为了了解材料特性的起源，三轴光谱仪（TAS）处的中子散射实验通过测量其动量（Q）和能量（E）空间中的强度分布来研究样品中的磁和晶格激发。但是，TAS实验的高需求和有限的光束时间可用性提出了自然的问题，即我们是否可以提高其效率或更好地利用实验者的时间。实际上，使用TAS，有许多科学问题需要在Q-E空间的特定区域中搜索感兴趣的信号，但是当手动完成时，这是耗时且效率低下的，因为测量点可能会放置在此类的无信息区域中作为背景。主动学习是一种有前途的通用机器学习方法，可以迭代地检测自主信号的信息区域，即不受人类干扰，从而避免了不必要的测量并加快实验。此外，自主模式允许实验者在此期间专注于其他相关任务。我们在本文中描述的方法利用了对数高斯过程，由于对数转换，该过程在信号区域中具有最大的近似不确定性。因此，将不确定性最大化为采集功能，因此直接产生了信息测量的位置。我们证明了我们方法对在Themal Tas Eiger（PSI）进行真实中子实验的结果的好处，以及在合成环境中基准的结果，包括许多不同的激发。

由于基础物理学的复杂性以及捕获中的复杂遮挡和照明，从稀疏多视频RGB视频中对流体的高保真重建仍然是一个巨大的挑战。现有的解决方案要么假设障碍和照明知识，要么仅专注于没有障碍物或复杂照明的简单流体场景，因此不适合具有未知照明或任意障碍的现实场景。我们提出了第一种通过从稀疏视频的端到端优化中利用管理物理（即，navier -stokes方程）来重建动态流体的第一种方法，而无需采取照明条件，几何信息或边界条件作为输入。我们使用神经网络作为流体的密度和速度解决方案函数以及静态对象的辐射场函数提供连续的时空场景表示。通过将静态和动态含量分开的混合体系结构，与静态障碍物的流体相互作用首次重建，而没有其他几何输入或人类标记。通过用物理知识的深度学习来增强随时间变化的神经辐射场，我们的方法受益于对图像和物理先验的监督。为了从稀疏视图中实现强大的优化，我们引入了逐层增长策略，以逐步提高网络容量。使用具有新的正则化项的逐步增长的模型，我们设法在不拟合的情况下解除了辐射场中的密度彩色歧义。在避免了次优速度之前，将预验证的密度到速度流体模型借用了，该数据低估了涡度，但可以微不足道地满足物理方程。我们的方法在一组代表性的合成和真实流动捕获方面表现出具有放松的约束和强大的灵活性的高质量结果。

在过去的十年中，深入的强化学习（RL）已经取得了长足的进步。同时，最先进的RL算法在培训时间融合方面需要大量的计算预算。最近的工作已经开始通过量子计算的角度来解决这个问题，这有望为几项传统上的艰巨任务做出理论上的速度。在这项工作中，我们研究了一类混合量子古典RL算法，我们共同称为变异量子Q-NETWORKS（VQ-DQN）。我们表明，VQ-DQN方法受到导致学习政策分歧的不稳定性的约束，研究了基于经典模拟的既定结果的重复性，并执行系统的实验以识别观察到的不稳定性的潜在解释。此外，与大多数现有的量子增强学习中现有工作相反，我们在实际量子处理单元（IBM量子设备）上执行RL算法，并研究模拟和物理量子系统之间因实施不足而进行的行为差异。我们的实验表明，与文献中相反的主张相反，与经典方法相比，即使在没有物理缺陷的情况下进行模拟，也不能最终决定是否已知量子方法，也可以提供优势。最后，我们提供了VQ-DQN作为可再现的测试床的强大，通用且经过充分测试的实现，以实现未来的实验。

尽管存在能够在许多医疗数据集上表现出很好的语义分割方法，但是通常，它们不设计用于直接用于临床实践。两个主要问题是通过不同的视觉外观的解开数据的概括，例如，使用不同的扫描仪获取的图像，以及计算时间和所需图形处理单元（GPU）存储器的效率。在这项工作中，我们使用基于SpatialConfiguration-Net（SCN）的多器官分段模型，该模型集成了标记器官中的空间配置的先验知识，以解决网络输出中的虚假响应。此外，我们修改了分割模型的体系结构，尽可能地减少其存储器占用空间，而不会急剧影响预测的质量。最后，我们实现了最小的推理脚本，我们优化了两者，执行时间和所需的GPU内存。

Robotic teleoperation is a key technology for a wide variety of applications. It allows sending robots instead of humans in remote, possibly dangerous locations while still using the human brain with its enormous knowledge and creativity, especially for solving unexpected problems. A main challenge in teleoperation consists of providing enough feedback to the human operator for situation awareness and thus create full immersion, as well as offering the operator suitable control interfaces to achieve efficient and robust task fulfillment. We present a bimanual telemanipulation system consisting of an anthropomorphic avatar robot and an operator station providing force and haptic feedback to the human operator. The avatar arms are controlled in Cartesian space with a direct mapping of the operator movements. The measured forces and torques on the avatar side are haptically displayed to the operator. We developed a predictive avatar model for limit avoidance which runs on the operator side, ensuring low latency. The system was successfully evaluated during the ANA Avatar XPRIZE competition semifinals. In addition, we performed in lab experiments and carried out a small user study with mostly untrained operators.

The purpose of this work was to tackle practical issues which arise when using a tendon-driven robotic manipulator with a long, passive, flexible proximal section in medical applications. A separable robot which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable. A control input which resolves the redundancy in the kinematics and a physical interpretation of this redundancy are provided. The effect of a static change in the proximal section angle on bending angle error was explored under four testing conditions for a sinusoidal input. Bending angle error increased for increasing proximal section angle for all testing conditions with an average error reduction of 41.48% for retension, 4.28% for hysteresis, and 52.35% for re-tension + hysteresis compensation relative to the baseline case. Two major sources of error in tracking the bending angle were identified: time delay from hysteresis and DC offset from the proximal section angle. Examination of these error sources revealed that the simple hysteresis compensation was most effective for removing time delay and re-tension compensation for removing DC offset, which was the primary source of increasing error. The re-tension compensation was also tested for dynamic changes in the proximal section and reduced error in the final configuration of the tip by 89.14% relative to the baseline case.

Learning enabled autonomous systems provide increased capabilities compared to traditional systems. However, the complexity of and probabilistic nature in the underlying methods enabling such capabilities present challenges for current systems engineering processes for assurance, and test, evaluation, verification, and validation (TEVV). This paper provides a preliminary attempt to map recently developed technical approaches in the assurance and TEVV of learning enabled autonomous systems (LEAS) literature to a traditional systems engineering v-model. This mapping categorizes such techniques into three main approaches: development, acquisition, and sustainment. We review the latest techniques to develop safe, reliable, and resilient learning enabled autonomous systems, without recommending radical and impractical changes to existing systems engineering processes. By performing this mapping, we seek to assist acquisition professionals by (i) informing comprehensive test and evaluation planning, and (ii) objectively communicating risk to leaders.

In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techniques make the often unrealistic assumption that the agent has access to full information about the environment. We remove this assumption by developing an algorithm for IRL in partially observable Markov decision processes (POMDPs). We address two limitations of existing IRL techniques. First, they require an excessive amount of data due to the information asymmetry between the expert and the learner. Second, most of these IRL techniques require solving the computationally intractable forward problem -- computing an optimal policy given a reward function -- in POMDPs. The developed algorithm reduces the information asymmetry while increasing the data efficiency by incorporating task specifications expressed in temporal logic into IRL. Such specifications may be interpreted as side information available to the learner a priori in addition to the demonstrations. Further, the algorithm avoids a common source of algorithmic complexity by building on causal entropy as the measure of the likelihood of the demonstrations as opposed to entropy. Nevertheless, the resulting problem is nonconvex due to the so-called forward problem. We solve the intrinsic nonconvexity of the forward problem in a scalable manner through a sequential linear programming scheme that guarantees to converge to a locally optimal policy. In a series of examples, including experiments in a high-fidelity Unity simulator, we demonstrate that even with a limited amount of data and POMDPs with tens of thousands of states, our algorithm learns reward functions and policies that satisfy the task while inducing similar behavior to the expert by leveraging the provided side information.

Speech-driven 3D facial animation has been widely explored, with applications in gaming, character animation, virtual reality, and telepresence systems. State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking style and facial idiosyncrasies of the target actor, thus, resulting in unrealistic and inaccurate lip movements. To address this, we present Imitator, a speech-driven facial expression synthesis method, which learns identity-specific details from a short input video and produces novel facial expressions matching the identity-specific speaking style and facial idiosyncrasies of the target actor. Specifically, we train a style-agnostic transformer on a large facial expression dataset which we use as a prior for audio-driven facial expressions. Based on this prior, we optimize for identity-specific speaking style based on a short reference video. To train the prior, we introduce a novel loss function based on detected bilabial consonants to ensure plausible lip closures and consequently improve the realism of the generated expressions. Through detailed experiments and a user study, we show that our approach produces temporally coherent facial expressions from input audio while preserving the speaking style of the target actors.

We study the problem of graph clustering under a broad class of objectives in which the quality of a cluster is defined based on the ratio between the number of edges in the cluster, and the total weight of vertices in the cluster. We show that our definition is closely related to popular clustering measures, namely normalized associations, which is a dual of the normalized cut objective, and normalized modularity. We give a linear time constant-approximate algorithm for our objective, which implies the first constant-factor approximation algorithms for normalized modularity and normalized associations.