New results for the magnitude of flicker noise, considered as resistance fluctuations, in random resistor networks are reported. Near the percolation threshold p c , the magnitude of the relative noise is shown to diverge as (p-p c )~K. The new exponent K is calculated by various methods: Monte Carlo simulations, effective-medium theory, and position-space renormalization group. Exponents pertaining to higher-order cumulants of the resistance fluctuations are also calculated. The possible implications of our results for ongoing experiments on metal-insulator mixtures and cermets are also discussed.
New bounds for the exponent characterizing the amplitude of the resistance noise near the percolation threshold of discrete random networks are found. The difference between the lower and upper bounds is very small, so that an accurate estimate of the noise exponent can be obtained in all dimensions. Continuum corrections to these exponents for the random-void class of systems are then calculated within the nodes-links-blobs model of percolating networks.
Many presume that parsing the shadows out of an image is a high-level task, because of the global nature of the shadow formation process. But shapefrom-shading algorithms are low-level, in the sense that they seek solutions (surface normals or depth values) directly from image intensities. A dilemma arises: since shape-from-shading involves an illumination term, shadows must first be identified. We show that a structure in.termediate between intensitees and surfaces -the shading flow field ~ provides a solution to this dilemma. Our analysis is based on the observation that the geometric information that can be derived froin images supports different inferences than the photometric information, and our specific goal will be to articulate this geometric structure and to show how shading flow fields can be reliably computed.
It is shown that the Negative Eigenvalue Theorem and transfer matrix methods may be considered within a unified framework and generalized to compute projected densities of states or, more generally, any linear combination of matrix elements of the inverse of large symmetric random matrices. As examples of applications, extensive simulations for one- and two-mode behaviour in the Raman spectrum of one-dimensional mixed crystals and a finite-size scaling analysis of critical exponents for the central force percolation universality class are presented
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