We analyze the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several particularities such as the concentric circular ring shape of the objects and high precision requirements due to which existing methods don't perform sufficiently well. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at http://github.com/alexander-g/INBD.

With an ever-growing number of new publications each day, scientific writing poses an interesting domain for authorship analysis of both single-author and multi-author documents. Unfortunately, most existing corpora lack either material from the science domain or the required metadata. Hence, we present SMAuC, a new metadata-rich corpus designed specifically for authorship analysis in scientific writing. With more than three million publications from various scientific disciplines, SMAuC is the largest openly available corpus for authorship analysis to date. It combines a wide and diverse range of scientific texts from the humanities and natural sciences with rich and curated metadata, including unique and carefully disambiguated author IDs. We hope SMAuC will contribute significantly to advancing the field of authorship analysis in the science domain.

流程挖掘的学科在医疗保健领域成功应用程序具有可靠的记录。在这样的研究领域，我们进行了与德国Uniklinik Aachen医院重症监护病房（ICU）病房有关的案例研究。这项工作的目的是双重的：开发一个规范模型，该模型代表了COVID-19患者治疗的临床指南，并分析观察到的行为（记录在医院的信息系统中）对此类准则的依从性。我们表明，通过一致性检查技术，可以分析COVID-19患者的护理过程，并强调与临床准则的主要偏差。结果为医生提供了改善过程并确保服务质量和患者满意度的有用指示。我们将结果模型作为开源BPMN文件共享。

本文通过讨论参加了为期三年的SubT竞赛的六支球队的不同大满贯策略和成果，报道了地下大满贯的现状。特别是，本文有四个主要目标。首先，我们审查团队采用的算法，架构和系统；特别重点是以激光雷达以激光雷达为中心的SLAM解决方案（几乎所有竞争中所有团队的首选方法），异质的多机器人操作（包括空中机器人和地面机器人）和现实世界的地下操作（从存在需要处理严格的计算约束的晦涩之处）。我们不会回避讨论不同SubT SLAM系统背后的肮脏细节，这些系统通常会从技术论文中省略。其次，我们通过强调当前的SLAM系统的可能性以及我们认为与一些良好的系统工程有关的范围来讨论该领域的成熟度。第三，我们概述了我们认为是基本的开放问题，这些问题可能需要进一步的研究才能突破。最后，我们提供了在SubT挑战和相关工作期间生产的开源SLAM实现和数据集的列表，并构成了研究人员和从业人员的有用资源。

知识表示和推理有悠久的历史，即研究如何通过机器对知识进行形式化，解释和语义分析。在自动化车辆领域，最近的进步表明，能够将相关知识形式化和利用相关知识作为处理交通界固有且复杂的环境的关键推动者。本文证明了本体论是a）对自动车辆环境中与关键相关的因素进行建模和形式化的强大工具。为此，我们利用著名的6层模型来创建环境环境的形式表示。在此表示形式中，本体论将域知识模型为逻辑公理，从而促进交通场景和场景中的关键因素的存在。为了执行自动分析，将联合描述逻辑和规则推理器与A-Priori谓词增强结合使用。我们详细介绍了模块化方法，提出了公开可用的实施，并通过大规模的无人机数据集评估了该方法的城市交通情况。

我们介绍了网络STEREO-21数据集，这是开放式出版物中大量的科学文本重复使用。它包含420万个独特的开放式出版物中发现的超过9100万例重复使用的文本段落。我们的数据集具有高度覆盖科学学科和重复使用的多种元素，以及全面的元数据，以使每个案例与每个案例进行背景化，解决了以前关于科学写作的最显着的缺点。Webis-Stereo-21允许从不同科学背景中解决广泛的研究问题，从而促进了对该现象的定性和定量分析，以及首次基于科学出版物中文本重复使用的基本率。

在公共道路上大规模的自动车辆部署有可能大大改变当今社会的运输方式。尽管这种追求是在几十年前开始的，但仍有公开挑战可靠地确保此类车辆在开放环境中安全运行。尽管功能安全性是一个完善的概念，但测量车辆行为安全的问题仍然需要研究。客观和计算分析交通冲突的一种方法是开发和利用所谓的关键指标。在与自动驾驶有关的各种应用中，当代方法利用了关键指标的潜力，例如用于评估动态风险或过滤大型数据集以构建方案目录。作为系统地选择适当的批判性指标的先决条件，我们在自动驾驶的背景下广泛回顾了批判性指标，其属性及其应用的现状。基于这篇综述，我们提出了一种适合性分析，作为一种有条不紊的工具，可以由从业者使用。然后，可以利用提出的方法和最新审查的状态来选择涵盖应用程序要求的合理的测量工具，如分析的示例性执行所证明。最终，高效，有效且可靠的衡量自动化车辆安全性能是证明其可信赖性的关键要求。

背景：虽然卷积神经网络（CNN）实现了检测基于磁共振成像（MRI）扫描的阿尔茨海默病（AD）痴呆的高诊断准确性，但它们尚未应用于临床常规。这是一个重要原因是缺乏模型可理解性。最近开发的用于导出CNN相关性图的可视化方法可能有助于填补这种差距。我们调查了具有更高准确性的模型还依赖于先前知识预定义的判别脑区域。方法：我们培训了CNN，用于检测痴呆症和Amnestic认知障碍（MCI）患者的N = 663 T1加权MRI扫描的AD，并通过交叉验证和三个独立样本验证模型的准确性= 1655例。我们评估了相关评分和海马体积的关联，以验证这种方法的临床效用。为了提高模型可理解性，我们实现了3D CNN相关性图的交互式可视化。结果：跨三个独立数据集，组分离表现出广告痴呆症与控制的高精度（AUC $ \ GEQUQ $ 0.92）和MCI与控制的中等精度（AUC $ \约0.75美元）。相关性图表明海马萎缩被认为是广告检测的最具信息性因素，其其他皮质和皮质区域中的萎缩额外贡献。海马内的相关评分与海马体积高度相关（Pearson的r $ \大约$ -0.86，p <0.001）。结论：相关性地图突出了我们假设先验的地区的萎缩。这加强了CNN模型的可理解性，这些模型基于扫描和诊断标签以纯粹的数据驱动方式培训。

Deep learning models are known to put the privacy of their training data at risk, which poses challenges for their safe and ethical release to the public. Differentially private stochastic gradient descent is the de facto standard for training neural networks without leaking sensitive information about the training data. However, applying it to models for graph-structured data poses a novel challenge: unlike with i.i.d. data, sensitive information about a node in a graph cannot only leak through its gradients, but also through the gradients of all nodes within a larger neighborhood. In practice, this limits privacy-preserving deep learning on graphs to very shallow graph neural networks. We propose to solve this issue by training graph neural networks on disjoint subgraphs of a given training graph. We develop three random-walk-based methods for generating such disjoint subgraphs and perform a careful analysis of the data-generating distributions to provide strong privacy guarantees. Through extensive experiments, we show that our method greatly outperforms the state-of-the-art baseline on three large graphs, and matches or outperforms it on four smaller ones.

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.