Characterization of a robust probabilistic framework for brain magnetic resonance image data distributions

Probabilistic characterisation of image data for accurate prognosis and treatment planning remains a long-standing problem in medical research, especially when the data distribution depicts flat-top and high-order contact. Such flat-top distributions are quite common in brain magnetic resonance (MR) image data, where the density drops sharply beyond the flat interval. Intuitively, it would indicate a bipartition of data into positive region containing observations definitely belonging to the image class and boundary region with observations possibly belonging to it. The flat peak would also imply that multiple values are equally most likely to belong to that class. However, the popular probability distributions used in such cases are unimodal, creating ambiguity about the positive region. In this work, we study the statistical properties and develop likelihood-based iterative estimation method for the parameters of a novel class of platykurtic probability distributions containing normal, called the stomped normal distribution, that provides more accurate modelling to the flat-top data distributions. The robustness of the proposed stomped normal model has been illustrated with six simulated and nine real brain MR volumes. Our analysis shows substantial improvement in explaining a variety of shapes of data distributions using the proposed probability model

Discovering similarities in Landsat satellite images using the Kmeans method

This article different ways for the treatment and identification of similarities in satellite images. By means of the systematic review of the literature it is possible to know the different existing forms for the treatment of this type of objects and by means of the implementation that is described, the operation of the K-means algorithm is shown to help the segmentation and analysis of characteristics associated to the color. In this type of objects, a descriptive analysis of the results thrown by the method is finally carried out

Experimental Tests of Particle Flow Calorimetry

Precision physics at future colliders requires highly granular calorimeters to support the Particle Flow Approach for event reconstruction. This article presents a review of about 10 - 15 years of R\&D, mainly conducted within the CALICE collaboration, for this novel type of detector. The performance of large scale prototypes in beam tests validate the technical concept of particle flow calorimeters. The comparison of test beam data with simulation, of e.g.\ hadronic showers, supports full detector studies and gives deeper insight into the structure of hadronic cascades than was possible previously.Comment: 55 pages, 83 figures, to appear in Reviews of Modern physic

Photon Physics in Heavy Ion Collisions at the LHC

Various pion and photon production mechanisms in high-energy nuclear collisions at RHIC and LHC are discussed. Comparison with RHIC data is done whenever possible. The prospect of using electromagnetic probes to characterize quark-gluon plasma formation is assessed.Comment: Writeup of the working group "Photon Physics" for the CERN Yellow Report on "Hard Probes in Heavy Ion Collisions at the LHC", 134 pages. One figure added in chapter 5 (comparison with PHENIX data). Some figures and correponding text corrected in chapter 6 (off-chemical equilibrium thermal photon rates). Some figures modified in chapter 7 (off-chemical equilibrium photon rates) and comparison with PHENIX data adde

Particle physics experiments at JLC

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On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Reconstruction and applications of QED final state radiation photons in Z->mumu decays produced in 7 TeV proton-proton collisions at the Large Hadron Collider

This thesis presents a method for including the final state radiation (FSR) photon in the reconstruction of Z bosons decaying into pairs of muons (Z ->mumu). An algorithm for the reconstruction of FSR photons has been developed. The FSR photon candidates with ET >1.3 GeV are reconstructed within dR mumu events with at least one FSR photon within fiducial region (ET>1.3 GeV and dR 4 GeV. Inclusion of these photons in the dimuon invariant mass calculation was shown to lead to improved Z-boson invariant mass resolution and scale: a Gaussian fit shows that the peak of the distribution moves from 89 GeV to 91.31±0.06 GeV with a standard deviation of 2.5±0.07 GeV. A 20% of all inclusive Z->mumu events are corrected in the tail of the invariant mass (MZZ()-> 4mu samples, more than 20% of the events are expected to find a reconstructed FSR photon around. The improvement of the Higgs invariant mass resolution and scale by adding the reconstructed FSR photons is presented