Single mode performance of a single mode injected locked quantum dot laser

An injection locked QD laser is demonstrated to provide single longitudinal mode operation with a 40dB SMSR and an improvement in RIN peak from 1.3-2.3GHz. Alpha factor is measured to be 0.8. © 2005 Optical Society of America.

20GHz colliding pulse mode-locking of ingaas quantum dot lasers

Colliding pulse mode-locking is demonstrated for the first time in quantum-dot lasers. Close to transform limited, 7ps, 20GHz pulses are achieved using an absorber length considerably longer than typically used in similar quantum-well lasers. © 2004 Optical Society of America.

All-fiber Yb-doped laser mode-locked by nanotubes

Single-wall carbon nanotubes (SWNTs) and graphene have emerged as promising saturable absorbers (SAs), due to their broad operation bandwidth and fast recovery times [1-3]. However, Yb-doped fiber lasers mode-locked using CNT and graphene SAs have generated relatively long pulses. All-fiber cavity designs are highly favored for their environmental robustness. Here, we demonstrate an all-fiber Yb-doped laser based on a SWNT saturable absorber, which allows generation of 8.7 ps-long pulses, externally compressed to 118 fs. To the best of our knowledge, these are the shortest pulses obtained with SWNT SAs from a Yb-doped fiber laser. © 2013 IEEE.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.