运行时验证（RV）有可能使安全关键系统的安全操作太复杂而无法正式验证，例如机器人操作系统2（ROS2）应用程序。编写正确的监视器本身可能很复杂，监视子系统中的错误威胁着整个任务。本文概述了一种正式的方法，该方法是根据用结构化的自然语言编写的要求为自动驾驶机器人生成运行时监视器的。我们的方法通过OGMA集成工具将正式需求启发工具（FRET）与Copilot（运行时验证框架）集成在一起。 FRET用于用明确的语义指定需求，然后将其自动转化为时间逻辑公式。 OGMA从FRET输出中生成监视规格，该规范已编译为硬实时C99。为了促进ROS2中的显示器的集成，我们已经扩展了OGMA，以生成定义监视节点的ROS2软件包，该节点在新数据可用时运行监视器，并发布任何违规结果。我们方法的目的是将生成的ROS2软件包视为黑匣子，并以最小的努力将它们集成到更大的ROS2系统中。

卷积神经网络（CNN）越来越多地用于自动化磁共振（MR）图像中脑结构的分割，以进行研究。在其他应用中，CNN模型在训练集中的代表性不足时已显示出对某些人口组的偏见。在这项工作中，我们研究了CNN大脑MR分割模型是否有可能在接受不平衡训练集训练时遏制性别或种族偏见。我们使用白人受试者中不同水平的性不平衡训练快速冲浪模型的多个实例。我们分别评估白人男性和白人女性测试集以评估性别偏见的性能，并在黑人男性和黑人女性测试套装上评估它们，以评估潜在的种族偏见。我们发现分割模型性能中的重大性别和种族偏见效应。这些偏见具有很强的空间成分，一些大脑区域表现出比其他大脑更强的偏见。总体而言，我们的结果表明，种族偏见比性偏见更为重要。我们的研究表明，在为基于CNN的大脑MR分割的训练集时考虑种族和性别平衡的重要性，以避免通过有偏见的研究研究结果来维持甚至加剧现有的健康不平等。

我们提出Simprov-可扩展的图像出处框架，将查询图像匹配回到可信的原始数据库，并在查询上确定可能的操作。 Simprov由三个阶段组成：检索Top-K最相似图像的可扩展搜索阶段；一个重新排列和近乎解复的检测阶段，用于识别候选人之间的原件；最后，在查询中定位区域的操纵检测和可视化阶段可能被操纵与原始区域不同。 Simprov对在线再分配过程中通常发生的良性图像转换非常强大，例如由于噪声和重新压缩降解而引起的工件，以及由于图像填充，翘曲，尺寸和形状的变化而引起的过度转换。通过对比较器体系结构中可区分的翘曲模块的端到端训练，可以实现对实地转换的鲁棒性。我们证明了对1亿张图像的数据集的有效检索和操纵检测。

大多数机器学习方法都用作建模的黑匣子。我们可能会尝试从基于物理学的训练方法中提取一些知识，例如神经颂（普通微分方程）。神经ODE具有可能具有更高类的代表功能的优势，与黑盒机器学习模型相比，扩展的可解释性，描述趋势和局部行为的能力。这种优势对于具有复杂趋势的时间序列尤其重要。但是，已知的缺点是与自回归模型和长期术语内存（LSTM）网络相比，广泛用于数据驱动的时间序列建模的高训练时间。因此，我们应该能够平衡可解释性和训练时间，以在实践中应用神经颂歌。该论文表明，现代神经颂歌不能简化为时间序列建模应用程序的模型。将神经ODE的复杂性与传统的时间序列建模工具进行比较。唯一可以提取的解释是操作员的特征空间，这对于大型系统来说是一个不适的问题。可以使用不同的经典分析方法提取光谱，这些方法没有延长时间的缺点。因此，我们将神经ODE缩小为更简单的线性形式，并使用合并的神经网络和ODE系统方法对时间序列建模进行了新的视图。

超材料是复合材料，具有工程化几何微观和中间结构，可以导致罕见的物理性质，如负泊松的比例或超低剪切电阻。周期性超材料由重复单元 - 细胞组成，并且这些单元电池内的几何图案影响弹性或声波和控制分散的传播。在这项工作中，我们开发了一种新的可解释，多分辨率的机器学习框架，用于在揭示其动态特性的材料的单元单元中查找模式。具体而言，我们提出了两个新的超材料的新可解释表示，称为形状频率特征和单元 - 单元格模板。使用这些要素类构建的机器学习模型可以准确地预测动态材料属性。这些特征表示（特别是单个单元格模板）具有有用的属性：它们可以在更高分辨率的设计上运行。通过学习可以通过形状频率特征或单元 - 单元模板可靠地传送到更精细的分辨率设计空间的关键粗略尺度模式，我们几乎可以自由地设计单元单元的精细分辨率特征而不改变粗略级别物理。通过这种多分辨率方法，我们能够设计具有目标频率范围的材料，其中允许或不允许波传播（频率带盖）。我们的方法产生了重大好处：（1）与材料科学的典型机器学习方法不同，我们的模型是可解释的，（2）我们的方法利用多分辨率属性，（3）我们的方法提供了设计灵活性。

Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs. We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples. We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet.

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems, e.g., image classification, natural language processing or human action recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method is based on deep Taylor decomposition and efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.

In the past years, deep learning has seen an increase of usage in the domain of histopathological applications. However, while these approaches have shown great potential, in high-risk environments deep learning models need to be able to judge their own uncertainty and be able to reject inputs when there is a significant chance of misclassification. In this work, we conduct a rigorous evaluation of the most commonly used uncertainty and robustness methods for the classification of Whole-Slide-Images under domain shift using the H\&E stained Camelyon17 breast cancer dataset. Although it is known that histopathological data can be subject to strong domain shift and label noise, to our knowledge this is the first work that compares the most common methods for uncertainty estimation under these aspects. In our experiments, we compare Stochastic Variational Inference, Monte-Carlo Dropout, Deep Ensembles, Test-Time Data Augmentation as well as combinations thereof. We observe that ensembles of methods generally lead to higher accuracies and better calibration and that Test-Time Data Augmentation can be a promising alternative when choosing an appropriate set of augmentations. Across methods, a rejection of the most uncertain tiles leads to a significant increase in classification accuracy on both in-distribution as well as out-of-distribution data. Furthermore, we conduct experiments comparing these methods under varying conditions of label noise. We observe that the border regions of the Camelyon17 dataset are subject to label noise and evaluate the robustness of the included methods against different noise levels. Lastly, we publish our code framework to facilitate further research on uncertainty estimation on histopathological data.

Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. A number of models exist that base charisma on various dimensions, often following the idea that charisma is given if someone could and would help others. Examples include influence (could help) and affability (would help) in scientific studies or power (could help), presence, and warmth (both would help) as a popular concept. Modelling high levels in these dimensions for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we, thereforem present a blueprint for building machines that can appear charismatic, but also analyse the charisma of others. To this end, we first provide the psychological perspective including different models of charisma and behavioural cues of it. We then switch to conversational charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behaviour by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.

Deep learning-based 3D human pose estimation performs best when trained on large amounts of labeled data, making combined learning from many datasets an important research direction. One obstacle to this endeavor are the different skeleton formats provided by different datasets, i.e., they do not label the same set of anatomical landmarks. There is little prior research on how to best supervise one model with such discrepant labels. We show that simply using separate output heads for different skeletons results in inconsistent depth estimates and insufficient information sharing across skeletons. As a remedy, we propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. The discovered latent 3D points capture the redundancy among skeletons, enabling enhanced information sharing when used for consistency regularization. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model, which outperforms prior work on a range of benchmarks, including the challenging 3D Poses in the Wild (3DPW) dataset. Our code and models are available for research purposes.