QCD sum rule calculation for the charmonium-like structures in the J/psi phi and J/psi omega invariant mass spectra

Using the QCD sum rules we test if the charmonium-like structure Y(4274), observed in the J/psi phi invariant mass spectrum, can be described with a D(s)(D) over bar (s0)(2317)+ h.c. molecular current with J(PC) = 0(-+). We consider the contributions of condensates up to dimension ten and we work at leading order in alpha(s). We keep terms which are linear in the strange quark mass m(s). The mass obtained for such state is mD(s)D(s0) = (4.78 +/- 0.54) GeV. We also consider a molecular 0(-+) D (D) over bar (0)(2400)+ h.c. current and we obtain m(DD0) = (4.55 +/- 0.49) GeV. Our study shows that the newly observed Y(4274) in the J/psi phi invariant mass spectrum can be, considering the uncertainties, described using a molecular charmonium current. (C) 2011 Elsevier B.V. All rights reserved.

A new scale-invariant ratio and finite-size scaling for the stochastic susceptible-infected-recovered model

The critical behavior of the stochastic susceptible-infected-recovered model on a square lattice is obtained by numerical simulations and finite-size scaling. The order parameter as well as the distribution in the number of recovered individuals is determined as a function of the infection rate for several values of the system size. The analysis around criticality is obtained by exploring the close relationship between the present model and standard percolation theory. The quantity UP, equal to the ratio U between the second moment and the squared first moment of the size distribution multiplied by the order parameter P, is shown to have, for a square system, a universal value 1.0167(1) that is the same for site and bond percolation, confirming further that the SIR model is also in the percolation class.

Systematics of nuclear densities, deformations and excitation energies within the context of the generalized rotation-vibration model

We present a large-scale systematics of charge densities, excitation energies and deformation parameters For hundreds of heavy nuclei The systematics is based on a generalized rotation vibration model for the quadrupole and octupole modes and takes into account second-order contributions of the deformations as well as the effects of finite diffuseness values for the nuclear densities. We compare our results with the predictions of classical surface vibrations in the hydrodynamical approximation. (C) 2010 Elsevier B V All rights reserved.

A conformal invariant growth model

We present a one-parameter extension of the raise and peel one-dimensional growth model. The model is defined in the configuration space of Dyck (RSOS) paths. Tiles from a rarefied gas hit the interface and change its shape. The adsorption rates are local but the desorption rates are non-local; they depend not only on the cluster hit by the tile but also on the total number of peaks (local maxima) belonging to all the clusters of the configuration. The domain of the parameter is determined by the condition that the rates are non-negative. In the finite-size scaling limit, the model is conformal invariant in the whole open domain. The parameter appears in the sound velocity only. At the boundary of the domain, the stationary state is an adsorbing state and conformal invariance is lost. The model allows us to check the universality of non-local observables in the raise and peel model. An example is given.

Conformational Polymorphism in Racemic Crystals of the Diuretic Drug Chlortalidone

Chlortalidone (HIGROTON) is a diuretic drug widely used in antihypertensive therapy. Thus far, only two solid-state polymorphs of chlortalidone have been reported. We elucidated the structure of chlortalidone form I and a new polymorph. This new phase, namely, chlortalidone form III, was also entirely characterized. It was possible to conclude that it is a conformer with a different orientation of the chlorobenzenesulfonamide moiety. Compared to form I, it has a rotation of about 90 degrees on the axis of the C-C bond bridging the substituted phenyl and isoindolinyl rings. This conformational feature is related to the crystal packing patterns of the chlortalidone forms. Furthermore, certain intermolecular hydrogen bonds are present in both polymorphs, giving rise to ribbons with chlortalidone enantiomers alternately placed into them. The chlortalidone form I and form III crystallize in the triclinic space group P (1) over bar as racemic mixtures. Additional conformational details also differentiate the chlortalidone conformers. Slight twists on the isoindolinyl and sulfamyl groups exist. Considering all structural relationships, the fingerprint plots derived from the Hirshfeld surfaces exhibited the characteristics of the chlortalidone form I and form III crystal structures.

Tunneling among rotation tori

We consider an integrable Hamiltonian system generated by the resonant normal form in order to study a particular mechanism of tunneling. We isolated near doublets of energy corresponding to rotation tori of the classical dynamics counterpart and the degeneracies breakdown is attributed to rotation-rotation tunneling. (C) 2008 Elsevier B.V. All rights reserved.

Paleomagnetic data and K-Ar ages from Mesozoic units of the Santa Marta massif: A preliminary interpretation for block rotation and translations

We report 6 K-Ar ages and paleomagnetic data from 28 sites collected in Jurassic, Lower Cretaceous and Paleocene rocks of the Santa Marta massif, to test previous hypothesis of rotations and translations of this massif, whose rock assemblage differs from other basement-cored ranges adjacent to the Guyana margin. Three magnetic components were identified in this study. A first component has a direction parallel to the present magnetic field and was uncovered in all units (D 352, I = 25.6, k = 57.35, a95 = 5.3, N = 12). A second component was isolated in Cretaceous limestone and Jurassic volcaniclastic rocks (D = 8.8, I = 8.3, k = 24.71, a95 = 13.7, N = 6), and it was interpreted as of Early Cretaceous age. In Jurassic sites with this component, Early Cretaceous K-Ar ages obtained from this and previous studies are interpreted as reset ages. The third component was uncovered in eight sites of Jurassic volcaniclastic rocks, and its direction indicates negative shallow to moderate inclinations and northeastward declinations. K-Ar ages in these sites are of Early (196.5 +/- 4.9 Ma) to early Late Jurassic age (156.6 +/- 8.9 Ma). Due to local structural complexity and too few Cretaceous outcrops to perform a reliable unconformity test, we only used two sites with (1) K-Ar ages, (2) less structural complexity, and (3) reliable structural data for Jurassic and Cretaceous rocks. The mean direction of the Jurassic component is (D = 20.4, I = -18.2, k = 46.9, a95 = 5.1, n = 18 specimens from two sites). These paleomagnetic data support previous models of northward along-margin translations of Grenvillian-cored massifs. Additionally, clockwise vertical-axis rotation of this massif, with respect to the stable craton, is also documented; the sense of rotation is similar to that proposed for the Perija Range and other ranges of the southern Caribbean margin. More data is needed to confirm the magnitudes of rotations and translations. (C) 2009 Elsevier Ltd. All rights reserved.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.

An Information Theory framework for two-stage binary image operator design

The design of translation invariant and locally defined binary image operators over large windows is made difficult by decreased statistical precision and increased training time. We present a complete framework for the application of stacked design, a recently proposed technique to create two-stage operators that circumvents that difficulty. We propose a novel algorithm, based on Information Theory, to find groups of pixels that should be used together to predict the Output Value. We employ this algorithm to automate the process of creating a set of first-level operators that are later combined in a global operator. We also propose a principled way to guide this combination, by using feature selection and model comparison. Experimental results Show that the proposed framework leads to better results than single stage design. (C) 2009 Elsevier B.V. All rights reserved.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88-99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0 degrees vs. 20 degrees, 0 degrees vs. 60 degrees, and 0 degrees vs. 100 degrees), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (08 vs. 1008), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100 degrees condition in relation to the 0 degrees condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100 degrees condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100 degrees condition.

Road Sign Recognition based onInvariant Features using SupportVector Machine

Since last two decades researches have been working on developing systems that can assistsdrivers in the best way possible and make driving safe. Computer vision has played a crucialpart in design of these systems. With the introduction of vision techniques variousautonomous and robust real-time traffic automation systems have been designed such asTraffic monitoring, Traffic related parameter estimation and intelligent vehicles. Among theseautomatic detection and recognition of road signs has became an interesting research topic.The system can assist drivers about signs they don’t recognize before passing them.Aim of this research project is to present an Intelligent Road Sign Recognition System basedon state-of-the-art technique, the Support Vector Machine. The project is an extension to thework done at ITS research Platform at Dalarna University [25]. Focus of this research work ison the recognition of road signs under analysis. When classifying an image its location, sizeand orientation in the image plane are its irrelevant features and one way to get rid of thisambiguity is to extract those features which are invariant under the above mentionedtransformation. These invariant features are then used in Support Vector Machine forclassification. Support Vector Machine is a supervised learning machine that solves problemin higher dimension with the help of Kernel functions and is best know for classificationproblems.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

Identification of cause of impairment in spiral drawings, using non-stationary feature extraction approach

Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is done under supervised learning, so a target class is used which is acquired from a neurologist using a web interface. After reducing the dimension of HHT features by using PCA, classification is performed. C4.5 classifier is used to perform the classification. Results of the classification are close to random guessing which shows that the computer method is unsuccessful in assessing the cause of drawing impairment in spirals when evaluated against human ratings. One promising reason is that there is no difference between the two classes of spiral drawings. Displaying patients self ratings along with the spirals in the web application is another possible reason for this, as the neurologist may have relied too much on this in his own ratings.