本文提出了一种新型的固定时间积分滑动模式控制器，以用于增强物理人类机器人协作。所提出的方法结合了遵守入学控制的外部力量和对整体滑动模式控制（ISMC）不确定性的高度鲁棒性的好处，从而使系统可以在不确定的环境中与人类伴侣合作。首先，在ISMC中应用固定时间滑动表面，以使系统的跟踪误差在固定时间内收敛，无论初始条件如何。然后，将固定的后台控制器（BSP）集成到ISMC中，作为标称控制器，以实现全局固定时间收敛。此外，为了克服奇异性问题，设计并集成到控制器中，这对于实际应用很有用。最后，提出的控制器已被验证，用于具有不确定性和外部力量的两连锁机器人操纵器。结果表明，在跟踪误差和收敛时间的意义上，所提出的控制器是优越的，同时，可以在共享工作区中遵守人类运动。

身体机器人的合作需要严格的安全保证，因为机器人和人类在共享工作区中工作。这封信提出了一个新颖的控制框架，以处理针对人类机器人互动的基于安全至关重要的位置的约束。所提出的方法基于入学控制，指数控制屏障功能（ECBF）和二次计划（QP），以在人与机器人之间的力相互作用期间达到合规性，同时保证安全约束。特别是，入学控制的配方被重写为二阶非线性控制系统，并且人与机器人之间的相互作用力被视为控制输入。通过使用欧洲央行-QP框架作为外部人类力量的补偿器，实时提供了用于入学控制的虚拟力反馈。因此，安全轨迹是从建议的低级控制器进行跟踪的建议的自适应入学控制方案中得出的。拟议方法的创新是，拟议的控制器将使机器人能够自然流动性遵守人类力量，而无需违反任何安全限制，即使在人类外部力量偶然迫使机器人违反约束的情况下。在对两链平面机器人操纵器的仿真研究中，我们的方法的有效性得到了证明。

联合学习（FL），其中多个机构在不共享数据的情况下协作训练机器学习模型正在变得流行。参与机构可能不会平等地做出贡献，有些贡献了更多的数据，一些更好的质量数据或一些更多样化的数据。为了公平地排名不同机构的贡献，沙普利价值（SV）已成为选择方法。精确的SV计算非常昂贵，尤其是在有数百个贡献者的情况下。现有的SV计算技术使用近似值。但是，在医疗保健中，贡献机构的数量可能不是巨大的规模，计算精确的SVS仍然很昂贵，但并非不可能。对于此类设置，我们提出了一种称为Safe的高效SV计算技术（用于使用Enembly的联合学习的Shapley值）。我们从经验上表明，安全计算接近精确SV的值，并且其性能优于当前SV近似值。这在医学成像环境中尤其重要，在医学成像环境中，整个机构之间的广泛异质性猖ramp，并且需要快速准确的数据评估来确定每个参与者在多机构协作学习中的贡献。

分析学习算法的挑战之一是客观值和随机噪声之间的循环纠缠。这也被称为“鸡肉和鸡蛋”现象，传统上，没有原则解决这个问题的方法。人们通过利用动态的特殊结构来解决问题，因此很难概括分析。在这项工作中，我们提出了一个简化的三步食谱，以解决“鸡肉和鸡蛋”问题，并为分析学习算法的随机动力学提供了一般框架。我们的框架构成了概率理论的标准技术，例如停止时间和Martingale浓度。我们通过对三个截然不同的学习问题进行统一分析，并具有强大的统一高概率收敛保证，从而证明了我们框架的力量和灵活性。这些问题是强烈凸功能，流主成分分析和带有随机梯度下降更新的线性匪徒的随机梯度下降。我们要么在所有三个动态上都改进或匹配最新界限。

Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.

We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.

We propose Hierarchical ProtoPNet: an interpretable network that explains its reasoning process by considering the hierarchical relationship between classes. Different from previous methods that explain their reasoning process by dissecting the input image and finding the prototypical parts responsible for the classification, we propose to explain the reasoning process for video action classification by dissecting the input video frames on multiple levels of the class hierarchy. The explanations leverage the hierarchy to deal with uncertainty, akin to human reasoning: When we observe water and human activity, but no definitive action it can be recognized as the water sports parent class. Only after observing a person swimming can we definitively refine it to the swimming action. Experiments on ActivityNet and UCF-101 show performance improvements while providing multi-level explanations.

Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e.g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a $\textit{renaissance}$ in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.

The ability to convert reciprocating, i.e., alternating, actuation into rotary motion using linkages is hindered fundamentally by their poor torque transmission capability around kinematic singularity configurations. Here, we harness the elastic potential energy of a linear spring attached to the coupler link of four-bar mechanisms to manipulate force transmission around the kinematic singularities. We developed a theoretical model to explore the parameter space for proper force transmission in slider-crank and rocker-crank four-bar kinematics. Finally, we verified the proposed model and methodology by building and testing a macro-scale prototype of a slider-crank mechanism. We expect this approach to enable the development of small-scale rotary engines and robotic devices with closed kinematic chains dealing with serial kinematic singularities, such as linkages and parallel manipulators.

Prevailing methods for assessing and comparing generative AIs incentivize responses that serve a hypothetical representative individual. Evaluating models in these terms presumes homogeneous preferences across the population and engenders selection of agglomerative AIs, which fail to represent the diverse range of interests across individuals. We propose an alternative evaluation method that instead prioritizes inclusive AIs, which provably retain the requisite knowledge not only for subsequent response customization to particular segments of the population but also for utility-maximizing decisions.