脑小血管疾病的成像标记提供了有关脑部健康的宝贵信息，但是它们的手动评估既耗时又受到实质性内部和间际变异性的阻碍。自动化评级可能受益于生物医学研究以及临床评估，但是现有算法的诊断可靠性尚不清楚。在这里，我们介绍了\ textIt {血管病变检测和分割}（\ textit {v textit {where valdo？}）挑战，该挑战是在国际医学图像计算和计算机辅助干预措施（MICCAI）的卫星事件中运行的挑战（MICCAI） 2021.这一挑战旨在促进大脑小血管疾病的小而稀疏成像标记的自动检测和分割方法的开发，即周围空间扩大（EPVS）（任务1），脑微粒（任务2）和预先塑造的鞋类血管起源（任务3），同时利用弱和嘈杂的标签。总体而言，有12个团队参与了针对一个或多个任务的解决方案的挑战（任务1 -EPVS 4，任务2 -Microbleeds的9个，任务3 -lacunes的6个）。多方数据都用于培训和评估。结果表明，整个团队和跨任务的性能都有很大的差异，对于任务1- EPV和任务2-微型微型且对任务3 -lacunes尚无实际的结果，其结果尤其有望。它还强调了可能阻止个人级别使用的情况的性能不一致，同时仍证明在人群层面上有用。

计算机辅助方法为诊断和预测脑疾病显示了附加的价值，因此可以支持临床护理和治疗计划中的决策。本章将洞悉方法的类型，其工作，输入数据（例如认知测试，成像和遗传数据）及其提供的输出类型。我们将专注于诊断的特定用例，即估计患者的当前“状况”，例如痴呆症的早期检测和诊断，对脑肿瘤的鉴别诊断以及中风的决策。关于预测，即对患者的未来“状况”的估计，我们将缩小用例，例如预测多发性硬化症中的疾病病程，并预测脑癌治疗后患者的结局。此外，根据这些用例，我们将评估当前的最新方法，并强调当前对这些方法进行基准测试的努力以及其中的开放科学的重要性。最后，我们评估了计算机辅助方法的当前临床影响，并讨论了增加临床影响所需的下一步。

大型医学成像数据集变得越来越多。这些数据集中的一个普遍挑战是确保每个样本满足没有重要人工制品的最低质量要求。尽管已经开发出广泛的现有自动方法来识别医学成像中的缺陷和人工制品，但它们主要依赖于渴望数据的方法。特别是，缺乏可用于培训的手工艺品的足够扫描，在临床研究中设计和部署机器学习方面造成了障碍。为了解决这个问题，我们提出了一个具有四个主要组成部分的新颖框架：（1）一组受磁共振物理启发的手工艺发电机，以损坏大脑MRI扫描和增强培训数据集，（2）一组抽象和工程的功能，紧凑地表示图像，（3）一个特征选择过程，取决于人工制品的类别以提高分类性能，以及（4）一组受过训练以识别人工制品的支持向量机（SVM）分类器。我们的新颖贡献是三重的：首先，我们使用新型的基于物理的人工制品发生器来生成以受控的人工制品作为数据增强技术的合成脑MRI扫描。这将避免使用稀有人工制品的劳动密集型收集和标记过程。其次，我们提出了开发的大量抽象和工程图像特征，以识别9种不同的结构MRI伪像。最后，我们使用一个基于人工制品的功能选择块，该块，对于每类的人工制品，可以找到提供最佳分类性能的功能集。我们对具有人工生成的人工制品的大量数据扫描进行了验证实验，并且在一项多发性硬化症临床试验中，专家确定了真实的人工制品，这表明拟议管道表现优于传统方法。

Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks for lidar segmentation are fine-tuned to specific datasets. Switching the lidar sensor without retraining on a big set of annotated data from the new sensor creates a domain shift, which causes the network performance to drop drastically. In this work we propose a new method for lidar domain adaption, in which we use annotated panoptic lidar datasets and recreate the recorded scenes in the structure of a different lidar sensor. We narrow the domain gap to the target data by recreating panoptic data from one domain in another and mixing the generated data with parts of (pseudo) labeled target domain data. Our method improves the nuScenes to SemanticKITTI unsupervised domain adaptation performance by 15.2 mean Intersection over Union points (mIoU) and by 48.3 mIoU in our semi-supervised approach. We demonstrate a similar improvement for the SemanticKITTI to nuScenes domain adaptation by 21.8 mIoU and 51.5 mIoU, respectively. We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation. Furthermore we successfully apply our proposed method to two entirely unlabeled datasets of two state of the art lidar sensors Velodyne Alpha Prime and InnovizTwo, and train well performing semantic segmentation networks for both.

Explainable AI (XAI) is slowly becoming a key component for many AI applications. Rule-based and modified backpropagation XAI approaches however often face challenges when being applied to modern model architectures including innovative layer building blocks, which is caused by two reasons. Firstly, the high flexibility of rule-based XAI methods leads to numerous potential parameterizations. Secondly, many XAI methods break the implementation-invariance axiom because they struggle with certain model components, e.g., BatchNorm layers. The latter can be addressed with model canonization, which is the process of re-structuring the model to disregard problematic components without changing the underlying function. While model canonization is straightforward for simple architectures (e.g., VGG, ResNet), it can be challenging for more complex and highly interconnected models (e.g., DenseNet). Moreover, there is only little quantifiable evidence that model canonization is beneficial for XAI. In this work, we propose canonizations for currently relevant model blocks applicable to popular deep neural network architectures,including VGG, ResNet, EfficientNet, DenseNets, as well as Relation Networks. We further suggest a XAI evaluation framework with which we quantify and compare the effect sof model canonization for various XAI methods in image classification tasks on the Pascal-VOC and ILSVRC2017 datasets, as well as for Visual Question Answering using CLEVR-XAI. Moreover, addressing the former issue outlined above, we demonstrate how our evaluation framework can be applied to perform hyperparameter search for XAI methods to optimize the quality of explanations.

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is commonly oversimplistic, ignoring the asymmetric importance of prediction errors and the heterogeneity of the datasets used for testing. We examine the potential of recasting interactions between vehicles as gap acceptance scenarios and evaluating models in this structured environment. To that end, we develop a framework facilitating the evaluation of any model, by any metric, and in any scenario. We then apply this framework to state-of-the-art prediction models, which all show themselves to be unreliable in the most safety-critical situations.

Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications. However, collaboration between these actors is difficult due to the heterogeneous nature of geospatial data modalities (e.g., multi-spectral images of various resolutions, timeseries, weather data) and diversity of tasks (e.g., regression of human activity indicators or detecting forest fires). In this work, we present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias, pre-trained on large amounts of unlabelled earth observation data in a self-supervised manner. We envision how such a model may facilitate cooperation between members of the community. We show preliminary results on the first step of the roadmap, where we instantiate an architecture that can process a wide variety of geospatial data modalities and demonstrate that it can achieve competitive performance with domain-specific architectures on tasks relating to the U.N.'s Sustainable Development Goals.

封闭的量子机械系统的物理学受哈密顿量的约束。但是，在大多数实际情况下，这种哈密顿量尚不清楚，最终所有的数据是从系统上的测量中获得的数据。在这项工作中，我们通过将基于机器学习的基于梯度的优化从机器学习中从张量量的网络中从机器学习中从基于梯度的优化中汇总到从基于梯度的优化的技术中汇总到从动力学数据中进行交互的多体汉密尔顿人来学习的家庭。我们的方法非常实用，实验友好且本质上可扩展，以使系统尺寸超过100次旋转。特别是，我们在综合数据上证明了算法的工作原理，即使仅限于一个简单的初始状态，少量的单量观测和时间演变为相对较短的时间。对于一维海森贝格模型的具体示例，我们的算法在系统大小和缩放的误差常数中作为数据集大小的反平方根。

从不同的随机初始化开始，经过随机梯度下降（SGD）训练的神经网络通常在功能上非常相似，从而提出了一个问题，即不同的SGD溶液之间是否存在有意义的差异。 Entezari等。最近猜想，尽管初始化不同，但在考虑到神经网络的置换不变性后，SGD发现的解决方案位于相同的损失谷中。具体而言，他们假设可以将SGD找到的任何两种解决方案排列，以使其参数之间的线性插值形成一条路径，而不会显着增加损失。在这里，我们使用一种简单但功能强大的算法来找到这样的排列，使我们能够获得直接的经验证据，证明该假设在完全连接的网络中是正确的。引人注目的是，我们发现在初始化时已经存在两个网络，并且平均它们随机，但适当排列的初始化的性能大大高于机会。相反，对于卷积架构，我们的证据表明该假设不存在。特别是在大型学习率制度中，SGD似乎发现了各种模式。

来自原子模拟数据的重建力场（FF）是一个挑战，因为准确的数据可能非常昂贵。在这里，机器学习（ML）模型可以帮助成为数据经济，因为可以使用基础对称性和物理保护定律成功限制它们。但是，到目前为止，每个针对ML模型新提出的描述符都需要进行繁琐且数学繁琐的重塑。因此，我们建议在ML建模过程中使用来自算法分化的现代技术 - 有效地以更高的计算效率的阶顺序自动地使用新颖的描述符或模型。这种范式的方法不仅可以使新的表示形式的多功能用法，对FF社区的有效计算（对FF社区的高价值都高），而且还可以简单地包含进一步的物理知识，例如高阶信息（例如〜Hessians） ，更复杂的部分微分方程约束等），甚至超出了提出的FF域。