基于模型的递归分区（MOB）是一种半参数统计方法，允许鉴定可以与广泛的结果度量结合的亚组，包括连续的时间赛车结果。当以离散量表测量时间时，方法和模型需要考虑这种差异，因为其他亚组可能是虚假的，并且效果偏见。 M-Fluctuation检验的BOB分裂标准的基础测试假定独立观察。但是，对于拟合离散的事件模型，必须对数据矩阵进行修改，从而导致增强数据矩阵违反独立性假设。我们提出了用于离散生存数据（MOB-DS）的MOB，该数据控制用于数据拆分的测试的I型错误率，因此，尽管存在不存在。 MOB-DS使用置换方法来说明增强的事件时间数据中的依赖项，以获取存在无子组的零假设下的分布。通过模拟，我们研究了新的MOB-DS的I型错误率以及不同生存曲线和事件速率的不同模式的标准BOB。我们发现，测试的I型错误率对MOB-DS得到了很好的控制，但是观察到BOB的错误率有了相当大的膨胀。为了说明所提出的方法，将MOB-DS应用于失业时间的数据。

在电缆驱动的平行机器人（CDPR）中，单个电缆故障通常会导致整个机器人的完全故障。但是，通常可以通过重新配置框架上的电缆附件来恢复丢失的静态工作空间（由于故障）。通过将运动冗余以在实时冗余分辨率控制器中操纵的移动线性滑块的形式添加到机器人中，从而引入了此功能。提出的工作将该控制器与在线故障检测框架相结合，以开发自动任务恢复的完整失误耐受控制方案。该解决方案通过将最终效应器的姿势估计与仅依靠最终效应器信息的交互式多重模型（IMM）算法相结合，从而提供了鲁棒性。然后将故障和姿势估计方案绑定到冗余分辨率方法中，以产生无缝的自动任务（轨迹）恢复方法，以实现电缆故障。

本文介绍了一个新的多模式介入放射学数据集，称为POCAP（端口导管放置）语料库。该语料库由德语，X射线图像的语音和音频信号组成，以及六名外科医生从31个POCAP干预措施收集的系统命令，平均持续时间为81.4 $ \ pm $ 41.0分钟。该语料库旨在为在手术室中开发智能语音助理提供资源。特别是，它可用于开发语音控制的系统，该系统使外科医生能够控制操作参数，例如C臂运动和表位置。为了记录数据集，我们获得了Erlangen大学医院和患者数据隐私的机构审查委员会和工人委员会的同意。我们描述了录制设置，数据结构，工作流程和预处理步骤，并使用预告片的模型以11.52 $ \％$单词错误率报告了第一个POCAP语料库语音识别分析结果。研究结果表明，数据有可能构建强大的命令识别系统，并将使用医学领域中的语音和图像处理来开发新颖的干预支持系统。

这项工作为使用总体标志特征的矢量观测的总比例提供了一种姿态估计问题的理论框架。首先，优化框架与从点云特征提取的观察矢量配制。然后，导出错误协方差表达式。经过证明通过导出的优化框架获得的姿态和位置解决方案，以达到在姿态误差的小角度近似下的CRAM \'ER-RAO下限所定义的边界。通过一系列向量观察扫描提供用于模拟该问题的测量数据，并且假设完全填充的观察噪声 - 协方差矩阵作为成本函数中的重量，以覆盖传感器不确定性的最常规情况。这里，以前的衍生来扩展姿势估计问题，以包括误差中的更通用相关性而不是涉及各向同性噪声假设的误差。所提出的解决方案在Monte-Carlo框架中模拟，以验证误差协方差分析。

With the advent of Neural Style Transfer (NST), stylizing an image has become quite popular. A convenient way for extending stylization techniques to videos is by applying them on a per-frame basis. However, such per-frame application usually lacks temporal-consistency expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal-consistency suffers from one or more of the following drawbacks. They (1) are only suitable for a limited range of stylization techniques, (2) can only be applied in an offline fashion requiring the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency-control. Note that existing consistent video-filtering approaches aim to completely remove flickering artifacts and thus do not respect any specific consistency-control aspect. For stylization tasks, however, consistency-control is an essential requirement where a certain amount of flickering can add to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that can stylize video streams while providing interactive consistency-control. Apart from stylization, our approach also supports various other image processing filters. For achieving interactive performance, we develop a lite optical-flow network that operates at 80 Frames per second (FPS) on desktop systems with sufficient accuracy. We show that the final consistent video-output using our flow network is comparable to that being obtained using state-of-the-art optical-flow network. Further, we employ an adaptive combination of local and global consistent features and enable interactive selection between the two. By objective and subjective evaluation, we show that our method is superior to state-of-the-art approaches.

Vision transformers have emerged as powerful tools for many computer vision tasks. It has been shown that their features and class tokens can be used for salient object segmentation. However, the properties of segmentation transformers remain largely unstudied. In this work we conduct an in-depth study of the spatial attentions of different backbone layers of semantic segmentation transformers and uncover interesting properties. The spatial attentions of a patch intersecting with an object tend to concentrate within the object, whereas the attentions of larger, more uniform image areas rather follow a diffusive behavior. In other words, vision transformers trained to segment a fixed set of object classes generalize to objects well beyond this set. We exploit this by extracting heatmaps that can be used to segment unknown objects within diverse backgrounds, such as obstacles in traffic scenes. Our method is training-free and its computational overhead negligible. We use off-the-shelf transformers trained for street-scene segmentation to process other scene types.

The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard. This paper proposes GCS-Q, a novel quantum-supported solution for Induced Subgraph Games (ISGs) in coalition structure generation. GCS-Q starts by considering the grand coalition as initial coalition structure and proceeds by iteratively splitting the coalitions into two nonempty subsets to obtain a coalition structure with a higher coalition value. In particular, given an $n$-agent ISG, the GCS-Q solves the optimal split problem $\mathcal{O} (n)$ times using a quantum annealing device, exploring $\mathcal{O}(2^n)$ partitions at each step. We show that GCS-Q outperforms the currently best classical solvers with its runtime in the order of $n^2$ and an expected worst-case approximation ratio of $93\%$ on standard benchmark datasets.

Cartesian impedance control is a type of motion control strategy for robots that improves safety in partially unknown environments by achieving a compliant behavior of the robot with respect to its external forces. This compliant robot behavior has the added benefit of allowing physical human guidance of the robot. In this paper, we propose a C++ implementation of compliance control valid for any torque-commanded robotic manipulator. The proposed controller implements Cartesian impedance control to track a desired end-effector pose. Additionally, joint impedance is projected in the nullspace of the Cartesian robot motion to track a desired robot joint configuration without perturbing the Cartesian motion of the robot. The proposed implementation also allows the robot to apply desired forces and torques to its environment. Several safety features such as filtering, rate limiting, and saturation are included in the proposed implementation. The core functionalities are in a re-usable base library and a Robot Operating System (ROS) ros_control integration is provided on top of that. The implementation was tested with the KUKA LBR iiwa robot and the Franka Emika Robot (Panda) both in simulation and with the physical robots.

Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and expensive to acquire. Development of active learning methods in such fields is highly computationally expensive and time consuming which obstructs the progression of research and leads to a lack of comparability between methods. In this work, we propose and investigate a sandbox setup for rapid development and transparent evaluation of active learning in deep object detection. Our experiments with commonly used configurations of datasets and detection architectures found in the literature show that results obtained in our sandbox environment are representative of results on standard configurations. The total compute time to obtain results and assess the learning behavior can thereby be reduced by factors of up to 14 when comparing with Pascal VOC and up to 32 when comparing with BDD100k. This allows for testing and evaluating data acquisition and labeling strategies in under half a day and contributes to the transparency and development speed in the field of active learning for object detection.

Using robots in educational contexts has already shown to be beneficial for a student's learning and social behaviour. For levitating them to the next level of providing more effective and human-like tutoring, the ability to adapt to the user and to express proactivity is fundamental. By acting proactively, intelligent robotic tutors anticipate possible situations where problems for the student may arise and act in advance for preventing negative outcomes. Still, the decisions of when and how to behave proactively are open questions. Therefore, this paper deals with the investigation of how the student's cognitive-affective states can be used by a robotic tutor for triggering proactive tutoring dialogue. In doing so, it is aimed to improve the learning experience. For this reason, a concept learning task scenario was observed where a robotic assistant proactively helped when negative user states were detected. In a learning task, the user's states of frustration and confusion were deemed to have negative effects on the outcome of the task and were used to trigger proactive behaviour. In an empirical user study with 40 undergraduate and doctoral students, we studied whether the initiation of proactive behaviour after the detection of signs of confusion and frustration improves the student's concentration and trust in the agent. Additionally, we investigated which level of proactive dialogue is useful for promoting the student's concentration and trust. The results show that high proactive behaviour harms trust, especially when triggered during negative cognitive-affective states but contributes to keeping the student focused on the task when triggered in these states. Based on our study results, we further discuss future steps for improving the proactive assistance of robotic tutoring systems.