12 research outputs found

    Search for chargino-neutralino production in ppbar collisions at sqrt(s) = 1.96 TeV

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    We present the results of a search for associated production of the chargino and neutralino supersymmetric particles using up to 1.1 fb-1 of integrated luminosity collected by the CDF II experiment at the Tevatron ppbar collider at a center-of-mass energy of 1.96 TeV. The search is conducted by analyzing events with a large transverse momentum imbalance and either three charged leptons or two charged leptons of the same electric charge. The numbers of observed events are found to be consistent with standard model expectations. Upper limits on the production cross section are derived in different theoretical models. In one of these models a lower limit on the mass of the chargino is set at 129 GeV/c^2 at the 95% confidence level.Comment: To be submitted to Phys.Rev.Let

    Automatic Artery/Vein Classification in 2D-DSA Images of Stroke Patients

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    To develop an objective system for perfusion assessment in digital subtraction angiography (DSA), artery-vein (A/V) classification is essential. In this study, an automated A/V classification system in 2D DSA images of stroke patients is proposed. After preprocessing through vessel segmentation with a Frangi fitler and Gaussian smoothing, a time-intensity curve (TIC) of each vessel pixel was extracted and relevant parameters were calculated. Different combinations of input parameters were systematically tested to come to the optimal set of input parameters. The parameters formed the input for k-means (KM) and fuzzy c-means (FCM) clustering. Both algorithms were tested for clustering into 2 to 7 clusters. Cluster labeling was performed based on the average time to peak (TTP) of a cluster. A reference standard consisted of manually annotated DSA images of the MR CLEAN registry. Outcome measures were accuracy, true artery rate (TAR) and true vein rate (TVR). The optimal value for k was found to be 2 for both KM and FCM clustering. The optimal parameter set was: variance, standard deviation, maximal slope, peak width, time to peak, arrival time, maximal intensity and area under the TIC. No significant difference was found between FCM and KM clustering and. Both FCM and KM clustering yielded an average accuracy of 76%, average TAR of 74% and average TVR of 80%

    State Preferences and Institution Evolution: From Security to Economic Interests

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