67,262 research outputs found

    Sound separation probe

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    Probe separates sound waves from turbulent flow pressure fluctuations in ducted airstreams by using principle that sound waves and turbulent flow pressure perturbations travel at different velocities

    Atom-Atom Scattering Under Cylindrical Harmonic Confinement: Numerical and Analytical Studies of the Confinement Induced Resonance

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    In a recent article [M. Olshanii, Phys. Rev. Lett. {\bf 81}, 938 (1998)], an analytic solution of atom-atom scattering with a delta-function pseudopotential interaction in the presence of transverse harmonic confinement yielded an effective coupling constant that diverged at a `confinement induced resonance.' In the present work, we report numerical results that corroborate this resonance for more realistic model potentials. In addition, we extend the previous theoretical discussion to include two-atom bound states in the presence of transverse confinement, for which we also report numerical results hereComment: New version with major revisions. We now provide a detailed physical interpretation of the confinement-induced resonance in tight atomic waveguide

    Machiavelli’s Walls: The Legacy of Realism in International Relations Theory

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    In this paper we argue that Niccolò Machiavelli has little to do with Realism in International Relations theory. By concentrating, as Machaivelli did, on the walls that define political relations—both inside and outside the polity—we find his insights deeply rooted in the specific political contexts of Sixteenth century Italy. Others may wish to generalize from them, but Machiavelli did not. In fact, as we show, Machiavelli was mindful of the difficulties of generalizing about walls and acknowledged the dangers political actors faced in navigating between the internal and external walls of the polity. We examine the geopolitical contours of Machiavelli’s walls and seek to demonstrate how morality is present in these historical spaces. In contrast to Realists, Machiavelli was ready and willing to make ethical judgments. We argue that theorists of international politics should exercise care in reaching for Machiavelli as the iconic thinker for making sense of anarchy in world politics. This article concludes by suggesting that the ideology of Machiavellianism has obscured deeper understanding of the particular contexts of Machiavelli’s own world

    Parallel Metric Tree Embedding based on an Algebraic View on Moore-Bellman-Ford

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    A \emph{metric tree embedding} of expected \emph{stretch~α1\alpha \geq 1} maps a weighted nn-node graph G=(V,E,ω)G = (V, E, \omega) to a weighted tree T=(VT,ET,ωT)T = (V_T, E_T, \omega_T) with VVTV \subseteq V_T such that, for all v,wVv,w \in V, dist(v,w,G)dist(v,w,T)\operatorname{dist}(v, w, G) \leq \operatorname{dist}(v, w, T) and operatornameE[dist(v,w,T)]αdist(v,w,G)operatorname{E}[\operatorname{dist}(v, w, T)] \leq \alpha \operatorname{dist}(v, w, G). Such embeddings are highly useful for designing fast approximation algorithms, as many hard problems are easy to solve on tree instances. However, to date the best parallel (polylogn)(\operatorname{polylog} n)-depth algorithm that achieves an asymptotically optimal expected stretch of αO(logn)\alpha \in \operatorname{O}(\log n) requires Ω(n2)\operatorname{\Omega}(n^2) work and a metric as input. In this paper, we show how to achieve the same guarantees using polylogn\operatorname{polylog} n depth and O~(m1+ϵ)\operatorname{\tilde{O}}(m^{1+\epsilon}) work, where m=Em = |E| and ϵ>0\epsilon > 0 is an arbitrarily small constant. Moreover, one may further reduce the work to O~(m+n1+ϵ)\operatorname{\tilde{O}}(m + n^{1+\epsilon}) at the expense of increasing the expected stretch to O(ϵ1logn)\operatorname{O}(\epsilon^{-1} \log n). Our main tool in deriving these parallel algorithms is an algebraic characterization of a generalization of the classic Moore-Bellman-Ford algorithm. We consider this framework, which subsumes a variety of previous "Moore-Bellman-Ford-like" algorithms, to be of independent interest and discuss it in depth. In our tree embedding algorithm, we leverage it for providing efficient query access to an approximate metric that allows sampling the tree using polylogn\operatorname{polylog} n depth and O~(m)\operatorname{\tilde{O}}(m) work. We illustrate the generality and versatility of our techniques by various examples and a number of additional results
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