无监督的图像传输可用于医疗应用内和模式间转移，其中大量配对训练数据不丰富。为了确保从输入到目标域的结构映射，现有的未配对医疗图像转移的方法通常基于周期矛盾，由于学习了反向映射，导致了其他计算资源和不稳定。本文介绍了一种新颖的单向域映射方法，在整个培训过程中不需要配对数据。通过采用GAN体系结构和基于贴片不变性的新颖发电机损失来确保合理的转移。更确切地说，对发电机的输出进行了评估和比较，并在不同的尺度上进行了比较，这使人们对高频细节以及隐式数据增强进行了越来越多的关注。这个新颖的术语还提供了通过对斑块残差建模输入依赖性量表图来预测不确定性的机会。提出的方法在三个著名的医疗数据库上进行了全面评估。这些数据集的卓越精度与未配对图像转移的四种不同的最新方法相比，这表明了这种方法对不确定性感知的医学图像翻译的巨大潜力。建议的框架的实施在此处发布：https：//github.com/anger-man/unsupervise-image-image-transfer-and-uq。

基于稀疏性的方法在信号处理领域具有悠久的历史，并已成功应用于各种图像重建问题。相关的稀疏转换或词典通常使用模型进行预训练，该模型反映了信号的假定特性，或者在重建过程中自适应地学习 - 产生所谓的盲人压缩传感方法。但是，通过这样做，将永远不会与生成信号的物理模型一起明确训练。此外，正确选择所涉及的正则化参数仍然是一项具有挑战性的任务。正规化方法的另一个最近出现的训练范式是使用迭代神经网络（INNS）（也称为展开网络），其中包含物理模型。在这项工作中，我们构建了一个可以用作有监督和物理知识的在线卷积词典学习算法的旅馆。我们通过将其应用于现实的大规模动态MR重建问题来评估所提出的方法，并将其与其他最近发表的作品进行了比较。我们表明，与Deep Inn相比，拟议的旅馆改进了两种常规的模型不足训练方法，并产生竞争成果。此外，它不需要选择正则化参数，并且与深度旅馆形成鲜明对比 - 每个网络组件都是完全可以解释的。

实时估计实际对象深度是各种自主系统任务（例如3D重建，场景理解和状况评估）的重要模块。在机器学习的最后十年中，将深度学习方法的广泛部署到计算机视觉任务中产生了成功，从而成功地从简单的RGB模式中实现了现实的深度综合。这些模型大多数基于配对的RGB深度数据和/或视频序列和立体声图像的可用性。到目前为止，缺乏序列，立体声数据和RGB深度对使深度估计成为完全无监督的单图像转移问题，到目前为止几乎没有探索过。这项研究以生成神经网络领域的最新进展为基础，以建立完全无监督的单发深度估计。使用Wasserstein-1距离（一种新型的感知重建项和手工制作的图像过滤器）实现并同时优化了两个用于RGB至深度和深度RGB传输的发电机。我们使用工业表面深度数据以及德克萨斯州3D面部识别数据库，人类肖像的Celebamask-HQ数据库和记录人体深度的超现实数据集来全面评估模型。对于每个评估数据集，与最先进的单图像转移方法相比，提出的方法显示出深度准确性的显着提高。

实时估计实际环境深度是各种自主系统任务（例如本地化，障碍检测和姿势估计）的重要模块。在机器学习的最后十年中，将深度学习方法的广泛部署到计算机视觉任务中，从简单的RGB模式中产生了成功的方法，以实现现实的深度综合。尽管这些模型中的大多数都基于配对的深度数据或视频序列和立体声图像的可用性，但缺乏以无监督方式面对单像深度综合的方法。因此，在这项研究中，将生成神经网络领域的最新进步杠杆化以完全无监督的单像深度综合。更确切地说，使用Wasserstein-1距离实现了两个用于RGB至深度和深度RGB传输的周期符合发电机，并同时优化。为了确保所提出的方法的合理性，我们将模型应用于自称的工业数据集以及著名的NYU DEPTH V2数据集，从而可以与现有方法进行比较。在这项研究中，观察到的成功表明，在现实世界应用中，不成对的单像深度估计的潜力很高。

用于量化大型内燃机的圆柱衬里的最先进的方法，需要拆卸和切割检查的衬里。接下来是基于实验室的高分辨率显微表面深度测量，该测量基于轴承载荷曲线（也称为Abbott-Firestone曲线），对磨损进行了定量评估。这种参考方法具有破坏性，耗时且昂贵。此处介绍的研究的目的是开发无损但可靠的方法来量化表面地形。提出了一个新型的机器学习框架，该框架允许预测代表衬里表面反射RGB图像的深度轮廓的轴承载荷曲线。这些图像可以使用简单的手持显微镜收集。涉及两个神经网络模块的联合深度学习方法也优化了表面粗糙度参数的预测质量。使用定制数据库对网络堆栈进行训练，该数据库包含422个完美对齐的深度轮廓和大型气体发动机衬里的反射图像对。观察到的方法的成功表明，其在服务过程中对发动机进行现场磨损评估的巨大潜力。

In recent years, several metrics have been developed for evaluating group fairness of rankings. Given that these metrics were developed with different application contexts and ranking algorithms in mind, it is not straightforward which metric to choose for a given scenario. In this paper, we perform a comprehensive comparative analysis of existing group fairness metrics developed in the context of fair ranking. By virtue of their diverse application contexts, we argue that such a comparative analysis is not straightforward. Hence, we take an axiomatic approach whereby we design a set of thirteen properties for group fairness metrics that consider different ranking settings. A metric can then be selected depending on whether it satisfies all or a subset of these properties. We apply these properties on eleven existing group fairness metrics, and through both empirical and theoretical results we demonstrate that most of these metrics only satisfy a small subset of the proposed properties. These findings highlight limitations of existing metrics, and provide insights into how to evaluate and interpret different fairness metrics in practical deployment. The proposed properties can also assist practitioners in selecting appropriate metrics for evaluating fairness in a specific application.

Partial differential equations (PDEs) are important tools to model physical systems, and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle, which works like a non-linear Fourier transform, to construct GP kernels mirroring standard spectral methods for GPs. Our approach can infer probable solutions of linear PDE systems from any data such as noisy measurements, or initial and boundary conditions. Constructing EPGP-priors is algorithmic, generally applicable, and comes with a sparse version (S-EPGP) that learns the relevant spectral frequencies and works better for big data sets. We demonstrate our approach on three families of systems of PDE, the heat equation, wave equation, and Maxwell's equations, where we improve upon the state of the art in computation time and precision, in some experiments by several orders of magnitude.

Classically, the development of humanoid robots has been sequential and iterative. Such bottom-up design procedures rely heavily on intuition and are often biased by the designer's experience. Exploiting the non-linear coupled design space of robots is non-trivial and requires a systematic procedure for exploration. We adopt the top-down design strategy, the V-model, used in automotive and aerospace industries. Our co-design approach identifies non-intuitive designs from within the design space and obtains the maximum permissible range of the design variables as a solution space, to physically realise the obtained design. We show that by constructing the solution space, one can (1) decompose higher-level requirements onto sub-system-level requirements with tolerance, alleviating the "chicken-or-egg" problem during the design process, (2) decouple the robot's morphology from its controller, enabling greater design flexibility, (3) obtain independent sub-system level requirements, reducing the development time by parallelising the development process.

Recent diffusion-based AI art platforms are able to create impressive images from simple text descriptions. This makes them powerful tools for concept design in any discipline that requires creativity in visual design tasks. This is also true for early stages of architectural design with multiple stages of ideation, sketching and modelling. In this paper, we investigate how applicable diffusion-based models already are to these tasks. We research the applicability of the platforms Midjourney, DALL-E 2 and StableDiffusion to a series of common use cases in architectural design to determine which are already solvable or might soon be. We also analyze how they are already being used by analyzing a data set of 40 million Midjourney queries with NLP methods to extract common usage patterns. With this insights we derived a workflow to interior and exterior design that combines the strengths of the individual platforms.

With the rise of AI and automation, moral decisions are being put into the hands of algorithms that were formerly the preserve of humans. In autonomous driving, a variety of such decisions with ethical implications are made by algorithms for behavior and trajectory planning. Therefore, we present an ethical trajectory planning algorithm with a framework that aims at a fair distribution of risk among road users. Our implementation incorporates a combination of five essential ethical principles: minimization of the overall risk, priority for the worst-off, equal treatment of people, responsibility, and maximum acceptable risk. To the best of the authors' knowledge, this is the first ethical algorithm for trajectory planning of autonomous vehicles in line with the 20 recommendations from the EU Commission expert group and with general applicability to various traffic situations. We showcase the ethical behavior of our algorithm in selected scenarios and provide an empirical analysis of the ethical principles in 2000 scenarios. The code used in this research is available as open-source software.