Multigrid algorithms for symmetric discontinuous Galerkin methods on graded meshes

We study a class of symmetric discontinuous Galerkin methods on graded meshes. Optimal order error estimates are derived in both the energy norm and the L 2 norm, and we establish the uniform convergence of V-cycle, F-cycle and W-cycle multigrid algorithms for the resulting discrete problems. Numerical results that confirm the theoretical results are also presented.

Some recent developments in solid state galvanic sensors

A critical revi<:w of the possibilities of measuring the ~artlal pressure of sulfur using solid state galvanic cells )'n;;cd on AgI, C" , B-alumina, CaO-Zr02' Na2S04-I and doped ;:":;, ,,,Ilil "Iltl ,,11: auxiliary "jectrodes are presentlOu. SOIll..., df tllc!iL' sYHtcmH h,}vu inherent limltntlol1$ when <:xl'o" ...d to environments contilining both oxygen and sulfur. Electrode polarization due to electronic conduction in the solid electrolyte is a significant factor limiting the ;lC'e,"'acy of isotlwrm:l1 cell",. The electrochemical flux of{lit' !'\)ndlwl Ill}: Ion LhnHO',h tht' ('!('ctrojyt(~ C:Ul },(,! llIinlnliz(,{j pfUjJL!f cell. dL:~) i.t',11. Noni!:iot.herm~ll cells \.Jlth temperaLure compensated reference electrodes have a number of advantages over thC'ir isothermal counterparts.

Relay Precoder Optimization in MIMO-Relay Networks With Imperfect CSI

In this paper, we consider robust joint designs of relay precoder and destination receive filters in a nonregenerative multiple-input multiple-output (MIMO) relay network. The network consists of multiple source-destination node pairs assisted by a MIMO-relay node. The channel state information (CSI) available at the relay node is assumed to be imperfect. We consider robust designs for two models of CSI error. The first model is a stochastic error (SE) model, where the probability distribution of the CSI error is Gaussian. This model is applicable when the imperfect CSI is mainly due to errors in channel estimation. For this model, we propose robust minimum sum mean square error (SMSE), MSE-balancing, and relay transmit power minimizing precoder designs. The next model for the CSI error is a norm-bounded error (NBE) model, where the CSI error can be specified by an uncertainty set. This model is applicable when the CSI error is dominated by quantization errors. In this case, we adopt a worst-case design approach. For this model, we propose a robust precoder design that minimizes total relay transmit power under constraints on MSEs at the destination nodes. We show that the proposed robust design problems can be reformulated as convex optimization problems that can be solved efficiently using interior-point methods. We demonstrate the robust performance of the proposed design through simulations.

Subspace-based DOA estimation using Fractional Lower Order statistics

Direction Of Arrival (DOA) estimation, using a sensor array, in the presence of non-Gaussian noise using Fractional Lower-Order Moments (FLOM)matrices is studied. In this paper, a new FLOM based technique using the Fractional Lower Order Infinity Norm based Covariance (FLIC) Matrix is proposed. The bounded property and the low-rank subspace structure of the FLIC matrix is derived. Performance of FLIC based DOA estimation using MUSIC, ESPRIT, is shown to be better than other FLOM based methods.

Robust Observer Design with Application to Fault Detection

A new scheme for robust estimation of the partial state of linear time-invariant multivariable systems is presented, and it is shown how this may be used for the detection of sensor faults in such systems. We consider an observer to be robust if it generates a faithful estimate of the plant state in the face of modelling uncertainty or plant perturbations. Using the Stable Factorization approach we formulate the problem of optimal robust observer design by minimizing an appropriate norm on the estimation error. A logical candidate is the 2-norm, corresponding to an H�¿ optimization problem, for which solutions are readily available. In the special case of a stable plant, the optimal fault diagnosis scheme reduces to an internal model control architecture.

Interaction of guar gum with hydrophobic solids

The interaction of guar gum with the hydrophobic solids namely talc, mica and graphite has been investigated through adsorption, electrokinetic and flotation experiments. The adsorption densities of guar gum onto the above hydrophobic minerals show that they are more or less independent of pH. The adsorption isotherms of guar gum onto talc, mica and graphite indicate that the adsorption densities increase with increase in guar gum concentration and all the isotherms follow the as L1 type according to Giles classification. The magnitude of the adsorption density of guar gum onto the above minerals may be arranged in the following sequence: talc > graphite > mica The effect of particle size on the adsorption density of guar gum onto these minerals has indicated that higher adsorption takes place in the coarser size fraction, consequent to an increase in the surface face-to-edge ratio. In the case of the talc and mica samples pretreated with EDTA and the leached graphite sample, a decrease in the adsorption density of guar gum is observed, due to a reduction in the metallic adsorption sites. The adsorption densities of guar gum increase with decrease in sample weight for all the three minerals. Electrokinetic measurements have indicated that the isoelectric points (iep) of these minerals lie between pH 2-3, Addition of guar gum decreases the negative electrophoretic mobility values in proportion to the guar gum concentration without any observable shift in the iep values, resembling the influence of an indifferent electrolyte. The flotation recovery is diminished in the presence of guar gum for all the three minerals, The magnitude of depression follows the same sequence as observed in the adsorption studies. The floatability of EDTA treated talc and mica samples as well as the leached graphite sample is enhanced, complementing the adsorption data, Possible mechanisms of interaction between the hydrophobic minerals and guar gum are discussed.

Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.

DNA targeting polyaza macrobicyclic dizinc(II) complexes promoting high in vitro caspase dependent anti-proliferative activity against human carcinoma cancer cells

A series of macrobicyclic dizinc(II) complexes Zn2L1-2B](ClO4)(4) (1-6) have been synthesized and characterized (L1-2 are polyaza macrobicyclic binucleating ligands, and B is the N,N-donor heterocyclic base (viz. 2,2'-bipyridine (bipy) and 1,10-phenanthroline (phen)). The DNA and protein binding, DNA hydrolysis and anticancer activity of these complexes were investigated. The interactions of complexes 1-6 with calf thymus DNA were studied by spectroscopic techniques, including absorption, fluorescence and CD spectroscopy. The DNA binding constant values of the complexes were found to range from 2.80 x 10(5) to 5.25 x 10(5) M-1, and the binding affinities are in the following order: 3 > 6 > 2 > 5 > 1 > 4. All the dizinc(II) complexes 1-6 are found to effectively promote the hydrolytic cleavage of plasmid pBR322 DNA under anaerobic and aerobic conditions. Kinetic data for DNA hydrolysis promoted by 3 and 6 under physiological conditions give observed rate constants (k(obs)) of 5.56 +/- 0.1 and 5.12 +/- 0.2 h(-1), respectively, showing a 10(7)-fold rate acceleration over the uncatalyzed reaction of dsDNA. Remarkably, the macrobicyclic dizinc(II) complexes 1-6 bind and cleave bovine serum albumin (BSA), and effectively promote the caspase-3 and caspase-9 dependent deaths of HeLa and BeWo cancer cells. The cytotoxicity of the complexes was further confirmed by lactate dehydrogenase enzyme levels in cancer cell lysate and content media.

Analysis of an Interior Penalty Method for Fourth Order Problems on Polygonal Domains

Error analysis for a stable C (0) interior penalty method is derived for general fourth order problems on polygonal domains under minimal regularity assumptions on the exact solution. We prove that this method exhibits quasi-optimal order of convergence in the discrete H (2), H (1) and L (2) norms. L (a) norm error estimates are also discussed. Theoretical results are demonstrated by numerical experiments.

Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices

In this paper we study the problem of designing SVM classifiers when the kernel matrix, K, is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the problem using the Robust Optimization methodology. This reduces the uncertain SVM problem into a deterministic conic quadratic problem which can be solved in principle by a polynomial time Interior Point (IP) algorithm. However, for large-scale classification problems, IP methods become intractable and one has to resort to first-order gradient type methods. The strategy we use here is to reformulate the robust counterpart of the uncertain SVM problem as a saddle point problem and employ a special gradient scheme which works directly on the convex-concave saddle function. The algorithm is a simplified version of a general scheme due to Juditski and Nemirovski (2011). It achieves an O(1/T-2) reduction of the initial error after T iterations. A comprehensive empirical study on both synthetic data and real-world protein structure data sets show that the proposed formulations achieve the desired robustness, and the saddle point based algorithm outperforms the IP method significantly.

Synthesis, characterization and phosphotriesterase mimetic activity of some Zn(II) and Cu(II) complexes

We report here the synthesis and characterization of a few phenolate-based ligands bearing tert- amino substituent and their Zn(II) and Cu(II) metal complexes. Three mono/binuclear Zn(II) and Cu(II) complexes Zn(L1)(H2O)].CH3OH.H2O (1) (H (2) L1 = 6,6(')-(((2-dimethylamino)ethylazanediyl)bis(methylene))bis(2, 4-dimethylphenol), Zn-2(L2)(2)] (2) (H (2) L2 = 2,2(')-(((2-dimethylamino)ethyl)azanediyl)bis(methylene)bis(4- methylphenol) and Cu-2(L3)(2).CH2 Cl-2] (3) (H (2) L3 = (6,6(')-(((2-(diethylamino)ethyl)azanediyl)bis(methylene)) bis(methylene))bis(2,4-dimethylphenol) were synthesized by using three symmetrical tetradendate ligands containing N2O2 donor sites. These complexes are characterized by a variety of techniques including; elemental analysis, mass spectrometry, H-1, C-13 NMR spectroscopic and single crystal X-ray analysis. The new complexes have been tested for the phosphotriesterase (PTE) activity with the help of P-31 NMR spectroscopy. The P-31 NMR studies show that mononuclear complex Zn(L1)(H2O)].CH3OH.H2O (1) can hydrolyse the phosphotriester i.e., p-nitrophenyl diphenylphosphate (PNPDPP), more efficiently than the binuclear complexes Zn-2(L2)(2)] (2) and Cu-2(L3)(2).CH2Cl2] (3). The mononuclear Zn(II) complex (1) having one coordinated water molecule exhibits significant PTE activity which may be due to the generation of a Zn(II)-bound hydroxide ion during the hydrolysis reactions in CHES buffer at pH 9.0.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

Sequential alternating proximal method for scalable sparse structural SVMs

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.

On the relation between K-means and PLSA

Non-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. It can be viewed as a generalization of the K-means clustering, Expectation Maximization based clustering and aspect modeling by Probabilistic Latent Semantic Analysis (PLSA). Specifically PLSA is related to NMF with KL-divergence objective function. Further it is shown that K-means clustering is a special case of NMF with matrix L2 norm based error function. In this paper our objective is to analyze the relation between K-means clustering and PLSA by examining the KL-divergence function and matrix L2 norm based error function.

DNA and protein targeting 1,2,4-triazole based water soluble dinickel(II) complexes enhances antiproliferation and lactate dehydrogenase inhibition

Water soluble dinickel(II) complexes Ni-2(L)(2)(1-2)](NO3)(4) (1-2), where L1-2 are triazole based dinucleating ligands, were synthesized and characterized. The DNA binding, protein binding, DNA hydrolysis and anticancer properties were investigated. The interactions of complexes 1 and 2 with calf thymus DNA were studied by spectroscopic techniques, including absorption and fluorescence spectroscopy. The DNA binding constant values of the complexes 1 and 2 were found to be 2.36 x 10(5) and 4.87 x 10(5) M-1 and the binding affinities are in the following order: 2 > 1. Both the dinickel(II) complexes 1 and 2, promoted the hydrolytic cleavage of plasmid pBR322 DNA under both anaerobic and aerobic conditions. Kinetic data for DNA hydrolysis promoted by 1 and 2 under physiological conditions give the observed rate constants (k(obs)) of 5.05 +/- 0.2 and 5.65 +/- 0.1 h(-1), respectively, which shows 10(8)-fold rate acceleration over the uncatalyzed reaction of ds-DNA. Meanwhile, the interactions of the complex with BSA have also been studied by spectroscopy. Both the complexes 1 and 2 display strong binding propensity and the binding constant (K-b), number of binding sites (n) were obtained are 0.71 x 10(6) 1.47] and 5.62 x 10(6) 1.98] M-1, respectively. The complexes 1 and 2 also promoted the apoptosis against human carcinoma (HeLa, and BeWo) cancer cells. Cytotoxicity of the complexes was further confirmed by lactate dehydrogenase enzyme level in cancer cell lysate and content media. (c) 2013 Elsevier Ltd. All rights reserved.