A convergent decomposition algorithm for support vector machines

In this work we consider nonlinear minimization problems with a single linear equality constraint and box constraints. In particular we are interested in solving problems where the number of variables is so huge that traditional optimization methods cannot be directly applied. Many interesting real world problems lead to the solution of large scale constrained problems with this structure. For example, the special subclass of problems with convex quadratic objective function plays a fundamental role in the training of Support Vector Machine, which is a technique for machine learning problems. For this particular subclass of convex quadratic problem, some convergent decomposition methods, based on the solution of a sequence of smaller subproblems, have been proposed. In this paper we define a new globally convergent decomposition algorithm that differs from the previous methods in the rule for the choice of the subproblem variables and in the presence of a proximal point modification in the objective function of the subproblems. In particular, the new rule for sequentially selecting the subproblems appears to be suited to tackle large scale problems, while the introduction of the proximal point term allows us to ensure the global convergence of the algorithm for the general case of nonconvex objective function. Furthermore, we report some preliminary numerical results on support vector classification problems with up to 100 thousands variables.     

A simple entry to chiral non-racemic 2-piperazinone derivatives

A simple synthetic approach to chiral, non-racemic, 2-piperazinones has been developed using natural amino acids methyl esters and nitroethylene as starting materials.     

Convenient ‘one-pot’ synthesis of 3,4-substituted tetrahydrothiophenes through tandem Michael-Henry and Michael-Michael reactions

In situ generated nitro alkenes underwent tandem Michael-Henry and Michael-Michael sequences leading to the ‘one-pot’ formation of 3,4-substituted tetrahydrothiophenes using the commercially available 1,4-dithiane-2,5-diol (the dimer of mercaptoacetaldehyde) or its 4-mercapto-2-butenoates derivatives as suitable bifunctional partners, respectively.     

Induced cosmology on a codimension-2 brane in a conical bulk

We study the cosmology of a 5-dimensional brane, which represents a regularization of a 4-dimensional codimension-2 brane, embedded in a conical bulk. The brane is assumed to be tensional, and to contain a curvature term. Cosmology is obtained by letting the brane move trough the bulk, and implementing dynamical junction conditions. Our results shows that, with suitable choices of the parameters, the resulting cosmological dynamics mimics fairly well standard 4-dimensional cosmology.     

Synthesis of gold nanoparticles using herbal Acorus calamus rhizome extract and coating on cotton fabric for antibacterial and UV blocking applications

Gold nanoparticles (AuNPs) have been synthesized by greener method using chloroauric acid as precursor and extract of Acorus calamus rhizome as reducing agent. Formation of AuNP was confirmed by the presence of Surface Plasmon Resonance (SPR) peak in UV–Visible spectral analysis. XRD and FT-IR spectral analyses were performed for characterization. SEM images show spherical morphology and HR-TEM images reveal nanosize of AuNPs. The AuNPs were then coated on cotton fabric by pad-dry-cure method and characterized by SEM with EDAX technique. The results reveal the deposition of AuNPs on the surface of cotton fabric. Uncoated cotton, neat extract coated cotton and extract containing AuNPs coated cotton fabrics were then tested for antibacterial activity against Gram positive (Staphylococcus aureus) and Gram negative (Escherichia coli) bacterial strains by AATCC 100 test method. It showed that the extract containing AuNPs coated cotton fabric had higher antibacterial activity than other test samples against E. coli. UV-DRS analysis performed on extract containing AuNPs coated cotton fabric showed improved UV-blocking property than uncoated cotton fabric and neat extract coated cotton fabric.     

Bayesian stratified random sampling using auxiliary information

Summary Smith (1976,J. R. Statist. Soc., A,139, 183–204) has argued that survey statisticians should attempt to model finite population structures in the same way that statisticians in other disciplines have to provide models of finite or infinite populations. Following this argument, we suggest in this paper that an obvious model for a stratified population when auxiliary information regarding variate values is available, is the one way analysis of covariance model with unequal variances and we consider the problem of estimating the finite population mean. Finally a possible extension of this result is discussed.     

A critical analysis of clustering phenomenon in Al2O3 nanofluids

Journal of Thermal Analysis and Calorimetry - In the present work, an investigation on the relationship between clustering phenomenon and thermal conductivity of nanofluids is presented....     

Siderophore-Mediated Iron Uptake Promotes Yeast–Bacterial Symbiosis

In the present study, siderophore produced by the marine yeast Aureobasidium pullulans was characterized as hydroxamate by chemical and bioassays. The hydroxamate assignment was supported by the appearance of peaks at 1,647.21?C1,625.99?cm^?1 and at 1,435.04?cm^?1 in the infrared spectrum. The purified siderophore exhibited specific growth-promoting activity under iron-limited conditions for siderophore auxotrophic probiotic bacteria. Cross-utilization of siderophore indicates a symbiotic relationship between the yeast A. pullulans and the selected probiotic bacterial strains. Statistical optimization of medium components for improved siderophore production in A. pullulans was depicted by response surface methodology. The shift in UV?CVis spectroscopy indicates the photoreactive property and subsequent oxidative cleavage of purified siderophore on exposure to sunlight.     

A decomposition algorithm for unconstrained optimization problems with partial derivative information

In this paper we consider the problem of minimizing a nonlinear function using partial derivative knowledge. Namely, the objective function is such that its derivatives with respect to a pre-specified block of variables cannot be computed. To solve the problem we propose a block decomposition method that takes advantage of both derivative-free and derivative-based iterations to account for the features of the objective function. Under standard assumptions, we manage to prove global convergence of the method to stationary points of the problem.