Determination of C-13 Chemical Shift Anisotropy Tensors and Molecular Order of 4-Hexyloxybenzoic Acid

4-Alkoxy benzoic acids belong to an important class of thermotropic liquid crystals that are structurally simple and often used as starting materials for many novel mesogens. 4-Hexyloxybenzoic acid (HBA) is a homologue of the same series and exhibits an enantiotropic nematic phase. As this molecule could serve as an ideal model compound, high resolution C-13 NMR studies of HEA in solution, solid, and liquid crystalline phases have been undertaken. In the solid state, two-dimensional separation of undistorted powder patterns by effortless recoupling (2D SUPER) experiments have been carried out to estimate the magnitude of the components of the chemical shift anisotropy (GSA) tensor of all the aromatic carbons. These values have been used subsequently for calculating the orientational order parameters in the liquid crystalline phase. The GSA values computed by density functional theory (DFT) calculations showed good agreement with the 2D SUPER values. Additionally, C-13-H-1 dipolar couplings in the nematic phase have been determined by separated local field (SLF) spectroscopy at various temperatures and were used for computing the order parameters, which compared well with those calculated by using the chemical shifts. It is anticipated that the CSA values determined for MBA would be useful for the assignment of carbon chemical shifts and for the study of order and dynamics of structurally similar novel mesogens in their nematic phases.

Phonon anomalies and structural transition in spin ice Dy2Ti2O7: a simultaneous pressure-dependent and temperature-dependent Raman study

We revisit the assignment of Raman phonons of rare-earth titanates by performing Raman measurements on single crystals of O18 isotope-rich spin ice Dy2Ti2O718 and nonmagnetic Lu2Ti2O718 pyrochlores and compare the results with their O16 counterparts. We show that the low-wavenumber Raman modes below 250 cm-1 are not due to oxygen vibrations. A mode near 200 cm-1, commonly assigned as F2g phonon, which shows highly anomalous temperature dependence, is now assigned to a disorder-induced Raman active mode involving Ti4+ vibrations. Moreover, we address here the origin of the new Raman mode, observed below TC similar to 110 K in Dy2Ti2O7, through a simultaneous pressure-dependent and temperature-dependent Raman study. Our study confirms the new mode to be a phonon mode. We find that dTC/dP = + 5.9 K/GPa. Temperature dependence of other phonons has also been studied at various pressures up to similar to 8 GPa. We find that pressure suppresses the anomalous temperature dependence. The role of the inherent vacant sites present in the pyrochlore structure in the anomalous temperature dependence is also discussed. Copyright (c) 2012 John Wiley & Sons, Ltd.

NMR Analysis of Cross Strand Aromatic Interactions in an 8 Residue Hairpin and a 14 Residue Three Stranded beta-Sheet Peptide

Cross strand aromatic interactions between a facing pair of phenylalanine residues in antiparallel beta-sheet structures have been probed using two structurally defined model peptides. The octapeptide Boc-(LFVPPLFV)-P-D-P-L-OMe (peptide 1) favors the beta-hairpin conformation nucleated by the type II' beta-turn formed by the (D)Pro-(L)Pro segment, placing Phe2 and Phe7 side chains in proximity. Two centrally positioned (D)Pro-(L)Pro segments facilitate the three stranded beta-sheet formation in the 14 residue peptide Boc-LFV(D)P(L)PLFVA(D)P(L)PLFV-OMe (peptide 2) in which the Phe2/Phe7 orientations are similar to that in the octapeptide. The anticipated folded conformations of peptides 1 and 2 are established by the delineation of intramolecularly hydrogen bonded NH groups and by the observation of specific cross strand NOEs. The observation of ring current shifted aromatic protons is a diagnostic of close approach of the Phe2 and Phe7 side chains. Specific assignment of aromatic proton resonances using HSQC and HSQC-TOCSY methods allow an analysis of interproton NOEs between the spatially proximate aromatic rings. This approach facilitates specific assignments in systems containing multiple aromatic rings in spectra at natural abundance. Evidence is presented for a dynamic process which invokes a correlated conformational change about the C-alpha-C-beta(chi(1)) bond for the pair of interacting Phe residues. NMR results suggest that aromatic ring orientations observed in crystals are maintained in solution. Anomalous temperature dependence of ring current induced proton chemical shifts suggests that solvophobic effects may facilitate aromatic ring clustering in apolar solvents.

Intermolecular charge transfer and vibrational analysis of hydrogen bonding in acetazolamide

In the present work the structural and spectral characteristics of acetazolamide have been studied by methods of infrared, Raman spectroscopy and quantum chemistry. Electrostatic potential surface, optimized geometry, harmonic vibrational frequencies, infrared intensities and activities of Raman scattering were calculated by density functional theory (DFT) employing B3LYP with complete relaxation in the potential energy surface using 6-311++G(d,p) basis set. Based on these results, we have discussed the correlation between the vibrational modes and the structure of the dimers of acetazolamide. The calculated vibrational spectra of three dimers of acetazolamide have been compared with observed spectra, and the assignment of observed bands was carried out using potential energy distribution. The observed spectra agree well with the values computed from the OFT. A comparison of observed and calculated vibrational spectra clearly shows the effect of hydrogen bonding. The frequency shifts observed for the different dimers are in accord with the hydrogen bonding in acetazolamide. Natural bond orbital (NBO) analyses reflect the charge transfer interaction in the individual hydrogen bond units and the stability of different dimers of acetazolamide. (C) 2012 Elsevier B.V. All rights reserved.

Chiral discrimination and the measurement of enantiomeric excess from a severely overcrowded NMR spectrum

NMR spectroscopic chiral visualization, unambiguous assignment of peaks pertaining to R and S enantiomers and the subsequent measurement of enantiomeric composition demands a highly resolved spectrum. The method fails when the spectrum is severely overcrowded or highly complex, thereby hampering the determination of enantiomeric excess. In order to circumvent such problems we propose the utility of pure shift spectrum obtained by resolving the chemical shift and coupling information in two orthogonal dimensions. The skew projected spectrum yields singlet's at the respective chemical shift positions, permitting the unravelling of the superimposed spectral transitions for each enantiomer and measurement of enantiomeric composition. (C) 2012 Elsevier B. V. All rights reserved.

A physics-based flexural phonon-dependent thermal conductivity model for single layer graphene

In this paper, we address a physics-based closed-form analytical model of flexural phonon-dependent diffusive thermal conductivity (kappa) of suspended rectangular single layer graphene sheet. A quadratic dependence of the out-of-plane phonon frequency, generally called flexural phonons, on the phonon wave vector has been taken into account to analyze the behavior of kappa at lower temperatures. Such a dependence has further been used for the determination of second-order three-phonon Umklapp and isotopic scatterings. We find that these behaviors in our model are best explained through the upper limit of Debye cut-off frequency in the second-order three-phonon Umklapp scattering of the long phonon waves that actually remove the thermal conductivity singularity by contributing a constant scattering rate at low frequencies and note that the out-of-plane Gruneisen parameter for these modes need not be too high. Using this, we clearly demonstrate that. follows a T-1.5 and T-2 law at lower and higher temperatures in the absence of isotopes, respectively. However in their presence, the behavior of kappa sharply deviates from the T-2 law at higher temperatures. The present geometry-dependent model of kappa is found to possess an excellent match with various experimental data over a wide range of temperatures which can be put forward for efficient electro-thermal analyses of encased/supported graphene.

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.

Dynamic mechanism design for markets with strategic resources

The assignment of tasks to multiple resources becomes an interesting game theoretic problem, when both the task owner and the resources are strategic. In the classical, nonstrategic setting, where the states of the tasks and resources are observable by the controller, this problem is that of finding an optimal policy for a Markov decision process (MDP). When the states are held by strategic agents, the problem of an efficient task allocation extends beyond that of solving an MDP and becomes that of designing a mechanism. Motivated by this fact, we propose a general mechanism which decides on an allocation rule for the tasks and resources and a payment rule to incentivize agents' participation and truthful reports. In contrast to related dynamic strategic control problems studied in recent literature, the problem studied here has interdependent values: the benefit of an allocation to the task owner is not simply a function of the characteristics of the task itself and the allocation, but also of the state of the resources. We introduce a dynamic extension of Mezzetti's two phase mechanism for interdependent valuations. In this changed setting, the proposed dynamic mechanism is efficient, within period ex-post incentive compatible, and within period ex-post individually rational.

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

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.

Minimizing the complexity of fast sphere decoding of STBCs

Decoding of linear space-time block codes (STBCs) with sphere-decoding (SD) is well known. A fast-version of the SD known as fast sphere decoding (FSD) has been recently studied by Biglieri, Hong and Viterbo. Viewing a linear STBC as a vector space spanned by its defining weight matrices over the real number field, we define a quadratic form (QF), called the Hurwitz-Radon QF (HRQF), on this vector space and give a QF interpretation of the FSD complexity of a linear STBC. It is shown that the FSD complexity is only a function of the weight matrices defining the code and their ordering, and not of the channel realization (even though the equivalent channel when SD is used depends on the channel realization) or the number of receive antennas. It is also shown that the FSD complexity is completely captured into a single matrix obtained from the HRQF. Moreover, for a given set of weight matrices, an algorithm to obtain a best ordering of them leading to the least FSD complexity is presented. The well known classes of low FSD complexity codes (multi-group decodable codes, fast decodable codes and fast group decodable codes) are presented in the framework of HRQF.

Near-optimal large-MIMO detection using randomized MCMC and randomized search algorithms

Low-complexity near-optimal detection of signals in MIMO systems with large number (tens) of antennas is getting increased attention. In this paper, first, we propose a variant of Markov chain Monte Carlo (MCMC) algorithm which i) alleviates the stalling problem encountered in conventional MCMC algorithm at high SNRs, and ii) achieves near-optimal performance for large number of antennas (e.g., 16×16, 32×32, 64×64 MIMO) with 4-QAM. We call this proposed algorithm as randomized MCMC (R-MCMC) algorithm. Second, we propose an other algorithm based on a random selection approach to choose candidate vectors to be tested in a local neighborhood search. This algorithm, which we call as randomized search (RS) algorithm, also achieves near-optimal performance for large number of antennas with 4-QAM. The complexities of the proposed R-MCMC and RS algorithms are quadratic/sub-quadratic in number of transmit antennas, which are attractive for detection in large-MIMO systems. We also propose message passing aided R-MCMC and RS algorithms, which are shown to perform well for higher-order QAM.

A Huber-loss-driven clustering technique and its application to robust cell detection in confocal microscopy images

We address the problem of detecting cells in biological images. The problem is important in many automated image analysis applications. We identify the problem as one of clustering and formulate it within the framework of robust estimation using loss functions. We show how suitable loss functions may be chosen based on a priori knowledge of the noise distribution. Specifically, in the context of biological images, since the measurement noise is not Gaussian, quadratic loss functions yield suboptimal results. We show that by incorporating the Huber loss function, cells can be detected robustly and accurately. To initialize the algorithm, we also propose a seed selection approach. Simulation results show that Huber loss exhibits better performance compared with some standard loss functions. We also provide experimental results on confocal images of yeast cells. The proposed technique exhibits good detection performance even when the signal-to-noise ratio is low.

Yielding and large deviations in micellar gels: a model

We present a simple model that can be used to account for the rheological behaviour observed in recent experiments on micellar gels. The model combines attachment detachment kinetics with stretching due to shear, and shows well-defined jammed and flowing states. The large-deviation function (LDF) for the coarse-grained velocity becomes increasingly non-quadratic as the applied force F is increased, in a range near the yield threshold. The power fluctuations are found to obey a steady-state fluctuation relation (FR) at small F. However, the FR is violated when F is near the transition from the flowing to the jammed state although the LDF still exists; the antisymmetric part of the LDF is found to be nonlinear in its argument. Our approach suggests that large fluctuations and motion in a direction opposite to an imposed force are likely to occur in a wider class of systems near yielding.

A generalized Stein's estimation approach to speech enhancement based on perceptual criteria

We address the problem of speech enhancement using a risk- estimation approach. In particular, we propose the use the Stein’s unbiased risk estimator (SURE) for solving the problem. The need for a suitable finite-sample risk estimator arises because the actual risks invariably depend on the unknown ground truth. We consider the popular mean-squared error (MSE) criterion first, and then compare it against the perceptually-motivated Itakura-Saito (IS) distortion, by deriving unbiased estimators of the corresponding risks. We use a generalized SURE (GSURE) development, recently proposed by Eldar for MSE. We consider dependent observation models from the exponential family with an additive noise model,and derive an unbiased estimator for the risk corresponding to the IS distortion, which is non-quadratic. This serves to address the speech enhancement problem in a more general setting. Experimental results illustrate that the IS metric is efficient in suppressing musical noise, which affects the MSE-enhanced speech. However, in terms of global signal-to-noise ratio (SNR), the minimum MSE solution gives better results.