21 resultados para Ulrich, Duke of Württemberg, 1487-1550


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Effective dialogue management is critically dependent on the information that is encoded in the dialogue state. In order to deploy reinforcement learning for policy optimization, dialogue must be modeled as a Markov Decision Process. This requires that the dialogue statemust encode all relevent information obtained during the dialogue prior to that state. This can be achieved by combining the user goal, the dialogue history, and the last user action to form the dialogue state. In addition, to gain robustness to input errors, dialogue must be modeled as a Partially Observable Markov Decision Process (POMDP) and hence, a distribution over all possible states must be maintained at every dialogue turn. This poses a potential computational limitation since there can be a very large number of dialogue states. The Hidden Information State model provides a principled way of ensuring tractability in a POMDP-based dialogue model. The key feature of this model is the grouping of user goals into partitions that are dynamically built during the dialogue. In this article, we extend this model further to incorporate the notion of complements. This allows for a more complex user goal to be represented, and it enables an effective pruning technique to be implemented that preserves the overall system performance within a limited computational resource more effectively than existing approaches. © 2011 ACM.

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This article presents a novel algorithm for learning parameters in statistical dialogue systems which are modeled as Partially Observable Markov Decision Processes (POMDPs). The three main components of a POMDP dialogue manager are a dialogue model representing dialogue state information; a policy that selects the system's responses based on the inferred state; and a reward function that specifies the desired behavior of the system. Ideally both the model parameters and the policy would be designed to maximize the cumulative reward. However, while there are many techniques available for learning the optimal policy, no good ways of learning the optimal model parameters that scale to real-world dialogue systems have been found yet. The presented algorithm, called the Natural Actor and Belief Critic (NABC), is a policy gradient method that offers a solution to this problem. Based on observed rewards, the algorithm estimates the natural gradient of the expected cumulative reward. The resulting gradient is then used to adapt both the prior distribution of the dialogue model parameters and the policy parameters. In addition, the article presents a variant of the NABC algorithm, called the Natural Belief Critic (NBC), which assumes that the policy is fixed and only the model parameters need to be estimated. The algorithms are evaluated on a spoken dialogue system in the tourist information domain. The experiments show that model parameters estimated to maximize the expected cumulative reward result in significantly improved performance compared to the baseline hand-crafted model parameters. The algorithms are also compared to optimization techniques using plain gradients and state-of-the-art random search algorithms. In all cases, the algorithms based on the natural gradient work significantly better. © 2011 ACM.

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We report the enhancement of sub-bandgap photoluminescence from silicon via the Purcell effect. We couple the defect emission from silicon, which is believed to be due to hydrogen incorporation into the lattice, to a photonic crystal (PhC) nanocavity. We observe an up to 300-fold enhancement of the emission at room temperature at 1550 nm, as compared to an unpatterned sample, which is then comparable to the silicon band-edge emission. We discuss the possibility of enhancing this emission even further by introducing additional defects by ion implantation, or by treating the silicon PhC nanocavity with hydrogen plasma. © 2011 Elsevier B.V.

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In order to guarantee a sustainable supply of future energy demand without compromising the environment, some actions for a substantial reduction of CO 2 emissions are nowadays deeply analysed. One of them is the improvement of the nuclear energy use. In this framework, innovative gas-cooled reactors (both thermal and fast) seem to be very attractive from the electricity production point of view and for the potential industrial use along the high temperature processes (e.g., H 2 production by steam reforming or I-S process). This work focuses on a preliminary (and conservative) evaluation of possible advantages that a symbiotic cycle (EPR-PBMR-GCFR) could entail, with special regard to the reduction of the HLW inventory and the optimization of the exploitation of the fuel resources. The comparison between the symbiotic cycle chosen and the reference one (once-through scenario, i.e., EPR-SNF directly disposed) shows a reduction of the time needed to reach a fixed reference level from ∼170000 years to ∼1550 years (comparable with typical human times and for this reason more acceptable by the public opinion). In addition, this cycle enables to have a more efficient use of resources involved: the total electric energy produced becomes equal to ∼630 TWh/year (instead of only ∼530 TWh/year using only EPR) without consuming additional raw materials. © 2009 Barbara Vezzoni et al.

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We demonstrate the design, fabrication and experimental characterization of a submicron-scale silicon waveguide that is fabricated by local oxidation of silicon. The use of local oxidation process allows defining the waveguide geometry and obtaining smooth sidewalls. The process can be tuned to precisely control the shape and the dimensions of the waveguide. The fabricated waveguides are measured using near field scanning optical microscope at 1550 nm wavelength. These measurements show mode width of 0.4 µm and effective refractive index of 2.54. Finally, we demonstrate the low loss characteristics of our waveguide by imaging the light scattering using an infrared camera.