7,328 research outputs found

    No Particle Production in Two Dimensions: Recursion Relations and Multi-Regge Limit

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    We introduce high-energy limits which allow us to derive recursion relations fixing the various couplings of Lagrangians of two-dimensional relativistic quantum field theories with no tree-level particle production in a very straightforward way. The sine-Gordon model, the Bullough-Dodd theory, Toda theories of various kinds and the U(N) non-linear sigma model can all be rediscovered in this way. The results here were the outcome of our explorations at the 2017 Perimeter Institute Winter School.Comment: 20 page

    Behavioural Flexibility in Bumblebees (Bombus impatiens)

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    Foraging bumblebees (Bombus impatiens) extract nectar and pollen from a wide variety of morphologically distinct flower species, referred to as flower handling. Bumblebees learn this behaviour and acquisition of multiple flower handling techniques is a demonstration of behavioural flexibility. The purpose of this thesis is to understand how bumblebees are able to forage flexibly. This research has three specific goals: (1) to identify the cognitive mechanisms that support flower handling learning, (2) to understand how bumblebees avoid interference costs between multiple handling techniques, and (3) to explore the relation between behavioural flexibility and the mushroom bodies of the bumblebee brain. To address the first two goals, I developed a laboratory model of flower handling. The model consisted of a tube with a plastic door insert that bumblebees moved to access a nectar reward. The door was designed to be similar to a flower petal that a bee would lift to access a nectary in a real flower. All bees demonstrated the same set of motor behaviours and showed improvement across trials by increasing the frequency with which they used the successful behaviour. The apparatus was then adapted to measure bees’ ability to switch between two handling tasks, representing two different flower morphologies. Two variations of the apparatus were used, each of which required a different innate motor pattern for successful removal of the door. Bees switched between the two tasks by changing only the frequency that they engaged in each successful motor behaviour. The role of the mushroom bodies in behavioural flexibility was examined by training bees on a measure of behavioural flexibility, reversal learning, and relating performance to volume of the mushroom bodies and their components. Performance on the reversal task did not correlate with mushroom body volume. My overall findings are that bumblebees use a combination of innate motor patterns and learned associations to forage on a variety of flower species and the flexibility of individual bumblebees is not related to individual variation in volume of the mushroom bodies and their components

    NICE : A Computational solution to close the gap from colour perception to colour categorization

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    The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms

    Flower pollination algorithm with pollinator attraction

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    The Flower Pollination Algorithm (FPA) is a highly efficient optimization algorithm that is inspired by the evolution process of flowering plants. In the present study, a modified version of FPA is proposed accounting for an additional feature of flower pollination in nature that is the so-called pollinator attraction. Pollinator attraction represents the natural tendency of flower species to evolve in order to attract pollinators by using their colour, shape and scent as well as nutritious rewards. To reflect this evolution mechanism, the proposed FPA variant with Pollinator Attraction (FPAPA) provides fitter flowers of the population with higher probabilities of achieving pollen transfer via biotic pollination than other flowers. FPAPA is tested against a set of 28 benchmark mathematical functions, defined in IEEE-CEC’13 for real-parameter single-objective optimization problems, as well as structural optimization problems. Numerical experiments show that the modified FPA represents a statistically significant improvement upon the original FPA and that it can outperform other state-of-the-art optimization algorithms offering better and more robust optimal solutions. Additional research is suggested to combine FPAPA with other modified and hybridized versions of FPA to further increase its performance in challenging optimization problems
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