13,201 research outputs found

    SCAN: Learning Hierarchical Compositional Visual Concepts

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    The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such representations are compositional and hierarchical, they can be recombined into an exponentially large set of new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such abstractions in the visual domain. SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner. Unlike state of the art multimodal generative model baselines, our approach requires very few pairings between symbols and images and makes no assumptions about the form of symbol representations. Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to break away from its training data distribution and imagine novel visual concepts through symbolically instructed recombination of previously learnt concepts

    Grasping asymmetric information in market impacts

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    The price impact for a single trade is estimated by the immediate response on an event time scale, i.e., the immediate change of midpoint prices before and after a trade. We work out the price impacts across a correlated financial market. We quantify the asymmetries of the distributions and of the market structures of cross-impacts, and find that the impacts across the market are asymmetric and non-random. Using spectral statistics and Shannon entropy, we visualize the asymmetric information in price impacts. Also, we introduce an entropy of impacts to estimate the randomness between stocks. We show that the useful information is encoded in the impacts corresponding to small entropy. The stocks with large number of trades are more likely to impact others, while the less traded stocks have higher probability to be impacted by others

    Inference of the drivers of collective movement in two cell types: Dictyostelium and melanoma

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    Collective cell movement is a key component of many important biological processes, including wound healing, the immune response and the spread of cancers. To understand and influence these movements, we need to be able to identify and quantify the contribution of their different underlying mechanisms. Here, we define a set of six candidate models—formulated as advection–diffusion–reaction partial differential equations—that incorporate a range of cell movement drivers. We fitted these models to movement assay data from two different cell types: Dictyostelium discoideum and human melanoma. Model comparison using widely applicable information criterion suggested that movement in both of our study systems was driven primarily by a self-generated gradient in the concentration of a depletable chemical in the cells' environment. For melanoma, there was also evidence that overcrowding influenced movement. These applications of model inference to determine the most likely drivers of cell movement indicate that such statistical techniques have potential to support targeted experimental work in increasing our understanding of collective cell movement in a range of systems

    Ellipticity and Deviations from Orthogonality in the Polarization Modes of PSR B0329+54

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    We report on an analysis of the polarization of single pulses of PSR B0329+54 at 328 MHz. We find that the distribution of polarization orientations in the central component diverges strongly from the standard picture of orthogonal polarization modes (OPMs), making a remarkable partial annulus on the Poincare sphere. A second, tightly clustered region of density appears in the opposite hemisphere, at a point antipodal to the centre of the annulus. We argue that this can be understood in terms of birefringent alterations in the relative phase of two elliptically polarized propagation modes in the pulsar magnetosphere (i.e. generalised Faraday rotation). The ellipticity of the modes implies a significant charge density in the plasma, while the presence of both senses of circular polarization, and the fact that only one mode shows the effect, supports the view that refracted ordinary-mode rays are involved in the production of the annulus. At other pulse longitudes the polarization (including the circular component) is broadly consistent with an origin in elliptical OPMs, shown here quantitatively for the first time, however considerable non-orthogonal contributions serve to broaden the orientation distribution in an isotropic manner.Comment: 13 pages, 5 figures, to appear in A&

    On Describing Multivariate Skewness: A Directional Approach

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    Most multivariate measures of skewness in the literature measure the overall skewness of a distribution. While these measures are perfectly adequate for testing the hypothesis of distributional symmetry, their relevance for describing skewed distributions is less obvious. In this article, we consider the problem of characterising the skewness of multivariate distributions. We define directional skewness as the skewness along a direction and analyse parametric classes of skewed distributions using measures based on directional skewness. The analysis brings further insight into the classes, allowing for a more informed selection of particular classes for particular applications. In the context of Bayesian linear regression under skewed error we use the concept of directional skewness twice. First in the elicitation of a prior on the parameters of the error distribution, and then in the analysis of the skewness of the posterior distribution of the regression residuals.Bayesian methods, Multivariate distribution, Multivariate regression, Prior elicitation, Skewness.

    Neural ODEs with stochastic vector field mixtures

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    It was recently shown that neural ordinary differential equation models cannot solve fundamental and seemingly straightforward tasks even with high-capacity vector field representations. This paper introduces two other fundamental tasks to the set that baseline methods cannot solve, and proposes mixtures of stochastic vector fields as a model class that is capable of solving these essential problems. Dynamic vector field selection is of critical importance for our model, and our approach is to propagate component uncertainty over the integration interval with a technique based on forward filtering. We also formalise several loss functions that encourage desirable properties on the trajectory paths, and of particular interest are those that directly encourage fewer expected function evaluations. Experimentally, we demonstrate that our model class is capable of capturing the natural dynamics of human behaviour; a notoriously volatile application area. Baseline approaches cannot adequately model this problem

    Recent advances in directional statistics

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    Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

    A generalised equivalent storm model for long-term statistics of ocean waves

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    This is the final version of the article. Available from Elsevier via the DOI in this record.To calculate the return periods of individual wave or crest heights, the long-term distribution of sea states must be combined with the short-term distribution of individual wave or crest heights conditional on sea state. This is normally achieved using an equivalent storm model to parameterise the distribution of the maximum wave or crest height in a storm. A new equivalent storm model is introduced that generalises the approach of Tromans and Vanderschuren (1995). The generalised equivalent storm (GES) method is significantly simpler than equivalent storm methods that model the temporal evolution of the significant wave height in a storm. The GES method is applied to long time series of wave buoy measurements for deep and shallow water sites and demonstrated to be more accurate than existing methods at representing the statistical characteristics of measured storms. Return periods of crest heights from the GES method are shown to be more robust to uncertainties in the fitted models of the equivalent storm parameters than estimates from temporal evolution methods such as the equivalent triangular storm and equivalent power storm model.This work was partly funded through EPSRC grant EP/R007519/1
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