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Undergraduate Topics in Computer Science (UTiCS) delivers high-quality instructional content for undergraduates studying in all areas of computing and information science. From core foundational and theoretical material to final-year topics and applications, UTiCS books take a fresh, concise, and modern approach and are ideal for self-study or for a one-or two-semester course. The texts are all authored by established experts in their fields, reviewed by an international advisory board, and contain numerous examples and problems. Many include fully worked solutions.More information about this series at

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In recent years there was a tremendous progress in robotic systems, and however also increased expectations: A robot should be easy to program and reliable in task execution. Learning from Demonstration (LfD) offers a very promising alternative to classical engineering approaches. LfD is a very natural way for humans to interact with robots and will be an essential part of future service robots. In this work we first review heteroscedastic Gaussian processes and show how these can be used to encode a task. We then introduce a new Gaussian process regression model that clusters the input space into smaller subsets similar to the work in [11]. In the next step we show how these approaches fit into the Learning by Demonstration framework of [2], [3]. At the end we present an experiment on a real robot arm that shows how all these approaches interact.

Immediate surgical treatment of patients with multiple injuries augments the proinflammatory immune response in the early phase of recovery as determined by increased IL-6 and sTREM-1 plasma levels. If not required solely for damage control, the early second hit from additional surgical stress might promote posttraumatic complications by surcharging the innate immune response to injury.