This paper proposes a new efficient merge-split sampler for both conjugate and nonconjugate Dirichlet process mixture (DPM) models. These Bayesian nonparametric models are usually fit usingMarkov chain Monte Carlo (MCMC) or sequential importance sampling (SIS). The latest generation of Gibbs and Gibbs-like samplers for both conjugate and nonconjugate DPM models effectively update the model para...

Execution time is no longer the only performance metric for computer systems. In fact, a trend is emerging to trade raw performance for energy savings. Techniques like Dynamic Power Management (DPM, switching to low power state) and Dynamic Voltage and Frequency Scaling (DVFS, throttling processor frequency) help modern systems to reduce their power consumption while adhering to performance req...

−Diesel particulate matter (DPM) often contains small amounts of metal as a minor component but this metal may contribute to adverse health effects. Knowledge of the mechanism for particle formation as well as the size preference of the trace metals is critical to understanding the potential for health concerns. To achieve this, the size and the composition of each particle should be optimally ...

We propose new global artificial boundary conditions (ABC’s) for computation of flows with propulsive jets. The algorithm is based on application of the difference potentials method (DPM). Previously, similar boundary conditions have been implemented for calculation of external compressible viscous flows around finite bodies. The proposed modification substantially extends the applicability ran...

Long-term reliability of processors in embedded systems is experiencing growing attention since decreasing feature sizes and increasing power consumption have a negative influence on the lifespan. Among other measures, the reliability can be influenced significantly by Dynamic Power Management (DPM), since it affects the processor’s temperature. Compared to single-core systems reconfigurable mu...

The Dirichlet process mixtures (DPM) can automatically infer the model complexity from data. Hence it has attracted significant attention recently, and is widely used for model selection and clustering. As a generative model, it generally requires prior base distribution to learn component parameters by maximizing posterior probability. In contrast, discriminative classifiers model the conditio...

The Dirichlet Process mixture (DPM) model is a popular nonparametric Bayesian tool for modeling unknown distributions through mixtures of components. Integrating out the latent location variables in a DPM model leads to a product partition model. This paper describes a modefinding algorithm which quickly finds either the maximizer of the partition posterior or the maximizer of the partition lik...

This study was conducted to investigate the effects of engine operating conditions and exhaust aftertreatments on the mutagenicity of diesel particulate matter (DPM) collected directly in an underground mine environment. A number of after-treatment devices are currently used on diesel engines in mines, but it is critical to determine whether reductions in DPM concentrations result in a correspo...

In this paper I present a software tool for simulating a dynamic parimutuel market (DPM) based on an underlying model for the spread of information through a community. I use a standard gossip model for the information flow through the community. Typically these models are used to model network congestion, but I have taken a more literal interpretation of the “gossip”. The typical scenario for ...

Subjects deciding whether two objects presented at angular disparity are identical or mirror versions of each other usually show response times that linearly increase with the angle between objects. This phenomenon has been termed mental rotation. While there is widespread agreement that parietal cortex plays a dominant role in mental rotation, reports concerning the involvement of motor areas ...