生成对抗网络（GAN）是图像合成的艺术状态。在这里，我们提出了DAPI2CK，这是一种基于GAN的新型方法，用于合成细胞角蛋白（CK）在非小细胞肺癌（NSCLC）图像中的免疫荧光（IF）DAPI染色中染色。我们使用合成CK来细分上皮区域，与专家注释相比，该区域与染色CK的分割相同的结果相同。考虑到（MIF）面板（MIF）中的标记数量有限，我们的方法允许另一个标记物替换CK，以解决肿瘤微环境（TME）的复杂性，以促进患者选择免疫疗法。与染色的CK相反，DAPI2CK不会遭受诸如非特异性CK染色或肿瘤CK表达丧失之类的问题。

使用生成的对抗神经网络和更精确的周期内，无监督和不配对的域翻译是组织病理学图像的染色翻译的最新技术。然而，它通常遭受循环一致但非结构保存错误的存在。我们为一组方法提出了一种替代方法，该方法依赖于分割一致性，可以保留病理结构。专注于免疫组织化学（IHC）和多重免疫荧光（MIF），我们引入了一种简单而有效的指导方案，作为一种损失函数，以利用污渍翻译和染色隔离的一致性。定性和定量实验显示了提出的方法改善两个域之间翻译的能力。

消费品的生产，运输，使用和处置对温室气体排放和资源耗竭有重大影响。现代零售平台在很大程度上依赖机器学习（ML）来搜索和推荐系统。因此，ML可以通过考虑产品搜索或建议中的可持续性方面来实施更可持续的消费模式的努力。但是，利用ML的潜力达到可持续性目标需要有关可持续性的数据。不幸的是，没有开放且公开可用的数据库可以按产品基础集成可持续性信息。在这项工作中，我们提出了填补这一空白的GreendB。根据数百万用户的搜索日志，我们优先考虑哪些产品用户最关心的是最关心的。 GreendB架构扩展了著名的schema.org产品定义，并且可以轻松地集成到现有的产品目录中，以改善可用于搜索和建议体验的可持续性信息。我们介绍了创建GREENDB数据集的刮擦系统的概念证明。

MRI扫描时间减少通常通过并行成像方法实现，通常基于逆图像空间（A.K.A.K空间）的均匀下采样和具有多个接收器线圈的同时信号接收。 Grappa方法通过跨越所有线圈的相邻获取信号的线性组合来插入缺失的k空间信号，并且可以通过k空间中的卷积来描述。最近，介绍了一种称为RAKI的更广泛的方法。 Raki是一种深入学习方法，将Grappa推广到附加的卷积层，在此期间应用非线性激活功能。这使得卷积神经网络能够实现缺失信号的非线性估计。与Grappa类似，Raki中的卷积核心使用从自动校准信号（ACS）获得的特定训练样本进行培训。 Raki与Grappa相比提供了卓越的重建质量，然而，由于其未知参数的数量增加，通常需要更多的AC。为了克服这一限制，本研究调查了训练数据对标准2D成像重建质量的影响，特别关注其金额和对比信息。此外，评估迭代k空间插值方法（araki），包括通过初始的格拉普重建训练数据增强，并通过迭代培训改进卷积滤波器。仅使用18,20和25个ACS线（8％），通过抑制在加速度因子R = 4和r = 5时发生的残余人工制品，并且与Grappa相比，通过定量质量指标加下划线，产生强烈的噪声抑制。与相约束的组合进一步改善。此外，在预扫描校准的情况下，伊拉克基显示比GRAPPA和RAKI更好的性能，并且在训练和缺乏采样的数据之间强烈不同的对比度。

从人类反馈中进行交互学习的能力将使代理在新环境中。例如，即使是新手用户也可以自然和互动地培训服务机器人。人类在循环增强学习（HRL）结合了人类的反馈和增强学习（RL）技术。最先进的互动学习技术遭受缓慢的学习速度，因此导致了人类的沮丧经历。我们通过扩展HRL框架TAMER来评估反馈来解决此问题，并有可能通过两种不同类型的反事实解释（基于动作和状态）来增强人类反馈。我们通过实验表明，我们的扩展提高了学习速度。

We address the challenge of building domain-specific knowledge models for industrial use cases, where labelled data and taxonomic information is initially scarce. Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discovering and linking new (so-called open-world) entities to the knowledge graph. We argue that - though neural approaches to text mining have yielded impressive results in the past years - current benchmarks do not reflect the typical challenges encountered in the industrial wild properly. Therefore, our first contribution is an open benchmark coined IRT2 (inductive reasoning with text) that (1) covers knowledge graphs of varying sizes (including very small ones), (2) comes with incidental, low-quality text mentions, and (3) includes not only triple completion but also ranking, which is relevant for supporting experts with discovery tasks. We investigate two neural models for inductive link prediction, one based on end-to-end learning and one that learns from the knowledge graph and text data in separate steps. These models compete with a strong bag-of-words baseline. The results show a significant advance in performance for the neural approaches as soon as the available graph data decreases for linking. For ranking, the results are promising, and the neural approaches outperform the sparse retriever by a wide margin.

Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the bio-medical domain, annotations are subjective and suffer from low inter- and intra-rater reliability. Since annotations only reflect the annotation entity's interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, three categories of PGT-aware strategies to evaluate and improve model performance are reviewed.

The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.

Efficient surrogate modelling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using Sparse Random Features for surrogate modelling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results show a superiority of the here described approach over state of the art surrogate modelling techniques, Polynomial Chaos Expansions and Neural Networks.

In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action domain have been developed over time that vary primarily in the way they parameterize their approximations of value distributions, and how they quantify the differences between those distributions. In this work we transfer three of the most well-known and successful of those algorithms (QR-DQN, IQN and FQF) to the continuous action domain by extending two powerful actor-critic algorithms (TD3 and SAC) with distributional critics. We investigate whether the relative performance of the methods for the discrete action space translates to the continuous case. To that end we compare them empirically on the pybullet implementations of a set of continuous control tasks. Our results indicate qualitative invariance regarding the number and placement of distributional atoms in the deterministic, continuous action setting.