Ultrafast all-optical wavelength conversion using an integrated amplifier/DFB laser

Wavelength conversion in the 1550 nm regime was achieved in an integrated semiconductor optical amplifier (SOA)/DFB laser by modulating the output power of the laser with a light beam of a different wavelength externally injected into the SOA section. A 12 dB output extinction ratio was obtained for an average coupled input power of 75 μW with the laser section driven at 65 mA and the amplifier section at 180 mA. The response time achieved was as low as 13 ps with the laser biased at 175 mA even with low extinction ratios. The laser exhibits a similar recovery time allowing potentially very high bit-rate operation.

10-Gbit/s transmission over 300-m standard multimode fiber using multilevel coding and 2-channel WDM

A combination of multilevel coding schemes and simple two-channel wavelength division multiplexing (WDM) at 1300 and 1550 nm was used to transmit an aggregate of 10 Gbit/s over 300 m of multimode fiber that is typical of that employed in current Local Area Networks (LANs). It was shown that this technique could be a simple solution for achieving 10 Gigabit ethernet links over installed multimode fiber building backbones.

Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager

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.

Natural actor and belief critic: Reinforcement algorithm for learning parameters of dialogue systems modelled as POMDPs

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.

Plasmonic band gaps and waveguide effects in carbon nanotube arrays based metamaterials.

Highly dense periodic arrays of multiwalled carbon nanotubes behave like low-density plasma of very heavy charged particles, acting as metamaterials. These arrays with nanoscale lattice constants can be designed to display extended plasmonic band gaps within the optical regime, encompassing the crucial optical windows (850 and 1550 nm) simultaneously. We demonstrate an interesting metamaterial waveguide effect displayed by these nanotube arrays containing line defects. The nanotube arrays with lattice constants of 400 nm and radius of 50 nm were studied. Reflection experiments conducted on the nanoscale structures were in agreement with numerical calculations.