In the field of proteomics, protein hierarchies based on sequence analysis have been extensively applied to automate the annotations of new proteins and facilitate the discovery and analysis of protein families. Howev...
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In the field of proteomics, protein hierarchies based on sequence analysis have been extensively applied to automate the annotations of new proteins and facilitate the discovery and analysis of protein families. However, the presence of ambiguous similarities in large databases increases the difficulty of delivering protein family hierarchies with favorable sensitivity and specificity. This work develops the HomoClust algorithm that exploits the homogeneity of protein sequences in generating protein family hierarchies. HomoClust improves the clustering quality of traditional hierarchical clustering algorithms by adopting different clustering mechanisms for different levels of sequence similarity. With considering homogeneity detection during clustering process, HomoClust increases the sensitivity of protein clusters without a drop in high specificity. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
This paper presents an efficient, simple, hierarchical, and sparse three-dimensional capacitance extraction algorithm, i.e., ICCAP. Most previous capacitance extraction algorithms, such as FastCap and HiCap, introduce...
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This paper presents an efficient, simple, hierarchical, and sparse three-dimensional capacitance extraction algorithm, i.e., ICCAP. Most previous capacitance extraction algorithms, such as FastCap and HiCap, introduce intermediate variables to facilitate the hierarchical potential calculation, but still preserve the basic panels as basis. In this paper, we. discover that those intermediate variables are a fundamentally much better basis than leaf panels. As a result, we are able to explicitly construct the sparse potential coefficient matrix and solve it with linear memory and linear run time in comparison with the most recent hierarchical O(nlogn) approach in PHiCap. Furthermore, the explicit sparse formulation of a potential matrix not only enables the usage of preconditioned Krylov subspace iterative methods, but also the reordering technique. A new reordering technique, i.e., level-oriented reordering (LOR), is proposed to further reduce over 20% of memory consumption and run time compared with no reordering techniques applied. In fact, LOR is even better than the state-of-the-art minimum degree reordering and more efficient. Without complicated orthonormalization matrix computation, ICCAP is very simple, efficient, and accurate. Experimental results demonstrate the superior run time and memory consumption over previous approaches while achieving similar accuracy.
In this paper, a new double- image Green's function approach is proposed to compute the frequency- dependent capacitance and conductance for the interconnects based on metal- insulator- semiconductor ( MIS) struct...
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In this paper, a new double- image Green's function approach is proposed to compute the frequency- dependent capacitance and conductance for the interconnects based on metal- insulator- semiconductor ( MIS) structure with a protective layer. The hierarchical algorithm is also adopted to speed the calculation of the infinite series resulted from the double- image Green's function. The parameters gained from this new approach are shown to be in good agreement with the data obtained by full- wave method and total charge Green's function method.
In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the c...
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In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the conventional algorithms. Use of a population-based centroid representation makes it possible to preserve the uncertainty inherent in data sets as long as possible before actual decisions are made. The k-populations algorithm was found to give markedly better clustering results through various experiments. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
This paper introduces the problem of data mining association rules. We adopt the iterative method to enlarge the size of the item set gradually and describe the hierarchical algorithm in detail. The hierarchical algor...
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ISBN:
(纸本)0819444804
This paper introduces the problem of data mining association rules. We adopt the iterative method to enlarge the size of the item set gradually and describe the hierarchical algorithm in detail. The hierarchical algorithm produces a larger provisional sets based on the obtained frequent item sets and make sure that those provisional sets which will never be frequent item set are ignored under the premise of the known information. Finally, an improving algorithm by decreasing the times of scanning databases is proposed. It's an improving algorithm which is to combine the last several procedures of iteration into a single scan of the database D. Mainly because that the more backwards the iterative processes approach the end, the less the provisional sets are there.
Instead of using the total charge Green抯 function method directly to compute the two-dimensional parameters of interconnects in multi-dielectric media, the Appel抯 hierarchical algorithm is adopted to
Instead of using the total charge Green抯 function method directly to compute the two-dimensional parameters of interconnects in multi-dielectric media, the Appel抯 hierarchical algorithm is adopted to
A large class of problems in parameter estimation concerns nonlinearly parametrized systems. Over the past few years, a stability framework for estimation and control of such systems has been established. We address t...
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A large class of problems in parameter estimation concerns nonlinearly parametrized systems. Over the past few years, a stability framework for estimation and control of such systems has been established. We address the issue of parameter convergence in such systems in this paper. Systems with both convex/concave and general parameterizations are considered. In the former case, sufficient conditions are derived under which parameter estimates converge to their true values using a min-max algorithm as in a previous. work by Armaswamy et al. In the latter case, to achieve parameter convergence a hierarchical min-max algorithm is proposed where the lower level consists of a min-max algorithm and the higher level component updates the bounds on the parameter region within which the unknown parameter is known to lie. Using this hierarchical algorithm, a necessary and sufficient condition is established for global parameter convergence in systems with a general nonlinear parameterization. In both cases, the conditions needed are shown to be stronger than linear persistent excitation conditions that guarantee parameter convergence in linearly parametrized systems. Explanations and examples of these conditions and simulation results are included to illustrate the nature of these conditions. A general definition of nonlinear persistent excitation that leads. to parameter convergence is proposed at the end of this paper.
We present a hierarchical top-down refinement algorithm for compressing 2D vector fields that preserves topology. Our approach is to reconstruct the data set using adaptive refinement that considers topology. The algo...
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We present a hierarchical top-down refinement algorithm for compressing 2D vector fields that preserves topology. Our approach is to reconstruct the data set using adaptive refinement that considers topology. The algorithms start with little data and subdivide regions that are most likely to reconstruct the original topology of the given data set. We use two different refinement techniques. The first technique uses bintree subdivision and linear interpolation. The second algorithm is driven by triangular quadtree subdivision with Coons patch quadratic interpolation. We employ local error metrics to measure the quality of compression and as a global metric we compute Earth Mover's Distance (EMD) to measure the deviation from the original topology. Experiments with both analytic and simulated data sets are presented. Results indicate that one can obtain significant compression with low errors without losing topological information. Advantages and disadvantages of different topology preserving compression algorithms are also discussed in the paper.
Multisensor data fusion is necessary to integrate data from different sensors and extract the relevant information on the targets. While a centralized processing approach is theoretically optimal, there are significan...
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Multisensor data fusion is necessary to integrate data from different sensors and extract the relevant information on the targets. While a centralized processing approach is theoretically optimal, there are significant advantages in distributing the fusion operations over multiple processing nodes. This paper discusses architectures for distributed fusion, whereby each node processes the data from its own set of sensors and communicates with other nodes to improve on the estimates. Information flow in distributed fusion systems is modelled using information graph. The approach is verified for intelligent alarm analysis process.
We present a computation model to describe a clustered memory hierarchy of distributed shared memory machines. The computation model includes the access to shared data stored in different levels of the hierarchy as we...
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ISBN:
(纸本)0769509886
We present a computation model to describe a clustered memory hierarchy of distributed shared memory machines. The computation model includes the access to shared data stored in different levels of the hierarchy as well as the transfer of entire blocks of data between different levels of the memory. Pure shared memory machines and pure message passing machines can be expressed within the model. As example we use the model to analyze a hierarchical matrix multiplication algorithm.
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