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Whereas scientists formulate laws and theories to account for observations, inventors create new technology to accomplish practical goals. Scientific discovery and technological innovation are among the most important accomplishments of the creative human mind. The aim of this paper is to compare how scientists produce discoveries with how inventors produce new technology. After briefly reviewing an account of thl: recent discovery of the bacterial theory of ulcers, we show that a similar account applies to the discovery that dinosaurs became extinct because of an asteroid collision. Both these discoveries involved a combination of serendipity, questioning and search. We then describe how these three processes also conttibuted to a very important recent technological innovation, the development of the programming language Java. The paper concludes with a more general assessment of the similarities and differences between cognitive processes involved in discovery and invention.Model-Based Reasoning in Scientific Discovery, edited by L. Magnani, N.

Abstract:This paper describes DIVA (Dynamic Imagery for Visual Analogy), a computational model of visual imagery based on the scene graph, a powerful representational structure widely used in computer graphics. Scene graphs make possible the visual display of complex objects, including the motions of individual objects. Our model combines a semantic-network memory system with computational procedures based on scene graphs. The model can account for people's ability to produce visual images of moving objects, in particular the ability to use dynamic visual analogies that compare two systems of objects in motion.

A: Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. K : Computerized Tomography (CT) and Computed Radiography (CR); Plasma diagnostics -interferometry, spectroscopy and imaging 1Corresponding author. 2See the author list of Overview of the JET preparation for Deuterium-Tritium Operation by E. Joffrin et al. in Nucl.

In the field of oncology, a close integration of cancer research and patient care is indispensable. Although an exchange of data between health care providers and other institutions such as cancer registries has already been established in Germany, it does not take advantage of internationally coordinated health data standards. Translational cancer research would also benefit from such standards in the context of secondary data use. This paper employs use cases from the German Cancer Consortium (DKTK) to show how this gap can be closed using a harmonised FHIR-based data model, and how to apply it to an existing federated data platform.