深度学习（DL）方法已显示出令人鼓舞的结果，以解决诸如从$ k $ -space数据中的MR图像重建等逆问题。但是，这些方法目前尚无重建质量的保证，并且这种算法的可靠性仅被了解不足。对抗攻击提供了一种有价值的工具，可以了解可能的故障模式和基于DL的重建算法的最坏情况。在本文中，我们描述了对多圈$ K $空间测量结果的对抗性攻击，并在最近提出的E2E-VARNET和更简单的基于UNET的模型上对其进行评估。与先前的工作相反，攻击旨在特异性改变诊断相关的区域。使用两种逼真的攻击模型（对抗性$ K $ - 空间噪声和对抗性旋转），我们能够证明，当前基于DL DL的最新重建算法确实对此类扰动敏感，而相关诊断信息可能会在某种程度上迷路。令人惊讶的是，在我们的实验中，UNET和更复杂的E2E-VARNET对此类攻击同样敏感。我们的发现进一步增加了以下证据：必须谨慎行事，因为基于DL的方法更接近临床实践。

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign with the purpose of enabling a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.

部署到现实世界的自主智能代理必须与对感官输入的对抗性攻击保持强大的态度。在加强学习中的现有工作集中于最小值扰动攻击，这些攻击最初是为了模仿计算机视觉中感知不变性的概念。在本文中，我们注意到，这种最小值扰动攻击可以由受害者琐碎地检测到，因为这些导致观察序列与受害者的行为不符。此外，许多现实世界中的代理商（例如物理机器人）通常在人类主管下运行，这些代理商不容易受到这种扰动攻击的影响。结果，我们建议专注于幻觉攻击，这是一种与受害者的世界模式一致的新型攻击形式。我们为这个新颖的攻击框架提供了正式的定义，在各种条件下探索了其特征，并得出结论，代理必须寻求现实主义反馈以对幻觉攻击具有强大的态度。

自动生物医学图像分析的领域至关重要地取决于算法验证的可靠和有意义的性能指标。但是，当前的度量使用通常是不明智的，并且不能反映基本的域名。在这里，我们提出了一个全面的框架，该框架指导研究人员以问题意识的方式选择绩效指标。具体而言，我们专注于生物医学图像分析问题，这些问题可以解释为图像，对象或像素级别的分类任务。该框架首先编译域兴趣 - 目标结构 - ，数据集和算法与输出问题相关的属性的属性与问题指纹相关，同时还将其映射到适当的问题类别，即图像级分类，语义分段，实例，实例细分或对象检测。然后，它指导用户选择和应用一组适当的验证指标的过程，同时使他们意识到与个人选择相关的潜在陷阱。在本文中，我们描述了指标重新加载推荐框架的当前状态，目的是从图像分析社区获得建设性的反馈。当前版本是在由60多个图像分析专家的国际联盟中开发的，将在社区驱动的优化之后公开作为用户友好的工具包提供。

虽然神经网络是强大的功能近似器，但底层建模假设最终定义了它们是参数化的假设类。在分类中，随着常用的SoftMax能够代表任何分类分布，这些假设很小。然而，在回归中，通常放置了要实现的连续分布类型的限制假设，如通过平均平均误差及其潜在的高斯度假的训练的主导选择。最近，建模前进允许对要建模的连续分布的类型无关，授予回归分类模型的灵活性。虽然过去的研究在表现方面强调了这种灵活的回归模型的益处，但在这里我们研究了模型选择对不确定性估计的影响。我们强调，根据模型拼写，炼狱不确定性没有妥善捕获，并且贝叶斯治疗错过的模型导致不可靠的认知不确定性估计。总体而言，我们的研究概述了回归中的建模选择如何影响不确定性估计，从而概述任何下游决策过程。

在医学图像中的对象的同时定位和分类，也称为医学对象检测，是高临床相关性，因为诊断决策通常依赖于物体的评级而不是例如像素。对于此任务，方法配置的繁琐和迭代过程构成了一个主要的研究瓶颈。最近，NNU-Net在巨大成功中解决了图像细分任务的挑战。在NNU-Net的议程之后，在这项工作中，我们系统化并自动化了医疗对象检测的配置过程。由此产生的自配置方法NNDetection，在没有任何手动干预到任意医学检测问题的情况下适应本身，同时实现结果腹板或优于现有技术。我们展示了NNDetection对两台公共基准，亚当和Luna16的有效性，并提出了关于综合方法评估的公共数据集的进一步医疗对象检测任务。代码是https://github.com/mic-dkfz/nndetection。

This paper presents a machine learning approach to multidimensional item response theory (MIRT), a class of latent factor models that can be used to model and predict student performance from observed assessment data. Inspired by collaborative filtering, we define a general class of models that includes many MIRT models. We discuss the use of penalized joint maximum likelihood (JML) to estimate individual models and cross-validation to select the best performing model. This model evaluation process can be optimized using batching techniques, such that even sparse large-scale data can be analyzed efficiently. We illustrate our approach with simulated and real data, including an example from a massive open online course (MOOC). The high-dimensional model fit to this large and sparse dataset does not lend itself well to traditional methods of factor interpretation. By analogy to recommender-system applications, we propose an alternative "validation" of the factor model, using auxiliary information about the popularity of items consulted during an open-book exam in the course.

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

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.