EXAMINING TRADER BEHAVIOR IN IDEA MARKETS: AN IMPLEMENTATION OF GE'S IMAGINATION MARKETS

We present the outcome of an idea market run for one of GE Energy's sub-businesses in July and August of 2006.  GE Energy used this market to elicit and rank-order technology and product ideas from across the sub-business. In this experiment, we examine the behavior of traders that have submitted the ideas on the market and their influence on the market's outcome. An idea’s submitter is clearly motivated to have his idea valued highly by the market, both by the funding given to the top idea as well as smaller prizes given to the top three ideas. In general, founders tended to buy their suggested ideas at prices above the volume-weighted-average-price (VWAP) in significant volumes. We discuss the implications and mitigation strategies. A survey of market participants yielded mixed results regarding the market's effectiveness at ranking ideas but very positive results regarding the quality of ideas proposed

Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies

Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion. This paper advocates for the training of surrogates that are consistent with the physical manifold -- i.e., predictions are always physically meaningful, and are cyclically consistent -- i.e., when the predictions of the surrogate, when passed through an independently trained inverse model give back the original input parameters. We find that these two consistencies lead to surrogates that are superior in terms of predictive performance, more resilient to sampling artifacts, and tend to be more data efficient. Using Inertial Confinement Fusion (ICF) as a test bed problem, we model a 1D semi-analytic numerical simulator and demonstrate the effectiveness of our approach. Code and data are available at https://github.com/rushilanirudh/macc/Comment: 10 pages, 6 figure

Coyote, Canis latrans - Rio Grande Turkey, Meleagris gallopavo intermedia, Interactions

Coyotes (Canis latrans) are widely known to be predators of Wild Turkeys (Meleagris gallopauo sspp.). We describe two observations of single Coyotes coming within 10 m of feeding Wild Turkey flocks without attempting to predate them in Stevens County, Kansas. We relate these observations to Coyote predation on turkeys and mobbing behavior

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process. We present a novel tournament method to train traditional as well as generative adversarial networks built on LBANN, a scalable deep learning framework optimized for HPC systems. LBANN combines multiple levels of parallelism and exploits some of the worlds largest supercomputers. We demonstrate our framework by creating a complex predictive model based on multi-variate data from high-energy-density physics containing hundreds of millions of images and hundreds of millions of scalar values derived from tens of millions of simulations of inertial confinement fusion. Our approach combines an HPC workflow and extends LBANN with optimized data ingestion and the new tournament-style training algorithm to produce a scalable neural network architecture using a CORAL-class supercomputer. Experimental results show that 64 trainers (1024 GPUs) achieve a speedup of 70.2 over a single trainer (16 GPUs) baseline, and an effective 109% parallel efficiency