数字来源比以往更普遍但有效地使用它们可能是挑战性的。一个核心挑战是数字化来源通常是分布式的，因此强迫研究人员花费时间收集，解释和对齐不同的来源。知识图可以通过提供人类和机器可以查询的单一连接的真理来加速研究。在两个设计 - 测试周期中，我们将四个数据集转换为历史海域域中的四个数据集成知识图。在这些周期期间的重点是创造可持续和可用的方法，可以在其他联系数据转换工作中采用。此外，我们的知识图表可用于海事历史学家和其他感兴趣的用户，以通过统一的门户调查荷兰东印度公司的日常业务。

在这项工作中，我们在文化象征主义的背景下填补了语义网络中的差距。建立早期的工作，我们介绍了模拟本体，这是一种模拟象征意义的背景知识，通过组合从Simulacra的权威理论和Jean Baudrillard的象征性和符号中所采取的符号结构和内容的象征性理论和象征性的象征性和内容来制定象征意义的背景知识。史蒂文古老的旧版典型的字典。我们通过将其转换为在我们的本体模式中来重新设计已经存在于异质资源中以产生溢流的象征性，这是完全致力于文化象征主义的第一个知识图。提出了在知识图上运行的第一个实验，以显示对象征主义定量研究的潜力。

临床笔记是健康记录的重要组成部分。本文评估了如何使用自然语言处理（NLP）来确定肿瘤患者急性护理使用（ACU）的风险，一旦化疗开始。使用结构化健康数据（SHD）的风险预测现在是标准的，但是使用自由文本格式的预测很复杂。本文探讨了自由文本注释用于预测ACU而不是SHD的使用。将深度学习模型与手动设计的语言功能进行了比较。结果表明，SHD模型最少胜过NLP模型。具有SHD的L1型逻辑回归的C统计量为0.748（95％-CI：0.735，0.762），而具有语言功能的相同模型达到0.730（95％-CI：0.717，0.745）和基于变形金属的模型模型达到了0.702（95％-CI：0.688，0.717）。本文展示了如何在临床应用中使用语言模型，并强调了不同患者群体的风险偏见如何不同，即使仅使用自由文本数据。

在本文中，我们呈现AIDA，它是一种积极推断的代理，可以通过与人类客户端的互动来迭代地设计个性化音频处理算法。 AIDA的目标应用是在助听器（HA）算法的调整参数的情况下，每当HA客户端对其HA性能不满意时，提出了最有趣的替代值。 AIDA解释搜索“最有趣的替代品”作为最佳（声学）背景感知贝叶斯试验设计的问题。在计算术语中，AIDA被实现为基于有源推断的药剂，具有预期的试验设计的自由能标准。这种类型的建筑受到高效（贝叶斯）试验设计的神经经济模型的启发，并意味着AIDA包括用于声学信号和用户响应的生成概率模型。我们提出了一种用于声学信号的新型生成模型作为基于高斯过程分类器的时变自自回归滤波器和用户响应模型的总和。已经在生成模型的因子图中实施了完整的AIDA代理，并且通过对因子图的变分消息来实现所有任务（参数学习，声学上下文分类，试验设计等）。所有验证和验证实验和演示都可以在我们的GitHub存储库中自由访问。

最近的估计报告说，公司损失了其收入的5％，用于职业欺诈。由于大多数中型和大型公司都采用企业资源计划（ERP）系统来跟踪有关其业务流程的大量信息，因此研究人员过去曾表现出对通过ERP系统数据自动检测欺诈的兴趣。然而，当前在该领域的研究受到了以下事实的阻碍：ERP系统数据不能公开用于开发和比较欺诈检测方法。因此，我们努力生成包括正常业务运营和欺诈的公共ERP系统数据。我们提出了一种通过认真的游戏来生成ERP系统数据的策略，与审计专家合作建模各种欺诈场景，并与多位研究参与者一起生成模拟的库存生产公司的数据。我们将生成的数据汇总到准备使用的数据集中，以用于ERP系统中的欺诈检测，并向公众提供原始数据和汇总数据，以允许对ERP系统数据上的欺诈检测方法进行公开开发和比较。

近年来，生成的对抗性网络（GANS）已经证明了令人印象深刻的实验结果，同时只有一些作品促进了统计学习理论。在这项工作中，我们提出了一种用于生成对抗性学习的无限尺寸理论框架。假设统一界限的$ k $-times $ \ alpha $ -h \“较旧的可分辨率和统一的正密度，我们表明Rosenblatt的转换引起了最佳发电机，可在$ \ alpha $的假设空间中可实现H \“较旧的微分发电机。通过一致的鉴别者假设空间的定义，我们进一步表明，在我们的框架中，由发电机引起的分布与来自对手学习过程的分布之间的jensen-shannon发散，并且数据生成分布会聚到零。在足够严格的规律性假设下对数据产生过程密度的假设，我们还基于浓度和链接提供会聚率。

Modeling lies at the core of both the financial and the insurance industry for a wide variety of tasks. The rise and development of machine learning and deep learning models have created many opportunities to improve our modeling toolbox. Breakthroughs in these fields often come with the requirement of large amounts of data. Such large datasets are often not publicly available in finance and insurance, mainly due to privacy and ethics concerns. This lack of data is currently one of the main hurdles in developing better models. One possible option to alleviating this issue is generative modeling. Generative models are capable of simulating fake but realistic-looking data, also referred to as synthetic data, that can be shared more freely. Generative Adversarial Networks (GANs) is such a model that increases our capacity to fit very high-dimensional distributions of data. While research on GANs is an active topic in fields like computer vision, they have found limited adoption within the human sciences, like economics and insurance. Reason for this is that in these fields, most questions are inherently about identification of causal effects, while to this day neural networks, which are at the center of the GAN framework, focus mostly on high-dimensional correlations. In this paper we study the causal preservation capabilities of GANs and whether the produced synthetic data can reliably be used to answer causal questions. This is done by performing causal analyses on the synthetic data, produced by a GAN, with increasingly more lenient assumptions. We consider the cross-sectional case, the time series case and the case with a complete structural model. It is shown that in the simple cross-sectional scenario where correlation equals causation the GAN preserves causality, but that challenges arise for more advanced analyses.

We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.

We propose Hierarchical ProtoPNet: an interpretable network that explains its reasoning process by considering the hierarchical relationship between classes. Different from previous methods that explain their reasoning process by dissecting the input image and finding the prototypical parts responsible for the classification, we propose to explain the reasoning process for video action classification by dissecting the input video frames on multiple levels of the class hierarchy. The explanations leverage the hierarchy to deal with uncertainty, akin to human reasoning: When we observe water and human activity, but no definitive action it can be recognized as the water sports parent class. Only after observing a person swimming can we definitively refine it to the swimming action. Experiments on ActivityNet and UCF-101 show performance improvements while providing multi-level explanations.

Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e.g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a $\textit{renaissance}$ in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.