This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced. On the other, a selection of important surveys and books, as well as two selected active research topics in the field, are presented.

Many state-of-the-art natural language understanding (NLU) models are based on pretrained neural language models. These models often make inferences using information from multiple sources. An important class of such inferences are those that require both background knowledge, presumably contained in a model's pretrained parameters, and instance-specific information that is supplied at inference time. However, the integration and reasoning abilities of NLU models in the presence of multiple knowledge sources have been largely understudied. In this work, we propose a test suite of coreference resolution tasks that require reasoning over multiple facts. Our dataset is organized into subtasks that differ in terms of which knowledge sources contain relevant facts. We evaluate state-of-the-art coreference resolution models on our dataset. Our results indicate that several models struggle to reason on-the-fly over knowledge observed both at pretrain time and at inference time. However, with task-specific training, a subset of models demonstrates the ability to integrate certain knowledge types from multiple sources.

已显示来自各种来源的多模式信息的集成可以提高机器学习模型的性能，因此近年来受到了越来越多的关注。通常，这样的模型使用深度模式特异性网络来获得单峰特征，这些特征合并以获得“晚融合”表示。但是，这些设计承担了各自单峰管道中信息损失的风险。另一方面，结合早期特征的“早期融合”方法遭受了与特征异质性和高样本复杂性相关的问题。在这项工作中，我们提出了一种迭代表示的改进方法，称为渐进式融合，该方法减轻了晚期融合表示的问题。我们的模型不足的技术引入了向后连接，使后期融合的表示形式可用于早期层，从而提高了这些阶段的表示表现力，同时保留了晚期融合设计的优势。我们在任务上测试渐进式融合，包括情感检测，多媒体分析以及与不同模型的时间序列融合，以证明其多功能性。我们表明，我们的方法始终提高性能，例如，在多模式时间序列预测中，MSE降低了5％，鲁棒性提高了40％。

人工智能算法越来越多地被公共机构作为决策助手，并承诺克服人类决策者的偏见。同时，他们可能会在人类算法中引入新的偏见。在心理学和公共行政文献上，我们调查了两个关键偏见：即使面对来自其他来源的警告信号（自动化偏见）的警告信号，对算法建议过度依赖，并选择性地采用算法建议时，这与刻板印象相对应（Selective Adherence）。我们通过在荷兰瓦中进行的三项实验研究评估这些研究，讨论了我们发现对公共部门决策在自动化时代的影响。总体而言，我们的研究表明，对已经脆弱和处境不利的公民自动化自动化的潜在负面影响。