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.

了解深度神经网络的结果是朝着更广泛接受深度学习算法的重要步骤。许多方法解决了解释人工神经网络的问题，但通常提供不同的解释。此外，不同的解释方法的超级公路可能导致互相冲突。在本文中，我们提出了一种使用受限制的Boltzmann机器（RBMS）来聚合不同解释算法的特征归属的技术，以实现对深神经网络的更可靠和坚固的解释。关于现实世界数据集的几个具有挑战性的实验表明，所提出的RBM方法优于流行的特征归因方法和基本集合技术。

在过去二十年中，识别具有不同纵向数据趋势的群体的方法已经成为跨越许多研究领域的兴趣。为了支持研究人员，我们总结了文献关于纵向聚类的指导。此外，我们提供了一种纵向聚类方法，包括基于基团的轨迹建模（GBTM），生长混合模拟（GMM）和纵向K平均值（KML）。该方法在基本级别引入，并列出了强度，限制和模型扩展。在最近数据收集的发展之后，将注意这些方法的适用性赋予密集的纵向数据（ILD）。我们展示了使用R.中可用的包在合成数据集上的应用程序的应用。

The ability to convert reciprocating, i.e., alternating, actuation into rotary motion using linkages is hindered fundamentally by their poor torque transmission capability around kinematic singularity configurations. Here, we harness the elastic potential energy of a linear spring attached to the coupler link of four-bar mechanisms to manipulate force transmission around the kinematic singularities. We developed a theoretical model to explore the parameter space for proper force transmission in slider-crank and rocker-crank four-bar kinematics. Finally, we verified the proposed model and methodology by building and testing a macro-scale prototype of a slider-crank mechanism. We expect this approach to enable the development of small-scale rotary engines and robotic devices with closed kinematic chains dealing with serial kinematic singularities, such as linkages and parallel manipulators.

Self-supervised learning (SSL) aims to produce useful feature representations without access to any human-labeled data annotations. Due to the success of recent SSL methods based on contrastive learning, such as SimCLR, this problem has gained popularity. Most current contrastive learning approaches append a parametrized projection head to the end of some backbone network to optimize the InfoNCE objective and then discard the learned projection head after training. This raises a fundamental question: Why is a learnable projection head required if we are to discard it after training? In this work, we first perform a systematic study on the behavior of SSL training focusing on the role of the projection head layers. By formulating the projection head as a parametric component for the InfoNCE objective rather than a part of the network, we present an alternative optimization scheme for training contrastive learning based SSL frameworks. Our experimental study on multiple image classification datasets demonstrates the effectiveness of the proposed approach over alternatives in the SSL literature.

With the increased use of machine learning systems for decision making, questions about the fairness properties of such systems start to take center stage. Most existing work on algorithmic fairness assume complete observation of features at prediction time, as is the case for popular notions like statistical parity and equal opportunity. However, this is not sufficient for models that can make predictions with partial observation as we could miss patterns of bias and incorrectly certify a model to be fair. To address this, a recently introduced notion of fairness asks whether the model exhibits any discrimination pattern, in which an individual characterized by (partial) feature observations, receives vastly different decisions merely by disclosing one or more sensitive attributes such as gender and race. By explicitly accounting for partial observations, this provides a much more fine-grained notion of fairness. In this paper, we propose an algorithm to search for discrimination patterns in a general class of probabilistic models, namely probabilistic circuits. Previously, such algorithms were limited to naive Bayes classifiers which make strong independence assumptions; by contrast, probabilistic circuits provide a unifying framework for a wide range of tractable probabilistic models and can even be compiled from certain classes of Bayesian networks and probabilistic programs, making our method much more broadly applicable. Furthermore, for an unfair model, it may be useful to quickly find discrimination patterns and distill them for better interpretability. As such, we also propose a sampling-based approach to more efficiently mine discrimination patterns, and introduce new classes of patterns such as minimal, maximal, and Pareto optimal patterns that can effectively summarize exponentially many discrimination patterns

In this paper the implementation of piezoelectrics to a state-of-the-art wafer gripper is investigated. The objective is to propose and validate a solution method, which includes a mechanical design and control system, to achieve at least 5% damping for two eigenmodes of a wafer gripper. This objective serves as a 'proof of concept' to show the possibilities of implementing a state-of-the-art damping method to an industrial application, which in turn can be used to dampen different thin structures. The coupling relation between the piezoelectrics and their host structure were used to design the placement of the piezoelectric patches, together with modal analysis data of the a state-of-the-art wafer gripper. This data had been measured through an experimental setup. Active damping has been succesfully implemented onto the wafer gripper where positive position feedback (PPF) is used as a control algorithm to dampen two eigenmodes.

当不可用的数据不可用时，在电子商务行业中通常使用强盗算法来培训机器学习（ML）系统。但是，行业的设置提出了各种挑战，使实践中实施强盗算法的挑战是非平凡的。在本文中，我们详细阐述了非政策优化，延迟奖励，概念漂移，奖励设计和业务规则限制的挑战。我们的主要贡献是对开放匪徒（OBP）框架的扩展。我们为一些上述挑战提供模拟组件，以使未来的从业者，研究人员和教育工作者提供资源，以应对电子商务行业遇到的挑战。

部分微分方程（PDE）参见在科学和工程中的广泛使用，以将物理过程的模拟描述为标量和向量场随着时间的推移相互作用和协调。由于其标准解决方案方法的计算昂贵性质，神经PDE代理已成为加速这些模拟的积极研究主题。但是，当前的方法并未明确考虑不同字段及其内部组件之间的关系，这些关系通常是相关的。查看此类相关场的时间演变通过多活动场的镜头，使我们能够克服这些局限性。多胎场由标量，矢量以及高阶组成部分组成，例如双分数和三分分射线。 Clifford代数可以描述它们的代数特性，例如乘法，加法和其他算术操作。据我们所知，本文介绍了此类多人表示的首次使用以及Clifford的卷积和Clifford Fourier在深度学习的背景下的转换。由此产生的Clifford神经层普遍适用，并将在流体动力学，天气预报和一般物理系统的建模领域中直接使用。我们通过经验评估克利福德神经层的好处，通过在二维Navier-Stokes和天气建模任务以及三维Maxwell方程式上取代其Clifford对应物中常见的神经PDE代理中的卷积和傅立叶操作。克利福德神经层始终提高测试神经PDE代理的概括能力。

因果代表学习揭示了低级观察背后的潜在高级因果变量，这对于一组感兴趣的下游任务具有巨大的潜力。尽管如此，从观察到的数据中确定真正的潜在因果表示是一个巨大的挑战。在这项工作中，我们专注于确定潜在的因果变量。为此，我们分析了潜在空间中的三个固有特性，包括传递性，置换和缩放。我们表明，传递性严重阻碍了潜在因果变量的可识别性，而排列和缩放指导指导了识别潜在因果变量的方向。为了打破传递性，我们假设潜在的潜在因果关系是线性高斯模型，其中高斯噪声的权重，平均值和方差受到额外观察到的变量的调节。在这些假设下，我们从理论上表明，潜在因果变量可以识别为微不足道的置换和缩放。基于这个理论结果，我们提出了一种新型方法，称为结构性因果变异自动编码器，该方法直接学习潜在因果变量，以及从潜在因果变量到观察到的映射。关于合成和实际数据的实验结果证明了可识别的结果以及所提出的学习潜在因果变量的能力。