Four-Parameter white blood cell differential counting based on light scattering measurements

Measurement of the depolarized orthogonal light scattering in flow cytometry enables one to discriminate human eosinephilic granulocytes from neutrophilic granulocytes. We use this method to perform a four-parameter differential white blood cell analysis. \ud A simple flow cytometer was built equipped with a 5-mW helium neon laser that measures simultaneously four light scattering parameters. Lymphocytes, monocytes, and granulocytes were identified by simultaneously measuring the light scattering intensity at angles between 1.0° and 2.6° and angles between 3.0° and 11.0°. Eosinophilic granulocytes were distinguished from neutrophilic granulocytes by simultaneous measurement of the orthogonal and depolarized orthogonal light scattering. \ud Comparison of a white blood cell differentiation of 45 donors obtained by the Technicon H-6000 and our instrument revealed good correlations. The correlation coefficients (r2) found were: 0.99 for lymphocytes, 0.76 for monocytes, 0.99 for neutrophilic granulocytes, and 0.98 for eosinophilic granulocytes. The results demonstrate that reliable white blood cell differentiation of the four most clinically relevant leukocytes can be obtained by measurement of light scattering properties of unstained leukocytes

Simple modules for the partition algebra and monotone convergence of Kronecker coefficients

We construct bases of the simple modules for partition algebras which are indexed by paths in an alcove geometry. This allows us to give a concrete interpretation (and new proof) of the monotone convergence property for Kronecker coefficients using stratifications of the cell modules of the partition algebra

Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization

Separating active regions that are quiet from potentially eruptive ones is a key issue in Space Weather applications. Traditional classification schemes such as Mount Wilson and McIntosh have been effective in relating an active region large scale magnetic configuration to its ability to produce eruptive events. However, their qualitative nature prevents systematic studies of an active region's evolution for example. We introduce a new clustering of active regions that is based on the local geometry observed in Line of Sight magnetogram and continuum images. We use a reduced-dimension representation of an active region that is obtained by factoring the corresponding data matrix comprised of local image patches. Two factorizations can be compared via the definition of appropriate metrics on the resulting factors. The distances obtained from these metrics are then used to cluster the active regions. We find that these metrics result in natural clusterings of active regions. The clusterings are related to large scale descriptors of an active region such as its size, its local magnetic field distribution, and its complexity as measured by the Mount Wilson classification scheme. We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the $R$ value. We provide some recommendations for which metrics, matrix factorization techniques, and regions of interest to use to study active regions.Comment: Accepted for publication in the Journal of Space Weather and Space Climate (SWSC). 33 pages, 12 figure

Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis

The flare-productivity of an active region is observed to be related to its spatial complexity. Mount Wilson or McIntosh sunspot classifications measure such complexity but in a categorical way, and may therefore not use all the information present in the observations. Moreover, such categorical schemes hinder a systematic study of an active region's evolution for example. We propose fine-scale quantitative descriptors for an active region's complexity and relate them to the Mount Wilson classification. We analyze the local correlation structure within continuum and magnetogram data, as well as the cross-correlation between continuum and magnetogram data. We compute the intrinsic dimension, partial correlation, and canonical correlation analysis (CCA) of image patches of continuum and magnetogram active region images taken from the SOHO-MDI instrument. We use masks of sunspots derived from continuum as well as larger masks of magnetic active regions derived from the magnetogram to analyze separately the core part of an active region from its surrounding part. We find the relationship between complexity of an active region as measured by Mount Wilson and the intrinsic dimension of its image patches. Partial correlation patterns exhibit approximately a third-order Markov structure. CCA reveals different patterns of correlation between continuum and magnetogram within the sunspots and in the region surrounding the sunspots. These results also pave the way for patch-based dictionary learning with a view towards automatic clustering of active regions.Comment: Accepted for publication in the Journal of Space Weather and Space Climate (SWSC). 23 pages, 11 figure

Relativistic and correlation effects on molecular properties - the interhalogens CIF, BrF, BrCI, IF, ICI and IBr

The effect of relativity on the properties of the interhalogens ClF, BrF, BrCl, IF, IBr, and IBr is studied by comparing relativistic and nonrelativistic calculations. Bond lengths, harmonic frequencies, and dissociation energies show that the bond is weakened in the relativistic formalism. Relativity increases the electric dipole moment whereas the electric quadrupole moment and dipole polarizability display an irregular behavior. The relativistic contributions to the electric dipole and quadrupole moment of the iodine containing molecules are 10%–20% of the total value, whereas the contributions in the other molecules cannot be neglected. The value of the electric quadrupole moment is dominated by the relativistic contributions

Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.Comment: in press for SWS

New Developments in MadGraph/MadEvent

We here present some recent developments of MadGraph/MadEvent since the latest published version, 4.0. These developments include: Jet matching with Pythia parton showers for both Standard Model and Beyond the Standard Model processes, decay chain functionality, decay width calculation and decay simulation, process generation for the Grid, a package for calculation of quarkonium amplitudes, calculation of Matrix Element weights for experimental events, automatic dipole subtraction for next-to-leading order calculations, and an interface to FeynRules, a package for automatic calculation of Feynman rules and model files from the Lagrangian of any New Physics model.Comment: 6 pages, 3 figures. Plenary talk given at SUSY08, Seoul, South Korea, June 2008. To appear in the proceeding