我们描述了与全球结构搜索方法结合使用的局部替代模型。该模型遵循高斯近似电势（GAP）形式主义，并基于原子位置描述符的平滑重叠，而使用Mini Batch $ K $ -MEANS则减少了本地环境的稀疏性。该模型是在原子全局优化X框架中实现的，并用作盆地跳结构搜索中局部放松的部分替代。该方法对于多种原子系统（包括分子，纳米颗粒，表面支撑的簇和表面薄膜）来说是可靠的。展示了本地替代模型的结构搜索环境中的好处。这包括从较小的系统转移学习的能力，以及执行并发多层计量搜索的可能性。

Manually analyzing spermatozoa is a tremendous task for biologists due to the many fast-moving spermatozoa, causing inconsistencies in the quality of the assessments. Therefore, computer-assisted sperm analysis (CASA) has become a popular solution. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30s of spermatozoa with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. VISEM-Tracking is an extension of the previously published VISEM dataset. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning model trained on the VISEM-Tracking dataset. As a result, the dataset can be used to train complex deep-learning models to analyze spermatozoa. The dataset is publicly available at https://zenodo.org/record/7293726.

The notion of concept drift refers to the phenomenon that the distribution generating the observed data changes over time. If drift is present, machine learning models may become inaccurate and need adjustment. Many technologies for learning with drift rely on the interleaved test-train error (ITTE) as a quantity which approximates the model generalization error and triggers drift detection and model updates. In this work, we investigate in how far this procedure is mathematically justified. More precisely, we relate a change of the ITTE to the presence of real drift, i.e., a changed posterior, and to a change of the training result under the assumption of optimality. We support our theoretical findings by empirical evidence for several learning algorithms, models, and datasets.

In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowledge extraction based on process mining has been proposed, among other techniques, to provide users with intuitive access to the information contained therein. At present, the majority of technologies aim to reconstruct explicit business process models. These are directly interpretable but limited concerning the integration of diverse and real-valued information sources. On the other hand, Machine Learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes. In this contribution, we evaluate the capability of modern Transformer architectures as well as more classical ML technologies of modeling process regularities, as can be quantitatively evaluated by their prediction capability. In addition, we demonstrate the capability of attentional properties and feature relevance determination by highlighting features that are crucial to the processes' predictive abilities. We demonstrate the efficacy of our approach using five benchmark datasets and show that the ML models are capable of predicting critical outcomes and that the attention mechanisms or XAI components offer new insights into the underlying processes.

Counterfactual explanations are a popular type of explanation for making the outcomes of a decision making system transparent to the user. Counterfactual explanations tell the user what to do in order to change the outcome of the system in a desirable way. However, it was recently discovered that the recommendations of what to do can differ significantly in their complexity between protected groups of individuals. Providing more difficult recommendations of actions to one group leads to a disadvantage of this group compared to other groups. In this work we propose a model-agnostic method for computing counterfactual explanations that do not differ significantly in their complexity between protected groups.

到目前为止，可解释的人工智能（XAI）主要集中在静态学习方案上。我们对逐步采样数据的动态场景感兴趣，学习是以增量而不是批处理模式进行的。我们寻求有效的增量算法来计算特征重要性（FI）度量，具体来说，基于缺乏特征的特征边缘化的增量FI度量，类似于置换功能的特征重要性（PFI）。我们提出了一种称为IPFI的高效，模型不足的算法，以逐步估算此度量，并在包括概念漂移（概念漂移）在内的动态建模条件下进行估算。我们证明了关于期望和差异方面的近似质量的理论保证。为了验证我们的理论发现和与传统批处理PFI相比，我们的方法的疗效，我们对具有和没有概念漂移的基准数据进行了多项实验研究。

基于机器学习的决策系统应用于安全关键领域需要可靠的高确定性预测。为此，可以通过拒绝选项来扩展系统，该选项允许系统拒绝输入，而只有一个具有不可接受的低确定性的预测。虽然能够拒绝不确定的样本很重要，但能够解释为什么拒绝特定样本也很重要。随着可解释的AI（XAI）的持续兴起，已经开发了许多基于机器学习系统的解释方法 - 但是，解释拒绝选项仍然是一个新的领域，在这种情况下，很少有先前的工作。在这项工作中，我们建议通过半就意义解释解释拒绝，这是基于示例的解释方法的实例，在XAI社区中尚未广泛考虑它们。我们提出了对任意拒绝选项的半同性恋解释的概念建模，并在基于共形预测的拒绝选项上对特定的实现进行了经验评估。

降低降低是一种流行的预处理，也是数据挖掘中广泛使用的工具。透明度通常是通过解释来实现的，如今已成为基于机器学习的系统（例如分类器和推荐系统）的广泛接受和关键要求。但是，降低维度和其他数据挖掘工具的透明度尚未得到太多考虑，但要了解其行为至关重要 - 特别是从业者可能想了解为什么特定样本被映射到特定位置。为了（本地）理解给定维度降低方法的行为，我们介绍了降低维度的对比解释的抽象概念，并将实现此概念的实现应用于解释两个维数据可视化的特定应用。

数据流分类是机器学习领域的重要问题。由于数据的非平稳性，其基础分布会随着时间的流逝而变化（概念漂移），因此该模型需要不断适应新的数据统计信息。基于流的主动学习（AL）方法通过交互式查询人类专家以在有限的预算内为最新样本提供新的数据标签来解决此问题。现有的AL策略假设可以立即可用标签，而在现实情况下，专家需要时间提供查询标签（验证延迟），而当请求的标签到达时，它们可能不再相关。在本文中，我们研究了在AL方法上存在概念漂移的情况下，有限，时间变化和未知验证延迟的影响。我们提出了繁殖（PR），这是一种独立的延迟效用估计器，它也预测了所请求但尚不清楚的标签。此外，我们提出了一种依赖漂移的动态预算策略，该策略在检测到的漂移后使用标签预算的可变分布。彻底的实验评估，包括合成和现实世界的非平稳数据集，以及验证延迟和预算的不同设置。我们从经验上表明，所提出的方法始终优于最先进的方法。此外，我们证明，随着时间的及时预算分配，可以提高AL策略的性能，而不会增加整体标签预算。

准确的交通预测是使流量管理等流量管理的关键要素，例如重新路由汽车减少道路拥堵或通过动态速度限制来调节流量以保持稳定的流量。表示流量数据的一种方法是以时间更改的热图可视化流量的属性（例如速度和音量）的形式。在最近的作品中，U-NET模型在热图预测的交通预测上显示了SOTA性能。我们建议将U-NET体系结构与图层相结合，该层面可以改善与香草U-NET相比，将空间概括到看不见的道路网络。特别是，我们专门将现有的图形操作对地理拓扑敏感，并概括合并和升级操作以适用于图形。