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The Sphere project stands at the intersection of the humanities and information sciences. The project aims to better understand the evolution of knowledge in the early modern period by studying a collection of 359 textbook editions published between 1472 and 1650 which were used to teach geocentric cosmology and astronomy at European universities. The relatively large size of the corpus at hand presents a challenge for traditional historical approaches, but provides a great opportunity to explore such a large collection of historical data using computational approaches. In this paper, we present a review of the different computational approaches, used in this project over the period of the last three years, that led to a better understanding of the dynamics of knowledge transfer and transformation in the early modern period. [ABSTRACT FROM AUTHOR]

Understanding the pathological properties of dysregulated protein networks in individual patients' tumors is the basis for precision therapy. Functional experiments are commonly used, but cover only parts of the oncogenic signaling networks, whereas methods that reconstruct networks from omics data usually only predict average network features across tumors. Here, we show that the explainable AI method layer-wise relevance propagation (LRP) can infer protein interaction networks for individual patients from proteomic profiling data. LRP reconstructs average and individual interaction networks with an AUC of 0.99 and 0.93, respectively, and outperforms state-of-the-art network prediction methods for individual tumors. Using data from The Cancer Proteome Atlas, we identify known and potentially novel oncogenic network features, among which some are cancer-type specific and show only minor variation among patients, while others are present across certain tumor types but differ among individual patients. Our approach may therefore support predictive diagnostics in precision oncology by inferring "patient-level" oncogenic mechanisms. [ABSTRACT FROM AUTHOR]

Machine learning has become an essential tool for extracting regularities in the data and for making inferences. Neural networks, in particular, provide the scalability and flexibility that is needed to convert complex datasets into structured and well-generalizing models. Pretrained models have strongly facilitated the application of neural networks to images and text data. Application to other types of data, e.g., in physics, remains more challenging and often requires ad-hoc approaches. In this chapter, we give an introduction to neural networks with a focus on the latter applications. We present practical steps that ease training of neural networks, and then review simple approaches to introduce prior knowledge into the model. The discussion is supported by theoretical arguments as well as examples showing how well-performing neural networks can be implemented easily in modern neural network frameworks. [ABSTRACT FROM AUTHOR]

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly intelligent behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner. Nonlinear machine learning methods have good predictive ability but the lack of transparency of the algorithms can limit their use. Here the authors investigate how these methods approach learning in order to assess the dependability of their decision making. [ABSTRACT FROM AUTHOR]