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https://www.selleckchem.com/products/CUDC-101.html Concentric tube robots, composed of nested pre-curved tubes, have the potential to perform minimally invasive surgery at difficult-to-reach sites in the human body. In order to plan motions that safely perform surgeries in constrained spaces that require avoiding sensitive structures, the ability to accurately estimate the entire shape of the robot is needed. Many state-of-the-art physics-based shape models are unable to account for complex physical phenomena and subsequently are less accurate than is required for safe surgery. In this work, we present a learned model that can estimate the entire shape of a concentric tube robot. The learned model is based on a deep neural network that is trained using a mixture of simulated and physical data. We evaluate multiple network architectures and demonstrate the model's ability to compute the full shape of a concentric tube robot with high accuracy.Computational models using text corpora have proved useful in understanding the nature of language and human concepts. One appeal of this work is that text, such as from newspaper articles, should reflect human behaviour and conceptual organization outside the laboratory. However, texts do not directly reflect human activity, but instead serve a communicative function and are highly curated or edited to suit an audience. Here, we apply methods devised for text to a data source that directly reflects thousands of individuals' activity patterns. Using product co-occurrence data from nearly 1.3-m supermarket shopping baskets, we trained a topic model to learn 25 high-level concepts (or topics). These topics were found to be comprehensible and coherent by both retail experts and consumers. The topics indicated that human concepts are primarily organized around goals and interactions (e.g. tomatoes go well with vegetables in a salad), rather than their intrinsic features (e.g. defining a tomato by the fact that it has seeds and is fl

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