49 resultados para Recall-Precision Curves


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Let C be a smooth, absolutely irreducible genus 3 curve over a number field M. Suppose that the Jacobian of C has complex multiplication by a sextic CM-field K. Suppose further that K contains no imaginary quadratic subfield. We give a bound on the primes p of M such that the stable reduction of C at p contains three irreducible components of genus 1.

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Let E/Q be an elliptic curve and p a rational prime of good ordinary reduction. For every imaginary quadratic field K/Q satisfying the Heegner hypothesis for E we have a corresponding line in E(K)\otimes Q_p, known as a shadow line. When E/Q has analytic rank 2 and E/K has analytic rank 3, shadow lines are expected to lie in E(Q)\otimes Qp. If, in addition, p splits in K/Q, then shadow lines can be determined using the anticyclotomic p-adic height pairing. We develop an algorithm to compute anticyclotomic p-adic heights which we then use to provide an algorithm to compute shadow lines. We conclude by illustrating these algorithms in a collection of examples.

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Compared with younger adults, older adults have a relative preference to attend to and remember positive over negative information. This is known as the “positivity effect,” and researchers have typically evoked socioemotional selectivity theory to explain it. According to socioemotional selectivity theory, as people get older they begin to perceive their time left in life as more limited. These reduced time horizons prompt older adults to prioritize achieving emotional gratification and thus exhibit increased positivity in attention and recall. Although this is the most commonly cited explanation of the positivity effect, there is currently a lack of clear experimental evidence demonstrating a link between time horizons and positivity. The goal of the current research was to address this issue. In two separate experiments, we asked participants to complete a writing activity, which directed them to think of time as being either limited or expansive (Experiments 1 and 2) or did not orient them to think about time in a particular manner (Experiment 2). Participants were then shown a series of emotional pictures, which they subsequently tried to recall. Results from both studies showed that regardless of chronological age, thinking about a limited future enhanced the relative positivity of participants’ recall. Furthermore, the results of Experiment 2 showed that this effect was not driven by changes in mood. Thus, the fact that older adults’ recall is typically more positive than younger adults’ recall may index naturally shifting time horizons and goals with age.

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The co-polar correlation coefficient (ρhv) has many applications, including hydrometeor classification, ground clutter and melting layer identification, interpretation of ice microphysics and the retrieval of rain drop size distributions (DSDs). However, we currently lack the quantitative error estimates that are necessary if these applications are to be fully exploited. Previous error estimates of ρhv rely on knowledge of the unknown "true" ρhv and implicitly assume a Gaussian probability distribution function of ρhv samples. We show that frequency distributions of ρhv estimates are in fact highly negatively skewed. A new variable: L = -log10(1 - ρhv) is defined, which does have Gaussian error statistics, and a standard deviation depending only on the number of independent radar pulses. This is verified using observations of spherical drizzle drops, allowing, for the first time, the construction of rigorous confidence intervals in estimates of ρhv. In addition, we demonstrate how the imperfect co-location of the horizontal and vertical polarisation sample volumes may be accounted for. The possibility of using L to estimate the dispersion parameter (µ) in the gamma drop size distribution is investigated. We find that including drop oscillations is essential for this application, otherwise there could be biases in retrieved µ of up to ~8. Preliminary results in rainfall are presented. In a convective rain case study, our estimates show µ to be substantially larger than 0 (an exponential DSD). In this particular rain event, rain rate would be overestimated by up to 50% if a simple exponential DSD is assumed.