Small-Angle Neutron-Scattering Studies of Mixed Micellar Structures Made of Dimeric Surfactants Having Imidazolium and Ammonium Headgroups

Planar imidazolium cation based gemini surfactants 16-Im-n-Im-16], 2Br(-) (where n = 2, 3, 4, 5, 6, 8, 10, and 12), exhibit different morphologies and internal packing arrangements by adopting different supramolecular assemblies in aqueous media depending on their number of spacer methylene units (CH2)(n). Detailed measurements of the small-angle neutron-scattering (SANS) cross sections from different imidazolium-based surfactant micelles in aqueous media (D2O) are reported. The SANS data, containing the information of aggregation behavior of such surfactants in the molecular level, have been analyzed on the basis of the Hayter and Penfold model for the macro ion solution to compute the interparticle structure factor S(Q) taking into account the screened Coulomb interactions between the dimeric surfactant micelles. The characteristic changes in the SANS spectra of the dimeric surfactant with n = 4 due to variation of temperature have also been investigated. These data are then compared with the SANS characterization data of the corresponding gemini micelles containing tetrahedral ammonium ion based polar headgroups. The critical micellar concentration of each surfactant micelle (cmc) has been determined using pyrene as an extrinsic fluorescence probe. The variation of cmc as a function of spacer chain length has been explained in terms of conformational variation and progressive looping of the spacer into the micellar interior upon increasing the n values. Small-angle neutron-scattering (SANS) cross sections from different mixed micelles composed of surfactants with ammonium headgroups, 16-A(0), 16-Am-n-Am-16], 2Br(-) (where n = 4), 16-I-0, and 16-Im-n-Im-16], 2Br(-) (where n = 4), in aqueous media (D2O) have also been analyzed. The aggregate composition matches with that predicted from the ideal mixing model.

Azimuthal Angle Probe of Anomalous HWW Couplings at a High Energy ep Collider

A high energy ep collider, such as the proposed LHeC, possesses the unique facility of permitting direct measurement of the HWW coupling without contamination from the HZZ coupling. At such a machine, the fusion of two W bosons through the HWW vertex would give rise to typical charged current events accompanied by a Higgs boson. We demonstrate that azimuthal angle correlations between the observable charged current final states could then be a sensitive probe of the nature of the HWW vertex and hence of the CP properties of the Higgs boson. DOI: 10.1103/PhysRevLett.109.261801

PULSE-PHASE-RESOLVED SPECTROSCOPY OF VELA X-1 WITH SUZAKU

We present a detailed pulse-phase-resolved spectral analysis of the persistent high-mass X-ray binary pulsar Vela X-1 observed with Suzaku during 2008 June. The pulse profiles exhibit both intensity and energy dependence with multiple peaks at low energies and double peaks at higher energies. The source shows some spectral evolution over the duration of the observation and care has been taken to average over data with minimum spectral variability for the analysis. We model the continuum with a phenomenological partial covering high-energy cutoff model and a more physical partial covering thermal Comptonization model (CompTT) excluding the time ranges having variable hardness ratio and intensity dependence. For both the models, we detect a cyclotron resonant scattering feature (CRSF) and its harmonic at similar to 25 keV and similar to 50 keV. Both the CRSF fundamental and harmonics parameters are strongly variable over the pulse phase, with the ratio of the two line energies deviating from the classical value of 2. The continuum parameters also show significant variation over the pulse phase and give us some idea about the changing physical conditions that are seen with the changing viewing angle at different pulse phases and obscuration by the accretion stream at some pulse phases.

A game theoretic approach for feature clustering and its application to feature selection

In this paper, we develop a game theoretic approach for clustering features in a learning problem. Feature clustering can serve as an important preprocessing step in many problems such as feature selection, dimensionality reduction, etc. In this approach, we view features as rational players of a coalitional game where they form coalitions (or clusters) among themselves in order to maximize their individual payoffs. We show how Nash Stable Partition (NSP), a well known concept in the coalitional game theory, provides a natural way of clustering features. Through this approach, one can obtain some desirable properties of the clusters by choosing appropriate payoff functions. For a small number of features, the NSP based clustering can be found by solving an integer linear program (ILP). However, for large number of features, the ILP based approach does not scale well and hence we propose a hierarchical approach. Interestingly, a key result that we prove on the equivalence between a k-size NSP of a coalitional game and minimum k-cut of an appropriately constructed graph comes in handy for large scale problems. In this paper, we use feature selection problem (in a classification setting) as a running example to illustrate our approach. We conduct experiments to illustrate the efficacy of our approach.

A Savitzky-Golay Filtering Perspective of Dynamic Feature Computation

We address the classical problem of delta feature computation, and interpret the operation involved in terms of Savitzky- Golay (SG) filtering. Features such as themel-frequency cepstral coefficients (MFCCs), obtained based on short-time spectra of the speech signal, are commonly used in speech recognition tasks. In order to incorporate the dynamics of speech, auxiliary delta and delta-delta features, which are computed as temporal derivatives of the original features, are used. Typically, the delta features are computed in a smooth fashion using local least-squares (LS) polynomial fitting on each feature vector component trajectory. In the light of the original work of Savitzky and Golay, and a recent article by Schafer in IEEE Signal Processing Magazine, we interpret the dynamic feature vector computation for arbitrary derivative orders as SG filtering with a fixed impulse response. This filtering equivalence brings in significantly lower latency with no loss in accuracy, as validated by results on a TIMIT phoneme recognition task. The SG filters involved in dynamic parameter computation can be viewed as modulation filters, proposed by Hermansky.

MULTI-OBJECTIVE GENETIC ALGORITHM FOR EFFICIENT POINT MATCHING IN MULTI-SENSOR SATELLITE IMAGE

This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient.

On the dynamic contact angle in simulation of impinging droplets with sharp interface methods

Effects of dynamic contact angle models on the flow dynamics of an impinging droplet in sharp interface simulations are presented in this article. In the considered finite element scheme, the free surface is tracked using the arbitrary Lagrangian-Eulerian approach. The contact angle is incorporated into the model by replacing the curvature with the Laplace-Beltrami operator and integration by parts. Further, the Navier-slip with friction boundary condition is used to avoid stress singularities at the contact line. Our study demonstrates that the contact angle models have almost no influence on the flow dynamics of the non-wetting droplets. In computations of the wetting and partially wetting droplets, different contact angle models induce different flow dynamics, especially during recoiling. It is shown that a large value for the slip number has to be used in computations of the wetting and partially wetting droplets in order to reduce the effects of the contact angle models. Among all models, the equilibrium model is simple and easy to implement. Further, the equilibrium model also incorporates the contact angle hysteresis. Thus, the equilibrium contact angle model is preferred in sharp interface numerical schemes.

Multi-objective optimization of satellite image registration using discrete Pparticle swarm optimisation

A new multi-sensor image registration technique is proposed based on detecting the feature corner points using modified Harris Corner Detector (HDC). These feature points are matched using multi-objective optimization (distance condition and angle criterion) based on Discrete Particle Swarm Optimization (DPSO). This optimization process is more efficient as it considers both the distance and angle criteria to incorporate multi-objective switching in the fitness function. This optimization process helps in picking up three corresponding corner points detected in the sensed and base image and thereby using the affine transformation, the sensed image is aligned with the base image. Further, the results show that the new approach can provide a new dimension in solving multi-sensor image registration problems. From the obtained results, the performance of image registration is evaluated and is concluded that the proposed approach is efficient.

Bearing capacity of two interfering strip footings from lower bound finite elements limit analysis

A rigorous lower bound solution, with the usage of the finite elements limit analysis, has been obtained for finding the ultimate bearing capacity of two interfering strip footings placed on a sandy medium. Smooth as well as rough footingsoil interfaces are considered in the analysis. The failure load for an interfering footing becomes always greater than that for a single isolated footing. The effect of the interference on the failure load (i) for rough footings becomes greater than that for smooth footings, (ii) increases with an increase in phi, and (iii) becomes almost negligible beyond S/B>3. Compared with various theoretical and experimental results reported in literature, the present analysis generally provides the lowest magnitude of the collapse load. Copyright (c) 2011 John Wiley & Sons, Ltd.

Discriminative feature analysis and selection for document classification

Classification of a large document collection involves dealing with a huge feature space where each distinct word is a feature. In such an environment, classification is a costly task both in terms of running time and computing resources. Further it will not guarantee optimal results because it is likely to overfit by considering every feature for classification. In such a context, feature selection is inevitable. This work analyses the feature selection methods, explores the relations among them and attempts to find a minimal subset of features which are discriminative for document classification.

Feature selection for unsupervised learning

In this paper, we present a methodology for identifying best features from a large feature space. In high dimensional feature space nearest neighbor search is meaningless. In this feature space we see quality and performance issue with nearest neighbor search. Many data mining algorithms use nearest neighbor search. So instead of doing nearest neighbor search using all the features we need to select relevant features. We propose feature selection using Non-negative Matrix Factorization(NMF) and its application to nearest neighbor search. Recent clustering algorithm based on Locally Consistent Concept Factorization(LCCF) shows better quality of document clustering by using local geometrical and discriminating structure of the data. By using our feature selection method we have shown further improvement of performance in the clustering.

Unsupervised feature selection for outlier detection in categorical data using mutual information

Outlier detection in high dimensional categorical data has been a problem of much interest due to the extensive use of qualitative features for describing the data across various application areas. Though there exist various established methods for dealing with the dimensionality aspect through feature selection on numerical data, the categorical domain is actively being explored. As outlier detection is generally considered as an unsupervised learning problem due to lack of knowledge about the nature of various types of outliers, the related feature selection task also needs to be handled in a similar manner. This motivates the need to develop an unsupervised feature selection algorithm for efficient detection of outliers in categorical data. Addressing this aspect, we propose a novel feature selection algorithm based on the mutual information measure and the entropy computation. The redundancy among the features is characterized using the mutual information measure for identifying a suitable feature subset with less redundancy. The performance of the proposed algorithm in comparison with the information gain based feature selection shows its effectiveness for outlier detection. The efficacy of the proposed algorithm is demonstrated on various high-dimensional benchmark data sets employing two existing outlier detection methods.

Bearing capacity of piles in soft clay underlaid by cohesive frictional soil

By using the axisymmetric quasi-lower bound finite-element limit analysis, the bearing capacity factors N-c(p) and N-gamma q(p) have been computed for axially loaded piles, with the shaft embedded in a fully cohesive soil medium and the tip placed over cohesive frictional soil strata. The results were obtained for various combinations of L/D, phi(l), and c(l)/c(u); the subscripts l and u refer to lower and upper soil strata, respectively. The factors N-c(p) and N-gamma q(p) increase continuously with increases in L/D and phi(l); the rate of increase of N-c(p) and N-gamma q(p) with L/D, however, decreases with an increase in L/D. For c(l)/c(u) > 100, the factor N-c(p) hardly depends on L/D.

Feature weighting for clustering by particle swarm optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. In this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.

Multiobjective discrete particle swarm optimization for multisensor image alignment

A new technique is proposed for multisensor image registration by matching the features using discrete particle swarm optimization (DPSO). The feature points are first extracted from the reference and sensed image using improved Harris corner detector available in the literature. From the extracted corner points, DPSO finds the three corresponding points in the sensed and reference images using multiobjective optimization of distance and angle conditions through objective switching technique. By this, the global best matched points are obtained which are used to evaluate the affine transformation for the sensed image. The performance of the image registration is evaluated and concluded that the proposed approach is efficient.