Formal geometric quantisation for proper actions

Published online: 23 April 2015We define formal geometric quantisation for proper Hamiltonian actions by possibly noncompact groups on possibly noncompact, prequantised symplectic manifolds, generalising work of Weitsman and Paradan. We study the functorial properties of this version of formal geometric quantisation, and relate it to a recent result by the authors via a version of the shifting trick. For (pre)symplectic manifolds of a certain form, quantisation commutes with reduction, in the sense that formal quantisation equals a more direct version of quantisation.Peter Hochs, Varghese Matha

Anomalous Defects and Their Quantized Transverse Conductivities

Using a description of defects in solids in terms of three-dimensional gravity, we study the propagation of electrons in the background of disclinations and screw dislocations. We study the situations where there are bound states that are effectively localized on the defect and hence can be described in terms of an effective 1+1 dimensional field theory for the low energy excitations. In the case of screw dislocations, we find that these excitations are chiral and can be described by an effective field theory of chiral fermions. Fermions of both chirality occur even for a given direction of the magnetic field. The ``net'' chirality of the system however is not always the same for a given direction of the magnetic field, but changes from one sign of the chirality through zero to the other sign as the Fermi momentum or the magnitude of the magnetic flux is varied. On coupling to an external electromagnetic field, the latter becomes anomalous, and predicts novel conduction properties for these materials.Comment: New material added. ReVTeX , 31 pgs., 4 figs.(uses epsf

Far Field Deposition Of Scoured Regolith Resulting From Lunar Landings

As a lunar lander approaches a dusty surface, the plume from the descent engine impinges on the ground, entraining loose regolith into a high velocity dust spray. Without the inhibition of a background atmosphere, the entrained regolith can travel many kilometers from the landing site. In this work, we simulate the flow field from the throat of the descent engine nozzle to where the dust grains impact the surface many kilometers away. The near field is either continuum or marginally rarefied and is simulated via a loosely coupled hybrid DSMC - Navier Stokes (DPLR) solver. Regions of two-phase and polydisperse granular flows are solved via DSMC. The far field deposition is obtained by using a staged calculation, where the first stages are in the near field where the flow is quasi-steady and the outer stages are unsteady. A realistic landing trajectory is approximated by a set of discrete hovering altitudes which range from 20m to 3m. The dust and gas motions are fully coupled using an interaction model that conserves mass, momentum, and energy statistically and inelastic collisions between dust particles are also accounted for. Simulations of a 4 engine configuration are also examined, and the erosion rates as well as near field particle fluxes are discussed.Astronom

A Novel Hybrid Classification Model For the Loan Repayment Capability Prediction System

Classification is a powerful tool in Data mining to predict the loan repayment capability of a banking customer. This paper evaluates the performance of various classification algorithms and selects the most appropriate one for predicting the class label of the credit data set as good or bad. Feature selection is a data pre-processing technique refers to the process of identifying the most beneficial features for a given task, while avoiding the noisy, irrelevant and redundant features of the dataset. These irrelevant noisy features results in a poor accuracy for the selected classifier. In order to improve the accuracy of a classifier, the feature selection plays a vital role as a data preprocessing step. Feature selection technique reduces the dimensionality of the feature set of the dataset. This paper has two objectives. First objective is to find out the best classifier algorithm for the credit data set using two different tools such as weka and R. Here the experiment proved that Random Forest performs better for loan repayment credibility prediction system. The second objective is to evaluate the performance of various feature selection methods based on Random Forest classification method. Also a novel hybrid model is developed for the same

Machine Learning Techniques to Evaluate the Approximation of Utilization Power in Circuits

The need for products that are more streamlined, more useful, and have longer battery lives is rising in today's culture. More components are being integrated onto smaller, more complex chips in order to do this. The outcome is higher total power consumption as a result of increased power dissipation brought on by dynamic and static currents in integrated circuits (ICs). For effective power planning and the precise application of power pads and strips by floor plan engineers, estimating power dissipation at an early stage is essential. With more information about the design attributes, power estimation accuracy increases. For a variety of applications, including function approximation, regularization, noisy interpolation, classification, and density estimation, they offer a coherent framework. RBFNN training is also quicker than training multi-layer perceptron networks. RBFNN learning typically comprises of a linear supervised phase for computing weights, followed by an unsupervised phase for determining the centers and widths of the Gaussian basis functions. This study investigates several learning techniques for estimating the synaptic weights, widths, and centers of RBFNNs. In this study, RBF networks—a traditional family of supervised learning algorithms—are examined.  Using centers found using k-means clustering and the square norm of the network coefficients, respectively, two popular regularization techniques are examined. It is demonstrated that each of these RBF techniques are capable of being rewritten as data-dependent kernels. Due to their adaptability and quicker training time when compared to multi-layer perceptron networks, RBFNNs present a compelling option to conventional neural network models. Along with experimental data, the research offers a theoretical analysis of these techniques, indicating competitive performance and a few advantages over traditional kernel techniques in terms of adaptability (ability to take into account unlabeled data) and computing complexity. The research also discusses current achievements in using soft k-means features for image identification and other tasks

Evolution of electromagnetic and Dirac perturbations around a black hole in Horava gravity

The evolution of electromagnetic and Dirac perturbations in the spacetime geometry of Kehagias-Sfetsos(KS) black hole in the deformed Horava-Lifshitz(HL) gravity is investigated and the associated quasinormal modes are evaluated using time domain integration and WKB methods. We find a considerable deviation in the nature of field evolution in HL theory from that in the Schwarzschild spacetime and QNMs region extends over a longer time in HL theory before the power-law tail decay begins. The dependence of the field evolution on the HL parameter $\alpha$ are studied. In the time domain picture we find that the length of QNM region increases with $\alpha$. But the late time decay of field follows the same power-law tail behavior as in the case of Schwarzschild black hole.Comment: The article was fully rewritten, references added, to appear in MPL

Scheduling of tasks in multiprocessor system using hybrid genetic algorithms

This paper presents an investigation into the optimal scheduling of realtime tasks of a multiprocessor system using hybrid genetic algorithms (GAs). A comparative study of heuristic approaches such as `Earliest Deadline First (EDF)¿ and `Shortest Computation Time First (SCTF)¿ and genetic algorithm is explored and demonstrated. The results of the simulation study using MATLAB is presented and discussed. Finally, conclusions are drawn from the results obtained that genetic algorithm can be used for scheduling of real-time tasks to meet deadlines, in turn to obtain high processor utilization

Systematic review of patient factors affecting adipose stem cell viability and function: implications for regenerative therapy

Background: The applications for fat grafting have increased recently, within both regenerative and reconstructive surgery. Although fat harvesting, processing and injection techniques have been extensively studied and standardised, this has not had a big impact on the variability of outcome following fat grafting. This suggests a possible larger role of patient characteristics on adipocyte and adipose-derived stem cell (ADSC) viability and function. This systematic review aims to collate current evidence on the effect of patient factors on adipocyte and ADSC behaviour. Methods: A systematic literature review was performed using MEDLINE, Cochrane Library and EMBASE. It includes outcomes observed in in vitro analyses, in vivo animal studies and clinical studies. Data from basic science work have been included in the discussion to enhance our understanding of the mechanism behind ADSC behaviour. Results: A total of 41 papers were included in this review. Accumulating evidence indicates decreased proliferation and differentiation potential of ADSCs with increasing age, body mass index, diabetes mellitus and exposure to radiotherapy and Tamoxifen, although this was not uniformly seen across all studies. Gender, donor site preference, HIV status and chemotherapy did not show a significant influence on fat retention. Circulating oestrogen levels have been shown to support both adipocyte function and graft viability. Evidence so far suggests no significant impact of total cholesterol, hypertension, renal disease, physical exercise and peripheral vascular disease on ADSC yield. Conclusions: A more uniform comparison of all factors highlighted in this review, with the application of a combination of tests for each outcome measure, is essential to fully understand factors that affect adipocyte and ADSC viability, as well as functionality. As these patient factors interact, future studies looking at adipocyte viability need to take them into consideration for conclusions to be meaningful. This would provide crucial information for surgeons when deciding appropriate volumes of lipoaspirate to inject, improve patient selection, and counsel patient expectations with regards to outcomes and likelihood for repeat procedures. An improved understanding will also assist in identification of patient groups that would benefit from graft enrichment and cryopreservation techniques

Bifurcations associated with sub-synchronous resonance

This paper describes a set of results of detecting nonlinear phenomena appearing in a turbine generator power system with series-capacitor compensation. The analysis was based on the Floquet theory as well as the Hopf bifurcation theorem. After the first Hopf bifurcation, the stable limit cycle bifurcates to a stable torus and an unstable limit cycle which connects to a stable limit cycle by a supercritical torus bifurcation. The stable limit cycle joins with an unstable limit cycle at a cyclic fold bifurcation. This unstable limit cycle is connected to the second Hopf. It has been also numerically demonstrated that such a strange sequence of periodic orbits is created by a q-axis damper winding.published_or_final_versio