Labile memory is thought to be held in the brain as persistent neural network activity. However, it is not known how biologically relevant memory circuits are organized and operate. Labile and persistent appetitive memory in Drosophila requires output after training from the α'β' subset of mushroom body (MB) neurons and from a pair of modulatory dorsal paired medial (DPM) neurons. DPM neurons i...

In this section, we briefly review Dirichlet process mixture model (DPM) and derive its marginal likelihood.

This paper presents a numerical analysis for complex 3D simulations of the Stirred Tank Reactor (STR) model by a modified discrete projection method (DPM) for rotating incompressible flow. For several prototypical configurations of the STR model, we examine the multigrid behaviour for the arising momentum and pressure Poisson subproblems for different values of the time step, the angular veloci...

We propose Dirichlet process mixture (DPM) models for prediction and cluster-wise variable selection, based on two choices of shrinkage baseline prior distributions the linear regression coefficients, namely, Horseshoe Normal-Gamma prior. show in a simulation study that each proposed DPM tends to outperform standard model non-shrinkage normal prior, terms predictive, clustering accuracy. This i...

We describe an implementation of the Deformable Parts Model [1] that operates in a user-defined time-frame. Our implementation uses a variety of mechanism to trade-off speed against accuracy. Our implementation can detect all 20 PASCAL 2007 objects simultaneously at 30Hz with an mAP of 0.26. At 15Hz, its mAP is 0.30; and at 100Hz, its mAP is 0.16. By comparison the reference implementation of [...

The Dirichlet distribution forms our first step toward understanding the DPM model. The Dirichlet distribution is a multi-parameter generalization of the Beta distribution and defines a distribution over distributions, i.e. the result of sampling a Dirichlet is a distribution on some discrete probability space. Let Θ = {θ1,θ2, . . . ,θn} be a probability distribution on the discrete space = { 1...