We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be...

Principal Components Analysis (PCA) consists in nding the orthogonal directions of highest variance in a distribution of vectors. In this paper, we propose to extract the principal components of a random vector that partially results from a previous PCA. We demonstrate that this contextual PCA pro vides an optimal linear encoding of temporal con text. A recurrent neural netw ork based on this p...

A total of twelve lactating Jersey cows were used in a 5-week experiment to determine the effects of severe feed restriction on the permeability of mammary gland cell tight junctions (TJs) and its effects on milk stability to the alcohol test. During the first 2 weeks, cows were managed and fed together and received the same diet according to their nutritional requirements (full diet: 15 kg of ...

Abstract Hydraulic fracturing and permeability enhancement are effective methods to improve low-permeability coal seams. However, few studies focused on increase permeability, there no suitable prediction for engineering applications. In this work, PFC2D software was used simulate seam hydraulic fracturing. The results were in a coupled mathematical model of the interaction between deformation ...

Feature reduction is often an essential part of solving a classification task. One common approach for doing this, is Principal Component Analysis. There the low variance directions in the data are removed and the high variance directions are retained. It is hoped that these high variance directions contain information about the class differences. For one-class classification or novelty detecti...

A new fault-relevant KPCA algorithm is proposed. Then the fault detection approach is proposed based on the fault-relevant KPCA algorithm. The proposed method further decomposes both the KPCA principal space and residual space into two subspaces. Comparedwith traditional statistical techniques, the fault subspace is separated based on the fault-relevant influence. This method can find fault-rel...

We explore a connection between the singular value decomposition (SVD) and functional principal component analysis (FPCA) models in high-dimensional brain imaging applications. We formally link right singular vectors to principal scores of FPCA. This, combined with the fact that left singular vectors estimate principal components, allows us to deploy the numerical efficiency of SVD to fully est...

Exogenous sphingosine-1-phosphate (S1P), a lipid mediator in blood, attenuates acute microvascular permeability increases via receptor S1P1 to stabilize the endothelium. To evaluate the contribution of erythrocytes as an endogenous source of S1P to the regulation of basal permeability, we measured permeability coefficients in intact individually perfused venular microvessels of rat mesentery. T...

The emerging instability of a forced throughflow in fluid saturated horizontal porous duct rectangular cross section is investigated. heated from below by assuming the boundaries to be at different temperatures. Both and vertical are impermeable basic flow parallel such boundaries. medium anisotropic with permeabilities directions. effect anisotropy on onset buoyancy-driven modal absolute analy...