699 research outputs found

    Twist-3 T-odd fragmentation functions G⊥G^\perp and G~⊥\tilde{G}^\perp in a spectator model

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    We present a calculation of the twist-3 T-odd chiral-even fragmentation functions G⊥G^{\perp} and G~⊥\tilde{G}^{\perp} using a spectator model. We consider the effect gluon exchange to calculate all necessary one-loop diagrams for the quark-quark and quark-gluon-quark correlation functions. We find that the gluon loops corrections generate non-zero contribution to these two fragmentation function. We numerically calculate their half-kTk_T moments by integrating over the transverse momentum and also verify the equation of motion relation among G⊥G^{\perp}, G~⊥\tilde{G}^{\perp} and the Collins function.Comment: 8 pages, 3 figures, match the version published in PL

    Learning to Warp for Style Transfer

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    Since its inception in 2015, Style Transfer has focused on texturing a content image using an art exemplar. Recently, the geometric changes that artists make have been acknowledged as an important component of style[42], [55], [62], [63]. Our contribution is to propose a neural network that, uniquely, learns a mapping from a 4D array of inter-feature distances to a non-parametric 2D warp field. The system is generic in not being limited by semantic class, a single learned model will suffice; all examples in this paper are output from one model.Our approach combines the benefits of the high speed of Liu et al. [42] with the non-parametric warping of Kim et al. [55]. Furthermore, our system extends the normal NST paradigm: although it can be used with a single exemplar, we also allow two style exemplars: one for texture and another geometry. This supports far greater flexibility in use cases than single exemplars can provide
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