Modeling sparse connectivity between underlying brain sources for EEG/MEG

We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure

How to Explain Individual Classification Decisions

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.Comment: 31 pages, 14 figure

Reduced kinetic model for the combustion of n-cetane and heptamethylnonane based on a primary reference fuel reduced kinetic model

A reduced kinetic model for the combustion of n-heptane, i-octane, n-cetane and heptamethylnonane was developed based on a prior model designed for use with a primary reference fuel consisting of n-heptane and i-octane. The present model, which can be easily employed in conjunction with a conventional computational fluid dynamics code, contains 59 chemical species and 96 reactions. Predicted ignition delay times under high pressure and temperature conditions were generated using this new kinetic model and compared with those obtained from full kinetic models. The results indicate that the general trends exhibited by the ignition delay times as temperature and pressure are varied are accurately predicted with this reduced model. The present model was also combined with a commercial computational fluid dynamics code and used to simulate the ignition of a diesel spray at high pressure and temperature. Finally, the effects of the cetane number of the fuel on the ignition process were investigated

In Search of Non-Gaussian Components of a High-Dimensional Distribution

Finding non-Gaussian components of high-dimensional data is an important preprocessing step for effcient information processing. This article proposes a new linear method to identify the ``non-Gaussian subspace´´ within a very general semi-parametric framework. Our proposed method, called NGCA (Non-Gaussian Component Analysis), is essentially based on a linear operator which, to any arbitrary nonlinear (smooth) function, associates a vector which belongs to the low dimensional non-Gaussian target subspace up to an estimation error. By applying this operator to a family of different nonlinear functions, one obtains a family of different vectors lying in a vicinity of the target space. As a final step, the target space itself is estimated by applying PCA to this family of vectors. We show that this procedure is consistent in the sense that the estimaton error tends to zero at a parametric rate, uniformly over the family, Numerical examples demonstrate the usefulness of our method.non-Gaussian components, dimension reduction

Endothelin

Endothelin-1 is the most potent vasoconstrictor agent currently identified, and it was originally isolated and characterized from the culture media of aortic endothelial cells. Two other isoforms, termed endothelin-2 and endothelin-3, were subsequently identified, along with structural homologues isolated from the venom of Actractapis eng-addensis known as the sarafotoxins. In this review, we will discuss the basic science of endothelins, endothelin-converting enzymes, and endothelin receptors. Only concise background information pertinent to clinical physician is provided. Next we will describe the pathophysiological roles of endothelin-1 in pulmonary arterial hypertension, heart failure, systemic hypertension, and female malignancies, with emphasis on ovarian cancer. The potential intervention with pharmacological therapeutics will be succinctly summarized to highlight the exciting pre-clinical and clinical studies within the endothelin field. Of note is the rapid development of selective endothelin receptor antagonists, which has led to an explosion of research in the field

Analysis of flow and heat transfer during the impingement of a diesel spray on a wall using large eddy simulation

Numerical calculations were carried out to investigate the formation of a fuel–air mixture as well as ignition and combustion processes associated with a diesel spray impinging on a wall. This was performed by modeling the spray formed by injecting n-heptane into a constant-volume vessel under high temperature and pressure, with the fuel droplets described by a discrete droplet model. The flow and turbulent diffusion processes were calculated based on the large eddy simulation method to simulate the formation of a local non-homogeneous mixture and the accompanying heat release. The flame structure and heat transfer to the wall during impingement were also assessed. The results show that heat transfer to the wall is increased in the peripheral region around the stagnation point, as a result of the high temperature and thin boundary layer. Conversely, in the outer region, the heat transfer decreases as the boundary layer becomes more developed