Generalized power method for sparse principal component analysis

In this paper we develop a new approach to sparse principal component analysis (sparse PCA). We propose two single-unit and two block optimization formulations of the sparse PCA problem, aimed at extracting a single sparse dominant principal component of a data matrix, or more components at once, respectively. While the initial formulations involve nonconvex functions, and are therefore computationally intractable, we rewrite them into the form of an optimization program involving maximization of a convex function on a compact set. The dimension of the search space is decreased enormously if the data matrix has many more columns (variables) than rows. We then propose and analyze a simple gradient method suited for the task. It appears that our algorithm has best convergence properties in the case when either the objective function or the feasible set are strongly convex, which is the case with our single-unit formulations and can be enforced in the block case. Finally, we demonstrate numerically on a set of random and gene expression test problems that our approach outperforms existing algorithms both in quality of the obtained solution and in computational speed. © 2010 Michel Journée, Yurii Nesterov, Peter Richtárik and Rodolphe Sepulchre.

Dispersion rheology of carbon nanotubes in a polymer matrix

We report on rheological properties of a dispersion of multiwalled carbon nanotubes in a viscous polymer matrix. Particular attention is paid to the process of nanotubes mixing and dispersion, which we monitor by the rheological signature of the composite. The response of the composite as a function of the dispersion mixing time and conditions indicates that a critical mixing time t* needs to be exceeded to achieve satisfactory dispersion of aggregates, this time being a function of nanotube concentration and the mixing shear stress. At shorter times of shear mixing t< t*, we find a number of nonequilibrium features characteristic of colloidal glass and jamming of clusters. A thoroughly dispersed nanocomposite, at t> t*, has several universal rheological features; at nanotube concentration above a characteristic value nc ∼2-3 wt. % the effective elastic gel network is formed, while the low-concentration composite remains a viscous liquid. We use this rheological approach to determine the effects of aging and reaggregation. © 2006 The American Physical Society.

Microwave power effects on the properties of phosphorus-doped SiC:H films prepared using ECR-CVD

There has been a growing interest in hydrogenated silicon carbide films (SiC:H) prepared using the electron cyclotron resonance-chemical vapour deposition (ECR-CVD) technique. Using the ECR-CVD technique, SiC:H films have been prepared from a mixture of methane, silane and hydrogen, with phosphine as the doping gas. The effects of changes in the microwave power (from 150 to 900 W) on the film properties were investigated in a series of phosphorus-doped SiC:H films. In particular, the changes in the deposition rate, optical bandgap, activation energy and conductivity were investigated in conjunction with results from Raman scattering and Fourier transform infra-red (FTIR) analysis. It was found that increase in the microwave power has the effect of enhancing the formation of the silicon microcrystalline phase in the amorphous matrix of the SiC:H films. This occurs in correspondence to a rapid increase in the conductivity and a reduction in the activation energy, both of which exhibit small variations in samples deposited at microwave powers exceeding 500 W. Analysis of IR absorption results suggests that hydrogen is bonded to silicon in the Si-H stretching mode and to carbon in the sp3 CHn rocking/wagging and bending mode in films deposited at higher microwave powers.

Active-passive 44 SOA-based switch with integrated power monitoring

The first monolithically integrated 44 switch with power monitoring function using on-chip PIN photodiodes is reported. Using 10Gb/s signals, under active power control an IPDR of 12dB for a 1dB power penalty is achieved. © 2012 OSA.

The influence of matrix integrity on stress-fiber remodeling in 3D.

Matrix anisotropy is important for long term in vivo functionality. However, it is not fully understood how to guide matrix anisotropy in vitro. Experiments suggest actin-mediated cell traction contributes. Although F-actin in 2D displays a stretch-avoidance response, 3D data are lacking. We questioned how cyclic stretch influences F-actin and collagen orientation in 3D. Small-scale cell-populated fibrous tissues were statically constrained and/or cyclically stretched with or without biochemical agents. A rectangular array of silicone posts attached to flexible membranes constrained a mixture of cells, collagen I and matrigel. F-actin orientation was quantified using fiber-tracking software, fitted using a bi-model distribution function. F-actin was biaxially distributed with static constraint. Surprisingly, uniaxial cyclic stretch, only induced a strong stretch-avoidance response (alignment perpendicular to stretching) at tissue surfaces and not in the core. Surface alignment was absent when a ROCK-inhibitor was added, but also when tissues were only statically constrained. Stretch-avoidance was also observed in the tissue core upon MMP1-induced matrix perturbation. Further, a strong stretch-avoidance response was obtained for F-actin and collagen, for immediate cyclic stretching, i.e. stretching before polymerization of the collagen. Results suggest that F-actin stress-fibers avoid cyclic stretch in 3D, unless collagen contact guidance dictates otherwise.

The influence of matrix integrity on stress-fiber remodeling in 3D

Matrix anisotropy is important for long term in vivo functionality. However, it is not fully understood how to guide matrix anisotropy in vitro. Experiments suggest actin-mediated cell traction contributes. Although F-actin in 2D displays a stretch-avoidance response, 3D data are lacking. We questioned how cyclic stretch influences F-actin and collagen orientation in 3D. Small-scale cell-populated fibrous tissues were statically constrained and/or cyclically stretched with or without biochemical agents. A rectangular array of silicone posts attached to flexible membranes constrained a mixture of cells, collagen I and matrigel. F-actin orientation was quantified using fiber-tracking software, fitted using a bi-model distribution function. F-actin was biaxially distributed with static constraint. Surprisingly, uniaxial cyclic stretch, only induced a strong stretch-avoidance response (alignment perpendicular to stretching) at tissue surfaces and not in the core. Surface alignment was absent when a ROCK-inhibitor was added, but also when tissues were only statically constrained. Stretch-avoidance was also observed in the tissue core upon MMP1-induced matrix perturbation. Further, a strong stretch-avoidance response was obtained for F-actin and collagen, for immediate cyclic stretching, i.e. stretching before polymerization of the collagen. Results suggest that F-actin stress-fibers avoid cyclic stretch in 3D, unless collagen contact guidance dictates otherwise. © 2012 Elsevier Ltd.

Thin film coil design considerations for wireless power transfer in flat panel display

Wireless power transfer is experimentally demonstrated by transmission between an AC power transmitter and receiver, both realised using thin film technology. The transmitter and receiver thin film coils are chosen to be identical in order to promote resonant coupling. Planar spiral coils are used because of the ease of fabrication and to reduce the metal layer thickness. The energy transfer efficiency as a function of transfer distance is analysed along with a comparison between the theoretical and the experimental results. © 2012 Materials Research Society.

Theory of high-power mode-locked lasers with a slow absorber

We develop an analytical theory of high-power passively mode-locked lasers with a slow absorber; the theory is valid at pulse energies well exceeding the saturation energy. We analyze the Haus modelocking master equation in the pulse-energy-domain representation, approximating the intensity profile function by a series in the vicinity of its peak value. We consider the high-power operation regime of subpicosecond blue-violet GaN mode-locked diode lasers, using the approach developed. © 2010 Springer Science+Business Media, Inc.

Multilinear function factorisation for time series feature extraction

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, a great deal of work has centred around designing time series classifiers using more and more complex feature extraction and machine learning schemes. This paper argues that complex learners and domain specific feature extraction schemes of this type are not necessarily needed for time series classification, as excellent classification results can be obtained by simply applying a number of existing matrix factorisation or linear projection techniques, which are simple and computationally inexpensive. We highlight this using a geometric separability measure and classification accuracies obtained though experiments on four different high dimensional multivariate time series datasets. © 2013 IEEE.

Fixed-rank matrix factorizations and Riemannian low-rank optimization

Motivated by the problem of learning a linear regression model whose parameter is a large fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function defined on the set of fixed-rank matrices. We adopt the geometric framework of optimization on Riemannian quotient manifolds. We study the underlying geometries of several well-known fixed-rank matrix factorizations and then exploit the Riemannian quotient geometry of the search space in the design of a class of gradient descent and trust-region algorithms. The proposed algorithms generalize our previous results on fixed-rank symmetric positive semidefinite matrices, apply to a broad range of applications, scale to high-dimensional problems, and confer a geometric basis to recent contributions on the learning of fixed-rank non-symmetric matrices. We make connections with existing algorithms in the context of low-rank matrix completion and discuss the usefulness of the proposed framework. Numerical experiments suggest that the proposed algorithms compete with state-of-the-art algorithms and that manifold optimization offers an effective and versatile framework for the design of machine learning algorithms that learn a fixed-rank matrix. © 2013 Springer-Verlag Berlin Heidelberg.