477,652 research outputs found

    Pose Embeddings: A Deep Architecture for Learning to Match Human Poses

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    We present a method for learning an embedding that places images of humans in similar poses nearby. This embedding can be used as a direct method of comparing images based on human pose, avoiding potential challenges of estimating body joint positions. Pose embedding learning is formulated under a triplet-based distance criterion. A deep architecture is used to allow learning of a representation capable of making distinctions between different poses. Experiments on human pose matching and retrieval from video data demonstrate the potential of the method

    Variational approximation for mixtures of linear mixed models

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    Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG

    The Galactic Halo in Mixed Dark Matter Cosmologies

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    A possible solution to the small scale problems of the cold dark matter (CDM) scenario is that the dark matter consists of two components, a cold and a warm one. We perform a set of high resolution simulations of the Milky Way halo varying the mass of the WDM particle (mWDMm_{\rm WDM}) and the cosmic dark matter mass fraction in the WDM component (fˉW\bar{f}_{\rm W}). The scaling ansatz introduced in combined analysis of LHC and astroparticle searches postulates that the relative contribution of each dark matter component is the same locally as on average in the Universe (e.g. fW,⊙=fˉWf_{\rm W,\odot} = \bar{f}_{\rm W}). Here we find however, that the normalised local WDM fraction (fW,⊙f_{\rm W,\odot} / fˉW\bar{f}_{\rm W}) depends strongly on mWDMm_{\rm WDM} for mWDM<m_{\rm WDM} < 1 keV. Using the scaling ansatz can therefore introduce significant errors into the interpretation of dark matter searches. To correct this issue a simple formula that fits the local dark matter densities of each component is provided.Comment: 19 pages, 10 figures, accepted for publication in JCA

    ScannerS: Constraining the phase diagram of a complex scalar singlet at the LHC

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    We present the first version of a new tool to scan the parameter space of generic scalar potentials, ScannerS. The main goal of ScannerS is to help distinguish between different patterns of symmetry breaking for each scalar potential. In this work we use it to investigate the possibility of excluding regions of the phase diagram of several versions of a complex singlet extension of the Standard Model, with future LHC results. We find that if another scalar is found, one can exclude a phase with a dark matter candidate in definite regions of the parameter space, while predicting whether a third scalar to be found must be lighter or heavier. The first version of the code is publicly available and contains various generic core routines for tree level vacuum stability analysis, as well as implementations of collider bounds, dark matter constraints, electroweak precision constraints and tree level unitarity.Comment: 24 pages, 4 figures, 3 tables. Project development webpage - http://gravitation.web.ua.pt/Scanner

    Eye guidance during real-world scene search:The role color plays in central and peripheral vision

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    The visual system utilizes environmental features to direct gaze efficiently when locating objects. While previous research has isolated various features' contributions to gaze guidance, these studies generally used sparse displays and did not investigate how features facilitated search as a function of their location on the visual field. The current study investigated how features across the visual field-particularly color-facilitate gaze guidance during real-world search. A gaze-contingent window followed participants' eye movements, restricting color information to specified regions. Scene images were presented in full color, with color in the periphery and gray in central vision or gray in the periphery and color in central vision, or in grayscale. Color conditions were crossed with a search cue manipulation, with the target cued either with a word label or an exact picture. Search times increased as color information in the scene decreased. A gaze-data based decomposition of search time revealed color-mediated effects on specific subprocesses of search. Color in peripheral vision facilitated target localization, whereas color in central vision facilitated target verification. Picture cues facilitated search, with the effects of cue specificity and scene color combining additively. When available, the visual system utilizes the environment's color information to facilitate different real-world visual search behaviors based on the location within the visual field
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