Learning a kernel matrix for nonlinear dimensionality reduction

We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear feature space, we show how to discover a mapping that unfolds the underlying manifold from which the data was sampled. The kernel matrix is constructed by maximizing the variance in feature space subject to local constraints that preserve the angles and distances between nearest neighbors. The main optimization involves an instance of semidefinite programming---a fundamentally different computation than previous algorithms for manifold learning, such as Isomap and locally linear embedding. The optimized kernels perform better than polynomial and Gaussian kernels for problems in manifold learning, but worse for problems in large margin classification. We explain these results in terms of the geometric properties of different kernels and comment on various interpretations of other manifold learning algorithms as kernel methods

Ferromagnetism in 2p Light Element-Doped II-oxide and III-nitride Semiconductors

II-oxide and III-nitride semiconductors doped by nonmagnetic 2p light elements are investigated as potential dilute magnetic semiconductors (DMS). Based on our first-principle calculations, nitrogen doped ZnO, carbon doped ZnO, and carbon doped AlN are predicted to be ferromagnetic. The ferromagnetism of such DMS materials can be attributed to a p-d exchange-like p-p coupling interaction which is derived from the similar symmetry and wave function between the impurity (p-like t_2) and valence (p) states. We also propose a co-doping mechanism, using beryllium and nitrogen as dopants in ZnO, to enhance the ferromagnetic coupling and to increase the solubility and activity

Dynamic update of shortest path tree in OSPF

2003-2004 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Fast computation of radar cross-section by fast multipole method in conjunction with lifting wavelet-like transform

The fast multipole method (FMM) in conjunction with the lifting wavelet-like transform scheme is proposed for the scattering analysis of differently shaped three-dimensional perfectly electrical conducting objects. As a flexible and efficient matrix compression technique, the proposed method can sparsify the aggregation matrix and disaggregation matrix in real time with compression ratio about 30%. The computational complexity and choice of proper wavelet are also discussed. Numerical simulation and complexity analysis have shown that the proposed method can speed up the aggregation and disaggregation steps of the FMM with lower memory requirements. © 2010 The Institution of Engineering and Technology.postprin

Enhanced Photoactivity on Ag/Ag3PO4 Composites by Plasmonic Effect

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Acute cortical blindness caused by pre-eclampsia in the antepartum; posterior reversible encephalopathy syndrome (PRES)

We present a case report of a patient presenting posterior reversible encephalopathy syndrome (PRES), a rare acute neurological condition associated with pre-eclampsia. A possible common aetiology and successful clinical management approach is reported