The decay of space charge in a glassy epoxy resin following voltage removal

The PEA technique is used to measure the distribution of space charge in an epoxy resin after polarisation for one week at an applied field of 7.14kV/mm over a range of temperatures. The decay of the space charge is followed for times up to 114 hours after removal of the voltage and analysed in terms of a number of alternative decay mechanisms. It is shown that the rate-determining stage of the decay mechanism is that of a thermally activated process that has been associated with charge de-trapping. At times greater than 102s the de-trapping process behaves as though the space charge field does not exist and the retention time of the space charge depends only upon the depth of the deepest occupied traps and the temperature

PCA Tomography and its application to nearby galactic nuclei

With the development of modern technologies such as IFUs, it is possible to obtain data cubes in which one produces images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We briefly describe a method of analysis of data cubes (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. We applied the method, for illustration purpose, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.Comment: 4 pages, 1 figure, 1 table, to be published in the Proceedings of the IAU Symposium no. 26

Robust multi-fidelity design of a micro re-entry unmanned space vehicle

This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preliminary results, the adopted technique is able to predict achievable performance of the small spacecraft and the requirements in terms of thermal protection materials

Effects of a Supermassive Black Hole Binary on a Nuclear Gas Disk

We study influence of a galactic central supermassive black hole (SMBH) binary on gas dynamics and star formation activity in a nuclear gas disk by making three-dimensional Tree+SPH simulations. Due to orbital motions of SMBHs, there are various resonances between gas motion and the SMBH binary motion. We have shown that these resonances create some characteristic structures of gas in the nuclear gas disk, for examples, gas elongated or filament structures, formation of gaseous spiral arms, and small gas disks around SMBHs. In these gaseous dense regions, active star formations are induced. As the result, many star burst regions are formed in the nuclear region.Comment: 19 pages, 11 figures, accepted for publication in Ap

Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application

Using Mixture Covariance Matrices to Improve Face and Facial Expression Recognitions

Abstract. In several pattern recognition problems, particularly in image recognition ones, there are often a large number of features available, but the number of training examples for each pattern is significantly less than the dimension of the feature space. This statement implies that the sample group covariance matrices often used in the Gaussian maximum probability classifier are singular. A common solution to this problem is to assume that all groups have equal covariance matrices and to use as their estimates the pooled covariance matrix calculated from the whole training set. This paper uses an alternative estimate for the sample group covariance matrices, here called the mixture covariance, given by an appropriate linear combination of the sample group and pooled covariance matrices. Experiments were carried out to evaluate the performance associated with this estimate in two biometric applications: face and facial expression. The average recognition rates obtained by using the mixture covariance matrices were higher than the usual estimates

Enhancing Success of Medicare\u27s Shared Decision Making Mandates Using Implementation Science: Examples Applying the Pragmatic Robust Implementation and Sustainability Model (PRISM)

The Centers for Medicare and Medicaid Services (CMS) has mandated shared decision making (SDM) using patient decision aids for three conditions (lung cancer screening, atrial fibrillation, and implantable defibrillators). These forward-thinking approaches are in response to a wealth of efficacy data demonstrating that decision aids can improve patient decision making. However, there has been little focus on how to implement these approaches in real-world practice. This article demonstrates how using an implementation science framework may help programs understand multilevel challenges and opportunities to improve adherence to the CMS mandates. Using the PRISM (Pragmatic Robust Implementation and Sustainability Model) framework, we discuss general challenges to implementation of SDM, issues specific to each mandate, and how to plan for, enhance, and assess SDM implementation outcomes. Notably, a theme of this discussion is that successful implementation is context-specific and to truly have successful and sustainable changes in practice, context variability, and adaptation to context must be considered and addressed

Clustering data by inhomogeneous chaotic map lattices

A new approach to clustering, based on the physical properties of inhomogeneous coupled chaotic maps, is presented. A chaotic map is assigned to each data-point and short range couplings are introduced. The stationary regime of the system corresponds to a macroscopic attractor independent of the initial conditions. The mutual information between couples of maps serves to partition the data set in clusters, without prior assumptions about the structure of the underlying distribution of the data. Experiments on simulated and real data sets show the effectiveness of the proposed algorithm.Comment: 8 pages, 6 figures. Revised version accepted for publication on Physical Review Letter