Pitman-Yor Diffusion Trees

We introduce the Pitman Yor Diffusion Tree (PYDT) for hierarchical clustering, a generalization of the Dirichlet Diffusion Tree (Neal, 2001) which removes the restriction to binary branching structure. The generative process is described and shown to result in an exchangeable distribution over data points. We prove some theoretical properties of the model and then present two inference methods: a collapsed MCMC sampler which allows us to model uncertainty over tree structures, and a computationally efficient greedy Bayesian EM search algorithm. Both algorithms use message passing on the tree structure. The utility of the model and algorithms is demonstrated on synthetic and real world data, both continuous and binary.

Detailed design of242mAm breeding in pressurized water reactors

There is growing interest in the use of 242mAm as a nuclear fuel. Because of its very high thermal fission cross section and its large number of neutrons released per fission, it can be used for various unique applications, such as space propulsion, medical applications, and compact energy sources. Since the thermal absorption cross section of 242mAm is very high, the best way to obtain 242mAm is by the capture of fast or epithermal neutrons in 241Am. However, fast spectrum reactors are not readily available. In this paper, we explore the possibility of producing 242mAm in existing pressurized water reactors (PWRs) with minimal interference in reactor performance. As suggested in previous studies on the subject, the 242mAm breeding targets are shielded with strong thermal absorbers in order to suppress the thermal neutron flux that causes 242mAm destruction. Since 242mAm enrichment within the Am target mainly depends on the neutron energy distribution, which in turn depends on the Am target thickness and on the neutron filter cutoff energy (thermal absorber type), this unique Am target design was developed. In our study, Cd, Sm, and Gd were considered as thermal neutron filters, as suggested by Cesana et al. The most favorable results were obtained by irradiating Am targets covered either with Gd or Cd. In these cases, up to 8.65% enrichment of 242mAm is obtained after 4.5 yr (three successive PWR fuel cycles) of irradiation. It was also found that significant quantities [up to 1.3 kg/GW (electric)-yr] of 242mAm can be obtained in PWR reactors without notable interference with reactor performance. However, in order to maintain the original fuel cycle length, the enrichment of the driver (UO2) fuel must be increased by ∼1%, raised from the conventional 4.5 to 5.5%, depending on the thermal neutron filter used. The most important reactivity feedback coefficients for fuel assemblies containing the 242mAm breeding targets were evaluated and found to be close to those of a standard PWR. Another product of neutron capture in the 241Am reaction is 238Pu. It was found that in a typical 1000 MW (electric) PWR core with one-third of the fuel assemblies containing 241Am targets, up to 15.1 kg of 238Pu enriched to 80% can be produced per year.

Light water breeding with nitride fuel

This scoping study proposes using mixed nitride fuel in Pu-based high conversion LWR designs in order to increase the breeding ratio. The higher density fuel reduces the hydrogen-to-heavy metal ratio in the reactor which results in a harder spectrum in which breeding is more effective. A Resource-renewable Boiling Water Reactor (RBWR) assembly was modeled in MCNP to demonstrate this effect in a typical high conversion LWR design. It was determined that changing the fuel from (U,TRU)O2 to (U,TRU)N in the assembly can increase its fissile inventory ratio (fissile Pu mass divided by initial fissile Pu mass) from 1.04 to up to 1.17. © 2011 Elsevier Ltd. All rights reserved.

MoT - Mixture of trees probabilistic graphical model for video segmentation

We present a novel mixture of trees (MoT) graphical model for video segmentation. Each component in this mixture represents a tree structured temporal linkage between super-pixels from the first to the last frame of a video sequence. Our time-series model explicitly captures the uncertainty in temporal linkage between adjacent frames which improves segmentation accuracy. We provide a variational inference scheme for this model to estimate super-pixel labels and their confidences in nearly realtime. The efficacy of our approach is demonstrated via quantitative comparisons on the challenging SegTrack joint segmentation and tracking dataset [23].