Hybrid Wavelet-Support Vector Classifiers

The Support Vector Machine (SVM) represents a new and very promising technique for machine learning tasks involving classification, regression or novelty detection. Improvements of its generalization ability can be achieved by incorporating prior knowledge of the task at hand. We propose a new hybrid algorithm consisting of signal-adapted wavelet decompositions and SVMs for waveform classification. The adaptation of the wavelet decompositions is tailormade for SVMs with radial basis functions as kernels. It allows the optimization Of the representation of the data before training the SVM and does not suffer from computationally expensive validation techniques. We assess the performance of our algorithm against the background of current concerns in medical diagnostics, namely the classification of endocardial electrograms and the detection of otoacoustic emissions. Here the performance of SVMs can significantly be improved by our adapted preprocessing step

Parameter Detection of Thin Films From Their X-Ray Reflectivity by Support Vector Machines

Reflectivity measurements are used in thin film investigations for determining the desity and the thickness of layered structures and the roughness of external and internal surfaces. From the mathematical point of view the deduction of these parameters from a measured reflectivity curve represents an inverSe ptoblem. At present, curve fitting procedures, based to a large extent on expert knowledge are commonly used in practice. These techniques suffer from a low degree of automation. In this paper we present a new approach to the evaluation of reflectivity measurements using support vector machines. For the estimation of the different thin film parameters we provide sparse approximations of vector-valued functions, where we work in parallel on the same data sets. Our support vector machines were trained by simulated reflectivity curves generated by the optical matrix method. The solution of the corresponding quadratic programming problem makes use of the SVMTorch algorithm. We present numerical investigations to assess the performance of our method using models of practical relevance. It is concluded that the approximation by support vector machines represents a very promising tool in X-ray reflectivity investigations and seems also to be applicable for a much broader range of parameter detection problems in X-ray analysis

Epithelial-mesenchymal transdifferentiation in pediatric lens epithelial cells

PURPOSE. Posterior capsule opacification (PCO) is a complication after cataract surgery, particularly in children. Epithelial-mesenchymal transition (EMT) of lens epithelial cells, mediated by transforming growth factor beta (TGF beta), contributes to PCO. However, its pathogenesis in children is poorly understood. We correlated cell growth in culture with patient characteristics, studied gene expression of pediatric lens epithelial cells (pLEC), and examined the effects of TGF beta-2 on these cells in vitro. METHODS. Clinical characteristics of children with cataracts correlated with growth behavior of pLEC in vitro. mRNA expression of epithelial (alpha B-crystallin, connexin-43) and mesenchymal (alpha(V)-integrin, alpha-smooth muscle actin, collagen-I alpha 2, fibronectin-1) markers was quantified in pLEC and in cell line HLE-B3 in the presence and absence of TGF beta-2. RESULTS. Fifty-four anterior lens capsules from 40 children aged 1 to 180 months were obtained. Cell outgrowth occurred in 44% of the capsules from patients <= 12 months and in 33% of capsules from children aged 13 to 60 months, but in only 6% of capsules from children over 60 months. TGF beta-2 significantly upregulated expression of alpha B-crystallin (HLE-B3), alpha(V)-integrin (HLE-B3), collagen-I alpha 2, and fibronectin-1 (in pLEC and HLE-B3 cells). CONCLUSIONS. Patient characteristics correlated with growth behavior of pLEC in vitro, paralleling a higher clinical incidence of PCO in younger children. Gene expression profiles of pLEC and HLE-B3 suggest that upregulation of alpha(V)-integrin, collagen-I alpha 2, and fibronectin-1 are involved in EMT

Multi-dimensional modeling and simulation of semiconductor nanophotonic devices

Self-consistent modeling and multi-dimensional simulation of semiconductor nanophotonic devices is an important tool in the development of future integrated light sources and quantum devices. Simulations can guide important technological decisions by revealing performance bottlenecks in new device concepts, contribute to their understanding and help to theoretically explore their optimization potential. The efficient implementation of multi-dimensional numerical simulations for computer-aided design tasks requires sophisticated numerical methods and modeling techniques. We review recent advances in device-scale modeling of quantum dot based single-photon sources and laser diodes by self-consistently coupling the optical Maxwell equations with semiclassical carrier transport models using semi-classical and fully quantum mechanical descriptions of the optically active region, respectively. For the simulation of realistic devices with complex, multi-dimensional geometries, we have developed a novel hp-adaptive finite element approach for the optical Maxwell equations, using mixed meshes adapted to the multi-scale properties of the photonic structures. For electrically driven devices, we introduced novel discretization and parameter-embedding techniques to solve the drift-diffusion system for strongly degenerate semiconductors at cryogenic temperature. Our methodical advances are demonstrated on various applications, including vertical-cavity surface-emitting lasers, grating couplers and single-photon sources