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.

Infinite push: a new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list

Ranking problems have become increasingly important in machine learning and data mining in recent years, with applications ranging from information retrieval and recommender systems to computational biology and drug discovery. In this paper, we describe a new ranking algorithm that directly maximizes the number of relevant objects retrieved at the absolute top of the list. The algorithm is a support vector style algorithm, but due to the different objective, it no longer leads to a quadratic programming problem. Instead, the dual optimization problem involves l1, ∞ constraints; we solve this dual problem using the recent l1, ∞ projection method of Quattoni et al (2009). Our algorithm can be viewed as an l∞-norm extreme of the lp-norm based algorithm of Rudin (2009) (albeit in a support vector setting rather than a boosting setting); thus we refer to the algorithm as the ‘Infinite Push’. Experiments on real-world data sets confirm the algorithm’s focus on accuracy at the absolute top of the list.

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.

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.

N-Terminal disordered domain of saccharomyces cerevisiae Hop1 protein Is dispensable for DNA binding, bridging, and synapsis of double-stranded DNA molecules but is ecessary for spore formation

The cytological architecture of the synaptonemal complex (SC), a meiosis-specific proteinaceous structure, is evolutionarily conserved among eukaryotes. However, little is known about the biochemical properties of SC components or the mechanisms underlying their roles in meiotic chromosome synapsis and recombination. Functional analysis of Saccharomyces cerevisiae Hop1, a key structural component of SC, has begun to reveal important insights into its function in interhomolog recombination. Previously, we showed that Hop1 is a structure-specific DNA-binding protein, exhibits higher binding affinity for the Holliday junction, and induces structural distortion at the core of the junction. Furthermore, Hop1 promotes DNA condensation and intra- and intermolecular synapsis between duplex DNA molecules. Here, we show that Hop1 possesses a modular domain organization, consisting of an intrinsically disordered N-terminal domain and a protease-resistant C-terminal domain (Hop1CTD). Furthermore, we found that Hop1CTD exhibits strong homotypic as well as heterotypic protein protein interactions, and its biochemical activities were similar to those of the full-length Hop1 protein. However, Hop1CTD failed to complement the meiotic recombination defects of the Delta hop1 strain, indicating that both N- and C-terminal domains of Hop1 are essential for meiosis and spore formation. Altogether, our findings reveal novel insights into the structure-function relationships of Hop1 and help to further our understanding of its role in meiotic chromosome synapsis and recombination.

Measurement Based Impromptu Deployment of a Multi-Hop Wireless Relay Network

We study the problem of optimal sequential (''as-you-go'') deployment of wireless relay nodes, as a person walks along a line of random length (with a known distribution). The objective is to create an impromptu multihop wireless network for connecting a packet source to be placed at the end of the line with a sink node located at the starting point, to operate in the light traffic regime. In walking from the sink towards the source, at every step, measurements yield the transmit powers required to establish links to one or more previously placed nodes. Based on these measurements, at every step, a decision is made to place a relay node, the overall system objective being to minimize a linear combination of the expected sum power (or the expected maximum power) required to deliver a packet from the source to the sink node and the expected number of relay nodes deployed. For each of these two objectives, two different relay selection strategies are considered: (i) each relay communicates with the sink via its immediate previous relay, (ii) the communication path can skip some of the deployed relays. With appropriate modeling assumptions, we formulate each of these problems as a Markov decision process (MDP). We provide the optimal policy structures for all these cases, and provide illustrations of the policies and their performance, via numerical results, for some typical parameters.

Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction

The sparse recovery methods utilize the l(p)-normbased regularization in the estimation problem with 0 <= p <= 1. These methods have a better utility when the number of independent measurements are limited in nature, which is a typical case for diffuse optical tomographic image reconstruction problem. These sparse recovery methods, along with an approximation to utilize the l(0)-norm, have been deployed for the reconstruction of diffuse optical images. Their performancewas compared systematically using both numerical and gelatin phantom cases to show that these methods hold promise in improving the reconstructed image quality.

Dielectric relaxation, phase transition and Rietveld studies of perovskite Pb0.94Sr0.06](Mn1/3Sb2/3)(0.05)(Zr0.52Ti0.48)(0.95)]O-3 ceramics

The frequency-dependent dielectric relaxation of Pb0.94Sr0.06](Mn1/3Sb2/3)(0.05)(Zr0.52Ti0.48)(0.95)]O-3 ceramics, synthesized in pure perovskite phase by a solid-state reaction technique is investigated in the temperature range from 303 to 773 K by alternating-current impedance spectroscopy. Using Cole-Cole model, an analysis of the imaginary part of the dielectric permittivity with frequency is performed assuming a distribution of relaxation times. The scaling behavior of the imaginary part of the electric modulus suggests that the relaxation describes the same mechanism at various temperatures. The variation of dielectric constant with temperature is explained considering the space-charge polarization. The SEM indicates that the sample has single phase with an average grain size similar to 14.2 mu m. The material exhibits tetragonal structure. A detailed temperature dependent dielectric study at various frequencies has also been performed. (C) 2013 Elsevier B.V. All rights reserved.

STOKES' SYSTEM IN A DOMAIN WITH OSCILLATING BOUNDARY: HOMOGENIZATION AND ERROR ANALYSIS OF AN INTERIOR OPTIMAL CONTROL PROBLEM

Homogenization and error analysis of an optimal interior control problem in the framework of Stokes' system, on a domain with rapidly oscillating boundary, are the subject matters of this article. We consider a three dimensional domain constituted of a parallelepiped with a large number of rectangular cylinders at the top of it. An interior control is applied in a proper subdomain of the parallelepiped, away from the oscillating volume. We consider two types of functionals, namely a functional involving the L-2-norm of the state variable and another one involving its H-1-norm. The asymptotic analysis of optimality systems for both cases, when the cross sectional area of the rectangular cylinders tends to zero, is done here. Our major contribution is to derive error estimates for the state, the co-state and the associated pressures, in appropriate functional spaces.

Large-MIMO Receiver based on Linear Regression of MMSE Residual

Multiple input multiple output (MIMO) systems with large number of antennas have been gaining wide attention as they enable very high throughputs. A major impediment is the complexity at the receiver needed to detect the transmitted data. To this end we propose a new receiver, called LRR (Linear Regression of MMSE Residual), which improves the MMSE receiver by learning a linear regression model for the error of the MMSE receiver. The LRR receiver uses pilot data to estimate the channel, and then uses locally generated training data (not transmitted over the channel), to find the linear regression parameters. The proposed receiver is suitable for applications where the channel remains constant for a long period (slow-fading channels) and performs quite well: at a bit error rate (BER) of 10(-3), the SNR gain over MMSE receiver is about 7 dB for a 16 x 16 system; for a 64 x 64 system the gain is about 8.5 dB. For large coherence time, the complexity order of the LRR receiver is the same as that of the MMSE receiver, and in simulations we find that it needs about 4 times as many floating point operations. We also show that further gain of about 4 dB is obtained by local search around the estimate given by the LRR receiver.

Dielectric relaxation and anti-ferromagnetic coupling of BiEu03 and BiGd03

BiEuO3 (BE) and BiGdO3 (BG) are synthesized by the solid-state reaction technique. Rietveld refinement of the X-ray diffraction data shows that the samples are crystallized in cubic phase at room temperature having Fm3m symmetry with the lattice parameters of 5.4925(2) and 5.4712(2) A for BE and BG, respectively. Raman spectra of the samples are investigated to obtain the phonon modes of the samples. The dielectric properties of the samples are investigated in the frequency range from 42 Hz to 1.1 MHz and in the temperature range from 303 K to 673 K. An analysis of the real and imaginary parts of impedance is performed assuming a distribution of relaxation times as confirmed by the Cole-Cole plots. The frequency-dependent maxima in the loss tangent are found to obey an Arrhenius law with activation energy similar to 1 eV for both the samples. The frequency-dependent electrical data are also analyzed in the framework of conductivity formalism. Magnetization of the samples are measured under the field cooled (EC) and zero field cooled (ZFC) modes in the temperature range from 5 K to 300 K applying a magnetic Field of 500 Oe. The FC and ZFC susceptibilities show that BE is a Van Vleck paramagnetic material with antiferromagnetic coupling at low temperature whereas BG is an anti-ferromagnetic system. The results are substantiated by the M-11 loops of the materials taken at 5 K in the ZFC mode. (C) 2014 Elsevier B.V. All rights reserved

Performance evaluation of typical approximation algorithms for nonconvex l(p)-minimization in diffuse optical tomography

The sparse estimation methods that utilize the l(p)-norm, with p being between 0 and 1, have shown better utility in providing optimal solutions to the inverse problem in diffuse optical tomography. These l(p)-norm-based regularizations make the optimization function nonconvex, and algorithms that implement l(p)-norm minimization utilize approximations to the original l(p)-norm function. In this work, three such typical methods for implementing the l(p)-norm were considered, namely, iteratively reweighted l(1)-minimization (IRL1), iteratively reweighted least squares (IRLS), and the iteratively thresholding method (ITM). These methods were deployed for performing diffuse optical tomographic image reconstruction, and a systematic comparison with the help of three numerical and gelatin phantom cases was executed. The results indicate that these three methods in the implementation of l(p)-minimization yields similar results, with IRL1 fairing marginally in cases considered here in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. (C) 2014 Optical Society of America

Nonequilibrium ``Melting'' of a Charge Density Wave Insulator via an Ultrafast Laser Pulse

We employ an exact solution of the simplest model for pump-probe time-resolved photoemission spectroscopy in charge-density-wave systems to show how, in nonequilibrium, the gap in the density of states disappears while the charge density remains modulated, and then the gap reforms after the pulse has passed. This nonequilibrium scenario qualitatively describes the common short-time experimental features in TaS2 and TbTe3, indicating a quasiuniversality for nonequilibrium ``melting'' with qualitative features that can be easily understood within a simple picture.

Structural refinement, optical and electrical properties of Ba1-x Sm-2x/3](Zr0.05Ti0.95)O-3 ceramics

Samarium doped barium zirconate titanate ceramics with general formula Ba1-x Sm-2x/3](Zr0.05Ti0.95)O-3 x = 0, 0.01, 0.02, and 0.03] were prepared by high energy ball milling method. X-ray diffraction patterns and micro-Raman spectroscopy confirmed that these ceramics have a single phase with a tetragonal structure. Rietveld refinement data were employed to model BaO12], SmO12], ZrO6], and TiO6] clusters in the lattice. Scanning electron microscopy shows a reduction in average grain size with the increase of Sm3+ ions into lattice. Temperature-dependent dielectric studies indicate a ferroelectric phase transition and the transition temperature decreases with an increase in Sm3+ ion content. The nature of the transition was investigated by the Curie-Weiss law and it is observed that the diffusivity increases with Sm3+ ion content. The ferroelectric hysteresis loop illustrates that the remnant polarization and coercive field increase with an increase in Sm3+ ions content. Optical properties of the ceramics were studied using ultraviolet-visible diffuse reflectance spectroscopy.