Learning a Hybrid Architecture for Sequence Regression and Annotation

When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states. Such settings arise in numerous domains, includ- ing many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible frame- work for jointly modeling both latent sequence features and the functional mapping that relates the summary response variables to the hidden state sequence. The algorithm is com- patible with a rich set of mapping functions. Results show that the availability of additional continuous response vari- ables can simultaneously improve the annotation of the se- quential observations and yield good prediction performance in both synthetic data and real-world datasets.Comment: AAAI 201

Turnover liquidity and the transmission of monetary policy

We provide empirical evidence of a novel liquidity-based transmission mechanism through which monetary policy influences asset markets, develop a model of this mechanism, and assess the ability of the quantitative theory to match the evidence

Rotorcraft Gearbox Foundation Design by a Network of Optimizations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83551/1/AIAA-2010-9310-152.pd