Electron Scattering and Hybrid Phonons in Low Dimensional Laser Structures made with GaAs/AlxGa1-xAs

We theoretically and numerically present the hybrid phonon modes for the double heterostructure GaAs/AlxGa1-xAs and their interactions with electrons. More specifically, we have calculated the electron capture within a symmetric quantum well via the emission of hybrid phonons. Our investigation shows that the capture rates via the hybrid phonons are matched to the rates predicted by the dielectric continuum (DC) model and the concentration of aluminium which is an important parameter for controlling the electron capture process in light emitting diodes (LED).Comment: 11 page

Surface scattering velocities in III-nitride quantum well laser structures via the emission of hybrid phonons

We have theoretically and numerically studied nitride-based quantum well (QW) laser structures. More specifically, we have used a QW made with III-nitride where the width of the barrier region is large relative to the electron mean free path, and we have calculated the electron surface capture velocities by considering an electron flux which is captured into the well region. The process is assisted by the emission of the longitudinal optical phonons as predicted by the hybrid (HB) model. The results of surface capture velocities via the emission of HB phonons are compared to the emission of the dielectric continuum phonons (Zakhleniuk et al 1999 Phys. Status Solidi a 176 79). Our investigation shows that the two different phonon models predict almost the same results for the non-retarded limit. Furthermore, the surface capture velocities strongly depend on the size of the structure and the heterostructure materials. Lastly, a comparison to the recent experimental values shows that our model could accurately describe the experimentally measured parameters of the quantum capture processes

L2-norm multiple kernel learning and its application to biomedical data fusion

<p>Abstract</p> <p>Background</p> <p>This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as <it>L</it>_∞, <it>L</it>₁, and <it>L</it>₂MKL. In particular, <it>L</it>₂MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing <it>L</it>_∞MKL method. In real biomedical applications, <it>L</it>₂MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.</p> <p>Results</p> <p>We provide a theoretical analysis of the relationship between the <it>L</it>₂optimization of kernels in the dual problem with the <it>L</it>₂coefficient regularization in the primal problem. Understanding the dual <it>L</it>₂problem grants a unified view on MKL and enables us to extend the <it>L</it>₂method to a wide range of machine learning problems. We implement <it>L</it>₂MKL for ranking and classification problems and compare its performance with the sparse <it>L</it>_∞and the averaging <it>L</it>₁MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. <it>L</it>₂MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel <it>L</it>₂MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing.</p> <p>Conclusions</p> <p>This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in <it>L</it>_∞MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing <it>L</it>₂kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL.</p> <p>Availability</p> <p>The MATLAB code of algorithms implemented in this paper is downloadable from <url>http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html</url>.</p

Machine learning approaches to medical decision making

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Controlling the Sensitivity of Support Vector Machines

For many applications it is important to accurately distinguish false negative results from false positives. This is particularly important for medical diagnosis where the correct balance between sensitivity and specificity plays an important role in evaluating the performance of a classifier. In this paper we discuss two schemes for adjusting the sensitivity and specificity of Support Vector Machines and the description of their performance using receiver operating characteristic (ROC) curves. We then illustrate their use on real-life medical diagnostic tasks. 1 Introduction. Since their introduction by Vapnik and coworkers [ Vapnik, 1995; Cortes and Vapnik, 1995 ] , Support Vector Machines (SVMs) have been successfully applied to a number of real world problems such as handwritten character and digit recognition [ Scholkopf, 1997; Cortes, 1995; LeCun et al., 1995; Vapnik, 1995 ] , face detection [ Osuna et al., 1997 ] and speaker identification [ Schmidt, 1996 ] . They exhibit a r..