成千上万的扫描历史地形图包含覆盖长时间的有价值的信息，例如如何随着时间的推移改变区域的水文。有效地解锁这些地图中的信息需要培训一种地理空间对象识别系统，该系统需要大量的注释数据。根据其坐标与地形图的重叠地理引用的外部矢量数据可以自动注释地图中的所需对象的位置。但是，直接重叠两个数据集会导致错位和错误的注释，因为出版年份和地形图的坐标投影系统与外部向量数据不同。我们提出了一种标签校正算法，它利用了地图的颜色信息和外部矢量数据的先前形状信息，以减少错位和错误的注释。实验表明，来自所提出的算法的注释精度比来自最先进的算法的注释高10％。因此，使用所提出的算法的注释的识别结果达到了比使用最先进的算法的注释更高的正确性。

许多开销图像的增加的可用性和可访问性使我们能够估计和评估地理空间目标对象组的空间排列，这可以使许多应用程序受益，例如交通监测和农业监测。空间排列估计是识别包含顶部图像中所需对象的区域的过程。传统的监督对象检测方法可以估计准确的空间布置，但需要大量的边界盒注释。最近的半监督聚类方法可以减少手动标签，但仍需要图像中所有对象类别的注释。本文介绍了目标导向生成模型（TGGM），在变分自动编码器（VAE）框架下，它使用高斯混合模型（GMM）来估计VAE中隐藏和解码器变量的分布。通过GMM模拟隐藏和解码器变量，可显着为空间排列估计减少所需的手动注释。与现有方法不同，培训过程只能在优化迭代中将其作为整体更新GMM（例如，“小贴士”），TGGM允许在相同的优化迭代中单独更新各个GMM组件。单独优化GMM组件允许TGGM在空间数据中利用语义关系，只需要几个标签启动和指导生成过程。我们的实验表明，TGGM实现了与最先进的半监督方法相当的结果，并根据$ F_ {1} $得分，胜过无监督方法10％，同时需要显着较少的标记数据。

历史地图包含难以找到覆盖长期时间的详细地理信息（例如，为美国历史地形图125年）。然而，这些地图通常存在于没有可搜索元数据的情况下扫描图像。现有方法制作历史地图可搜索依靠繁琐的手动工作（包括人群）来生成元数据（例如，地理域和关键字）。光学字符识别（OCR）软件可以缓解所需的手动工作，但识别结果是单个单词而不是位置短语（例如，“黑色”和“山”与“黑山”）。本文介绍了一种端到端的方法来解决发现和索引历史地图图像的真实问题。此方法自动处理历史地图图像以提取其文本内容，并生成一组与大型外部地理空间知识库相关的元数据。 RDF（资源描述框架）格式中的链接元数据支持用于查找和索引历史地图的复杂查询，例如检索加利福尼亚州高于1000米的山峰的所有历史地图。我们在称为MapKurator的系统中实现了方法。我们使用来自多个来源的历史地图评估了MapKurator，各种地图样式，尺度和覆盖范围。我们的结果显示出对最先进的方法的显着改善。该代码已被公开可用作Kartta Labs项目的模块，以https://github.com/kartta-labs/project。

Diversity Searcher is a tool originally developed to help analyse diversity in news media texts. It relies on a form of automated content analysis and thus rests on prior assumptions and depends on certain design choices related to diversity and fairness. One such design choice is the external knowledge source(s) used. In this article, we discuss implications that these sources can have on the results of content analysis. We compare two data sources that Diversity Searcher has worked with - DBpedia and Wikidata - with respect to their ontological coverage and diversity, and describe implications for the resulting analyses of text corpora. We describe a case study of the relative over- or under-representation of Belgian political parties between 1990 and 2020 in the English-language DBpedia, the Dutch-language DBpedia, and Wikidata, and highlight the many decisions needed with regard to the design of this data analysis and the assumptions behind it, as well as implications from the results. In particular, we came across a staggering over-representation of the political right in the English-language DBpedia.

Artificial intelligence(AI) systems based on deep neural networks (DNNs) and machine learning (ML) algorithms are increasingly used to solve critical problems in bioinformatics, biomedical informatics, and precision medicine. However, complex DNN or ML models that are unavoidably opaque and perceived as black-box methods, may not be able to explain why and how they make certain decisions. Such black-box models are difficult to comprehend not only for targeted users and decision-makers but also for AI developers. Besides, in sensitive areas like healthcare, explainability and accountability are not only desirable properties of AI but also legal requirements -- especially when AI may have significant impacts on human lives. Explainable artificial intelligence (XAI) is an emerging field that aims to mitigate the opaqueness of black-box models and make it possible to interpret how AI systems make their decisions with transparency. An interpretable ML model can explain how it makes predictions and which factors affect the model's outcomes. The majority of state-of-the-art interpretable ML methods have been developed in a domain-agnostic way and originate from computer vision, automated reasoning, or even statistics. Many of these methods cannot be directly applied to bioinformatics problems, without prior customization, extension, and domain adoption. In this paper, we discuss the importance of explainability with a focus on bioinformatics. We analyse and comprehensively overview of model-specific and model-agnostic interpretable ML methods and tools. Via several case studies covering bioimaging, cancer genomics, and biomedical text mining, we show how bioinformatics research could benefit from XAI methods and how they could help improve decision fairness.

Kernel machines have sustained continuous progress in the field of quantum chemistry. In particular, they have proven to be successful in the low-data regime of force field reconstruction. This is because many physical invariances and symmetries can be incorporated into the kernel function to compensate for much larger datasets. So far, the scalability of this approach has however been hindered by its cubical runtime in the number of training points. While it is known, that iterative Krylov subspace solvers can overcome these burdens, they crucially rely on effective preconditioners, which are elusive in practice. Practical preconditioners need to be computationally efficient and numerically robust at the same time. Here, we consider the broad class of Nystr\"om-type methods to construct preconditioners based on successively more sophisticated low-rank approximations of the original kernel matrix, each of which provides a different set of computational trade-offs. All considered methods estimate the relevant subspace spanned by the kernel matrix columns using different strategies to identify a representative set of inducing points. Our comprehensive study covers the full spectrum of approaches, starting from naive random sampling to leverage score estimates and incomplete Cholesky factorizations, up to exact SVD decompositions.

We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans. Through a visual evaluation by 3D experts, we show that our method retrieves annotations that are at least as accurate as manual annotations, and can thus be used as ground truth without the burden of manually annotating 3D data. We do this using an analysis-by-synthesis approach, which compares renderings of the CAD models with the captured scene. We introduce a 'cloning procedure' that identifies objects that have the same geometry, to annotate these objects with the same CAD models. This allows us to obtain complete annotations for the ScanNet dataset and the recent ARKitScenes dataset.

Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasive and the laboratory analysis can be slow or inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool which is quick and simple to acquire. However, the problem of estimating continuous electrolyte concentrations directly from ECGs is not well-studied. We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations. Methods: We explore the use of deep neural networks (DNNs) for this task. We analyze the regression performance across four electrolytes, utilizing a novel dataset containing over 290000 ECGs. For improved understanding, we also study the full spectrum from continuous predictions to binary classification of extreme concentration levels. To enhance clinical usefulness, we finally extend to a probabilistic regression approach and evaluate different uncertainty estimates. Results: We find that the performance varies significantly between different electrolytes, which is clinically justified in the interplay of electrolytes and their manifestation in the ECG. We also compare the regression accuracy with that of traditional machine learning models, demonstrating superior performance of DNNs. Conclusion: Discretization can lead to good classification performance, but does not help solve the original problem of predicting continuous concentration levels. While probabilistic regression demonstrates potential practical usefulness, the uncertainty estimates are not particularly well-calibrated. Significance: Our study is a first step towards accurate and reliable ECG-based prediction of electrolyte concentration levels.

Earthquakes, fire, and floods often cause structural collapses of buildings. The inspection of damaged buildings poses a high risk for emergency forces or is even impossible, though. We present three recent selected missions of the Robotics Task Force of the German Rescue Robotics Center, where both ground and aerial robots were used to explore destroyed buildings. We describe and reflect the missions as well as the lessons learned that have resulted from them. In order to make robots from research laboratories fit for real operations, realistic test environments were set up for outdoor and indoor use and tested in regular exercises by researchers and emergency forces. Based on this experience, the robots and their control software were significantly improved. Furthermore, top teams of researchers and first responders were formed, each with realistic assessments of the operational and practical suitability of robotic systems.

Fine-grained semantic segmentation of a person's face and head, including facial parts and head components, has progressed a great deal in recent years. However, it remains a challenging task, whereby considering ambiguous occlusions and large pose variations are particularly difficult. To overcome these difficulties, we propose a novel framework termed Mask-FPAN. It uses a de-occlusion module that learns to parse occluded faces in a semi-supervised way. In particular, face landmark localization, face occlusionstimations, and detected head poses are taken into account. A 3D morphable face model combined with the UV GAN improves the robustness of 2D face parsing. In addition, we introduce two new datasets named FaceOccMask-HQ and CelebAMaskOcc-HQ for face paring work. The proposed Mask-FPAN framework addresses the face parsing problem in the wild and shows significant performance improvements with MIOU from 0.7353 to 0.9013 compared to the state-of-the-art on challenging face datasets.