150,716 research outputs found

    The Relativistic Rotation

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    The classical rotation is not self-consistent in the framework of the special theory of relativity. the Relativistic rotation is obtained, which takes the relativistic effect into account. It is demonstrated that the angular frequency of classical rotation is only valid in local approximation. The properties of the relativistic rotation and the relativistic transverse Doppler shift are discussed in this work

    Morphology, structure, optical, and electrical properties of AgSbO₃

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    The morphology of defect pyrochlore-type, AgSbO₃ microparticle/nanoparticles obtained via solid state reaction evolve from irregular to Fullerene-like polyhedra before finally decomposing into metal-organic framework-5 like particles with increase in sintering temperature. The defect pyrochlore-type AgSbO₃ particles are slightly Ag deficient while the valence of the antimony ion is shown to be +5 giving rise to a probable stoichiometry of Ag₁ˍₓ SbVO₃ˍₓ/₂, with x∼0.01–0.04. A highly structured diffuse intensity distribution observed via electron diffraction is interpreted in terms of correlated displacements of one-dimensional (1D) silver ion chains along ⟨110⟩ directions. A redshifting in the absorption edges in UV-visible absorption spectra is observed for samples prepared at sintering temperatures higher than 1000 °C and attributed to the surface plasma resonance effect associated with small amounts of excess metallic Ag on the Ag₁ˍₓ SbVO₃ˍₓ/₂ particles. An electrical properties investigation of the silver antimonate samples via dielectric, conductivity, and electric modulus spectroscopy shows a prominent dielectric relaxation associated with grain boundaries. The silver ion conductivity is associated with correlated displacements of 1D silver ion chains along ⟨110⟩ directions.Z.G.Y., Y.L., and R.L.W. acknowledge financial support from the Australian Research Council ARC in the form of ARC Discovery Grants

    Image tag completion by local learning

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    The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local linear function is used in the neighborhood of each image to predict the tag scoring vectors of its neighboring images. We construct a unified objective function for the learning of both tag scoring vectors and local linear function parame- ters. In the objective, we impose the learned tag scoring vectors to be consistent with the known associations to the tags of each image, and also minimize the prediction error of each local linear function, while reducing the complexity of each local function. The objective function is optimized by an alternate optimization strategy and gradient descent methods in an iterative algorithm. We compare the proposed algorithm against different state-of-the-art tag completion methods, and the results show its advantages
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