Basic principles of ultrafast Raman loss spectroscopy

When a light beam passes through any medium, the effects of interaction of light with the material depend on the field intensity. At low light intensities the response of materials remain linear to the amplitude of the applied electromagnetic field. But for sufficiently high intensities, the optical properties of materials are no longer linear to the amplitude of applied electromagnetic field. In such cases, the interaction of light waves with matter can result in the generation of new frequencies due to nonlinear processes such as higher harmonic generation and mixing of incident fields. One such nonlinear process, namely, the third order nonlinear spectroscopy has become a popular tool to study molecular structure. Thus, the spectroscopy based on the third order optical nonlinearity called stimulated Raman spectroscopy (SRS) is a tool to extract the structural and dynamical information about a molecular system. Ultrafast Raman loss spectroscopy (URLS) is analogous to SRS but is more sensitive than SRS. In this paper, we present the theoretical basis of SRS (URLS) techniques which have been developed in our laboratory.

Coupled phonons, magnetic excitations, and ferroelectricity in AlFeO3: Raman and first-principles studies

We determine the nature of coupled phonons and magnetic excitations in AlFeO3 using inelastic light scattering from 5 to 315 K covering a spectral range from 100 to 2200 cm(-1) and complementary first-principles density functional theory-based calculations. A strong spin-phonon coupling and magnetic ordering-induced phonon renormalization are evident in (1) anomalous temperature dependence of many modes with frequencies below 850 cm(-1), particularly near the magnetic transition temperature T-c approximate to 250 K, and (2) distinct changes in band positions of high-frequency Raman bands between 1100 and 1800 cm(-1); in particular, a broad mode near 1250 cm(-1) appears only below T-c, attributed to the two-magnon Raman scattering. We also observe weak anomalies in the mode frequencies similar to 100 K due to a magnetically driven ferroelectric phase transition. Understanding of these experimental observations has been possible on the basis of first-principles calculations of the phonons' spectrum and their coupling with spins.

On maximum entropy and minimum KL-divergence optimization by Grobner basis methods

In this paper we study constrained maximum entropy and minimum divergence optimization problems, in the cases where integer valued sufficient statistics exists, using tools from computational commutative algebra. We show that the estimation of parametric statistical models in this case can be transformed to solving a system of polynomial equations. We give an implicit description of maximum entropy models by embedding them in algebraic varieties for which we give a Grobner basis method to compute it. In the cases of minimum KL-divergence models we show that implicitization preserves specialization of prior distribution. This result leads us to a Grobner basis method to embed minimum KL-divergence models in algebraic varieties. (C) 2012 Elsevier Inc. All rights reserved.

First principles calculations of H-storage in sorption materials

A review of various contributions of first principles calculations in the area of hydrogen storage, particularly for the carbon-based sorption materials, is presented. Carbon-based sorption materials are considered as promising hydrogen storage media due to their light weight and large surface area. Depending upon the hybridization state of carbon, these materials can bind the hydrogen via various mechanisms, including physisorption, Kubas and chemical bonding. While attractive binding energy range of Kubas bonding has led to design of several promising storage systems, in reality the experiments remain very few due to materials design challenges that are yet to be overcome. Finally, we will discuss the spillover process, which deals with the catalytic chemisorption of hydrogen, and arguably is the most promising approach for reversibly storing hydrogen under ambient conditions.

Maximum weight independent sets in hole- and dart-free graphs

The Maximum Weight Independent Set (MWIS) problem on graphs with vertex weights asks for a set of pairwise nonadjacent vertices of maximum total weight. The complexity of the MWIS problem for hole-free graphs is unknown. In this paper, we first prove that the MWIS problem for (hole, dart, gem)-free graphs can be solved in O(n(3))-time. By using this result, we prove that the MWIS problem for (hole, dart)-free graphs can be solved in O(n(4))-time. Though the MWIS problem for (hole, dart, gem)-free graphs is used as a subroutine, we also give the best known time bound for the solvability of the MWIS problem in (hole, dart, gem)-free graphs. (C) 2012 Elsevier B.V. All rights reserved.

Selective excitation and detection of maximum quantum coherence of a group of scalar coupled protons in chiral molecules: an NMR experiment for enantiodiscrimination

The H-1 NMR spectroscopic discrimination of enantiomers in the solution state and the measurement of enantiomeric composition is most often hindered due to either very small chemical shift differences between the discriminated peaks or severe overlap of transitions from other chemically non-equivalent protons. In addition the use of chiral auxiliaries such as, crown ether and chiral lanthanide shift reagent may often cause enormous line broadening or give little degree of discrimination beyond the crown ether substrate ratio, hampering the discrimination. In circumventing such problems we are proposing the utilization of the difference in the additive values of all the chemical shifts of a scalar coupled spin system. The excitation and detection of appropriate highest quantum coherence yields the measurable difference in the frequencies between two transitions, one pertaining to each enantiomer in the maximum quantum dimension permitting their discrimination and the F-2 cross section at each of these frequencies yields an enantiopure spectrum. The advantage of the utility of the proposed method is demonstrated on several chiral compounds where the conventional one dimensional H-1 NMR spectra fail to differentiate the enantiomers.

Pressure induced structural phase transformation in TiN: a first-principles study

Titanium nitride (TiN), which is widely used for hard coatings, reportedly undergoes a pressure-induced structural phase transformation, from a NaCl to a CsCl structure, at similar to 7 GPa. In this paper, we use first-principles calculations based on density functional theory with a generalized gradient approximation of the exchange correlation energy to determine the structural stability of this transformation. Our results show that the stress required for this structural transformation is substantially lower (by more than an order of magnitude) when it is deviatoric in nature vis-a-vis that under hydrostatic pressure. Local stability of the structure is assessed with phonon dispersion determined at different pressures, and we find that CsCl structure of TiN is expected to distort after the transformation. From the electronic structure calculations, we estimate the electrical conductivity of TiN in the CsCl structure to be about 5 times of that in NaCl structure, which should be observable experimentally. (C) 2013 American Institute of Physics. http://dx.doi.org/10.1063/1.4798591]

Maximum likelihood algorithms for joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing system

Maximum likelihood (ML) algorithms, for the joint estimation of synchronisation impairments and channel in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system, are investigated in this work. A system model that takes into account the effects of carrier frequency offset, sampling frequency offset, symbol timing error and channel impulse response is formulated. Cramer-Rao lower bounds for the estimation of continuous parameters are derived, which show the coupling effect among different impairments and the significance of the joint estimation. The authors propose an ML algorithm for the estimation of synchronisation impairments and channel together, using the grid search method. To reduce the complexity of the joint grid search in the ML algorithm, a modified ML (MML) algorithm with multiple one-dimensional searches is also proposed. Further, a stage-wise ML (SML) algorithm using existing algorithms, which estimate less number of parameters, is also proposed. Performance of the estimation algorithms is studied through numerical simulations and it is found that the proposed ML and MML algorithms exhibit better performance than SML algorithm.

Learning from positive and unlabelled examples using maximum margin clustering

Learning from Positive and Unlabelled examples (LPU) has emerged as an important problem in data mining and information retrieval applications. Existing techniques are not ideally suited for real world scenarios where the datasets are linearly inseparable, as they either build linear classifiers or the non-linear classifiers fail to achieve the desired performance. In this work, we propose to extend maximum margin clustering ideas and present an iterative procedure to design a non-linear classifier for LPU. In particular, we build a least squares support vector classifier, suitable for handling this problem due to symmetry of its loss function. Further, we present techniques for appropriately initializing the labels of unlabelled examples and for enforcing the ratio of positive to negative examples while obtaining these labels. Experiments on real-world datasets demonstrate that the non-linear classifier designed using the proposed approach gives significantly better generalization performance than the existing relevant approaches for LPU.

Combining ``chimie douce'' and green principles for the developing world: Improving industrial viability of photocatalytic water remediation

We highlight the need for a comprehensive, multi-disciplinary approach for the development of cost-effective water remediation methods. Combining ``chimie douce'' and green chemical principles seems essential for making these technologies economically viable and socially relevant (especially in the developing world). A comprehensive approach to water remediation will take into account issues such as nanotoxicity, chemical yield, cost, and ease of deployment in reactors. By considering technological challenges that lie ahead, we will attempt to identify directions that are likely to make photocatalytic water remediation a more global technology than it currently is. (C) 2013 Elsevier Ltd. All rights reserved

A Phase Transition for the Uniform Distribution in the Pattern Maximum Likelihood Problem

In this paper, we consider the setting of the pattern maximum likelihood (PML) problem studied by Orlitsky et al. We present a well-motivated heuristic algorithm for deciding the question of when the PML distribution of a given pattern is uniform. The algorithm is based on the concept of a ``uniform threshold''. This is a threshold at which the uniform distribution exhibits an interesting phase transition in the PML problem, going from being a local maximum to being a local minimum.

Common recognition principles across diverse sequence and structural families of sialic acid binding proteins

Sialic acids form a large family of 9-carbon monosaccharides and are integral components of glycoconjugates. They are known to bind to a wide range of receptors belonging to diverse sequence families and fold classes and are key mediators in a plethora of cellular processes. Thus, it is of great interest to understand the features that give rise to such a recognition capability. Structural analyses using a non-redundant data set of known sialic acid binding proteins was carried out, which included exhaustive binding site comparisons and site alignments using in-house algorithms, followed by clustering and tree computation, which has led to derivation of sialic acid recognition principles. Although the proteins in the data set belong to several sequence and structure families, their binding sites could be grouped into only six types. Structural comparison of the binding sites indicates that all sites contain one or more different combinations of key structural features over a common scaffold. The six binding site types thus serve as structural motifs for recognizing sialic acid. Scanning the motifs against a non-redundant set of binding sites from PDB indicated the motifs to be specific for sialic acid recognition. Knowledge of determinants obtained from this study will be useful for detecting function in unknown proteins. As an example analysis, a genome-wide scan for the motifs in structures of Mycobacterium tuberculosis proteome identified 17 hits that contain combinations of the features, suggesting a possible function of sialic acid binding by these proteins.

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.