Delivering configurable solutions, that is products tailored to the requirements of a particular customer, is a priority of most B2B and B2C markets. These markets now heavily rely on interactive configurators that help customers build complete and correct products. Reliability is thus a critical requirement for configurators. Yet, our experience in industry reveals that many configurators are developed in an ad hoc manner, raising correctness and maintenance issues. In this paper, we present a vision to re-engineering more reliable configurators and the challenges it poses. The first challenge is to reverse engineer from an existing configurator the variability information, including complex rules, and to consolidate it in a variability model, namely a feature model. The second challenge is to forward engineer a new configurator that uses the feature model to generate a customized graphical user interface and the underlying reasoning engine.
If robots are to cooperate with humans in an increasingly human-like manner, then significant progress must be made in their abilities to observe and learn to perform novel goal directed actions in a flexible and adaptive manner. The current research addresses this challenge. In CHRIS.I [1], we developed a platform-independent perceptual system that learns from observation to recognize human actions in a way which abstracted from the specifics of the robotic platform, learning actions including "put X on Y" and "take X". In the current research, we extend this system from action perception to execution, consistent with current developmental research in human understanding of goal directed action and teleological reasoning. We demonstrate the platform independence with experiments on three different robots. In Experiments 1 and 2 we complete our previous study of perception of actions "put" and "take" demonstrating how the system learns to execute these same actions, along with new related actions "cover" and "uncover" based on the composition of action primitives "grasp X" and "release X at Y". Significantly, these compositional action execution specifications learned on one iCub robot are then executed on another, based on the abstraction layer of motor primitives. Experiment 3 further validates the platformindependence of the system, as a new action that is learned on the iCub in Lyon is then executed on the Jido robot in Toulouse. In Experiment 4 we extended the definition of action perception to include the notion of agency, again inspired by developmental studies of agency attribution, exploiting the Kinect motion capture system for tracking human motion. Finally in Experiment 5 we demonstrate how the combined representation of action in terms of perception and execution provides the basis for imitation. This provides the basis for an open ended cooperation capability where new actions can be learned and integrated into shared plans for cooperation. Part of the novelty of this research is the robots' use of spoken language understanding and visual perception to generate action representations in a platform independent manner basedManuscript received March 15, 2011. This work was fully supported by European FP7 ICT project CHRIS).
It is often difficult to achieve realistic simulation in teaching endodontic surgery. There is relatively little material available to students other than animal tissue or human cadavers. We propose a working model for the teaching of this discipline, constructed from casts of a natural skull which reproduce anatomical features such as gingiva, maxillary sinus, or mandibular canal, for example. This model, made of polyurethane resin containing mineral particles, which closely simulates the radiological and tactile differences of the relative densities of spongy bone, cortical bone and teeth, permits close simulation of the conditions under which endodontic surgery may be performed.
Hydroxyurea inhibits antigen-induced lymphoproliferation in vitro at a concentration at which it does not inhibit PHA-induced HIV replication. Hydroxyurea may inhibit HIV-1 in CD4+ T cells in vivo not only by decreasing the amount of intracellular deoxynucleotides, but more specifically by interfering with antigen-dependent T-cell activation, thereby causing a reduction in the number of HIV target cells.
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