280,181 research outputs found

    Drell-Yan production of multi Z'-bosons at the LHC within Non-Universal ED and 4D Composite Higgs Models

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    The Drell-Yan di-lepton production at hadron colliders is by far the preferred channel to search for new heavy spin-1 particles. Traditionally, such searches have exploited the Narrow Width Approximation (NWA) for the signal, thereby neglecting the effect of the interference between the additional Z'-bosons and the Standard Model Z and {\gamma}. Recently, it has been established that both finite width and interference effects can be dealt with in experimental searches while still retaining the model independent approach ensured by the NWA. This assessment has been made for the case of popular single Z'-boson models currently probed at the CERN Large Hadron Collider (LHC). In this paper, we test the scope of the CERN machine in relation to the above issues for some benchmark multi Z'-boson models. In particular, we consider Non-Universal Extra Dimensional (NUED) scenarios and the 4-Dimensional Composite Higgs Model (4DCHM), both predicting a multi-Z' peaking structure. We conclude that in a variety of cases, specifically those in which the leptonic decays modes of one or more of the heavy neutral gauge bosons are suppressed and/or significant interference effects exist between these or with the background, especially present when their decay widths are significant, traditional search approaches based on the assumption of rather narrow and isolated objects might require suitable modifications to extract the underlying dynamics

    Occlusion Coherence: Detecting and Localizing Occluded Faces

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    The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occluders have lagged behind those for object appearance and shape. In this paper we describe a hierarchical deformable part model for face detection and landmark localization that explicitly models part occlusion. The proposed model structure makes it possible to augment positive training data with large numbers of synthetically occluded instances. This allows us to easily incorporate the statistics of occlusion patterns in a discriminatively trained model. We test the model on several benchmarks for landmark localization and detection including challenging new data sets featuring significant occlusion. We find that the addition of an explicit occlusion model yields a detection system that outperforms existing approaches for occluded instances while maintaining competitive accuracy in detection and landmark localization for unoccluded instances

    Segmentation of Radiographs of Hands with Joint Damage Using Customized Active Appearance Models

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    This paper is part of a project that investigates the possibilities of automating the assessment of joint damagein hand radiographs. Our goal is to design a robust segmentationalgorithm for the hand skeleton. The algorithm is\ud based on active appearance models (AAM) [1], which have been used for hand segmentation before [2]. The results will be used in the future for radiographic assessment of rheumatoid arthritis and the early detection of joint damage. New in this work with respect to [2] is the use of multiple object warps for each individual bone in a single AAM. This method prevents modelling and reconstruction defects caused when warping overlapping objects. This makes the algorithm more robust in cases where joint damage is present. The current implementation of the model includes the metacarpals, the phalanges, and the carpal region. For a first experimental evaluation a collection of 50 hand radiographs has been gathered. The image data set was split into a training set (40) and a test set (10) in order to evaluate the algorithm’s performance. First results show that in 8 images from the test set the bone contours are detected correctly within 1.3 mm (1 STD) at 15 pixels/cm resolution. In two images not all contours are detected correctly. Possibly this is caused by extreme deviations in these images that have not yet been incorporated in the model due to a limited training set. More training examples are needed to optimize the AAM and improve the quality and reliability of the results

    Ball-Scale Based Hierarchical Multi-Object Recognition in 3D Medical Images

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    This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.Comment: This paper was published and presented in SPIE Medical Imaging 201

    An experimental survey of the production of alpha decaying heavy elements in the reactions of 238^{238}U +232^{232}Th at 7.5-6.1 MeV/nucleon

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    The production of alpha particle decaying heavy nuclei in reactions of 7.5-6.1 MeV/nucleon 238^{238}U +232^{232}Th has been explored using an in-beam detection array composed of YAP scintillators and gas ionization chamber-Si telescopes. Comparisons of alpha energies and half-lives for the observed products with those of the previously known isotopes and with theoretically predicted values indicate the observation of a number of previously unreported alpha emitters. Alpha particle decay energies reaching as high as 12 MeV are observed. Many of these are expected to be from decay of previously unseen relatively neutron rich products. While the contributions of isomeric states require further exploration and specific isotope identifications need to be made, the production of heavy isotopes with quite high atomic numbers is suggested by the data.Comment: 12 pages, 12 figure

    Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

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    One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
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