Clustering Based Large Margin Classification: A Scalable Approach using SOCP Formulation

This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture distributions, where each component of the mixture has a spherical covariance, the second order statistics of the components can be estimated efficiently using clustering algorithms like BIRCH. For each cluster, the second order moments are used to derive a second order cone constraint via a Chebyshev-Cantelli inequality. This constraint ensures that any data point in the cluster is classified correctly with a high probability. This leads to a large margin SOCP formulation whose size depends on the number of clusters rather than the number of training data points. Hence, the proposed formulation scales well for large datasets when compared to the state-of-the-art classifiers, Support Vector Machines (SVMs). Experiments on real world and synthetic datasets show that the proposed algorithm outperforms SVM solvers in terms of training time and achieves similar accuracies.

Optimal association of mobile wireless devices with a WLAN-3G access network

In this paper, we consider the problem of association of wireless stations (STAs) with an access network served by a wireless local area network (WLAN) and a 3G cellular network. There is a set of WLAN Access Points (APs) and a set of 3G Base Stations (BSs) and a number of STAs each of which needs to be associated with one of the APs or one of the BSs. We concentrate on downlink bulk elastic transfers. Each association provides each ST with a certain transfer rate. We evaluate an association on the basis of the sum log utility of the transfer rates and seek the utility maximizing association. We also obtain the optimal time scheduling of service from a 3G BS to the associated STAs. We propose a fast iterative heuristic algorithm to compute an association. Numerical results show that our algorithm converges in a few steps yielding an association that is within 1% (in objective value) of the optimal (obtained through exhaustive search); in most cases the algorithm yields an optimal solution.

Scalable processes for fabricating non-volatile memory devices using self-assembled 2D arrays of gold nanoparticles as charge storage nodes

We propose robust and scalable processes for the fabrication of floating gate devices using ordered arrays of 7 nm size gold nanoparticles as charge storage nodes. The proposed strategy can be readily adapted for fabricating next generation (sub-20 nm node) non-volatile memory devices.

Trapped fermions in a synthetic non-Abelian gauge field

On increasing the coupling strength (lambda) of a non-Abelian gauge field that induces a generalized Rashba spin-orbit interaction, the topology of the Fermi surface of a homogeneous gas of noninteracting fermions of density rho similar to k(F)(3) undergoes a change at a critical value, lambda(T) approximate to k(F) [Phys. Rev. B 84, 014512 ( 2011)]. In this paper we analyze how this phenomenon affects the size and shape of a cloud of spin-1/2 fermions trapped in a harmonic potential such as those used in cold atom experiments. We develop an adiabatic formulation, including the concomitant Pancharatnam-Berry phase effects, for the one-particle states in the presence of a trapping potential and the gauge field, obtaining approximate analytical formulas for the energy levels for some high symmetry gauge field configurations of interest. An analysis based on the local density approximation reveals that, for a given number of particles, the cloud shrinks in a characteristic fashion with increasing.. We explain the physical origins of this effect by a study of the stress tensor of the system. For an isotropic harmonic trap, the local density approximation predicts a spherical cloud even for anisotropic gauge field configurations. We show, via a calculation of the cloud shape using exact eigenstates, that for certain gauge field configurations there is a systematic and observable anisotropy in the cloud shape that increases with increasing gauge coupling lambda. The reasons for this anisotropy are explained using the analytical energy levels obtained via the adiabatic approximation. These results should be useful in the design of cold atom experiments with fermions in non-Abelian gauge fields. An important spin-off of our adiabatic formulation is that it reveals exciting possibilities for the cold-atom realization of interesting condensed matter Hamiltonians by using a non-Abelian gauge field in conjunction with another potential. In particular, we show that the use of a spherical non-Abelian gauge field with a harmonic trapping potential produces a monopole field giving rise to a spherical geometry quantum Hall-like Hamiltonian in the momentum representation.

Editorial optical computing circuits, devices and systems

Information forms the basis of modern technology. To meet the ever-increasing demand for information, means have to be devised for a more efficient and better-equipped technology to intelligibly process data. Advances in photonics have made their impact on each of the four key applications in information processing, i.e., acquisition, transmission, storage and processing of information. The inherent advantages of ultrahigh bandwidth, high speed and low-loss transmission has already established fiber-optics as the backbone of communication technology. However, the optics to electronics inter-conversion at the transmitter and receiver ends severely limits both the speed and bit rate of lightwave communication systems. As the trend towards still faster and higher capacity systems continues, it has become increasingly necessary to perform more and more signal-processing operations in the optical domain itself, i.e., with all-optical components and devices that possess a high bandwidth and can perform parallel processing functions to eliminate the electronic bottleneck.

Polymer blend based photovoltaic devices.

Solar cells on thin conformable substrates require conventional plastics such asPS and PMMA that provide better mechanical and environmental stability with cost reduction. We can also tune charge transfer between PPV derivatives and fullerene derivatives via morphology control of the plastics in the solar cells. Our group has conducted morphology evolution studies in nano- and microscale light emitting domains in poly (2-methoxy, 5-(2'-ethyl-hexyloxy)-p-phenylenevinylene) (MEH-PPV) and poly (methyl methacrylate) (PMMA) blends. Our current research has been focused on tricomponent-photoactive solar cells which comprise MEH-PPV, PMMA, and [6,6]-phenyl C61-butyric acid methyl ester (PCBM, Figure 1) in the photoactive layer. Morphology control of the photoactive materials and fine tuning of photovoltaic properties for the solar cells are our primary interest. Similar work has been done by the Sariciftci research group. Additionally, a study on inter- and intramolecular photoinduced charge transfer using MEH-PPV derivatives that have different conjugation lengths (Figure 1, n=1 and 0.85) has been performed.

Towards sustainability: a new, solid-state synthetic route for supported metal nanocatalysts

Noble metal ions like Pt(IV) and Pd(II) were impregnated on gamma-alumina and aerosol 300 silica surfaces. Reduction of these ions using ammonia borane in the solid state resulted in the formation of the respective metal nanoparticles embedded in BNHx polymer which is dispersed on the oxide support. Removal of the BNH polymer was accomplished by washing the samples repeatedly with methanol. In this process the polymer undergoes solvolysis to release H-2 accompanied by the formation of ammonium methoxy borate salt, which has been removed by repeated methanol washings. As a result, metal nanoparticles well dispersed on gamma-alumina and aerosol 300 silica were obtained. These samples have been characterized by a combination of techniques, including electron microscopy, powder X-ray diffraction, NMR spectroscopy and surface area analyser.

Curcumin-glucoside, A Novel Synthetic Derivative of Curcumin, Inhibits alpha-Synuclein Oligomer Formation: Relevance to Parkinson's Disease

alpha-Synuclein aggregation is centrally implicated in Parkinson's disease (PD). It involves multi-step nucleated polymerization process via the formation of dimers, soluble toxic oligomers and insoluble fibrils. In the present study, we synthesized a novel compound viz., Curcumin-glucoside (Curc-gluc), a modified form of curcumin and studied its anti-aggregating potential with alpha-synuclein. Under aggregating conditions in vitro, Curc-gluc prevents oligomer formation as well as inhibits fibril formation indicating favorable stoichiometry for inhibition. The binding efficacies of Curc-gluc to both alpha-synuclein monomeric and oligomeric forms were characterized by micro-calorimetry. It was observed that titration of Curc-gluc with alpha-synuclein monomer yielded very low heat values with low binding while, in case of oligomers, Curc-gluc showed significant binding. Addition of Curc-gluc inhibited aggregation in a dose-dependent manner and enhanced alpha-synuclein solubility, which propose that Curc-gluc solubilizes the oligomeric form by disintegrating preformed fibrils and this is a novel observation. Overall, the data suggest that Curc-gluc binds to alpha-synuclein oligomeric form and prevents further fibrillization of alpha-synuclein; this might aid the development of disease modifying agents in preventing or treating PD.

Learning polyhedral classifiers using logistic function

In this paper we propose a new algorithm for learning polyhedral classifiers. In contrast to existing methods for learning polyhedral classifier which solve a constrained optimization problem, our method solves an unconstrained optimization problem. Our method is based on a logistic function based model for the posterior probability function. We propose an alternating optimization algorithm, namely, SPLA1 (Single Polyhedral Learning Algorithm1) which maximizes the loglikelihood of the training data to learn the parameters. We also extend our method to make it independent of any user specified parameter (e.g., number of hyperplanes required to form a polyhedral set) in SPLA2. We show the effectiveness of our approach with experiments on various synthetic and real world datasets and compare our approach with a standard decision tree method (OC1) and a constrained optimization based method for learning polyhedral sets.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Reactive power optimization with different objectives in large power systems including HVDC systems and FACTS devices

Present day power systems are growing in size and complexity of operation with inter connections to neighboring systems, introduction of large generating units, EHV 400/765 kV AC transmission systems, HVDC systems and more sophisticated control devices such as FACTS. For planning and operational studies, it requires suitable modeling of all components in the power system, as the number of HVDC systems and FACTS devices of different type are incorporated in the system. This paper presents reactive power optimization with three objectives to minimize the sum of the squares of the voltage deviations (ve) of the load buses, minimization of sum of squares of voltage stability L-indices of load buses (¿L2), and also the system real power loss (Ploss) minimization. The proposed methods have been tested on typical sample system. Results for Indian 96-bus equivalent system including HVDC terminal and UPFC under normal and contingency conditions are presented.

Oscillatory exchange bias and training effects in nanocrystalline Pr0.5Ca0.5MnO3

We report on exchange bias effects in 10 nm particles of Pr0.5Ca0.5MnO3 which appear as a result of competing interactions between the ferromagnetic (FM)/anti-ferromagnetic (AFM) phases. The fascinating new observation is the demonstration of the temperature dependence of oscillatory exchange bias (OEB) and is tunable as a function of cooling field strength below the SG phase, may be attributable to the presence of charge/spin density wave (CDW/SDW) in the AFM core of PCMO10. The pronounced training effect is noticed at 5 K from the variation of the EB field as a function of number of field cycles (n) upon the field cooling (FC) process. For n > 1, power-law behavior describes the experimental data well; however, the breakdown of spin configuration model is noticed at n >= 1. Copyright 2012 Author(s). This article is distributed under a Creative Commons Attribution 3.0 Unported License. http://dx.doi.org/10.1063/1.3696033]