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Titlebook: Robotic Surgery of the Head and Neck; A Comprehensive Guid Gregory A. Grillone,Scharukh Jalisi Book 2015 Springer Science+Business Media Ne

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Claudio Vicini M.D.,Filippo Montevecchi M.D.,Mohamed Eesa M.D.,Iacopo Dallan M.D.oncerns in this population. This chapter will review a brief history of mental health with a focus on key aspects related to geriatric issues, explore some common mental health concerns experienced within elderly populations, and provide an overview of depression in the elderly. Also included is an
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Laureano A. Giraldez-Rodriguez M.D.,Brett Miles D.D.S., M.D.,Eric M. Genden M.D.nt learning scheme that we consider is based on iterative Q-learning, which enables the controller to learn the optimal control parameters based on the input-state or input–output data. The proposed scheme has the advantage that it is immune to the exploration bias. As a result, the need of discount
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Tom Thomas M.D., M.P.H.,Donald J. Annino Jr. M.D., D.M.D.ess of the closed-loop system when the OAC uses periodic and event sampled feedback. Finally, future work involving an image sensor as part of the OAC loop using deep neural network based RL is reported. A simulation example is included to illustrate the effectiveness of the proposed approach.
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William S. Duke M.D.,David J. Terris M.D., F.A.C.S. the agents must act in a decentralized manner. In this chapter, we demonstrate a method, centralized expert supervises multi-agents (CESMA), to obtain decentralized multi-agents through a top-down approach; we first obtain a solution with a centralized controller, and then decentralize this using i
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