Abstract-Tying suture knots is a time-consuming task performed frequently during Minimally Invasive Surgery (MIS).Automating this task could greatly reduce total surgery time for patients. Current solutions to this problem replay manually programmed trajectories, but a more general and robust approach is to use supervised machine learning to smooth surgeongiven training trajectories and generalize from them. Since knottying generally requires a controller with internal memory to distinguish between identical inputs that require different actions at different points along a trajectory, it would be impossible to teach the system using traditional feedforward neural nets or support vector machines. Instead we exploit more powerful, recurrent neural networks (RNNs) with adaptive internal states. Results obtained using LSTM RNNs trained by the recent Evolino algorithm show that this approach can significantly increase the efficiency of suture knot tying in MIS over preprogrammed control.

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Time-dependent density-functional theory is used to calculate the energy loss of antiprotons and protons traversing metal clusters of variable size. We find that the effective energy loss per unit path length inside the cluster shows no significant cluster size effects over the wide range of projectile velocities studied. This allows us to compare the calculated stopping power with the experimental values for a solid metal target. Excellent agreement between the theoretical results and recent experimental data is found for velocities below the inner-shell excitation threshold. We thus present a nonperturbative quantum-mechanical approach to obtain the energy loss of charges in solids.