863 research outputs found

    Continuous images of Cantor's ternary set

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    The Hausdorff-Alexandroff Theorem states that any compact metric space is the continuous image of Cantor's ternary set CC. It is well known that there are compact Hausdorff spaces of cardinality equal to that of CC that are not continuous images of Cantor's ternary set. On the other hand, every compact countably infinite Hausdorff space is a continuous image of CC. Here we present a compact countably infinite non-Hausdorff space which is not the continuous image of Cantor's ternary set

    Diffraction of return time measures

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    Letting TT denote an ergodic transformation of the unit interval and letting f ⁣:[0,1)Rf \colon [0,1)\to \mathbb{R} denote an observable, we construct the ff-weighted return time measure μy\mu_y for a reference point y[0,1)y\in[0,1) as the weighted Dirac comb with support in Z\mathbb{Z} and weights fTz(y)f \circ T^z(y) at zZz\in\mathbb{Z}, and if TT is non-invertible, then we set the weights equal to zero for all z<0z < 0. Given such a Dirac comb, we are interested in its diffraction spectrum which emerges from the Fourier transform of its autocorrelation and analyse it for the dependence on the underlying transformation. For certain rapidly mixing transformations and observables of bounded variation, we show that the diffraction of μy\mu_{y} consists of a trivial atom and an absolutely continuous part, almost surely with respect to yy. This contrasts what occurs in the setting of regular model sets arising from cut and project schemes and deterministic incommensurate structures. As a prominent example of non-mixing transformations, we consider the family of rigid rotations Tα ⁣:xx+αmod1T_{\alpha} \colon x \to x + \alpha \bmod{1} with rotation number αR+\alpha \in \mathbb{R}^+. In contrast to when TT is mixing, we observe that the diffraction of μy\mu_{y} is pure point, almost surely with respect to yy. Moreover, if α\alpha is irrational and the observable ff is Riemann integrable, then the diffraction of μy\mu_{y} is independent of yy. Finally, for a converging sequence (αi)iN(\alpha_{i})_{i \in \mathbb{N}} of rotation numbers, we provide new results concerning the limiting behaviour of the associated diffractions.Comment: 11 pages, 2 figure

    Smaller Extended Formulations for the Spanning Tree Polytope of Bounded-genus Graphs

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    We give an O(g1/2n3/2+g3/2n1/2)O(g^{1/2} n^{3/2} + g^{3/2} n^{1/2})-size extended formulation for the spanning tree polytope of an nn-vertex graph embedded on a surface of genus gg, improving on the known O(n2+gn)O(n^2 + g n)-size extended formulations following from Wong and Martin.Comment: v3: fixed some typo

    On the asymptotics of the α\alpha-Farey transfer operator

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    We study the asymptotics of iterates of the transfer operator for non-uniformly hyperbolic α\alpha-Farey maps. We provide a family of observables which are Riemann integrable, locally constant and of bounded variation, and for which the iterates of the transfer operator, when applied to one of these observables, is not asymptotic to a constant times the wandering rate on the first element of the partition α\alpha. Subsequently, sufficient conditions on observables are given under which this expected asymptotic holds. In particular, we obtain an extension theorem which establishes that, if the asymptotic behaviour of iterates of the transfer operator is known on the first element of the partition α\alpha, then the same asymptotic holds on any compact set bounded away from the indifferent fixed point

    Using the online cross-entropy method to learn relational policies for playing different games

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    By defining a video-game environment as a collection of objects, relations, actions and rewards, the relational reinforcement learning algorithm presented in this paper generates and optimises a set of concise, human-readable relational rules for achieving maximal reward. Rule learning is achieved using a combination of incremental specialisation of rules and a modified online cross-entropy method, which dynamically adjusts the rate of learning as the agent progresses. The algorithm is tested on the Ms. Pac-Man and Mario environments, with results indicating the agent learns an effective policy for acting within each environment
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