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  • Blackwell Publishing Ltd  (1)
  • National Academy of Sciences  (1)
  • 1
    Digitale Medien
    Digitale Medien
    Oxford, UK : Blackwell Publishing Ltd
    Geophysical journal international 113 (1993), S. 0 
    ISSN: 1365-246X
    Quelle: Blackwell Publishing Journal Backfiles 1879-2005
    Thema: Geologie und Paläontologie
    Notizen: Recent spectral studies of vertical transect profiles of landscapes and mountains have shown them to be self-affine fractals, i.e. the rms height fluctuation Δh(L) averaged over a distance L scales as Δh(L)∼Lx with X≈ 0.5 ± 0.1, related to the fractal dimension Df= 2 -X≈ 1.5 of the horizontal contours. We propose that self-affine rough landscapes are created by the interplay of non-linearity and noise. To illustrate this idea and model the formation of such structures, we suggest a non-linear stochastic equation ∂h/∂t=DΔ2h+λ(Δh)2+ν(r, t), which is the generalization of the deterministic Culling's linear equation. The non-linear term λ(Δh)2 comes from the requirement that erosion is proportional to the exposed area of the landscape; the noise term ν(r, t) accounts for the fact that erosion is locally irregular, as a result of the heterogeneity of soils and distribution of storms. Using this general framework, we recover the scaling law Δh(L∼Lx with X≥ 0.4. Several novel avenues of research emerge from this analysis to further quantify geological data.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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  • 2
    Publikationsdatum: 2015-02-06
    Beschreibung: The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners.
    Print ISSN: 0027-8424
    Digitale ISSN: 1091-6490
    Thema: Biologie , Medizin , Allgemeine Naturwissenschaft
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
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