computingnumerical solutions of the thermal radiative transfer equations on a finely resolved grid can be costly due to high computational and memory requirements. A numerical reduced order method that has recently b...
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Grid cells in the entorhinal cortex are known for their hexagonal spatial activity patterns and are thought to provide a neural metric for space, and support path integration. In this study, we further investigate gri...
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We study a thermo-poroelasticity model which describes the interaction between the deformation of an elastic porous material and fluid flow under non-isothermal conditions. The model involves several parameters that c...
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The discriminator lemma is normally used to prove lower bounds for circuits with small-weight threshold gates. In this note we adapt the lemma to circuits with a general (large-weight) threshold gate at the top. The n...
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The discriminator lemma is normally used to prove lower bounds for circuits with small-weight threshold gates. In this note we adapt the lemma to circuits with a general (large-weight) threshold gate at the top. The new lemma is used to give a new proof of a previously known lower bound for the size of a threshold of parity gates that computes inner product mod 2, and to prove that a small-depth AND-OR circuit for parity must have exponential size even if we allow a threshold gate at the top. The latter result is a generalization to large weights of a result by Green, and depends heavily on Hastad's switching-lemma. (C) 1997 Elsevier science B.V.
The paper outlines a method for designing near optimal nonlinear classifiers based on a self-organizing technique for estimating probability density functions when only weak assumptions are made about the densities. T...
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The paper outlines a method for designing near optimal nonlinear classifiers based on a self-organizing technique for estimating probability density functions when only weak assumptions are made about the densities. The classical parametric and nonparametric methods for estimating density functions have a number of drawbacks;parametric methods give weak results on unknown distributions, while nonparametric methods require extensive amounts of design samples, storage capacity, and computing power. The present method avoids these disadvantages by parameterizing a set of component densities from which the actual densities are constructed. The parameters of the component densities are optimized by a self-organizing algorithm, reducing to a minimum the labeling of design samples. All the required computations are realized with the simple "sum of product" units commonly used in connectionist models. The density approximations produced by the method are illustrated in two dimensions for a multispectral image classification task. The practical use of the method is illustrated by a small speech recognition problem, that of recognizing 18 Swedish consonants. Related issues of invariant projections, cross-class pooling of data, and subspace partitioning are also discussed.
We prove that maximum H-matching (the problem of determining the maximum number of node-disjoint copies of the fixed graph H contained in a variable graph) is a Max SNP-hard problem for any graph H that has three or m...
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We prove that maximum H-matching (the problem of determining the maximum number of node-disjoint copies of the fixed graph H contained in a variable graph) is a Max SNP-hard problem for any graph H that has three or more nodes in some connected component. If H is connected and the degrees of the nodes in H are bounded by a constant the problem is Max SNP-complete.
We introduce an algebraic construction for the Hausdorff extension H(A) of a many-sorted universal algebra A with respect to a family T of Hausdorff topologies on the carrier sets of A. This construction can be combin...
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We introduce an algebraic construction for the Hausdorff extension H(A) of a many-sorted universal algebra A with respect to a family T of Hausdorff topologies on the carrier sets of A. This construction can be combined with other algebraic constructions, such as the initial model construction, to provide methods for the algebraic specification of uncountable algebras, e.g. algebras of reals, function spaces and screams.
We present a multi-scale representation of grey-level shape, called the scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. Th...
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We present a multi-scale representation of grey-level shape, called the scale-space primal sketch, that makes explicit features in scale-space as well as the relations between features at different levels of scale. The representation gives a qualitative description of the image structure that allows for extraction of significant image structure - stable scales and regions of interest - in a solely bottom-up data-driven manner. Hence, it can be seen as preceding further processing, which can then be properly tuned. Experiments on real imagery demonstrate that the proposed theory gives intuitively reasonable results.
The cylindrical algebraic decomposition method decomposes E r into regions over which a given polynomial has constant sign by extension of one complicated decomposition of E r-1 . We investigate a method which decompo...
The cylindrical algebraic decomposition method decomposes E r into regions over which a given polynomial has constant sign by extension of one complicated decomposition of E r-1 . We investigate a method which decomposes E r into sign-invariant region by combining several but simpler decompositions of E r-1 . We can obtain a sign-invariaat decomposition of E 2 defined by a bivariate polynomial of total degree n and coefficient size d in time O(n 12 (d + log n) 2 log n) . Preliminary experiments suggest that the method is useful in practice.
The edge focusing method produces a series of edge images ranging from coarser to finer scale resolution. The displacements of these extracted edges in this series are discussed. A three-step method of labelling the e...
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The edge focusing method produces a series of edge images ranging from coarser to finer scale resolution. The displacements of these extracted edges in this series are discussed. A three-step method of labelling the extracted edges as coming from objects or as coming from shadows and other illumination phenomena using this series is tried. More precisely, we show that it seems possible to label edges into the categories ‘diffuse’ and ‘non-diffuse’ from a binary multi-scale representation, i.e. without using the image intensities directly.
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