1,811,924 research outputs found

    Elastic Multi-Body Interactions on a Lattice

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    We show that by coupling two hyperfine states of an atom in an optical lattice one can independently control two-, three-, and four-body on-site interactions in a non-perturbative manner. In particular, under typical conditions of current experiments one can have a purely three- or four-body interacting gas of 39^{39}K atoms characterized by on-site interaction shifts of several 100Hz.Comment: 6 pages, 3 figure

    Multi-body Non-rigid Structure-from-Motion

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    Conventional structure-from-motion (SFM) research is primarily concerned with the 3D reconstruction of a single, rigidly moving object seen by a static camera, or a static and rigid scene observed by a moving camera --in both cases there are only one relative rigid motion involved. Recent progress have extended SFM to the areas of {multi-body SFM} (where there are {multiple rigid} relative motions in the scene), as well as {non-rigid SFM} (where there is a single non-rigid, deformable object or scene). Along this line of thinking, there is apparently a missing gap of "multi-body non-rigid SFM", in which the task would be to jointly reconstruct and segment multiple 3D structures of the multiple, non-rigid objects or deformable scenes from images. Such a multi-body non-rigid scenario is common in reality (e.g. two persons shaking hands, multi-person social event), and how to solve it represents a natural {next-step} in SFM research. By leveraging recent results of subspace clustering, this paper proposes, for the first time, an effective framework for multi-body NRSFM, which simultaneously reconstructs and segments each 3D trajectory into their respective low-dimensional subspace. Under our formulation, 3D trajectories for each non-rigid structure can be well approximated with a sparse affine combination of other 3D trajectories from the same structure (self-expressiveness). We solve the resultant optimization with the alternating direction method of multipliers (ADMM). We demonstrate the efficacy of the proposed framework through extensive experiments on both synthetic and real data sequences. Our method clearly outperforms other alternative methods, such as first clustering the 2D feature tracks to groups and then doing non-rigid reconstruction in each group or first conducting 3D reconstruction by using single subspace assumption and then clustering the 3D trajectories into groups.Comment: 21 pages, 16 figure

    Direct CPV in two-body and multi-body charm decays at LHCb

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    The Standard Model predicts CP asymmetries in charm decays of O(10−3)O(10^{-3}) and the observation of significantly larger CP violation could indicate non-Standard Model physics effects. During 2011 and 2012, the LHCb experiment collected a sample corresponding to 3/fb yielding the world's largest sample of decays of charmed hadrons. This allowed the CP violation in charm to be studied with unprecedented precision in many two- body and multibody decay modes. The most recent LHCb searches for direct CP violation are presented in these proceedings.Comment: 6 pages, Contribution to proceedings of the 8th International Workshop on the CKM Unitarity Triangle (CKM 2014), Vienna, Austria, September 8-12, 201

    Multi-body dynamics in full-vehicle handling analysis

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    This paper presents a multidegrees-of-freedom non-linear multibody dynamic model of a vehicle, comprising front and rear suspensions, steering system, road wheels, tyres and vehicle inertia. The model incorporates all sources of compliance, stiffness and damping, all with non-linear characteristics. The vehicle model is created in ADAMS (automatic dynamic analysis of mechanical systems) formulation. The model is used for the purpose of vehicle handling analysis. Simulation runs, in-line with vehicle manoeuvres specified under ISO and British Standards, have been undertaken and reported in the paper

    Multi-body dynamics in full-vehicle handling analysis

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    Linear dynamic modeling of spacecraft with various flexible appendages and on-board angular momentums

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    We present here a method and some tools developed to build linear models of multi-body systems for space applications (typically satellites). The multi-body system is composed of a main body (hub) fitted with rigid and flexible appendages (solar panels, antennas, propellant tanks,...) and on-board angular momentums (flywheels, control moment gyros). Each appendage can be connected to the hub by a cantilever joint or a pivot joint. More generally, our method can be applied to any open mechanical chain. In our approach, the rigid six degrees of freedom (d.o.f) (three translational and three rotational) are treated all together. That is very convenient to build linear models of complex multi-body systems. Then, the dynamics model used to design AOCS, i.e. the model between forces and torques (applied on the hub) and angular and linear position and velocity of the hub, can be derived very easily. This model can be interpreted using block diagram representation

    Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

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    Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

    Soft-photon corrections in multi-body meson decays

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    The effects due to soft-photon emission (and the related virtual corrections) in multi-body decays of B, D, and K mesons are analysed. We present analytic expressions for the universal O(alpha) correction factors which can be applied to all multi-body decay modes where a tight soft-photon energy cut in the decaying-particle rest-frame is applied. All-order resummations valid in the limit of small and large velocities of the final-state particles are also discussed. The phenomenological implications of these correction factors in the distortion of Dalitz-plot distributions of K -> 3 pi decays are briefly analysed.Comment: 8 pages, 2 figures (v2: minor modifications - published version

    Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification

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    Person Re-identification (ReID) is to identify the same person across different cameras. It is a challenging task due to the large variations in person pose, occlusion, background clutter, etc How to extract powerful features is a fundamental problem in ReID and is still an open problem today. In this paper, we design a Multi-Scale Context-Aware Network (MSCAN) to learn powerful features over full body and body parts, which can well capture the local context knowledge by stacking multi-scale convolutions in each layer. Moreover, instead of using predefined rigid parts, we propose to learn and localize deformable pedestrian parts using Spatial Transformer Networks (STN) with novel spatial constraints. The learned body parts can release some difficulties, eg pose variations and background clutters, in part-based representation. Finally, we integrate the representation learning processes of full body and body parts into a unified framework for person ReID through multi-class person identification tasks. Extensive evaluations on current challenging large-scale person ReID datasets, including the image-based Market1501, CUHK03 and sequence-based MARS datasets, show that the proposed method achieves the state-of-the-art results.Comment: Accepted by CVPR 201
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