A method for the automatic supervised detection of multiple mineral targets in hyperspectral mineral data is presented in this paper. The method makes use of wavelet analysis, wavelet-based denoising using thresholdin...
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A method for the automatic supervised detection of multiple mineral targets in hyperspectral mineral data is presented in this paper. The method makes use of wavelet analysis, wavelet-based denoising using thresholding of wavelet detail coefficients, and feature reduction based on sequential forward selection, which utilises an extension of receiver operating characteristic curves to fuzzy set membership in order to measure discriminating capability. The method is shown to run in time linear to the number of hyperspectral bands, per pixel. Furthermore, an extension of this method to linear unmixing is presented, based on minimising the least-squares error between abundance estimates and actual spectra by varying a thresholding parameter to eliminate outliers and imposing a sum-to-one constraint on the abundances.
Polynomially large ground-state energy gaps are rare in many-body quantum systems, but useful in quantum information and an interesting feature of the one-dimensional quantum Ising model. We show analytically that the...
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Polynomially large ground-state energy gaps are rare in many-body quantum systems, but useful in quantum information and an interesting feature of the one-dimensional quantum Ising model. We show analytically that the gap is generically polynomially large not just for the quantum Ising model, but for one-, two-, and three-dimensional interaction lattices and Hamiltonians with certain random interactions. We extend the analysis to Hamiltonian evolutions and we use the Jordan-Wigner transformation and a related transformation for spin-3/2 particles to show that our results can be restated using spin operators in a surprisingly simple manner. These results also yield a new perspective on the one-dimensional cluster state.
We examine the analysis of hyperspectral data produced by the Hy-perspectral Core Imager of AngloGold Ashanti. The dimension of the data is reduced using diffusion maps and the data is then clustered using three divis...
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We examine the analysis of hyperspectral data produced by the Hy-perspectral Core Imager of AngloGold Ashanti. The dimension of the data is reduced using diffusion maps and the data is then clustered using three divisive clustering strategies. Divisive k-means, PDDP and the NCut algorithm are used. It is shown that the clusterings produced are reasonably accurate compared to a reference clustering, but superior with respect to an internal quality evaluation. Moreover, using a divisive algorithm makes it possible to keep track of inter-cluster similarities. It is also shown that by embedding sample spectra in a dataset it is possible to identify particular minerals within the cluster.
In the last few years, many studies in the cognitive and system neuroscience found that a consistent network of brain regions, referred to as the default network, showed high levels of activity when no explicit task w...
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In the last few years, many studies in the cognitive and system neuroscience found that a consistent network of brain regions, referred to as the default network, showed high levels of activity when no explicit task was performed. Some scientists believed that the resting state activity might reflect some neural functions that consolidate the past, stabilize brain ensembles and prepare us for the future. Here, we modeled default network as undirected weighted graph and then used graph theory to investigate the topological properties of the default network of the two groups of people with different intelligence levels. We found that, in both groups, the posterior cingulate cortex showed the greatest degree in comparison to the other brain regions in the default network, and that the medial temporal lobes and cerebellar tonsils were topologically separations from the other brain regions in the default network. More importantly, we found that the strength of some functional connectivities and the global efficiency of default network were significantly different between the superior intelligence group and the average intelligence group, which indicates that the functional integration of the default network might be related to the individual intelligent performance.
The use of laser‐driven Inertial Confinement Fusion (ICF) for space propulsion has been the subject of several earlier conceptual design studies, (see: Orth, 1998; and other references therein). However, these studie...
The use of laser‐driven Inertial Confinement Fusion (ICF) for space propulsion has been the subject of several earlier conceptual design studies, (see: Orth, 1998; and other references therein). However, these studies were based on older ICF technology using either “direct “or “in‐direct x‐ray driven” type target irradiation. Important new directions have opened for laser ICF in recent years following the development of “chirped” lasers capable of ultra short pulses with powers of TW up to few PW which leads to the concept of “fast ignition (FI)” to achieve higher energy gains from target implosions. In a recent publication the authors showed that use of a modified type of FI, termed “block ignition” (Miley et al., 2008), could meet many of the requirements anticipated (but not then available) by the designs of the Vehicle for Interplanetary Space Transport Applications (VISTA) ICF fusion propulsion ship (Orth, 2008) for deep space missions. Subsequently the first author devised and presented concepts for imbedding high density condensed matter “clusters” of deuterium into the target to obtain ultra high local fusion reaction rates (Miley, 2008). Such rates are possible due to the high density of the clusters (over an order of magnitude above cryogenic deuterium). Once compressed by the implosion, the yet higher density gives an ultra high reaction rate over the cluster volume since the fusion rate is proportional to the square of the fuel density. Most recently, a new discovery discussed here indicates that the target matrix could be composed of B11 with proton clusters imbedded. This then makes p‐B11 fusion practical, assuming all of the physics issues such as stability of the clusters during compression are resolved. Indeed, p‐B11 power is ideal for fusion propulsion since it has a minimum of unwanted side products while giving most of the reaction energy to energetic alpha particles which can be directed into an exhaust (propulsion) nozzle. Power plants using p‐
We considered the case of a multi-stage hostel space allocation problem based on data set obtained from a tertiary institution. Genetic Algorithm was applied at different levels of the allocation. We studied the effec...
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Spectral embedding and spectral clustering are common methods for non-linear dimensionality reduction and clustering of complex high dimensional datasets. In this paper we provide a diffusion based probabilistic analy...
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ISBN:
(纸本)9783540737490
Spectral embedding and spectral clustering are common methods for non-linear dimensionality reduction and clustering of complex high dimensional datasets. In this paper we provide a diffusion based probabilistic analysis of algorithms that use the normalized graph Laplacian. Given the pairwise adjacency matrix of all points in a dataset, we define a random walk on the graph of points and a diffusion distance between any two points. We show that the diffusion distance is equal to the Euclidean distance in the embedded space with all eigenvectors of the normalized graph Laplacian. This identity shows that characteristic relaxation times and processes of the random walk on the graph are the key concept that governs the properties of these spectral clustering and spectral embedding algorithms. Specifically, for spectral clustering to succeed, a necessary condition is that the mean exit times from each cluster need to be significantly larger than the largest (slowest) of all relaxation times inside all of the individual clusters. For complex, multiscale data, this condition may not hold and multiscale methods need to be developed to handle such situations.
The stochastic generalized Ginzburg-Landau equation with additive noise can be solved pathwise and the unique solution generates a random system. Then we prove the random system possesses a global random attractor in ...
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The stochastic generalized Ginzburg-Landau equation with additive noise can be solved pathwise and the unique solution generates a random system. Then we prove the random system possesses a global random attractor in H 0 1 .
We present a study on the use of component technology for encapsulating platform-specific hardware-accelerated algorithms on hybrid HPC systems. Our research shows that component technology can have significant benefi...
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ISBN:
(纸本)9781605583112
We present a study on the use of component technology for encapsulating platform-specific hardware-accelerated algorithms on hybrid HPC systems. Our research shows that component technology can have significant benefits from a software engineering point-of-view to increase encapsulation, portability and reduce or eliminate platform dependence for hardware-accelerated algorithms. As a demonstration of this concept, we discuss our experience in designing, implementing and integrating an FPGA-accelerated kernel for Polygraph, an application in computational proteomics. Copyright 2008 ACM.
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