Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favourably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates

Evidence for involvement of both IKCa and SKCa channels in hyperpolarizing responses of the rat middle cerebral artery

Endothelium-derived hyperpolarizing factor responses in the rat middle cerebral artery are blocked by inhibiting IKCa channels alone, contrasting with peripheral vessels where block of both IKCa and SKCa is required. As the contribution of IKCa and SKCa to endothelium-dependent hyperpolarization differs in peripheral arteries, depending on the level of arterial constriction, we investigated the possibility that SKCa might contribute to equivalent hyperpolarization in cerebral arteries under certain conditions. METHODS: Rat middle cerebral arteries (approximately 175 microm) were mounted in a wire myograph. The effect of KCa channel blockers on endothelium-dependent responses to the protease-activated receptor 2 agonist, SLIGRL (20 micromol/L), were then assessed as simultaneous changes in tension and membrane potential. These data were correlated with the distribution of arterial KCa channels revealed with immunohistochemistry. RESULTS: SLIGRL hyperpolarized and relaxed cerebral arteries undergoing variable levels of stretch-induced tone. The relaxation was unaffected by specific inhibitors of IKCa (TRAM-34, 1 micromol/L) or SKCa (apamin, 50 nmol/L) alone or in combination. In contrast, the associated smooth-muscle hyperpolarization was inhibited, but only with these blockers in combination. Blocking nitric oxide synthase (NOS) or guanylyl cyclase evoked smooth-muscle depolarization and constriction, with both hyperpolarization and relaxation to SLIGRL being abolished by TRAM-34 alone, whereas apamin had no effect. Immunolabeling showed SKCa and IKCa within the endothelium. CONCLUSIONS: In the absence of NO, IKCa underpins endothelium-dependent hyperpolarization and relaxation in cerebral arteries. However, when NOS is active SKCa contributes to hyperpolarization, whatever the extent of background contraction. These changes may have relevance in vascular disease states where NO release is compromised and when the levels of SKCa expression may be altered

The Coupled Cluster Method in Hamiltonian Lattice Field Theory

The coupled cluster or exp S form of the eigenvalue problem for lattice Hamiltonian QCD (without quarks) is investigated. A new construction prescription is given for the calculation of the relevant coupled cluster matrix elements with respect to an orthogonal and independent loop space basis. The method avoids the explicit introduction of gauge group coupling coefficients by mapping the eigenvalue problem onto a suitable set of character functions, which allows a simplified procedure. Using appropriate group theoretical methods, we show that it is possible to set up the eigenvalue problem for eigenstates having arbitrary lattice momentum and lattice angular momentum.Comment: LaTeX, no figur

An efficient multigrid strategy for large-scale molecular mechanics optimization

Static mechanical properties of materials require large-scale nonlinear optimization of the molecular mechanics model under various controls. This paper presents an efficient multigrid strategy to solve such problems. This strategy approximates solutions on grids in a quasi-atomistic and inexact manner, transfers solutions on grids following a coarse-to-fine (oneway) schedule, and finds physically relevant minimizers with linear scaling complexity. Compared to the full multigrid method which has the same complexity, the prefactor of this strategy is orders of magnitude smaller. Consequently, the required CPU time of this strategy is orders of magnitude smaller than that of the full multigrid method, and is smaller than that of the brute-force optimization for systems with more than 200,000 atoms. Considerable savings are found if the number of atoms becomes even larger due to the super-linear scaling complexity of the brute-force optimization. For systems with 1,000,000 atoms (over three million degrees of freedom), on average a more than 70% reduction of CPU time is observed regardless of the type of defects, including vacancies, dislocations, and cracks. In addition, linear scalability of the proposed strategy is tested in the presence of a dislocation pair for systems with more than 100 million atoms (over 400 million degrees of freedom)

On linear coupling of acoustic and cyclotron waves in plasma flows

It is found that in magnetized electrostatic plasma flows the velocity shear couples ion-acoustic waves with ion-cyclotron waves and leads, under favorable conditions, to their efficient reciprocal transformations. It is shown that in a two-dimensional setup this coupling has a remarkable feature: it is governed by equations that are exactly similar to the ones describing coupling of sound waves with internal gravity waves [Rogava & Mahajan: Phys. Rev. E vol.55, 1185 (1997)] in neutral fluid flows. Using another noteworthy quantum mechanical analogy we calculate transformation coefficients and give fully analytic, quantitative description of the coupling efficiency for flows with low shearing rates.Comment: 5 pages, no figures. Submitted to "Physics of Plasmas

The Coupled Cluster Method in Hamiltonian Lattice Field Theory: SU(2) Glueballs

The glueball spectrum within the Hamiltonian formulation of lattice gauge theory (without fermions) is calculated for the gauge group SU(2) and for two spatial dimensions. The Hilbert space of gauge-invariant functions of the gauge field is generated by its parallel-transporters on closed paths along the links of the spatial lattice. The coupled cluster method is used to determine the spectrum of the Kogut-Susskind Hamiltonian in a truncated basis. The quality of the description is studied by computing results from various truncations, lattice regularisations and with an improved Hamiltonian. We find consistency for the mass ratio predictions within a scaling region where we obtain good agreement with standard lattice Monte Carlo results.Comment: 13 pages, 7 figure

Kernel density construction using orthogonal forward regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate

Visualising the structure of document search results: A comparison of graph theoretic approaches

This is the post-print of the article - Copyright @ 2010 Sage PublicationsPrevious work has shown that distance-similarity visualisation or ‘spatialisation’ can provide a potentially useful context in which to browse the results of a query search, enabling the user to adopt a simple local foraging or ‘cluster growing’ strategy to navigate through the retrieved document set. However, faithfully mapping feature-space models to visual space can be problematic owing to their inherent high dimensionality and non-linearity. Conventional linear approaches to dimension reduction tend to fail at this kind of task, sacrificing local structural in order to preserve a globally optimal mapping. In this paper the clustering performance of a recently proposed algorithm called isometric feature mapping (Isomap), which deals with non-linearity by transforming dissimilarities into geodesic distances, is compared to that of non-metric multidimensional scaling (MDS). Various graph pruning methods, for geodesic distance estimation, are also compared. Results show that Isomap is significantly better at preserving local structural detail than MDS, suggesting it is better suited to cluster growing and other semantic navigation tasks. Moreover, it is shown that applying a minimum-cost graph pruning criterion can provide a parameter-free alternative to the traditional K-neighbour method, resulting in spatial clustering that is equivalent to or better than that achieved using an optimal-K criterion