目的：尽管机器学习模型有潜力，但缺乏普遍性阻碍了他们在临床实践中的广泛采用。我们研究了三个方法论陷阱：（1）违反独立性假设，（2）具有不适当的性能指标或基线进行比较的模型评估，以及（3）批次效应。材料和方法：使用几个回顾性数据集，我们在有或没有陷阱的情况下实现机器学习模型，以定量说明这些陷阱对模型通用性的影响。结果：更具体地说，违反独立假设，在将数据分别分为火车，验证和测试集中，在预测局部恢复和预测局部恢复和表面上，将数据分别划分为火车，验证和测试集，在将数据分别分为火车，验证和测试集中，在F1分别误导和表面上获得误解和表面收益，从而违反独立假设。预测头颈癌的3年总生存期以及46.0％的总体生存率为5.0％，从而区分肺癌的组织病理学模式。此外，在培训，验证和测试集中为受试者分发数据点导致F1分数的表面增长21.8％。此外，我们展示了绩效指标选择和基线的重要性。在存在批处理效应的情况下，为肺炎检测而建立的模型导致F1得分为98.7％。但是，当将同一模型应用于正常患者的新数据集时，仅正确地将3.86％的样品分类。结论：这些方法上的陷阱无法使用内部模型评估来捕获，这种模型的不准确预测可能会导致错误的结论和解释。因此，对于开发可推广的模型是必要的，理解和避免这些陷阱是必要的。

在医学图像处理的领域中，医疗设备制造商在许多情况下通过仅运输编译软件来保护他们的知识产权，即可以执行的二进制代码，但难以通过潜在的攻击者理解。在本文中，我们研究了该过程能够保护图像处理算法的程度如何。特别是，我们研究了从双能量CT数据的单能量图像和碘映射的计算是否可以通过机器学习方法反向设计。我们的结果表明，两者只能在所有研究中以非常高的精度使用一个单片图像作为训练数据，以非常高的精度，在所有调查的情况下，结构相似度大于0.98。

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

In this paper, we present a novel control architecture for the online adaptation of bipedal locomotion on inclined obstacles. In particular, we introduce a novel, cost-effective, and versatile foot sensor to detect the proximity of the robot's feet to the ground (bump sensor). By employing this sensor, feedback controllers are implemented to reduce the impact forces during the transition of the swing to stance phase or steeping on inclined unseen obstacles. Compared to conventional sensors based on contact reaction force, this sensor detects the distance to the ground or obstacles before the foot touches the obstacle and therefore provides predictive information to anticipate the obstacles. The controller of the proposed bump sensor interacts with another admittance controller to adjust leg length. The walking experiments show successful locomotion on the unseen inclined obstacle without reducing the locomotion speed with a slope angle of 12. Foot position error causes a hard impact with the ground as a consequence of accumulative error caused by links and connections' deflection (which is manufactured by university tools). The proposed framework drastically reduces the feet' impact with the ground.

Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction of the time series, and they cannot measure the level of reliability or the probability of correct detection. Although the Bayesian neural network (BNN)-based approaches are well known for time series uncertainty estimation, they are computationally intractable. In this paper, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy. For time series forecasting, we employ an NN, which consists of several Long Short-Term Memory (LSTM) layers followed by various dense layers. We employ the MC dropout inside each LSTM layer and before the dense layers for uncertainty estimation. With the proposed uncertainty region and by utilizing a post-processing filter, we can effectively capture the anomaly points. Numerical results show that our proposed time series AD approach outperforms the existing methods from both prediction accuracy and AD perspectives.

The robustness and accuracy of a vision system for motion estimation of a tumbling target satellite are enhanced by an adaptive Kalman filter. This allows a vision-guided robot to complete the grasping of the target even if occlusion occurs during the operation. A complete dynamics model, including aspects of orbital mechanics, is incorporated for accurate estimation. Based on the model, an adaptive Kalman filter is developed that estimates not only the system states but also all the model parameters such as the inertia ratio, center-of-mass, and the rotation of the principal axes of the target satellite. An experiment is conducted by using a robotic arm to move a satellite mockup according to orbital mechanics while the satellite pose is measured by a laser camera system. The measurements are sent to the Kalman filter, which, in turn, drives another robotic arm to grasp the target. The results demonstrate successful grasping even if the vision system is blocked for several seconds.

Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to some considerations such as cost, radiation dose and modality missing. Recently, medical image synthesis has aroused gaining research interest to cope with this limitation. In this paper, we propose a bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN), to synthesize medical images from unpaired data. Specifically, a dual contrast loss is introduced into the discriminators to indirectly build constraints between real source and synthetic images by taking advantage of samples from the source domain as negative samples and enforce the synthetic images to fall far away from the source domain. In addition, cross-entropy and structural similarity index (SSIM) are integrated into the DC-cycleGAN in order to consider both the luminance and structure of samples when synthesizing images. The experimental results indicate that DC-cycleGAN is able to produce promising results as compared with other cycleGAN-based medical image synthesis methods such as cycleGAN, RegGAN, DualGAN, and NiceGAN. The code will be available at https://github.com/JiayuanWang-JW/DC-cycleGAN.

本文提出了一种控制操纵器系统，掌握刚体有效载荷的方法，因此，由于外部施加的力与另一个自由浮动的刚体（具有不同的惯性特性）相同，因此组合系统的运动与另一个相同。这允许在1-G实验室环境中测试下的缩放航天器原型的零G仿真。由运动反馈和力量/力矩反馈组成的控制器调整了测试航天器的运动，以匹配飞行航天器的运动，即使后者具有灵活的附属物（例如太阳能电池板），而前者则是刚性的。整体系统的稳定性进行了分析研究，结果表明，只要两个航天器的惯性特性不同，并且尊重有效载荷与操纵器的惯性比率的上行，则该系统保持稳定。还提出了重要的实际问题，例如校准和对传感器噪声和量化的敏感性分析。

虽然对多语言视觉语言预测的模型实现了一些好处，但是当将多句预训练的视力语言模型应用于非英语数据时，各种任务和语言的最新基准测试表明，跨语性概括不佳，并且在有监督之间存在很大的差距（ ）英语表现和（零射）跨语性转移。在这项工作中，我们探讨了这些模型在零拍的跨语性视觉响应（VQA）任务上的糟糕性能，其中模型在英语视觉问题数据上进行了微调，并对7种类型上多样的语言进行了评估。我们通过三种策略改善了跨语性转移：（1）我们引入了语言的先验目标，以增加基于相似性损失以指导模型在培训期间的跨渗透损失，（2）我们学习了一个特定于任务的子网络，改善跨语性概括并减少不修改模型的方差，（3）我们使用合成代码混合来扩大培训示例，以促进源和目标语言之间的嵌入。我们使用预审计的多语言多模式变压器UC2和M3P进行的XGQA实验证明了针对7种语言提出的微调策略的一致有效性，以稀疏模型优于现有的转移方法。复制我们发现的代码和数据已公开可用。

使用自适应机器学习解决了在不准确运动学模型的情况下，在存在不正确的运动学模型的情况下形成封闭运动链的合作操纵器的自我调整控制问题。两个级联估计器在线更新了与互连操纵器的相对位置/方向不确定性有关的运动学参数，以调整合作控制器，以通过最小值驱动力来实现准确的运动跟踪。该技术允许对所涉及的操纵器的相对运动学进行准确的校准，而无需高精度的终点传感或力测量，因此在经济上是合理的。研究整个实时估计器/控制器系统的稳定性表明，可以确保自适应控制过程的收敛性和稳定性，如果i）角速度向量的方向不会随着时间的推移而保持恒定；参数误差是由一些已知参数的缩放器函数上限。自适应控制器被证明是无奇异性的，即使控制定律涉及在估计参数下计算的矩阵的近似。实验结果证明了传统的反向动态控制方案对运动不准确的跟踪性能的敏感性，而自我调整合作控制器的跟踪误差显着降低。