280,181 research outputs found
Drell-Yan production of multi Z'-bosons at the LHC within Non-Universal ED and 4D Composite Higgs Models
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
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
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
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 U +Th at 7.5-6.1 MeV/nucleon
The production of alpha particle decaying heavy nuclei in reactions of
7.5-6.1 MeV/nucleon U +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
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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