Quantum Monte Carlo methods and lithium cluster properties. [Atomic clusters
Energy Technology Data Exchange (ETDEWEB)
Owen, R.K.
1990-12-01
Properties of small lithium clusters with sizes ranging from n = 1 to 5 atoms were investigated using quantum Monte Carlo (QMC) methods. Cluster geometries were found from complete active space self consistent field (CASSCF) calculations. A detailed development of the QMC method leading to the variational QMC (V-QMC) and diffusion QMC (D-QMC) methods is shown. The many-body aspect of electron correlation is introduced into the QMC importance sampling electron-electron correlation functions by using density dependent parameters, and are shown to increase the amount of correlation energy obtained in V-QMC calculations. A detailed analysis of D-QMC time-step bias is made and is found to be at least linear with respect to the time-step. The D-QMC calculations determined the lithium cluster ionization potentials to be 0.1982(14) (0.1981), 0.1895(9) (0.1874(4)), 0.1530(34) (0.1599(73)), 0.1664(37) (0.1724(110)), 0.1613(43) (0.1675(110)) Hartrees for lithium clusters n = 1 through 5, respectively; in good agreement with experimental results shown in the brackets. Also, the binding energies per atom was computed to be 0.0177(8) (0.0203(12)), 0.0188(10) (0.0220(21)), 0.0247(8) (0.0310(12)), 0.0253(8) (0.0351(8)) Hartrees for lithium clusters n = 2 through 5, respectively. The lithium cluster one-electron density is shown to have charge concentrations corresponding to nonnuclear attractors. The overall shape of the electronic charge density also bears a remarkable similarity with the anisotropic harmonic oscillator model shape for the given number of valence electrons.
Quantum Monte Carlo methods and lithium cluster properties
Energy Technology Data Exchange (ETDEWEB)
Owen, Richard Kent [Univ. of California, Berkeley, CA (United States)
1990-12-01
Properties of small lithium clusters with sizes ranging from n = 1 to 5 atoms were investigated using quantum Monte Carlo (QMC) methods. Cluster geometries were found from complete active space self consistent field (CASSCF) calculations. A detailed development of the QMC method leading to the variational QMC (V-QMC) and diffusion QMC (D-QMC) methods is shown. The many-body aspect of electron correlation is introduced into the QMC importance sampling electron-electron correlation functions by using density dependent parameters, and are shown to increase the amount of correlation energy obtained in V-QMC calculations. A detailed analysis of D-QMC time-step bias is made and is found to be at least linear with respect to the time-step. The D-QMC calculations determined the lithium cluster ionization potentials to be 0.1982(14) [0.1981], 0.1895(9) [0.1874(4)], 0.1530(34) [0.1599(73)], 0.1664(37) [0.1724(110)], 0.1613(43) [0.1675(110)] Hartrees for lithium clusters n = 1 through 5, respectively; in good agreement with experimental results shown in the brackets. Also, the binding energies per atom was computed to be 0.0177(8) [0.0203(12)], 0.0188(10) [0.0220(21)], 0.0247(8) [0.0310(12)], 0.0253(8) [0.0351(8)] Hartrees for lithium clusters n = 2 through 5, respectively. The lithium cluster one-electron density is shown to have charge concentrations corresponding to nonnuclear attractors. The overall shape of the electronic charge density also bears a remarkable similarity with the anisotropic harmonic oscillator model shape for the given number of valence electrons.
Method for discovering relationships in data by dynamic quantum clustering
Weinstein, Marvin; Horn, David
2014-10-28
Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.
Anharmonic effects in the quantum cluster equilibrium method
von Domaros, Michael; Perlt, Eva
2017-03-01
The well-established quantum cluster equilibrium (QCE) model provides a statistical thermodynamic framework to apply high-level ab initio calculations of finite cluster structures to macroscopic liquid phases using the partition function. So far, the harmonic approximation has been applied throughout the calculations. In this article, we apply an important correction in the evaluation of the one-particle partition function and account for anharmonicity. Therefore, we implemented an analytical approximation to the Morse partition function and the derivatives of its logarithm with respect to temperature, which are required for the evaluation of thermodynamic quantities. This anharmonic QCE approach has been applied to liquid hydrogen chloride and cluster distributions, and the molar volume, the volumetric thermal expansion coefficient, and the isobaric heat capacity have been calculated. An improved description for all properties is observed if anharmonic effects are considered.
The DSUBm approximation scheme for the coupled cluster method and applications to quantum magnets
Directory of Open Access Journals (Sweden)
R.F. Bishop
2009-01-01
Full Text Available A new approximate scheme, DSUBm, is described for the coupled cluster method. We apply it to two well-studied (spin-1/2 Heisenberg antiferromagnet spin-lattice models, namely: the XXZ and the XY models on the square lattice in two dimensions. Results are obtained in each case for the ground-state energy, the sublattice magnetization and the quantum critical point. They are in good agreement with those from such alternative methods as spin-wave theory, series expansions, exact diagonalization techniques, quantum Monte Carlo methods and those from the CCM using the LSUBm scheme.
Equilibrium properties of quantum water clusters by the variational Gaussian wavepacket method.
Frantsuzov, Pavel A; Mandelshtam, Vladimir A
2008-03-07
The variational Gaussian wavepacket (VGW) method in combination with the replica-exchange Monte Carlo is applied to calculations of the heat capacities of quantum water clusters, (H(2)O)(8) and (H(2)O)(10). The VGW method is most conveniently formulated in Cartesian coordinates. These in turn require the use of a flexible (i.e., unconstrained) water potential. When the latter is fitted as a linear combination of Gaussians, all the terms involved in the numerical solution of the VGW equations of motion are analytic. When a flexible water model is used, a large difference in the timescales of the inter- and intramolecular degrees of freedom generally makes the system very difficult to simulate numerically. Yet, given this difficulty, we demonstrate that our methodology is still practical. We compare the computed heat capacities to those for the corresponding classical systems. As expected, the quantum effects shift the melting temperatures toward the lower values.
Quantum cluster algebras and quantum nilpotent algebras
Goodearl, Kenneth R.; Yakimov, Milen T.
2014-01-01
A major direction in the theory of cluster algebras is to construct (quantum) cluster algebra structures on the (quantized) coordinate rings of various families of varieties arising in Lie theory. We prove that all algebras in a very large axiomatically defined class of noncommutative algebras possess canonical quantum cluster algebra structures. Furthermore, they coincide with the corresponding upper quantum cluster algebras. We also establish analogs of these results for a large class of Poisson nilpotent algebras. Many important families of coordinate rings are subsumed in the class we are covering, which leads to a broad range of applications of the general results to the above-mentioned types of problems. As a consequence, we prove the Berenstein–Zelevinsky conjecture [Berenstein A, Zelevinsky A (2005) Adv Math 195:405–455] for the quantized coordinate rings of double Bruhat cells and construct quantum cluster algebra structures on all quantum unipotent groups, extending the theorem of Geiß et al. [Geiß C, et al. (2013) Selecta Math 19:337–397] for the case of symmetric Kac–Moody groups. Moreover, we prove that the upper cluster algebras of Berenstein et al. [Berenstein A, et al. (2005) Duke Math J 126:1–52] associated with double Bruhat cells coincide with the corresponding cluster algebras. PMID:24982197
Quantum cluster algebras and quantum nilpotent algebras.
Goodearl, Kenneth R; Yakimov, Milen T
2014-07-08
A major direction in the theory of cluster algebras is to construct (quantum) cluster algebra structures on the (quantized) coordinate rings of various families of varieties arising in Lie theory. We prove that all algebras in a very large axiomatically defined class of noncommutative algebras possess canonical quantum cluster algebra structures. Furthermore, they coincide with the corresponding upper quantum cluster algebras. We also establish analogs of these results for a large class of Poisson nilpotent algebras. Many important families of coordinate rings are subsumed in the class we are covering, which leads to a broad range of applications of the general results to the above-mentioned types of problems. As a consequence, we prove the Berenstein-Zelevinsky conjecture [Berenstein A, Zelevinsky A (2005) Adv Math 195:405-455] for the quantized coordinate rings of double Bruhat cells and construct quantum cluster algebra structures on all quantum unipotent groups, extending the theorem of Geiß et al. [Geiß C, et al. (2013) Selecta Math 19:337-397] for the case of symmetric Kac-Moody groups. Moreover, we prove that the upper cluster algebras of Berenstein et al. [Berenstein A, et al. (2005) Duke Math J 126:1-52] associated with double Bruhat cells coincide with the corresponding cluster algebras.
Directory of Open Access Journals (Sweden)
M. Kulmala
2007-05-01
Full Text Available We study the ammonia addition reactions of H2SO4·NH3 molecular clusters containing up to four ammonia and two sulfuric acid molecules using the ab initio method RI-MP2 (Resolution of Identity 2nd order MÃƒÂ¸ller-Plesset perturbation theory. Together with results from previous studies, we use the computed values to estimate an upper limit for the ammonia content of small atmospheric clusters, without having to explicitly include water molecules in the quantum chemical simulations. Our results indicate that the NH3:H2SO4 mole ratio of small molecular clusters in typical atmospheric conditions is probably around 1:2. High ammonia concentrations or low temperatures may lead to the formation of ammonium bisulfate (1:1 clusters, but our results rule out the formation of ammonium sulfate clusters (2:1 anywhere in the atmosphere. A sensitivity analysis indicates that the qualitative conclusions of this study are not affected even by relatively large errors in the calculation of electronic energies or vibrational frequencies.
Cluster State Quantum Computation
2014-02-01
important result is called the threshold theorem of quantum computation [Aliferis06]. Fault-tolerant schemes for OWQC using photons have recently...defined in terms of the standard Fubini -Study distance Approved for Public Release; Distribution Unlimited. 25 ( ) ( ) 1
Quantum annealing for combinatorial clustering
Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph
2018-02-01
Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.
Cluster State Quantum Computing
2012-12-01
discuss the potential advantages of such a system and the difficulties of the design. When an incident photon strikes a Niobium nitride ( NbN ...counted. Present superconducting nanowire systems, such as NbN , have reasonably good counting efficiency [Dauler10], [Marsili11], by which we mean...L. O’Brein, A. Furusawa, J. Vuchovic, “Photonic quantum technologies ,” Nat. Photonics 3 Dec. (2009) doi:10.1038/nphoton.2009.229. [Pawlowski09
SAYANTAN GUPTA
2017-01-01
This paper on the Theory of Quantum Computation provides an outline of modern day Quantum computation with its drawbacks and limitations and provides various solutions using existing or newer Models and Approaches. This paper will enlighten us to get a broader aspect of the recent day technology of computer evolution and why this QC still remains a mere concept.
Quantum cluster algebra structures on quantum nilpotent algebras
Goodearl, K R
2017-01-01
All algebras in a very large, axiomatically defined class of quantum nilpotent algebras are proved to possess quantum cluster algebra structures under mild conditions. Furthermore, it is shown that these quantum cluster algebras always equal the corresponding upper quantum cluster algebras. Previous approaches to these problems for the construction of (quantum) cluster algebra structures on (quantized) coordinate rings arising in Lie theory were done on a case by case basis relying on the combinatorics of each concrete family. The results of the paper have a broad range of applications to these problems, including the construction of quantum cluster algebra structures on quantum unipotent groups and quantum double Bruhat cells (the Berenstein-Zelevinsky conjecture), and treat these problems from a unified perspective. All such applications also establish equality between the constructed quantum cluster algebras and their upper counterparts.
Yakobi, Hana; Eliav, Ephraim; Kaldor, Uzi
2011-02-07
Quantum dots with three-dimensional isotropic harmonic confining potentials and up to 60 electrons are studied. The Dirac-Coulomb Hamiltonian serves as a framework, so that relativistic effects are included, and electron correlation is treated at a high level by the Fock-space coupled cluster method, with single and double excitations summed to all orders. Large basis sets composed of spherical Gaussian functions are used. Energies of ground and excited states are calculated. The orbital order is 1s, 2p, 3d, 3s, 4f, 4p, 5g, ... , and closed-shell structures appear for 2, 8, 18, 20, 34, 40, and 58 electrons. Relativistic effects are negligible for low strengths of the harmonic potential and increase rapidly for stronger potentials. Breit contributions, coming from the lowest order relativistic correction to the interelectronic repulsion terms, are also studied. Correlation effects are significant for these systems, in particular for weak confining potentials and for small systems, where they constitute up to 6% of the total energies. Their relative weight goes down (although they increase in absolute value) for larger systems or confining potentials. Planned applications to quantum dots with impurities are discussed briefly.
Binary systems from quantum cluster equilibrium theory.
Brüssel, Marc; Perlt, Eva; Lehmann, Sebastian B C; von Domaros, Michael; Kirchner, Barbara
2011-11-21
An extension of the quantum cluster equilibrium theory to treat binary mixtures is introduced in this work. The necessary equations are derived and a possible implementation is presented. In addition an alternative sampling procedure using widely available experimental data for the quantum cluster equilibrium approach is suggested and tested. An illustrative example, namely, the binary mixture of water and dimethyl sulfoxide, is given to demonstrate the new approach. A basic cluster set is introduced containing the relevant cluster motifs. The populations computed by the quantum cluster equilibrium approach are compared to the experimental data. Furthermore, the excess Gibbs free energy is computed and compared to experiments as well.
Double-partition Quantum Cluster Algebras
DEFF Research Database (Denmark)
Jakobsen, Hans Plesner; Zhang, Hechun
2012-01-01
A family of quantum cluster algebras is introduced and studied. In general, these algebras are new, but sub-classes have been studied previously by other authors. The algebras are indexed by double parti- tions or double flag varieties. Equivalently, they are indexed by broken lines L. By grouping...... together neighboring mutations into quantum line mutations we can mutate from the cluster algebra of one broken line to another. Compatible pairs can be written down. The algebras are equal to their upper cluster algebras. The variables of the quantum seeds are given by elements of the dual canonical basis....
Quantum phenomena in magnetic nano clusters
Indian Academy of Sciences (India)
Unknown
Quantum phenomena in magnetic nano clusters. 461. Figure 3. Schematic exchange interactions in a V15 cluster. There is no direct exchange interaction amongst the triangle spins. Interactions not shown explicitly can be generated from the C3 symmetry of the system. simplify the calculations, the strongly coupled ...
Matisz, Gergely; Kelterer, Anne-Marie; Fabian, Walter M F; Kunsági-Máté, Sándor
2011-04-14
The Quantum Cluster Equilibrium (QCE) model was applied to the liquid phase for the first few members of the homologous series of unbranched aliphatic primary alcohols, methanol, ethanol, propan-1-ol, and butan-1-ol. Cluster structures and energies were calculated by density functional theory [B3LYP/6-311++G(2d,2p)]. For butan-1-ol the dispersion interaction was also considered with the B3LYP-D method. In agreement with previous findings, cyclic cluster structures are the most probable ones. In addition, weak C-H...O interactions as well as dispersion interactions between the longer alkyl chains were found to be important in the cluster formation. The reliability of the model was assessed by the calculated constant pressure heat capacity (C(p)) values. Larger deviations between theory and experiment were found for higher homologes (propan-1-ol, butan-1-ol) with the B3LYP method. When the B3LYP-D method was applied for butan-1-ol, adequate agreement was found between experimental and calculated C(p) values.
Classical and quantum physics of hydrogen clusters.
Mezzacapo, Fabio; Boninsegni, Massimo
2009-04-22
We present results of a comprehensive theoretical investigation of the low temperature (T) properties of clusters of para-hydrogen (p-H(2)), both pristine as well as doped with isotopic impurities (i.e., ortho-deuterium, o-D(2)). We study clusters comprising up to N = 40 molecules, by means of quantum simulations based on the continuous-space Worm algorithm. Pristine p-H(2) clusters are liquid-like and superfluid in the [Formula: see text] limit. The superfluid signal is uniform throughout these clusters; it is underlain by long cycles of permutation of molecules. Clusters with more than 22 molecules display solid-like, essentially classical behavior at temperatures down to T∼1 K; some of them are seen to turn liquid-like at sufficiently low T (quantum melting).
Spickermann, Christian; Perlt, Eva; von Domaros, Michael; Roatsch, Martin; Friedrich, Joachim; Kirchner, Barbara
2011-04-12
Treating the bulk phase with high-level ab initio methods, such as coupled cluster, is a nontrivial task because of the computational costs of these electronic structure methods. In this part of our hydrogen fluoride study we make use of the quantum cluster equilibrium method, which employs electronic structure input of small clusters and combines it with simple statistical mechanics in order to describe condensed phase phenomena. If no parameter adjustment is applied, then the lower quantum chemical methods, such as density functional theory in conjunction with the generalized gradient approximation, provide wrong results in accordance with the description of the strength of the interaction in the clusters. While density functional theory describes the liquid phase too dense due to overbinding of the clusters, the coupled cluster method and the perturbation theory at the complete basis set limit agree well with experimental observations. If we allow the two parameters in the quantum cluster equilibrium method to vary, then these are able to compensate the overbinding, thereby leading to very good agreement with experiment. Correlated methods in combination with small basis sets giving rise to too weakly bound clusters cannot reach this accuracy even if the parameters are flexible. Only at the complete basis set limit, the performance of the correlated methods is again excellent.
Bishop, R. F.; Li, P. H. Y.; Campbell, C. E.
2014-10-01
We outline how the coupled cluster method of microscopic quantum many-body theory can be utilized in practice to give highly accurate results for the ground-state properties of a wide variety of highly frustrated and strongly correlated spin-lattice models of interest in quantum magnetism, including their quantum phase transitions. The method itself is described, and it is shown how it may be implemented in practice to high orders in a systematically improvable hierarchy of (so-called LSUBm) approximations, by the use of computer-algebraic techniques. The method works from the outset in the thermodynamic limit of an infinite lattice at all levels of approximation, and it is shown both how the "raw" LSUBm results are themselves generally excellent in the sense that they converge rapidly, and how they may accurately be extrapolated to the exact limit, m → ∞, of the truncation index m, which denotes the only approximation made. All of this is illustrated via a specific application to a two-dimensional, frustrated, spin-half J1XXZ-J2XXZ model on a honeycomb lattice with nearest-neighbor and next-nearest-neighbor interactions with exchange couplings J1 > 0 and J2 ≡ κJ1 > 0, respectively, where both interactions are of the same anisotropic XXZ type. We show how the method can be used to determine the entire zero-temperature ground-state phase diagram of the model in the range 0 ≤ κ ≤ 1 of the frustration parameter and 0 ≤ Δ ≤ 1 of the spin-space anisotropy parameter. In particular, we identify a candidate quantum spin-liquid region in the phase space.
The polarizable embedding coupled cluster method
DEFF Research Database (Denmark)
Sneskov, Kristian; Schwabe, Tobias; Kongsted, Jacob
2011-01-01
We formulate a new combined quantum mechanics/molecular mechanics (QM/MM) method based on a self-consistent polarizable embedding (PE) scheme. For the description of the QM region, we apply the popular coupled cluster (CC) method detailing the inclusion of electrostatic and polarization effects...
The quantum structure of anionic hydrogen clusters
Calvo, F.; Yurtsever, E.
2018-03-01
A flexible and polarizable interatomic potential has been developed to model hydrogen clusters interacting with one hydrogen anion, (H2)nH-, in a broad range of sizes n = 1-54 and parametrized against coupled cluster quantum chemical calculations. Using path-integral molecular dynamics simulations at 1 K initiated from the putative classical global minima, the equilibrium structures are found to generally rely on icosahedral shells with the hydrogen molecules pointing toward the anion, producing geometric magic numbers at sizes n = 12, 32, and 44 that are in agreement with recent mass spectrometry measurements. The energetic stability of the clusters is also connected with the extent of vibrational delocalization, measured here by the fluctuations among inherent structures hidden in the vibrational wave function. As the clusters grow, the outer molecules become increasingly free to rotate, and strong finite size effects are also found between magic numbers, associated with more prominent vibrational delocalization. The effective icosahedral structure of the 44-molecule cluster is found to originate from quantum nuclear effects as well, the classical structure showing no particular symmetry.
Kryuchkova, Natalya A.; Syrokvashin, Mikhail M.; Gushchin, Artem L.; Korotaev, Evgeniy V.; Kalinkin, Alexander V.; Laricheva, Yuliya A.; Sokolov, Maxim N.
2018-02-01
Charge state studies of compounds [Mo3S4(tu)8(H2O)]Cl4·4H2O (1), [Mo3S4Cl3(dbbpy)3]Cl·5H2O (2), [Mo3S4(CuCl)Cl3(dbbpy)3][CuCl2] (3), containing {Mo3S4}4+ and {Mo3CuS4}5+ cluster cores bearing terminal thiourea (tu) or 4,4‧-di-tert-butyl-2,2‧-bipyridine (dbbpy) ligands, have been performed by X-ray photoelectron and X-ray emission spectroscopies combined with quantum chemical calculations. The best agreement between theory and experiments has been obtained using the B3LYP method. According to the experimental and calculated data, the Mo atoms are in the oxidation state 4+ for all compounds. The energies and shapes of the Cu2p lines indicate formal oxidation states of Cu as 1+. The coordination of Cu(I) to the cluster {Mo3S4} in 3 does not lead to significant changes in the charge state of the molybdenum atoms and the {Mo3S4} unit can be considered as a tridentate metallothia crown ether.
Kryuchkova, Natalya A; Syrokvashin, Mikhail M; Gushchin, Artem L; Korotaev, Evgeniy V; Kalinkin, Alexander V; Laricheva, Yuliya A; Sokolov, Maxim N
2018-02-05
Charge state studies of compounds [Mo3S4(tu)8(H2O)]Cl4·4H2O (1), [Mo3S4Cl3(dbbpy)3]Cl·5H2O (2), [Mo3S4(CuCl)Cl3(dbbpy)3][CuCl2] (3), containing {Mo3S4}4+ and {Mo3CuS4}5+ cluster cores bearing terminal thiourea (tu) or 4,4'-di-tert-butyl-2,2'-bipyridine (dbbpy) ligands, have been performed by X-ray photoelectron and X-ray emission spectroscopies combined with quantum chemical calculations. The best agreement between theory and experiments has been obtained using the B3LYP method. According to the experimental and calculated data, the Mo atoms are in the oxidation state 4+ for all compounds. The energies and shapes of the Cu2p lines indicate formal oxidation states of Cu as 1+. The coordination of Сu(I) to the cluster {Mo3S4} in 3 does not lead to significant changes in the charge state of the molybdenum atoms and the {Mo3S4} unit can be considered as a tridentate metallothia crown ether. Copyright © 2017 Elsevier B.V. All rights reserved.
Quantum mechanical cluster calculations of critical scintillationprocesses
Energy Technology Data Exchange (ETDEWEB)
Derenzo, Stephen E.; Klintenberg, Mattias K.; Weber, Marvin J.
2000-02-22
This paper describes the use of commercial quantum chemistrycodes to simu-late several critical scintillation processes. The crystalis modeled as a cluster of typically 50 atoms embedded in an array oftypically 5,000 point charges designed to reproduce the electrostaticfield of the infinite crystal. The Schrodinger equation is solved for theground, ionized, and excited states of the system to determine the energyand electron wavefunction. Computational methods for the followingcritical processes are described: (1) the formation and diffusion ofrelaxed holes, (2) the formation of excitons, (3) the trapping ofelectrons and holes by activator atoms, (4) the excitation of activatoratoms, and (5) thermal quenching. Examples include hole diffusion in CsI,the exciton in CsI, the excited state of CsI:Tl, the energy barrier forthe diffusion of relaxed holes in CaF2 and PbF2, and prompt hole trappingby activator atoms in CaF2:Eu and CdS:Te leading to an ultra-fast (<50ps) scintillation risetime.
Directory of Open Access Journals (Sweden)
Dong Yumin
2014-01-01
Full Text Available A quantum optimization scheme in network cluster server task scheduling is proposed. We explore and research the distribution theory of energy field in quantum mechanics; specially, we apply it to data clustering. We compare the quantum optimization method with genetic algorithm (GA, ant colony optimization (ACO, simulated annealing algorithm (SAA. At the same time, we prove its validity and rationality by analog simulation and experiment.
Plasmon enhanced silver quantum cluster fluorescence for biochemical applications
DEFF Research Database (Denmark)
Bernard, S.; Kutter, J.P.; Mogensen, Klaus Bo
2014-01-01
quantum clusters are subsequently synthesized at the surface of the nanoparticles by photoactivation in presence of Ag+ cations in solution. The photogeneration of these silver quantum clusters leads to a great increase in the fluorescent signal. This photoactivated surface can then be used for sensing...... purposes. It was found, that in presence of a strong nucleophile (such as CN-), silver quantum clusters are dissolved into non-fluorescing AgCN complexes, resulting in a fast and observable decrease of the fluorescent signal....
Plasmon enhanced silver quantum cluster fluorescence for biochemical applications
DEFF Research Database (Denmark)
Bernard, S.; Kutter, Jörg P.; Mogensen, K. B.
2014-01-01
quantum clusters are subsequently synthesized at the surface of the nanoparticles by photoactivation in presence of Ag+ cations in solution. The photogeneration of these silver quantum clusters leads to a great increase in the fluorescent signal. This photoactivated surface can then be used for sensing...
Sanfilippo, Antonio [Richland, WA; Calapristi, Augustin J [West Richland, WA; Crow, Vernon L [Richland, WA; Hetzler, Elizabeth G [Kennewick, WA; Turner, Alan E [Kennewick, WA
2009-12-22
Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.
Greedy bases in rank 2 quantum cluster algebras.
Lee, Kyungyong; Li, Li; Rupel, Dylan; Zelevinsky, Andrei
2014-07-08
We identify a quantum lift of the greedy basis for rank 2 coefficient-free cluster algebras. Our main result is that our construction does not depend on the choice of initial cluster, that it builds all cluster monomials, and that it produces bar-invariant elements. We also present several conjectures related to this quantum greedy basis and the triangular basis of Berenstein and Zelevinsky.
Weaving quantum optical frequency combs into continuous-variable hypercubic cluster states
Wang, Pei; Chen, Moran; Menicucci, Nicolas C.; Pfister, Olivier
2014-09-01
Cluster states with higher-dimensional lattices that cannot be physically embedded in three-dimensional space have important theoretical interest in quantum computation and quantum simulation of topologically ordered condensed-matter systems. We present a simple, scalable, top-down method of entangling the quantum optical frequency comb into hypercubic-lattice continuous-variable cluster states of a size of about 104 quantum field modes, using existing technology. A hypercubic lattice of dimension D (linear, square, cubic, hypercubic, etc.) requires but D optical parametric oscillators with bichromatic pumps whose frequency splittings alone determine the lattice dimensionality and the number of copies of the state.
Semi-supervised clustering methods
Bair, Eric
2013-01-01
Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830
Stochastic methods in quantum mechanics
Gudder, Stanley P
2005-01-01
Practical developments in such fields as optical coherence, communication engineering, and laser technology have developed from the applications of stochastic methods. This introductory survey offers a broad view of some of the most useful stochastic methods and techniques in quantum physics, functional analysis, probability theory, communications, and electrical engineering. Starting with a history of quantum mechanics, it examines both the quantum logic approach and the operational approach, with explorations of random fields and quantum field theory.The text assumes a basic knowledge of fun
Blind Quantum Signature with Controlled Four-Particle Cluster States
Li, Wei; Shi, Jinjing; Shi, Ronghua; Guo, Ying
2017-08-01
A novel blind quantum signature scheme based on cluster states is introduced. Cluster states are a type of multi-qubit entangled states and it is more immune to decoherence than other entangled states. The controlled four-particle cluster states are created by acting controlled-Z gate on particles of four-particle cluster states. The presented scheme utilizes the above entangled states and simplifies the measurement basis to generate and verify the signature. Security analysis demonstrates that the scheme is unconditional secure. It can be employed to E-commerce systems in quantum scenario.
Splitting Methods for Convex Clustering.
Chi, Eric C; Lange, Kenneth
Clustering is a fundamental problem in many scientific applications. Standard methods such as k-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal. Recently introduced convex relaxations of k-means and hierarchical clustering shrink cluster centroids toward one another and ensure a unique global minimizer. In this work we present two splitting methods for solving the convex clustering problem. The first is an instance of the alternating direction method of multipliers (ADMM); the second is an instance of the alternating minimization algorithm (AMA). In contrast to previously considered algorithms, our ADMM and AMA formulations provide simple and unified frameworks for solving the convex clustering problem under the previously studied norms and open the door to potentially novel norms. We demonstrate the performance of our algorithm on both simulated and real data examples. While the differences between the two algorithms appear to be minor on the surface, complexity analysis and numerical experiments show AMA to be significantly more efficient. This article has supplemental materials available online.
Theoretically extensible quantum digital signature with starlike cluster states
Yang, Yu-Guang; Liu, Zhi-Chao; Li, Jian; Chen, Xiu-Bo; Zuo, Hui-Juan; Zhou, Yi-Hua; Shi, Wei-Min
2017-01-01
Chen et al. (Phys Rev A 73:012303, 2006) constructed this "starlike cluster" state, which involves one qubit located at the center and n neighboring two-qubit arms. This genuine entangled state has been used for the construction of 2D and 3D cluster states, topological one-way computation, and dynamical quantum secret sharing. In this paper, we investigate the usefulness of this starlike cluster state and propose a theoretically extensible quantum digital signature scheme. The proposed scheme can be theoretically generalized to more than three participants. Moreover, it retains the merits of no requirements such as authenticated quantum channels and long-term quantum memory. We also give a security proof for the proposed scheme against repudiation and forgery.
Clustering in Hilbert space of a quantum optimization problem
Morampudi, S. C.; Hsu, B.; Sondhi, S. L.; Moessner, R.; Laumann, C. R.
2017-10-01
The solution space of many classical optimization problems breaks up into clusters which are extensively distant from one another in the Hamming metric. Here, we show that an analogous quantum clustering phenomenon takes place in the ground-state subspace of a certain quantum optimization problem. This involves extending the notion of clustering to Hilbert space, where the classical Hamming distance is not immediately useful. Quantum clusters correspond to macroscopically distinct subspaces of the full quantum ground-state space which grow with the system size. We explicitly demonstrate that such clusters arise in the solution space of random quantum satisfiability (3-QSAT) at its satisfiability transition. We estimate both the number of these clusters and their internal entropy. The former are given by the number of hard-core dimer coverings of the core of the interaction graph, while the latter is related to the underconstrained degrees of freedom not touched by the dimers. We additionally provide numerical evidence suggesting that the 3-QSAT satisfiability transition may coincide with the product satisfiability transition, which would imply the absence of an intermediate entangled satisfiable phase.
Transport through quantum dots: a combined DMRG and embedded-cluster approximation study
Heidrich-Meisner, F.; Martins, G. B.; Büsser, C. A.; Al-Hassanieh, K. A.; Feiguin, A. E.; Chiappe, G.; Anda, E. V.; Dagotto, E.
2009-02-01
The numerical analysis of strongly interacting nanostructures requires powerful techniques. Recently developed methods, such as the time-dependent density matrix renormalization group (tDMRG) approach or the embedded-cluster approximation (ECA), rely on the numerical solution of clusters of finite size. For the interpretation of numerical results, it is therefore crucial to understand finite-size effects in detail. In this work, we present a careful finite-size analysis for the examples of one quantum dot, as well as three serially connected quantum dots. Depending on “odd-even” effects, physically quite different results may emerge from clusters that do not differ much in their size. We provide a solution to a recent controversy over results obtained with ECA for three quantum dots. In particular, using the optimum clusters discussed in this paper, the parameter range in which ECA can reliably be applied is increased, as we show for the case of three quantum dots. As a practical procedure, we propose that a comparison of results for static quantities against those of quasi-exact methods, such as the ground-state density matrix renormalization group (DMRG) method or exact diagonalization, serves to identify the optimum cluster type. In the examples studied here, we find that to observe signatures of the Kondo effect in finite systems, the best clusters involving dots and leads must have a total z-component of the spin equal to zero.
Miura, Shinichi
2007-03-21
In this paper, we present a path integral hybrid Monte Carlo (PIHMC) method for rotating molecules in quantum fluids. This is an extension of our PIHMC for correlated Bose fluids [S. Miura and J. Tanaka, J. Chem. Phys. 120, 2160 (2004)] to handle the molecular rotation quantum mechanically. A novel technique referred to be an effective potential of quantum rotation is introduced to incorporate the rotational degree of freedom in the path integral molecular dynamics or hybrid Monte Carlo algorithm. For a permutation move to satisfy Bose statistics, we devise a multilevel Metropolis method combined with a configurational-bias technique for efficiently sampling the permutation and the associated atomic coordinates. Then, we have applied the PIHMC to a helium-4 cluster doped with a carbonyl sulfide molecule. The effects of the quantum rotation on the solvation structure and energetics were examined. Translational and rotational fluctuations of the dopant in the superfluid cluster were also analyzed.
Numerical linked-cluster approach to quantum lattice models.
Rigol, Marcos; Bryant, Tyler; Singh, Rajiv R P
2006-11-03
We present a novel algorithm that allows one to obtain temperature dependent properties of quantum lattice models in the thermodynamic limit from exact diagonalization of small clusters. Our numerical linked-cluster approach provides a systematic framework to assess finite-size effects and is valid for any quantum lattice model. Unlike high temperature expansions, which have a finite radius of convergence in inverse temperature, these calculations are accurate at all temperatures provided the range of correlations is finite. We illustrate the power of our approach studying spin models on kagomé, triangular, and square lattices.
Quantum chemical calculation of the equilibrium structures of small metal atom clusters
Kahn, L. R.
1982-01-01
Metal atom clusters are studied based on the application of ab initio quantum mechanical approaches. Because these large 'molecular' systems pose special practical computational problems in the application of the quantum mechanical methods, there is a special need to find simplifying techniques that do not compromise the reliability of the calculations. Research is therefore directed towards various aspects of the implementation of the effective core potential technique for the removal of the metal atom core electrons from the calculations.
Control of entanglement transitions in quantum spin clusters
Irons, Hannah R.; Quintanilla, Jorge; Perring, Toby G.; Amico, Luigi; Aeppli, Gabriel
2017-12-01
Quantum spin clusters provide a platform for the experimental study of many-body entanglement. Here we address a simple model of a single-molecule nanomagnet featuring N interacting spins in a transverse field. The field can control an entanglement transition (ET). We calculate the magnetization, low-energy gap, and neutron-scattering cross section and find that the ET has distinct signatures, detectable at temperatures as high as 5% of the interaction strength. The signatures are stronger for smaller clusters.
Operator methods in quantum mechanics
Schechter, Martin
2003-01-01
This advanced undergraduate and graduate-level text introduces the power of operator theory as a tool in the study of quantum mechanics, assuming only a working knowledge of advanced calculus and no background in physics. The author presents a few simple postulates describing quantum theory, gradually introducing the mathematical techniques that help answer questions important to the physical theory; in this way, readers see clearly the purpose of the method and understand the accomplishment. The entire book is devoted to the study of a single particle moving along a straight line. By posing q
Quantum Monte Carlo methods algorithms for lattice models
Gubernatis, James; Werner, Philipp
2016-01-01
Featuring detailed explanations of the major algorithms used in quantum Monte Carlo simulations, this is the first textbook of its kind to provide a pedagogical overview of the field and its applications. The book provides a comprehensive introduction to the Monte Carlo method, its use, and its foundations, and examines algorithms for the simulation of quantum many-body lattice problems at finite and zero temperature. These algorithms include continuous-time loop and cluster algorithms for quantum spins, determinant methods for simulating fermions, power methods for computing ground and excited states, and the variational Monte Carlo method. Also discussed are continuous-time algorithms for quantum impurity models and their use within dynamical mean-field theory, along with algorithms for analytically continuing imaginary-time quantum Monte Carlo data. The parallelization of Monte Carlo simulations is also addressed. This is an essential resource for graduate students, teachers, and researchers interested in ...
Colloquium: Quantum interference of clusters and molecules
Hornberger, Klaus; Gerlich, Stefan; Haslinger, Philipp; Nimmrichter, Stefan; Arndt, Markus
2011-01-01
We review recent progress and future prospects of matter wave interferometry with complex organic molecules and inorganic clusters. Three variants of a near-field interference effect, based on diffraction by material nanostructures, at optical phase gratings, and at ionizing laser fields are considered. We discuss the theoretical concepts underlying these experiments and the experimental challenges. This includes optimizing interferometer designs as well as understanding the role of decoheren...
Quantum broadcast scheme and multi-output quantum teleportation via four-qubit cluster state
Yu, Yan; Zha, Xin Wei; Li, Wei
2017-02-01
In this paper, two theoretical schemes of the arbitrary single-qubit states via four-qubit cluster state are proposed. One is three-party quantum broadcast scheme, which realizes the broadcast among three participants. The other is multi-output quantum teleportation. Both allow two distant receivers to simultaneously and deterministically obtain the arbitrary single-qubit states, respectively. Compared with former schemes of an arbitrary single-qubit state, the proposed schemes realize quantum multi-cast communication efficiently, which enables Bob and Charlie to obtain the states simultaneously in the case of just knowing Alice's measurement results. The proposed schemes play an important role in quantum information, specially in secret sharing and quantum teleportation.
Simple scheme for expanding photonic cluster states for quantum information
Energy Technology Data Exchange (ETDEWEB)
Kalasuwan, P.; Laing, A.; Coggins, J.; Callaway, M.; O' Brien, J. L. [Centre for Quantum Photonics, H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB (United Kingdom); Mendoza, G. [Centre for Quantum Photonics, H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB (United Kingdom); California Institute of Technology, Pasadena, California 91125 (United States); Nagata, T.; Takeuchi, S. [Research Institute for Electronic Science, Hokkaido University, Sapporo 060-0812 (Japan); Institute of Scientific and Industrial Research, Osaka University, Mihogaoka 8-1, Ibaraki, Osaka 567-0047 (Japan); Stefanov, A. [Federal Office of Metrology METAS, Laboratory Time and Frequency, Lindenweg 50, 3084 Wabern (Switzerland)
2010-06-15
We show how an entangled cluster state encoded in the polarization of single photons can be straightforwardly expanded by deterministically entangling additional qubits encoded in the path degree of freedom of the constituent photons. This can be achieved using a polarization-path controlled-phase gate. We experimentally demonstrate a practical and stable realization of this approach by using a Sagnac interferometer to entangle a path qubit and polarization qubit on a single photon. We demonstrate precise control over phase of the path qubit to change the measurement basis and experimentally demonstrate properties of measurement-based quantum computing using a two-photon, three-qubit cluster state.
New Eavesdropper Detection Method in Quantum Cryptograph
Directory of Open Access Journals (Sweden)
Cătălin Anghel
2011-12-01
Full Text Available ecurity of quantum cryptographic algorithms is one of the main research directions in quantum cryptography. Security growth of the quantum key distribution systems can be realized by detecting the eavesdropper quickly, precisely and without letting any secret information in the hands of the enemy. This paper proposes a new method, named QBTT, to detect the enemy who try to tap the communication channel. The QBTT method can be implemented in every type of quantum key distribution scheme.
Energy Technology Data Exchange (ETDEWEB)
Blaise, Philippe [Universite Joseph Fourier, Grenoble 1, 74 Annecy (France)
1998-09-29
The aim of this thesis is to study metallic sodium clusters by numerical simulation. We have developed two ab initio molecular dynamics programs within the formalism of density functional theory. The first is based on the semi-classical extended Thomas-Fermi approach. We use a real-space grid and a Car-Parrinello-like scheme. The computational cost is O(N), and we have built a pseudopotential that speeds up the calculations. By neglecting quantum shell effects, we are able to study a very large set of clusters. We show that sodium cluster energies fit well a liquid drop formula, by adjusting a few parameters. We have investigated breathing modes, surface oscillations and the net charge density. We have shown that the surface energy varies strongly with temperature, and that clusters have a lower melting point than bulk material. We have calculated fission barriers by a constraint method. The second program is based on the quantum Kohn-Sham approach. We use a real-space grid, and combine a generalized Broyden scheme for assuring self-consistency with an iterative Davidson-Lanczos algorithm for solving the Eigen-problem. The cost of the method is much higher. First of all, we have calculated some stable structures for small clusters and their energetics. We obtained very good agreement with previous works. Then, we have investigated highly charged cluster dynamics. We have identified a chaotic fission process. For high fissility systems, we observe a multi-fragmentation dynamics and we find preferential emission of monomers on a characteristic time scale less than a pico-second. This has been simulated for the first time, with the help of our adaptive grid method which follows each fragment as they move apart during the fragmentation. (author) 87 refs., 57 figs., 4 tabs.
Riwinoto
2012-01-01
Sekarang ini, metode clustering telah diimplementasikan dalam riset DNA. Data dari DNA didapat melalui teknik microarray. Dengan menggunakan metode teknik SVD, dimensi data dikurangi sehingga mempermudah proses komputasi. Dalam paper ini, ditampilkan hasil clustering tanpa pengarahan terhadap gen-gen dari data bakteri ragi dengan menggunakan metode quantum clustering. Sebagai pembanding, dilakukan juga clustering menggunakan metoda Support Vector Clustering. Selain itu juga ditampilkan data h...
Visual cluster analysis and pattern recognition methods
Osbourn, Gordon Cecil; Martinez, Rubel Francisco
2001-01-01
A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.
Das, Bodhisatwa; Fernandez, Francis; John, Annie; Sharma, Chandra P
2012-01-01
Quantum clusters are sub-nano sized materials mostly synthesized from noble metals with luminescence property and high quantum yield. They are important to biomedical scientists because of their excellent optical properties. Here we represent a tool for cell imaging purpose using protein stabilized gold quantum clusters. Intestinal protease Trypsin was used to develop clusters. They were conjugated to cyclic RGD peptides by EDAC coupling. Cell imaging property was checked by transfecting the RGD-conjugated quantum clusters to bone marrow stem cells. For characterization of RGD-conjugated quantum clusters UV-Vis, Fluorescence and FTIR spectroscopy was performed. DLS and Zeta potential measurement also have been done. To check the bio compatibility of the quantum clusters MTT assay, AFM and blood cell adhesion study were performed. The samples are found out to be good for cell imaging as well as bio compatible and hemo-compatible.
A one-parameter quantum cluster equilibrium approach.
Brüssel, Marc; Perlt, Eva; von Domaros, Michael; Brehm, Martin; Kirchner, Barbara
2012-10-28
The established quantum cluster equilibrium approach is further developed in this work. The equations are reformulated to result in a one-parameter expression, i.e., with one of two empirical parameters eliminated. Instead of a parametrized constant mean field interaction we present two further approaches using temperature dependent mean field functions. The suggested functions are assessed by means of two test systems, namely hydrogen fluoride and water which are investigated concerning their liquid phase properties as well as the phenomenon of evaporation. The obtained thermodynamic data are compared with each other for the different mean field functions including the conventional approach as well as to experimental data.
Quantum cluster equilibrium model of N-methylformamide-water binary mixtures.
von Domaros, Michael; Jähnigen, Sascha; Friedrich, Joachim; Kirchner, Barbara
2016-02-14
The established quantum cluster equilibrium (QCE) approach is refined and applied to N-methylformamide (NMF) and its aqueous solution. The QCE method is split into two iterative cycles: one which converges to the liquid phase solution of the QCE equations and another which yields the gas phase. By comparing Gibbs energies, the thermodynamically stable phase at a given temperature and pressure is then chosen. The new methodology avoids metastable solutions and allows a different treatment of the mean-field interactions within the gas and liquid phases. These changes are of crucial importance for the treatment of binary mixtures. For the first time in a QCE study, the cis-trans-isomerism of a species (NMF) is explicitly considered. Cluster geometries and frequencies are calculated using density functional theory (DFT) and complementary coupled cluster single point energies are used to benchmark the DFT results. Independent of the selected quantum-chemical method, a large set of clusters is required for an accurate thermodynamic description of the binary mixture. The liquid phase of neat NMF is found to be dominated by the cyclic trans-NMF pentamer, which can be interpreted as a linear trimer that is stabilized by explicit solvation of two further NMF molecules. This cluster reflects the known hydrogen bond network preferences of neat NMF.
Quantum cluster equilibrium model of N-methylformamide–water binary mixtures
Energy Technology Data Exchange (ETDEWEB)
Domaros, Michael von; Kirchner, Barbara, E-mail: kirchner@thch.uni-bonn.de [Mulliken Center for Theoretical Chemistry, Universität Bonn, Beringstr. 4, D-53115 Bonn (Germany); Jähnigen, Sascha [Martin-Luther-Universität Halle-Wittenberg, von-Danckelmann-Platz 4, D-06120 Halle (Germany); Friedrich, Joachim [Technische Universität Chemnitz, Straße der Nationen 62, D-09111 Chemnitz (Germany)
2016-02-14
The established quantum cluster equilibrium (QCE) approach is refined and applied to N-methylformamide (NMF) and its aqueous solution. The QCE method is split into two iterative cycles: one which converges to the liquid phase solution of the QCE equations and another which yields the gas phase. By comparing Gibbs energies, the thermodynamically stable phase at a given temperature and pressure is then chosen. The new methodology avoids metastable solutions and allows a different treatment of the mean-field interactions within the gas and liquid phases. These changes are of crucial importance for the treatment of binary mixtures. For the first time in a QCE study, the cis-trans-isomerism of a species (NMF) is explicitly considered. Cluster geometries and frequencies are calculated using density functional theory (DFT) and complementary coupled cluster single point energies are used to benchmark the DFT results. Independent of the selected quantum-chemical method, a large set of clusters is required for an accurate thermodynamic description of the binary mixture. The liquid phase of neat NMF is found to be dominated by the cyclic trans-NMF pentamer, which can be interpreted as a linear trimer that is stabilized by explicit solvation of two further NMF molecules. This cluster reflects the known hydrogen bond network preferences of neat NMF.
Many Worlds, the Cluster-state Quantum Computer, and the Problem of the Preferred Basis
Cuffaro, Michael
2011-01-01
I argue that the many worlds explanation of quantum computation is not licensed by, and in fact is conceptually inferior to, the many worlds interpretation of quantum mechanics from which it is derived. I argue that the many worlds explanation of quantum computation is incompatible with the recently developed cluster state model of quantum computation. Based on these considerations I conclude that we should reject the many worlds explanation of quantum computation.
Lehmann, S B C; Spickermann, C; Kirchner, B
2009-06-09
Different cluster sets containing only 2-fold coordinated water, 2- and 3-fold coordinated water, and 2-fold, 3-fold, and tetrahedrally coordinated water molecules were investigated by applying second-order Møller-Plesset perturbation theory and density functional theory based on generalized gradient approximation functionals in the framework of the quantum cluster equilibrium theory. We found an improvement of the calculated isobars at low temperatures if tetrahedrally coordinated water molecules were included in the set of 2-fold hydrogen-bonded clusters. This was also reflected in a reduced parameter for the intercluster interaction. If all parameters were kept constant and only the electronic structure methods were varied, large basis set dependencies in the liquid state for the density functional theory results were found. The behavior of the intercluster parameter was also examined for the case that cooperative effects were neglected. The values were 3 times as large as in the calculations including the total electronic structure. Furthermore, these effects are more severe in the tetrahedrally coordinated clusters. Different populations were considered, one weighted by the total number of clusters and one depending on the monomers.
Supported quantum clusters of silver as enhanced catalysts for reduction
Directory of Open Access Journals (Sweden)
Leelavathi Annamalai
2011-01-01
Full Text Available Abstract Quantum clusters (QCs of silver such as Ag7(H2MSA7, Ag8(H2MSA8 (H2MSA, mercaptosuccinic acid were synthesized by the interfacial etching of Ag nanoparticle precursors and were loaded on metal oxide supports to prepare active catalysts. The supported clusters were characterized using high resolution transmission electron microscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, and laser desorption ionization mass spectrometry. We used the conversion of nitro group to amino group as a model reaction to study the catalytic reduction activity of the QCs. Various aromatic nitro compounds, namely, 3-nitrophenol (3-np, 4-nitrophenol (4-np, 3-nitroaniline (3-na, and 4-nitroaniline (4-na were used as substrates. Products were confirmed using UV-visible spectroscopy and electrospray ionization mass spectrometry. The supported QCs remained active and were reused several times after separation. The rate constant suggested that the reaction followed pseudo-first-order kinetics. The turn-over frequency was 1.87 s-1 per cluster for the reduction of 4-np at 35°C. Among the substrates investigated, the kinetics followed the order, SiO2 > TiO2 > Fe2O3 > Al2O3.
Quantum Monte Carlo Calculations of Excitations in Hydrogenated Germanium Clusters
Vincent, Jordan; Kim, Jeongnim; Martin, Richard
2006-03-01
Quantum Monte Carlo (QMC) calculations are presented for energies of ground and excited states of Ge atom and hydrogen passivated closed-shell molecules and clusters: GeH4, Ge2H6, Ge5H12, Ge10H16 and Ge29H36. We compare the results for excitations with previous QMC and time-dependant Density Functional Theory (TD- DFT) done for the corresponding Silicon clusters [1,2]; in particular; we find that preliminary results for lowest excitation enregy of Ge29H36 5.08[29]eV is lower than the gap 5.4eV reported for Si[2]. Core-valence partitioning for Ge is implemented by replacing the core-states with a Hartree-Fock pseudopotential plus a Core Polarization Potential (CPP)[3]. Core-valence correlation treated by the CPP is shown to be essential for accurate atomic energies and significant for the molecules, but smaller in the clusters. [1] Porter et. al., PRB 64, 035320 (2001). [2] Williamson et. al., PRL 89, 196803 (2002). [3] Shirley and Martin, PRB 47, 15413 (1993)
Clustering of Ions at Atomic-Dimensions in Quantum Plasmas
Shukla, P K
2012-01-01
By means of particle simulations of the equations of motion for ions interacting with the newly discovered Shukla-Eliasson (SE) force in a dense quantum plasma, we demonstrate that the SE force is powerful to bring ions closer at atomic dimensions. Specifically, we present simulation results on the dynamics of an ensemble of ions in the presence of the SE force without and with confining external potentials and collisions between the ions and degenerate electrons. Our particle simulations reveal that under the SE force, ions attract each other, come closer and form ionic clusters in the bath of degenerate electrons that shield the ions. Furthermore, an external confining potential produces robust ion clusters that can have cigar-like and ball-like shapes. The binding between the ions on account of the SE force may provide possibility of non-Coulombic explosions of ionic clusters for inertial confined fusion (ICF) schemes when high-energy density plasmas (density exceeding $10^{23}$ per cubic centimeters) are ...
Robustness of Charge-Qubit Cluster States to Double Quantum Point Contact Measurement
Tanamoto, Tetsufumi
2010-01-01
We theoretically investigate the robustness of cluster states in charge qubit system based on quantum dot (QD) and double quantum point contact (DQPC). Trap state is modeled by an island structure in DQPC and represents a dynamical fluctuation. We found that the dynamical fluctuations affect the cluster states more than static fluctuation caused by QD size fluctuation.
Using Dynamic Quantum Clustering to Analyze Hierarchically Heterogeneous Samples on the Nanoscale
Energy Technology Data Exchange (ETDEWEB)
Hume, Allison; /Princeton U. /SLAC
2012-09-07
Dynamic Quantum Clustering (DQC) is an unsupervised, high visual data mining technique. DQC was tested as an analysis method for X-ray Absorption Near Edge Structure (XANES) data from the Transmission X-ray Microscopy (TXM) group. The TXM group images hierarchically heterogeneous materials with nanoscale resolution and large field of view. XANES data consists of energy spectra for each pixel of an image. It was determined that DQC successfully identifies structure in data of this type without prior knowledge of the components in the sample. Clusters and sub-clusters clearly reflected features of the spectra that identified chemical component, chemical environment, and density in the image. DQC can also be used in conjunction with the established data analysis technique, which does require knowledge of components present.
Comparing the performance of biomedical clustering methods
DEFF Research Database (Denmark)
Wiwie, Christian; Baumbach, Jan; Röttger, Richard
2015-01-01
expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future....... This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide......-ranging comparison we were able to develop a short guideline for biomedical clustering tasks. ClustEval allows biomedical researchers to pick the appropriate tool for their data type and allows method developers to compare their tool to the state of the art....
Metropolis Methods for Quantum Monte Carlo Simulations
Ceperley, D.M.
2003-01-01
Since its first description fifty years ago, the Metropolis Monte Carlo method has been used in a variety of different ways for the simulation of continuum quantum many-body systems. This paper will consider some of the generalizations of the Metropolis algorithm employed in quantum Monte Carlo: Variational Monte Carlo, dynamical methods for projector monte carlo ({\\it i.e.} diffusion Monte Carlo with rejection), multilevel sampling in path integral Monte Carlo, the sampling of permutations, ...
Advanced cluster methods for correlated-electron systems
Energy Technology Data Exchange (ETDEWEB)
Fischer, Andre
2015-04-27
In this thesis, quantum cluster methods are used to calculate electronic properties of correlated-electron systems. A special focus lies in the determination of the ground state properties of a 3/4 filled triangular lattice within the one-band Hubbard model. At this filling, the electronic density of states exhibits a so-called van Hove singularity and the Fermi surface becomes perfectly nested, causing an instability towards a variety of spin-density-wave (SDW) and superconducting states. While chiral d+id-wave superconductivity has been proposed as the ground state in the weak coupling limit, the situation towards strong interactions is unclear. Additionally, quantum cluster methods are used here to investigate the interplay of Coulomb interactions and symmetry-breaking mechanisms within the nematic phase of iron-pnictide superconductors. The transition from a tetragonal to an orthorhombic phase is accompanied by a significant change in electronic properties, while long-range magnetic order is not established yet. The driving force of this transition may not only be phonons but also magnetic or orbital fluctuations. The signatures of these scenarios are studied with quantum cluster methods to identify the most important effects. Here, cluster perturbation theory (CPT) and its variational extention, the variational cluster approach (VCA) are used to treat the respective systems on a level beyond mean-field theory. Short-range correlations are incorporated numerically exactly by exact diagonalization (ED). In the VCA, long-range interactions are included by variational optimization of a fictitious symmetry-breaking field based on a self-energy functional approach. Due to limitations of ED, cluster sizes are limited to a small number of degrees of freedom. For the 3/4 filled triangular lattice, the VCA is performed for different cluster symmetries. A strong symmetry dependence and finite-size effects make a comparison of the results from different clusters difficult
Quantum transport in randomly diluted quantum percolation clusters in two dimensions
Cuansing, Eduardo; Nakanishi, Hisao
2008-02-01
We study the hopping transport of a quantum particle through finite, randomly diluted percolation clusters in two dimensions. We investigate how the transmission coefficient T behaves as a function of the energy E of the particle, the occupation concentration p of the disordered cluster, the size of the underlying lattice, and the type of connection chosen between the cluster and the input and output leads. We investigate both the point-to-point contacts and the busbar type of connection. For highly diluted clusters we find the behavior of the transmission to be independent of the type of connection. As the amount of dilution is decreased we find sharp variations in transmission. These variations are the remnants of the resonances at the ordered, zero-dilution, limit. For particles with energies within 0.25≤E≤1.75 (relative to the hopping integral) and with underlying square lattices of size 20×20, the configurations begin transmitting near pα=0.60 with T against p curves following a common pattern as the amount of dilution is decreased. Near pβ=0.90 this pattern is broken and the transmission begins to vary with the energy. In the asymptotic limit of very large clusters we find the systems to be totally reflecting in almost all cases. A few clear exceptions we find are when the amount of dilution is very low, when the particle has energy close to a resonance value at the ordered limit, and when the particle has energy at the middle of the band. These three cases, however, may not exhaust all possible exceptions.
METHOD OF CONSTRUCTION OF GENETIC DATA CLUSTERS
Directory of Open Access Journals (Sweden)
N. A. Novoselova
2016-01-01
Full Text Available The paper presents a method of construction of genetic data clusters (functional modules using the randomized matrices. To build the functional modules the selection and analysis of the eigenvalues of the gene profiles correlation matrix is performed. The principal components, corresponding to the eigenvalues, which are significantly different from those obtained for the randomly generated correlation matrix, are used for the analysis. Each selected principal component forms gene cluster. In a comparative experiment with the analogs the proposed method shows the advantage in allocating statistically significant different-sized clusters, the ability to filter non- informative genes and to extract the biologically interpretable functional modules matching the real data structure.
Quantum correlated cluster mean-field theory applied to the transverse Ising model.
Zimmer, F M; Schmidt, M; Maziero, Jonas
2016-06-01
Mean-field theory (MFT) is one of the main available tools for analytical calculations entailed in investigations regarding many-body systems. Recently, there has been a surge of interest in ameliorating this kind of method, mainly with the aim of incorporating geometric and correlation properties of these systems. The correlated cluster MFT (CCMFT) is an improvement that succeeded quite well in doing that for classical spin systems. Nevertheless, even the CCMFT presents some deficiencies when applied to quantum systems. In this article, we address this issue by proposing the quantum CCMFT (QCCMFT), which, in contrast to its former approach, uses general quantum states in its self-consistent mean-field equations. We apply the introduced QCCMFT to the transverse Ising model in honeycomb, square, and simple cubic lattices and obtain fairly good results both for the Curie temperature of thermal phase transition and for the critical field of quantum phase transition. Actually, our results match those obtained via exact solutions, series expansions or Monte Carlo simulations.
Cluster-state quantum computing enhanced by high-fidelity generalized measurements.
Biggerstaff, D N; Kaltenbaek, R; Hamel, D R; Weihs, G; Rudolph, T; Resch, K J
2009-12-11
We introduce and implement a technique to extend the quantum computational power of cluster states by replacing some projective measurements with generalized quantum measurements (POVMs). As an experimental demonstration we fully realize an arbitrary three-qubit cluster computation by implementing a tunable linear-optical POVM, as well as fast active feedforward, on a two-qubit photonic cluster state. Over 206 different computations, the average output fidelity is 0.9832+/-0.0002; furthermore the error contribution from our POVM device and feedforward is only of O(10(-3)), less than some recent thresholds for fault-tolerant cluster computing.
Cluster-State Quantum Computing Enhanced by High-Fidelity Generalized Measurements
Biggerstaff, D. N.; Kaltenbaek, R.; Hamel, D. R.; Weihs, G.; Rudolph, T.; Resch, K. J.
2009-12-01
We introduce and implement a technique to extend the quantum computational power of cluster states by replacing some projective measurements with generalized quantum measurements (POVMs). As an experimental demonstration we fully realize an arbitrary three-qubit cluster computation by implementing a tunable linear-optical POVM, as well as fast active feedforward, on a two-qubit photonic cluster state. Over 206 different computations, the average output fidelity is 0.9832±0.0002; furthermore the error contribution from our POVM device and feedforward is only of O(10-3), less than some recent thresholds for fault-tolerant cluster computing.
Colliding holes in Riemann surfaces and quantum cluster algebras
Chekhov, Leonid; Mazzocco, Marta
2018-01-01
In this paper, we describe a new type of surgery for non-compact Riemann surfaces that naturally appears when colliding two holes or two sides of the same hole in an orientable Riemann surface with boundary (and possibly orbifold points). As a result of this surgery, bordered cusps appear on the boundary components of the Riemann surface. In Poincaré uniformization, these bordered cusps correspond to ideal triangles in the fundamental domain. We introduce the notion of bordered cusped Teichmüller space and endow it with a Poisson structure, quantization of which is achieved with a canonical quantum ordering. We give a complete combinatorial description of the bordered cusped Teichmüller space by introducing the notion of maximal cusped lamination, a lamination consisting of geodesic arcs between bordered cusps and closed geodesics homotopic to the boundaries such that it triangulates the Riemann surface. We show that each bordered cusp carries a natural decoration, i.e. a choice of a horocycle, so that the lengths of the arcs in the maximal cusped lamination are defined as λ-lengths in Thurston–Penner terminology. We compute the Goldman bracket explicitly in terms of these λ-lengths and show that the groupoid of flip morphisms acts as a generalized cluster algebra mutation. From the physical point of view, our construction provides an explicit coordinatization of moduli spaces of open/closed string worldsheets and their quantization.
Message clusterization method based on archive transformation
Directory of Open Access Journals (Sweden)
Олексій Олександрович Сірий
2015-06-01
Full Text Available This article represents the method of the text’s parameters identification and their classification with the help of archiving. Using the direct bond between the archiving with LZ77 and Huffman algorithm and entropy, the text’s characteristics are identified, and they help to define its language, style, authorship, and cluster data files by their topic relevance
Phase space theory of evaporation in neon clusters: the role of quantum effects.
Calvo, F; Parneix, P
2009-12-31
Unimolecular evaporation of neon clusters containing between 14 and 148 atoms is theoretically investigated in the framework of phase space theory. Quantum effects are incorporated in the vibrational densities of states, which include both zero-point and anharmonic contributions, and in the possible tunneling through the centrifugal barrier. The evaporation rates, kinetic energy released, and product angular momentum are calculated as a function of excess energy or temperature in the parent cluster and compared to the classical results. Quantum fluctuations are found to generally increase both the kinetic energy released and the angular momentum of the product, but the effects on the rate constants depend nontrivially on the excess energy. These results are interpreted as due to the very few vibrational states available in the product cluster when described quantum mechanically. Because delocalization also leads to much narrower thermal energy distributions, the variations of evaporation observables as a function of canonical temperature appear much less marked than in the microcanonical ensemble. While quantum effects tend to smooth the caloric curve in the product cluster, the melting phase change clearly keeps a signature on these observables. The microcanonical temperature extracted from fitting the kinetic energy released distribution using an improved Arrhenius form further suggests a backbending in the quantum Ne(13) cluster that is absent in the classical system. Finally, in contrast to delocalization effects, quantum tunneling through the centrifugal barrier does not play any appreciable role on the evaporation kinetics of these rather heavy clusters.
A Novel Quantum Blind Signature Scheme with Four-Particle Cluster States
Fan, Ling
2016-03-01
In an arbitrated quantum signature scheme, the signer signs the message and the receiver verifies the signature's validity with the assistance of the arbitrator. We present an arbitrated quantum blind signature scheme by measuring four-particle cluster states and coding. By using the special relationship of four-particle cluster states, we cannot only support the security of quantum signature, but also guarantee the anonymity of the message owner. It has a wide application to E-payment system, E-government, E-business, and etc.
Five-wave-packet quantum error correction based on continuous-variable cluster entanglement.
Hao, Shuhong; Su, Xiaolong; Tian, Caixing; Xie, Changde; Peng, Kunchi
2015-10-26
Quantum error correction protects the quantum state against noise and decoherence in quantum communication and quantum computation, which enables one to perform fault-torrent quantum information processing. We experimentally demonstrate a quantum error correction scheme with a five-wave-packet code against a single stochastic error, the original theoretical model of which was firstly proposed by S. L. Braunstein and T. A. Walker. Five submodes of a continuous variable cluster entangled state of light are used for five encoding channels. Especially, in our encoding scheme the information of the input state is only distributed on three of the five channels and thus any error appearing in the remained two channels never affects the output state, i.e. the output quantum state is immune from the error in the two channels. The stochastic error on a single channel is corrected for both vacuum and squeezed input states and the achieved fidelities of the output states are beyond the corresponding classical limit.
Quantum algorithms and the finite element method
Montanaro, Ashley; Pallister, Sam
2015-01-01
The finite element method is used to approximately solve boundary value problems for differential equations. The method discretises the parameter space and finds an approximate solution by solving a large system of linear equations. Here we investigate the extent to which the finite element method can be accelerated using an efficient quantum algorithm for solving linear equations. We consider the representative general question of approximately computing a linear functional of the solution t...
Fuzzy Clustering - Principles, Methods and Examples
DEFF Research Database (Denmark)
Kroszynski, Uri; Zhou, Jianjun
1998-01-01
One of the most remarkable advances in the field of identification and control of systems -in particular mechanical systems- whose behaviour can not be described by means of the usual mathematical models, has been achieved by the application of methods of fuzzy theory.In the framework of a study ...... of the methods. The examples were solved by hand and served as a test bench for exploration of the MATLAB capabilities included in the Fuzzy Control Toolbox. The fuzzy clustering methods described include Fuzzy c-means (FCM), Fuzzy c-lines (FCL) and Fuzzy c-elliptotypes (FCE)....
System and method for making quantum dots
Bakr, Osman M.
2015-05-28
Embodiments of the present disclosure provide for methods of making quantum dots (QDs) (passivated or unpassivated) using a continuous flow process, systems for making QDs using a continuous flow process, and the like. In one or more embodiments, the QDs produced using embodiments of the present disclosure can be used in solar photovoltaic cells, bio-imaging, IR emitters, or LEDs.
Perspective: Quantum mechanical methods in biochemistry and biophysics
Cui, Qiang
2016-01-01
In this perspective article, I discuss several research topics relevant to quantum mechanical (QM) methods in biophysical and biochemical applications. Due to the immense complexity of biological problems, the key is to develop methods that are able to strike the proper balance of computational efficiency and accuracy for the problem of interest. Therefore, in addition to the development of novel ab initio and density functional theory based QM methods for the study of reactive events that involve complex motifs such as transition metal clusters in metalloenzymes, it is equally important to develop inexpensive QM methods and advanced classical or quantal force fields to describe different physicochemical properties of biomolecules and their behaviors in complex environments. Maintaining a solid connection of these more approximate methods with rigorous QM methods is essential to their transferability and robustness. Comparison to diverse experimental observables helps validate computational models and mechanistic hypotheses as well as driving further development of computational methodologies. PMID:27782516
Perspective: Quantum mechanical methods in biochemistry and biophysics.
Cui, Qiang
2016-10-14
In this perspective article, I discuss several research topics relevant to quantum mechanical (QM) methods in biophysical and biochemical applications. Due to the immense complexity of biological problems, the key is to develop methods that are able to strike the proper balance of computational efficiency and accuracy for the problem of interest. Therefore, in addition to the development of novel ab initio and density functional theory based QM methods for the study of reactive events that involve complex motifs such as transition metal clusters in metalloenzymes, it is equally important to develop inexpensive QM methods and advanced classical or quantal force fields to describe different physicochemical properties of biomolecules and their behaviors in complex environments. Maintaining a solid connection of these more approximate methods with rigorous QM methods is essential to their transferability and robustness. Comparison to diverse experimental observables helps validate computational models and mechanistic hypotheses as well as driving further development of computational methodologies.
Perspective: Quantum mechanical methods in biochemistry and biophysics
Cui, Qiang
2016-10-01
In this perspective article, I discuss several research topics relevant to quantum mechanical (QM) methods in biophysical and biochemical applications. Due to the immense complexity of biological problems, the key is to develop methods that are able to strike the proper balance of computational efficiency and accuracy for the problem of interest. Therefore, in addition to the development of novel ab initio and density functional theory based QM methods for the study of reactive events that involve complex motifs such as transition metal clusters in metalloenzymes, it is equally important to develop inexpensive QM methods and advanced classical or quantal force fields to describe different physicochemical properties of biomolecules and their behaviors in complex environments. Maintaining a solid connection of these more approximate methods with rigorous QM methods is essential to their transferability and robustness. Comparison to diverse experimental observables helps validate computational models and mechanistic hypotheses as well as driving further development of computational methodologies.
Fault-tolerant measurement-based quantum computing with continuous-variable cluster states.
Menicucci, Nicolas C
2014-03-28
A long-standing open question about Gaussian continuous-variable cluster states is whether they enable fault-tolerant measurement-based quantum computation. The answer is yes. Initial squeezing in the cluster above a threshold value of 20.5 dB ensures that errors from finite squeezing acting on encoded qubits are below the fault-tolerance threshold of known qubit-based error-correcting codes. By concatenating with one of these codes and using ancilla-based error correction, fault-tolerant measurement-based quantum computation of theoretically indefinite length is possible with finitely squeezed cluster states.
Recent advances in coupled-cluster methods
Bartlett, Rodney J
1997-01-01
Today, coupled-cluster (CC) theory has emerged as the most accurate, widely applicable approach for the correlation problem in molecules. Furthermore, the correct scaling of the energy and wavefunction with size (i.e. extensivity) recommends it for studies of polymers and crystals as well as molecules. CC methods have also paid dividends for nuclei, and for certain strongly correlated systems of interest in field theory.In order for CC methods to have achieved this distinction, it has been necessary to formulate new, theoretical approaches for the treatment of a variety of essential quantities
Lattice Methods for Quantum Chromodynamics
DeGrand, Thomas
2006-01-01
Numerical simulation of lattice-regulated QCD has become an important source of information about strong interactions. In the last few years there has been an explosion of techniques for performing ever more accurate studies on the properties of strongly interacting particles. Lattice predictions directly impact many areas of particle and nuclear physics theory and phenomenology. This book provides a thorough introduction to the specialized techniques needed to carry out numerical simulations of QCD: a description of lattice discretizations of fermions and gauge fields, methods for actually do
Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
Directory of Open Access Journals (Sweden)
Enseih Davoodi Jam
2012-11-01
Full Text Available Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on numerical attributes. Recently, there have been several proposals to develop clustering methods that support mixed attributes. There are
three basic groups of clustering methods: partitional methods, hierarchical methods and densitybased methods. This paper proposes a hybrid clustering algorithm that combines the advantages of hierarchical clustering and fuzzy clustering techniques and considers mixed attributes. The proposed algorithms improve the fuzzy algorithm by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, and it can determine the number of clusters based on the complexity of cluster structure. Our approach is organized in two phases: first, the division of data in two clusters; then the determination of the worst cluster and splitting. The number of clusters is unknown, but our algorithms can find this parameter based on the complexity of cluster structure. We demonstrate the effectiveness of the clustering approach by evaluating datasets of linked data. We applied the proposed algorithms on three different datasets. Experimental results the proposed algorithm is suitable for link discovery between datasets of linked data. Clustering can decrease the number of comparisons before link discovery.
Research on Palmprint Identification Method Based on Quantum Algorithms
Directory of Open Access Journals (Sweden)
Hui Li
2014-01-01
Full Text Available Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%.
Research on Palmprint Identification Method Based on Quantum Algorithms
Zhang, Zhanzhan
2014-01-01
Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%. PMID:25105165
Single pass kernel k-means clustering method
Indian Academy of Sciences (India)
Abstract. In unsupervised classification, kernel k-means clustering method has been shown to perform better than conventional k-means clustering method in iden- tifying non-isotropic clusters in a data set. The space and time requirements of this method are O(n2), where n is the data set size. Because of this quadratic time ...
Quantum fluctuation effects on nuclear fragment and atomic cluster formation
Energy Technology Data Exchange (ETDEWEB)
Ohnishi, Akira [Hokkaido Univ., Sapporo (Japan). Dept. of Physics; Randrup, J.
1997-05-01
We investigate the nuclear fragmentation and atomic cluster formation by means of the recently proposed quantal Langevin treatment. It is shown that the effect of the quantal fluctuation is in the opposite direction in nuclear fragment and atomic cluster size distribution. This tendency is understood through the effective classical temperature for the observables. (author)
Coronene molecules in helium clusters: Quantum and classical studies of energies and configurations
Energy Technology Data Exchange (ETDEWEB)
Rodríguez-Cantano, Rocío; Pérez de Tudela, Ricardo; Bartolomei, Massimiliano; Hernández, Marta I.; Campos-Martínez, José; González-Lezana, Tomás, E-mail: t.gonzalez.lezana@csic.es; Villarreal, Pablo [Instituto de Física Fundamental, IFF-CSIC, Serrano 123, 28006 Madrid (Spain); Hernández-Rojas, Javier; Bretón, José [Departamento de Física and IUdEA, Universidad de La Laguna, 38205 Tenerife (Spain)
2015-12-14
Coronene-doped helium clusters have been studied by means of classical and quantum mechanical (QM) methods using a recently developed He–C{sub 24}H{sub 12} global potential based on the use of optimized atom-bond improved Lennard-Jones functions. Equilibrium energies and geometries at global and local minima for systems with up to 69 He atoms were calculated by means of an evolutive algorithm and a basin-hopping approach and compared with results from path integral Monte Carlo (PIMC) calculations at 2 K. A detailed analysis performed for the smallest sizes shows that the precise localization of the He atoms forming the first solvation layer over the molecular substrate is affected by differences between relative potential minima. The comparison of the PIMC results with the predictions from the classical approaches and with diffusion Monte Carlo results allows to examine the importance of both the QM and thermal effects.
Fast method for quantum mechanical molecular dynamics
Niklasson, Anders M. N.; Cawkwell, Marc J.
2012-11-01
As the processing power available for scientific computing grows, first-principles Born-Oppenheimer molecular dynamics simulations are becoming increasingly popular for the study of a wide range of problems in materials science, chemistry, and biology. Nevertheless, the computational cost of Born-Oppenheimer molecular dynamics still remains prohibitively large for many potential applications. Here we show how to avoid a major computational bottleneck: the self-consistent-field optimization prior to force calculations. The optimization-free quantum mechanical molecular dynamics method gives trajectories that are almost indistinguishable from an “exact” microcanonical Born-Oppenheimer molecular dynamics simulation even when low-prefactor linear scaling sparse matrix algebra is used. Our findings show that the computational gap between classical and quantum mechanical molecular dynamics simulations can be significantly reduced.
Noiseless signal shaping and cluster-state generation with a quantum memory cell
Manukhova, A. D.; Tikhonov, K. S.; Golubeva, T. Yu.; Golubev, Yu. M.
2017-08-01
In this article, we employ multimode radiation of a synchronously pumped optical parametric oscillator (SPOPO) to build a cluster state through a conversion on the base of a quantum memory cell. We demonstrate that by choosing an appropriate driving field we can ensure the effective writing of only one supermode from the entire set of the SPOPO squeezed supermodes. Further, by changing the driving field profile at the readout, we convert the time profile of the retrieved signal while maintaining its quantum state. We demonstrate the possibilities of using the presented scheme by the example of creating a four-mode linear cluster state of light.
Asymptotic methods for wave and quantum problems
Karasev, M V
2003-01-01
The collection consists of four papers in different areas of mathematical physics united by the intrinsic coherence of the asymptotic methods used. The papers describe both the known results and most recent achievements, as well as new concepts and ideas in mathematical analysis of quantum and wave problems. In the introductory paper "Quantization and Intrinsic Dynamics" a relationship between quantization of symplectic manifolds and nonlinear wave equations is described and discussed from the viewpoint of the weak asymptotics method (asymptotics in distributions) and the semiclassical approxi
MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS
Directory of Open Access Journals (Sweden)
Jana Halčinová
2014-06-01
Full Text Available The aim of the present article is to show the possibility of using the methods of cluster analysis in classification of stocks of finished products. Cluster analysis creates groups (clusters of finished products according to similarity in demand i.e. customer requirements for each product. Manner stocks sorting of finished products by clusters is described a practical example. The resultants clusters are incorporated into the draft layout of the distribution warehouse.
Principles and methods of quantum information technologies
Semba, Kouichi
2016-01-01
This book presents the research and development-related results of the “FIRST” Quantum Information Processing Project, which was conducted from 2010 to 2014 with the support of the Council for Science, Technology and Innovation of the Cabinet Office of the Government of Japan. The project supported 33 research groups and explored five areas: quantum communication, quantum metrology and sensing, coherent computing, quantum simulation, and quantum computing. The book is divided into seven main sections. Parts I through V, which consist of twenty chapters, focus on the system and architectural aspects of quantum information technologies, while Parts VI and VII, which consist of eight chapters, discuss the superconducting quantum circuit, semiconductor spin and molecular spin technologies. Readers will be introduced to new quantum computing schemes such as quantum annealing machines and coherent Ising machines, which have now arisen as alternatives to standard quantum computers and are designed to successf...
Size specific emission in peptide capped gold quantum clusters with tunable photoswitching behavior.
Baral, Abhishek; Basu, Kingshuk; Ghosh, Sirshendu; Bhattacharyya, Kalishankar; Roy, Subhasish; Datta, Ayan; Banerjee, Arindam
2017-03-30
Three different types of fluorescent gold clusters (namely blue, green and red emitting) have been prepared from a gold precursor (chloroauric acid) under moderate conditions in aqueous medium. A cysteine containing dipeptide has been used for the formation of these quantum clusters as this peptide molecule contains a thiol group in the side chain to cap these nascently formed clusters and the free amino and carboxylic moieties assist in water solubility. Thus, the clusters are also environmentally friendly as the capped peptide is made up of only naturally occurring protein amino acids. These clusters have been well characterized by using UV-visible, fluorescence, X-ray photoelectron spectroscopy (XPS)spectroscopy, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and ultrahigh resolution field emission gun-transmission electron microscopy (UHR-FEG-TEM). Arrangements of gold atoms and their interaction with the corresponding ligands in three different fluorescent clusters have been predicted computationally. The excited state behavior of three different clusters has also been studied using time dependent density functional theory (TD-DFT). Time correlated single photon counting (TCSPC) and computational studies suggest intersystem crossing (S1 → T1) in the case of red-emitting Au23 clusters. Interestingly, these gold clusters exhibit semiconducting and photoswitching properties (Ion/Ioff), which are shown to be controlled by varying the size of these clusters. This holds future promise of using these gold cluster based nanomaterials for optoelectronic applications.
Miura, Shinichi
2007-03-21
In this paper, quantum fluctuations of a carbonyl sulfide molecule in helium-4 clusters are studied as a function of cluster size N in a small-to-large size regime (2or=20, which is larger than the experimental nanodroplet value. Superfluid response of the doped cluster is found to show remarkable anisotropy especially for Nfluctuation of the molecule. On the other hand, the superfluid fraction regarding the axis parallel to the molecular axis reaches 0.9 at N=5, arising from the bosonic exchange cycles of the helium atoms around the molecular axis. The anisotropy in the superfluid response is found to be the direct consequence of the configurations of the bosonic exchange cycles.
Spadafore, Maxwell; Najarian, Kayvan; Boyle, Alan P
2017-11-29
Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions. Here, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF. We show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions. The interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.
Integrated management of thesis using clustering method
Astuti, Indah Fitri; Cahyadi, Dedy
2017-02-01
Thesis is one of major requirements for student in pursuing their bachelor degree. In fact, finishing the thesis involves a long process including consultation, writing manuscript, conducting the chosen method, seminar scheduling, searching for references, and appraisal process by the board of mentors and examiners. Unfortunately, most of students find it hard to match all the lecturers' free time to sit together in a seminar room in order to examine the thesis. Therefore, seminar scheduling process should be on the top of priority to be solved. Manual mechanism for this task no longer fulfills the need. People in campus including students, staffs, and lecturers demand a system in which all the stakeholders can interact each other and manage the thesis process without conflicting their timetable. A branch of computer science named Management Information System (MIS) could be a breakthrough in dealing with thesis management. This research conduct a method called clustering to distinguish certain categories using mathematics formulas. A system then be developed along with the method to create a well-managed tool in providing some main facilities such as seminar scheduling, consultation and review process, thesis approval, assessment process, and also a reliable database of thesis. The database plays an important role in present and future purposes.
One- and two-particle correlation functions in the dynamical quantum cluster approach
Energy Technology Data Exchange (ETDEWEB)
Hochkeppel, Stephan
2008-07-25
This thesis is dedicated to a theoretical study of the 1-band Hubbard model in the strong coupling limit. The investigation is based on the Dynamical Cluster Approximation (DCA) which systematically restores non-local corrections to the Dynamical Mean Field approximation (DMFA). The DCA is formulated in momentum space and is characterised by a patching of the Brillouin zone where momentum conservation is only recovered between two patches. The approximation works well if k-space correlation functions show a weak momentum dependence. In order to study the temperature and doping dependence of the spin- and charge excitation spectra, we explicitly extend the Dynamical Cluster Approximation to two-particle response functions. The full irreducible two-particle vertex with three momenta and frequencies is approximated by an effective vertex dependent on the momentum and frequency of the spin and/or charge excitations. The effective vertex is calculated by using the Quantum Monte Carlo method on the finite cluster whereas the analytical continuation of dynamical quantities is performed by a stochastic version of the maximum entropy method. A comparison with high temperature auxiliary field quantum Monte Carlo data serves as a benchmark for our approach to two-particle correlation functions. Our method can reproduce basic characteristics of the spin- and charge excitation spectrum. Near and beyond optimal doping, our results provide a consistent overall picture of the interplay between charge, spin and single-particle excitations: a collective spin mode emerges at optimal doping and sufficiently low temperatures in the spin response spectrum and exhibits the energy scale of the magnetic exchange interaction J. Simultaneously, the low energy single-particle excitations are characterised by a coherent quasiparticle with bandwidth J. The origin of the quasiparticle can be quite well understood in a picture of a more or less antiferromagnetic ordered background in which holes
Quantum teleportation and information splitting via four-qubit cluster state and a Bell state
Ramírez, Marlon David González; Falaye, Babatunde James; Sun, Guo-Hua; Cruz-Irisson, M.; Dong, Shi-Hai
2017-10-01
Quantum teleportation provides a "bodiless" way of transmitting the quantum state from one object to another, at a distant location, using a classical communication channel and a previously shared entangled state. In this paper, we present a tripartite scheme for probabilistic teleportation of an arbitrary single qubit state, without losing the information of the state being teleported, via a fourqubit cluster state of the form | ϕ>1234 = α|0000>+ β|1010>+ γ|0101>- η|1111>, as the quantum channel, where the nonzero real numbers α, β, γ, and η satisfy the relation j αj2 + | β|2 + | γ|2 + | η|2 = 1. With the introduction of an auxiliary qubit with state |0>, using a suitable unitary transformation and a positive-operator valued measure (POVM), the receiver can recreate the state of the original qubit. An important advantage of the teleportation scheme demonstrated here is that, if the teleportation fails, it can be repeated without teleporting copies of the unknown quantum state, if the concerned parties share another pair of entangled qubit. We also present a protocol for quantum information splitting of an arbitrary two-particle system via the aforementioned cluster state and a Bell-state as the quantum channel. Problems related to security attacks were examined for both the cases and it was found that this protocol is secure. This protocol is highly efficient and easy to implement.
Carbon quantum dots and a method of making the same
Zidan, Ragaiy; Teprovich, Joseph A.; Washington, Aaron L.
2017-08-22
The present invention is directed to a method of preparing a carbon quantum dot. The carbon quantum dot can be prepared from a carbon precursor, such as a fullerene, and a complex metal hydride. The present invention also discloses a carbon quantum dot made by reacting a carbon precursor with a complex metal hydride and a polymer containing a carbon quantum dot made by reacting a carbon precursor with a complex metal hydride.
New methods for quantum mechanical reaction dynamics
Energy Technology Data Exchange (ETDEWEB)
Thompson, Ward Hugh [Univ. of California, Berkeley, CA (United States). Dept. of Chemistry
1996-12-01
Quantum mechanical methods are developed to describe the dynamics of bimolecular chemical reactions. We focus on developing approaches for directly calculating the desired quantity of interest. Methods for the calculation of single matrix elements of the scattering matrix (S-matrix) and initial state-selected reaction probabilities are presented. This is accomplished by the use of absorbing boundary conditions (ABC) to obtain a localized (L^{2}) representation of the outgoing wave scattering Green`s function. This approach enables the efficient calculation of only a single column of the S-matrix with a proportionate savings in effort over the calculation of the entire S-matrix. Applying this method to the calculation of the initial (or final) state-selected reaction probability, a more averaged quantity, requires even less effort than the state-to-state S-matrix elements. It is shown how the same representation of the Green`s function can be effectively applied to the calculation of negative ion photodetachment intensities. Photodetachment spectroscopy of the anion ABC^{-} can be a very useful method for obtaining detailed information about the neutral ABC potential energy surface, particularly if the ABC^{-} geometry is similar to the transition state of the neutral ABC. Total and arrangement-selected photodetachment spectra are calculated for the H_{3}O^{-} system, providing information about the potential energy surface for the OH + H_{2} reaction when compared with experimental results. Finally, we present methods for the direct calculation of the thermal rate constant from the flux-position and flux-flux correlation functions. The spirit of transition state theory is invoked by concentrating on the short time dynamics in the area around the transition state that determine reactivity. These methods are made efficient by evaluating the required quantum mechanical trace in the basis of eigenstates of the
Versatile Formal Methods Applied to Quantum Information.
Energy Technology Data Exchange (ETDEWEB)
Witzel, Wayne [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Rudinger, Kenneth Michael [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Sarovar, Mohan [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
2015-11-01
Using a novel formal methods approach, we have generated computer-veri ed proofs of major theorems pertinent to the quantum phase estimation algorithm. This was accomplished using our Prove-It software package in Python. While many formal methods tools are available, their practical utility is limited. Translating a problem of interest into these systems and working through the steps of a proof is an art form that requires much expertise. One must surrender to the preferences and restrictions of the tool regarding how mathematical notions are expressed and what deductions are allowed. Automation is a major driver that forces restrictions. Our focus, on the other hand, is to produce a tool that allows users the ability to con rm proofs that are essentially known already. This goal is valuable in itself. We demonstrate the viability of our approach that allows the user great exibility in expressing state- ments and composing derivations. There were no major obstacles in following a textbook proof of the quantum phase estimation algorithm. There were tedious details of algebraic manipulations that we needed to implement (and a few that we did not have time to enter into our system) and some basic components that we needed to rethink, but there were no serious roadblocks. In the process, we made a number of convenient additions to our Prove-It package that will make certain algebraic manipulations easier to perform in the future. In fact, our intent is for our system to build upon itself in this manner.
Multidistribution Center Location Based on Real-Parameter Quantum Evolutionary Clustering Algorithm
Directory of Open Access Journals (Sweden)
Huaixiao Wang
2014-01-01
Full Text Available To determine the multidistribution center location and the distribution scope of the distribution center with high efficiency, the real-parameter quantum-inspired evolutionary clustering algorithm (RQECA is proposed. RQECA is applied to choose multidistribution center location on the basis of the conventional fuzzy C-means clustering algorithm (FCM. The combination of the real-parameter quantum-inspired evolutionary algorithm (RQIEA and FCM can overcome the local search defect of FCM and make the optimization result independent of the choice of initial values. The comparison of FCM, clustering based on simulated annealing genetic algorithm (CSAGA, and RQECA indicates that RQECA has the same good convergence as CSAGA, but the search efficiency of RQECA is better than that of CSAGA. Therefore, RQECA is more efficient to solve the multidistribution center location problem.
Atmospheric Cluster Dynamics Code: a flexible method for solution of the birth-death equations
McGrath, M. J.; Olenius, T.; Ortega, I. K.; Loukonen, V.; Paasonen, P.; Kurtén, T.; Kulmala, M.; Vehkamäki, H.
2012-03-01
The Atmospheric Cluster Dynamics Code (ACDC) is presented and explored. This program was created to study the first steps of atmospheric new particle formation by examining the formation of molecular clusters from atmospherically relevant molecules. The program models the cluster kinetics by explicit solution of the birth-death equations, using an efficient computer script for their generation and the MATLAB ode15s routine for their solution. Through the use of evaporation rate coefficients derived from formation free energies calculated by quantum chemical methods for clusters containing dimethylamine or ammonia and sulphuric acid, we have explored the effect of changing various parameters at atmospherically relevant monomer concentrations. We have included in our model clusters with 0-4 base molecules and 0-4 sulfuric acid molecules for which we have commensurable quantum chemical data. The tests demonstrate that large effects can be seen for even small changes in different parameters, due to the non-linearity of the system. In particular, changing the temperature had a significant impact on the steady-state concentrations of all clusters, while the boundary effects (allowing clusters to grow to sizes beyond the largest cluster that the code keeps track of, or forbidding such processes), coagulation sink terms, non-monomer collisions, sticking probabilities and monomer concentrations did not show as large effects under the conditions studied. Removal of coagulation sink terms prevented the system from reaching the steady state when all the initial cluster concentrations were set to the default value of 1 m-3, which is probably an effect caused by studying only relatively small cluster sizes.
Design Methods for Reliable Quantum Circuits
Paler, Alexandru
2015-01-01
Quantum computing is an emerging technology that has the potential to change the perspectives and applications of computing in general. A wide range of applications are enabled: from faster algorithmic solutions of classically still difficult problems to theoretically more secure communication protocols. A quantum computer uses the quantum mechanical effects of particles or particle-like systems, and a major similarity between quantum and classical computers consists of both being abstracted ...
Fuzzy Clustering Methods and their Application to Fuzzy Modeling
DEFF Research Database (Denmark)
Kroszynski, Uri; Zhou, Jianjun
1999-01-01
prediction of outputs. This article presents an overview of some of the most popular clustering methods, namely Fuzzy Cluster-Means (FCM) and its generalizations to Fuzzy C-Lines and Elliptotypes. The algorithms for computing cluster centers and principal directions from a training data-set are described...
Volokitin, V.; Liniov, A.; Meyerov, I.; Hartmann, M.; Ivanchenko, M.; Hänggi, P.; Denisov, S.
2017-11-01
Quantum systems out of equilibrium are presently a subject of active research, both in theoretical and experimental domains. In this work, we consider time-periodically modulated quantum systems that are in contact with a stationary environment. Within the framework of a quantum master equation, the asymptotic states of such systems are described by time-periodic density operators. Resolution of these operators constitutes a nontrivial computational task. Approaches based on spectral and iterative methods are restricted to systems with the dimension of the hosting Hilbert space dim H =N ≲300 , while the direct long-time numerical integration of the master equation becomes increasingly problematic for N ≳400 , especially when the coupling to the environment is weak. To go beyond this limit, we use the quantum trajectory method, which unravels the master equation for the density operator into a set of stochastic processes for wave functions. The asymptotic density matrix is calculated by performing a statistical sampling over the ensemble of quantum trajectories, preceded by a long transient propagation. We follow the ideology of event-driven programming and construct a new algorithmic realization of the method. The algorithm is computationally efficient, allowing for long "leaps" forward in time. It is also numerically exact, in the sense that, being given the list of uniformly distributed (on the unit interval) random numbers, {η1,η2,...,ηn} , one could propagate a quantum trajectory (with ηi's as norm thresholds) in a numerically exact way. By using a scalable N -particle quantum model, we demonstrate that the algorithm allows us to resolve the asymptotic density operator of the model system with N =2000 states on a regular-size computer cluster, thus reaching the scale on which numerical studies of modulated Hamiltonian systems are currently performed.
Volokitin, V; Liniov, A; Meyerov, I; Hartmann, M; Ivanchenko, M; Hänggi, P; Denisov, S
2017-11-01
Quantum systems out of equilibrium are presently a subject of active research, both in theoretical and experimental domains. In this work, we consider time-periodically modulated quantum systems that are in contact with a stationary environment. Within the framework of a quantum master equation, the asymptotic states of such systems are described by time-periodic density operators. Resolution of these operators constitutes a nontrivial computational task. Approaches based on spectral and iterative methods are restricted to systems with the dimension of the hosting Hilbert space dimH=N≲300, while the direct long-time numerical integration of the master equation becomes increasingly problematic for N≳400, especially when the coupling to the environment is weak. To go beyond this limit, we use the quantum trajectory method, which unravels the master equation for the density operator into a set of stochastic processes for wave functions. The asymptotic density matrix is calculated by performing a statistical sampling over the ensemble of quantum trajectories, preceded by a long transient propagation. We follow the ideology of event-driven programming and construct a new algorithmic realization of the method. The algorithm is computationally efficient, allowing for long "leaps" forward in time. It is also numerically exact, in the sense that, being given the list of uniformly distributed (on the unit interval) random numbers, {η_{1},η_{2},...,η_{n}}, one could propagate a quantum trajectory (with η_{i}'s as norm thresholds) in a numerically exact way. By using a scalable N-particle quantum model, we demonstrate that the algorithm allows us to resolve the asymptotic density operator of the model system with N=2000 states on a regular-size computer cluster, thus reaching the scale on which numerical studies of modulated Hamiltonian systems are currently performed.
An image segmentation method based on network clustering model
Jiao, Yang; Wu, Jianshe; Jiao, Licheng
2018-01-01
Network clustering phenomena are ubiquitous in nature and human society. In this paper, a method involving a network clustering model is proposed for mass segmentation in mammograms. First, the watershed transform is used to divide an image into regions, and features of the image are computed. Then a graph is constructed from the obtained regions and features. The network clustering model is applied to realize clustering of nodes in the graph. Compared with two classic methods, the algorithm based on the network clustering model performs more effectively in experiments.
Progressive clustering based method for protein function prediction.
Saini, Ashish; Hou, Jingyu
2013-02-01
In recent years, significant effort has been given to predicting protein functions from protein interaction data generated from high throughput techniques. However, predicting protein functions correctly and reliably still remains a challenge. Recently, many computational methods have been proposed for predicting protein functions. Among these methods, clustering based methods are the most promising. The existing methods, however, mainly focus on protein relationship modeling and the prediction algorithms that statically predict functions from the clusters that are related to the unannotated proteins. In fact, the clustering itself is a dynamic process and the function prediction should take this dynamic feature of clustering into consideration. Unfortunately, this dynamic feature of clustering is ignored in the existing prediction methods. In this paper, we propose an innovative progressive clustering based prediction method to trace the functions of relevant annotated proteins across all clusters that are generated through the progressive clustering of proteins. A set of prediction criteria is proposed to predict functions of unannotated proteins from all relevant clusters and traced functions. The method was evaluated on real protein interaction datasets and the results demonstrated the effectiveness of the proposed method compared with representative existing methods.
A Latent Variable Clustering Method for Wireless Sensor Networks
DEFF Research Database (Denmark)
Vasilev, Vladislav; Iliev, Georgi; Poulkov, Vladimir
2016-01-01
In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work...... obtain by running simulations of a time dynamic sensor network. The performance of the proposed method outperforms the existing clustering methods, such as the Girvan-Newmans algorithm, the Kargers algorithm and the Spectral Clustering method, in terms of packet acceptance probability and delay....
Recommender engine for continuous-time quantum Monte Carlo methods
Huang, Li; Yang, Yi-feng; Wang, Lei
2017-03-01
Recommender systems play an essential role in the modern business world. They recommend favorable items such as books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accuracy. Exploiting the quantum to classical mapping inherent in the continuous-time quantum Monte Carlo methods, we construct a classical molecular gas model to reproduce the quantum distributions. We then utilize powerful molecular simulation techniques to propose efficient quantum Monte Carlo updates. The recommender engine approach provides a general way to speed up the quantum impurity solvers.
Classical and quantum transport on square lattices and disordered clusters in two dimensions
Cuansing, Eduardo C., Jr.
The transport of a particle through disordered clusters can be treated either classically or quantum mechanically, depending on the size of the systems involved. In this thesis we employ both treatments. In the classical part we extend ordinary site percolation on a square lattice to fully coordinated (FC) percolation and to iterated fully coordinated (IFC) percolation models. FC percolation comes about by adding a full coordination requirement to ordinary site percolation. In IFC percolation we iterate this requirement one more time. We find all three models to belong to the same universality class. We also find a developing Euclidean signature as we iterate the models from ordinary to FC and then to IFC percolation. In the quantum part we study the transmittance of a particle traversing through square lattices and through disordered clusters. The square lattices and disordered clusters are attached to two semi-infinite chains serving as the input and output leads. The leads and the clusters are coupled together through either point to point contacts or busbar connections. In transport through square lattices we find resonant transmission and reflection whenever the energy of the incident particle is close to a doubly-degenerate eigenvalue of the uncoupled lattice. We also find the transmission to be sensitive to the type of coupling chosen. In transport through disordered clusters we find the transmission to decrease as the clusters become larger. This hints that states are localized. Furthermore, we find the transmission to be independent of the coupling chosen in the presence of strong disorder. This independence is lost in weakly disordered clusters. We also find hints of localized-to-localized transitions as we vary the degree of disorder. However, the clusters we have been studying are still too small to make definite conclusions. We thus find it necessary to extend our analyses to larger-sized clusters.
A simulation study of three methods for detecting disease clusters
Directory of Open Access Journals (Sweden)
Samuelsen Sven O
2006-04-01
Full Text Available Abstract Background Cluster detection is an important part of spatial epidemiology because it can help identifying environmental factors associated with disease and thus guide investigation of the aetiology of diseases. In this article we study three methods suitable for detecting local spatial clusters: (1 a spatial scan statistic (SaTScan, (2 generalized additive models (GAM and (3 Bayesian disease mapping (BYM. We conducted a simulation study to compare the methods. Seven geographic clusters with different shapes were initially chosen as high-risk areas. Different scenarios for the magnitude of the relative risk of these areas as compared to the normal risk areas were considered. For each scenario the performance of the methods were assessed in terms of the sensitivity, specificity, and percentage correctly classified for each cluster. Results The performance depends on the relative risk, but all methods are in general suitable for identifying clusters with a relative risk larger than 1.5. However, it is difficult to detect clusters with lower relative risks. The GAM approach had the highest sensitivity, but relatively low specificity leading to an overestimation of the cluster area. Both the BYM and the SaTScan methods work well. Clusters with irregular shapes are more difficult to detect than more circular clusters. Conclusion Based on our simulations we conclude that the methods differ in their ability to detect spatial clusters. Different aspects should be considered for appropriate choice of method such as size and shape of the assumed spatial clusters and the relative importance of sensitivity and specificity. In general, the BYM method seems preferable for local cluster detection with relatively high relative risks whereas the SaTScan method appears preferable for lower relative risks. The GAM method needs to be tuned (using cross-validation to get satisfactory results.
An XQDD-Based Verification Method for Quantum Circuits
Wang, Shiou-An; Lu, Chin-Yung; Tsai, I.-Ming; Kuo, Sy-Yen
Synthesis of quantum circuits is essential for building quantum computers. It is important to verify that the circuits designed perform the correct functions. In this paper, we propose an algorithm which can be used to verify the quantum circuits synthesized by any method. The proposed algorithm is based on BDD (Binary Decision Diagram) and is called X-decomposition Quantum Decision Diagram (XQDD). In this method, quantum operations are modeled using a graphic method and the verification process is based on comparing these graphic diagrams. We also develop an algorithm to verify reversible circuits even if they have a different number of garbage qubits. In most cases, the number of nodes used in XQDD is less than that in other representations. In general, the proposed method is more efficient in terms of space and time and can be used to verify many quantum circuits in polynomial time.
Visual cluster analysis and pattern recognition template and methods
Osbourn, Gordon Cecil; Martinez, Rubel Francisco
1999-01-01
A method of clustering using a novel template to define a region of influence. Using neighboring approximation methods, computation times can be significantly reduced. The template and method are applicable and improve pattern recognition techniques.
Phase space methods for degenerate quantum gases
Dalton, Bryan J; Barnett, Stephen M
2015-01-01
Recent experimental progress has enabled cold atomic gases to be studied at nano-kelvin temperatures, creating new states of matter where quantum degeneracy occurs - Bose-Einstein condensates and degenerate Fermi gases. Such quantum states are of macroscopic dimensions. This book presents the phase space theory approach for treating the physics of degenerate quantum gases, an approach already widely used in quantum optics. However, degenerate quantum gases involve massive bosonic and fermionic atoms, not massless photons. The book begins with a review of Fock states for systems of identical atoms, where large numbers of atoms occupy the various single particle states or modes. First, separate modes are considered, and here the quantum density operator is represented by a phase space distribution function of phase space variables which replace mode annihilation, creation operators, the dynamical equation for the density operator determines a Fokker-Planck equation for the distribution function, and measurable...
Quantum effect on the internal proton transfer and structural fluctuation in the H+ 5 cluster.
Ohta, Yasuhito; Ohta, Koji; Kinugawa, Kenichi
2004-12-08
The thermal equilibrium state of H+(5) is investigated by means of an ab initio path integral molecular dynamics (PIMD) method, in which degrees of freedom of both nuclei and electrons at finite temperature are quantized within the adiabatic approximation. The second-order Moller-Plesset force field has been employed for the present ab initio PIMD. At 5-200 K, H+(5) is shown to have the structure that the proton is surrounded by the two H(2) units without any exchange of an atom between the central proton and the H(2) unit. At 5 K, the quantum tunneling of the central proton occurs more easily when the distance between the two H(2) units is shortened. At the high temperature of 200 K, the central proton is more delocalized in space between the two H(2) units, with less correlation with the stretching of the distance between the two H(2) units. As for the rotation of the H(2) units around the C(2) axis of H+(5) , the dihedral angle distribution is homogeneous at all temperatures, suggesting that the two H(2) units freely rotate around the C(2) axis, while this quantum effect on the rotation of the H(2) units becomes more weakened with increasing temperature. The influence of the structural fluctuation of H+(5) on molecular orbital energies has been examined to conclude that the highest occupied molecular orbital-lowest unoccupied molecular orbital energy gap is largely reduced with the increase of temperature because of the spatial expansion of the whole cluster. (c) 2004 American Institute of Physics.
CCM: A Text Classification Method by Clustering
DEFF Research Database (Denmark)
Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock
2011-01-01
In this paper, a new Cluster based Classification Model (CCM) for suspicious email detection and other text classification tasks, is presented. Comparative experiments of the proposed model against traditional classification models and the boosting algorithm are also discussed. Experimental results...... show that the CCM outperforms traditional classification models as well as the boosting algorithm for the task of suspicious email detection on terrorism domain email dataset and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. The overall finding is that applying a cluster based...
DEFF Research Database (Denmark)
Bernard, S.; Kutter, J. P.; Mogensen, K. B.
2014-01-01
Plasmonics is combined with polymer synthesis for rapid fabrication of highly fluorescing silver quantum cluster/polymer composites inside microfluidic channels. UV-light assisted synthesis of polymers has been investigated by a number of groups previously [1], however, plasmon assisted synthesis...... has not been presented before. This should allow highly localized fabrication of porous polymers that are defined by the location of the nanoplasmonic metal film. Silver quantum clusters (AgQCs) consisting of 2-10 atoms can be highly fluorescing in the visible wavelength range and possess a greater...... oil-immersion microscopy through a ∼100 μm thick glass lid of the chip, while the bottom substrate contains the plasmonic silver nanoparticle film....
Nanvakenari, Milad; Houshmand, Monireh
In this paper, a three-party controlled quantum secure direct communication and authentication (QSDCA) protocol is proposed by using four particle cluster states via a quantum one-time pad and local unitary operations. In the present scheme, only under the permission of the controller, the sender and the receiver can implement secure direct communication successfully. But under any circumstances, Charlie cannot obtain the secret message. Eavesdropping detection and identity authentication are achieved with the help of the previously shared reusable base identity strings of users. This protocol is unconditionally secure in both ideal and practical noisy cases. In one transmission, a qubit of each four particle cluster state is used as controller’s permission and the same qubit with another qubit are used to recover two classical bits of information. In the proposed scheme, the efficiency is improved compared with the previous works.
Multi-party Measurement-Device-Independent Quantum Key Distribution Based on Cluster States
Liu, Chuanqi; Zhu, Changhua; Ma, Shuquan; Pei, Changxing
2017-12-01
We propose a novel multi-party measurement-device-independent quantum key distribution (MDI-QKD) protocol based on cluster states. A four-photon analyzer which can distinguish all the 16 cluster states serves as the measurement device for four-party MDI-QKD. Any two out of four participants can build secure keys after the analyzers obtains successful outputs and the two participants perform post-processing. We derive a security analysis for the protocol, and analyze the key rates under different values of polarization misalignment. The results show that four-party MDI-QKD is feasible over 280 km in the optical fiber channel when the key rate is about 10- 6 with the polarization misalignment parameter 0.015. Moreover, our work takes an important step toward a quantum communication network.
A graph clustering method for community detection in complex networks
Zhou, HongFang; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn
2017-03-01
Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.
Bidirectional Controlled Quantum Information Transmission by Using a Five-Qubit Cluster State
Sang, Zhi-wen
2017-11-01
We demonstrate that an entangled five-qubit cluster state can be used to realize the deterministic bidirectional controlled quantum information transmission by performing only Bell-state measurement and single-qubit measurements. In our protocol, Alice can teleport an arbitrary unknown single-qubit state to Bob and at the same time Bob can remotely prepare an arbitrary known single-qubit state for Alice via the control of the supervisor Charlie.
Quantum cluster equilibrium theory treatment of hydrogen-bonded liquids: water, methanol and ethanol
Borowski, Piotr; Jaroniec, Justyna; Janowski, Tomasz; Woliński, Krzysztof
2003-01-01
The quantum cluster equilibrium (QCE) theory was used in order to predict the composition of the hydrogen bonded liquids: water, methanol and ethanol. The calculations were based on high accuracy theoretical data obtained at the DFT/B3LYP/6-311G(d,p) level of theory. All investigated liquids are predicted to be composed of big clusters: hexamers in the case of water, tetramers, pentamers, hexamers and heptamers in the case of methanol and pentamers in the case of ethanol. The content of big clusters in a liquid phase as predicted by QCE is overestimated. We have found two confirmations of this. First of all, the behaviour of the liquid water isobar clearly demonstrates that there should be a substantial amount of small clusters in order to obtain the correct temperature dependence of the molar volume. Indeed, the theoretical molar volume close to the boiling point is by about 0.6 cm3 lower than the experimental one. The molar volume is too low due to the overestimated population of big clusters resulting in too high a liquid density. Second, the temperature dependence of the chemical shift of the hydroxyl protons in liquid methanol and ethanol, obtained as the population weighted average of the chemical shift of individual clusters, is shifted down field as compared to experiment by as much as 2 ppm. This is because big clusters with strongly deshielded hydroxyl protons contribute too much to the weighted average. Possible shortcomings of the QCE approach are discussed.
Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan
2018-02-01
Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.
Quantum kinetic theory of metal clusters in an intense electromagnetic field
Directory of Open Access Journals (Sweden)
M.Bonitz
2004-01-01
Full Text Available A quantum kinetic theory for weakly inhomogeneous charged particle systems is derived within the framework of nonequilibrium Green's functions. The results are of relevance for valence electrons of metal clusters as well as for confined Coulomb systems, such as electrons in quantum dots or ultracold ions in traps and similar systems. To be specific, here we concentrate on the application to metal clusters, but the results are straightforwardly generalized. Therefore, we first give an introduction to the physics of correlated valence electrons of metal clusters in strong electromagnetic fields. After a brief overview on the jellium model and the standard density functional approach to the ground state properties, we focus on the extension of the theory to nonequilibrium. To this end a general gauge-invariant kinetic theory is developed. The results include the equations of motion of the two-time correlation functions, the equation for the Wigner function and an analysis of the spectral function. Here, the concept of an effective quantum potential is introduced which retains the convenient local form of the propagators. This allows us to derive explicit results for the spectral function of electrons in a combined strong electromagnetic field and a weakly inhomogeneous confinement potential.
Progeny Clustering: A Method to Identify Biological Phenotypes
Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.
2015-01-01
Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476
Functional renormalization group methods in quantum chromodynamics
Energy Technology Data Exchange (ETDEWEB)
Braun, J.
2006-12-18
We apply functional Renormalization Group methods to Quantum Chromodynamics (QCD). First we calculate the mass shift for the pion in a finite volume in the framework of the quark-meson model. In particular, we investigate the importance of quark effects. As in lattice gauge theory, we find that the choice of quark boundary conditions has a noticeable effect on the pion mass shift in small volumes. A comparison of our results to chiral perturbation theory and lattice QCD suggests that lattice QCD has not yet reached volume sizes for which chiral perturbation theory can be applied to extrapolate lattice results for low-energy observables. Phase transitions in QCD at finite temperature and density are currently very actively researched. We study the chiral phase transition at finite temperature with two approaches. First, we compute the phase transition temperature in infinite and in finite volume with the quark-meson model. Though qualitatively correct, our results suggest that the model does not describe the dynamics of QCD near the finite-temperature phase boundary accurately. Second, we study the approach to chiral symmetry breaking in terms of quarks and gluons. We compute the running QCD coupling for all temperatures and scales. We use this result to determine quantitatively the phase boundary in the plane of temperature and number of quark flavors and find good agreement with lattice results. (orig.)
Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann
2017-07-01
Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.
Application of the cluster variation method to interstitial solid solutions
Pekelharing, M.I.
2008-01-01
A thermodynamic model for interstitial alloys, based on the Cluster Variation Method (CVM), has been developed, capable of incorporating short range ordering (SRO), long range ordering (LRO), and the mutual interaction between the host and the interstitial sublattices. The obtained cluster-based
Vasconcelos, Diego Andrade; Kubota, Tatiana; Santos, Douglas C; Araujo, Marcia V G; Teixeira, Zaine; Gimenez, Iara F
2016-01-20
Here we report the use of β-cyclodextrin polyurethane nanosponges cross-linked with 1,6-hexamethylene diisocyanate as a template for the preparation of Aun quantum clusters, by the core-etching of glutathione-capped Au nanoparticles. The study of temporal evolution of the core-etching process using different Au concentrations indicated that formation of Aun clusters embedded in the nanosponge is favored by the use of lower Au concentrations, since it began at shorter times and lead to higher cluster loading. An estimation of the number of Au atoms based on the maximum photoluminescence wavelength suggested that, depending on the Au concentration and the core etching time, clusters with 11-15 atoms were formed. After excluding the possibility of an inclusion complex formation, evaluation of the catalytic activity of nanosponge-loaded Aun clusters toward the reduction of 4-nitrophenol has shown that the reaction is catalyzed by the Aun clusters with no induction time, following the Langmuir-Hinshelwood kinetic model. Copyright © 2015 Elsevier Ltd. All rights reserved.
Luminescent quantum clusters of gold in transferrin family protein, lactoferrin exhibiting FRET
Xavier, Paulrajpillai Lourdu; Chaudhari, Kamalesh; Verma, Pramod Kumar; Pal, Samir Kumar; Pradeep, Thalappil
2010-12-01
We report the synthesis of highly luminescent, water soluble quantum clusters (QCs) of gold, which are stabilized by an iron binding transferrin family protein, lactoferrin (Lf). The synthesized AuQC@Lfclusters were characterized using UV-Visiblespectroscopy, X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), photoluminescence (PL), matrix assisted laser desorption ionizationmass spectrometry (MALDI-MS), FTIR spectroscopy and circular dichroism (CD) spectroscopy along with picosecond-resolved lifetime measurements. Detailed investigations with FTIR and CD spectroscopy have revealed changes in the secondary structure of the protein in the cluster. We have also studied Förster resonance energy transfer (FRET) occurring between the protein and the cluster. The ability of the clusters to sense cupric ions selectively at ppm concentrations was tested. The stability of clusters in widely varying pH conditions and their continued luminescence make it feasible for them to be used for intracellular imaging and molecular delivery, particularly in view of Lf protection.We report the synthesis of highly luminescent, water soluble quantum clusters (QCs) of gold, which are stabilized by an iron binding transferrin family protein, lactoferrin (Lf). The synthesized AuQC@Lfclusters were characterized using UV-Visiblespectroscopy, X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), photoluminescence (PL), matrix assisted laser desorption ionizationmass spectrometry (MALDI-MS), FTIR spectroscopy and circular dichroism (CD) spectroscopy along with picosecond-resolved lifetime measurements. Detailed investigations with FTIR and CD spectroscopy have revealed changes in the secondary structure of the protein in the cluster. We have also studied Förster resonance energy transfer (FRET) occurring between the protein and the cluster. The ability of the clusters to sense cupric ions selectively at ppm concentrations was tested. The
Quantum Monte Carlo method for attractive Coulomb potentials
Kole, J.S.; Raedt, H. De
2001-01-01
Starting from an exact lower bound on the imaginary-time propagator, we present a path-integral quantum Monte Carlo method that can handle singular attractive potentials. We illustrate the basic ideas of this quantum Monte Carlo algorithm by simulating the ground state of hydrogen and helium.
The factorization method, self-similar potentials and quantum algebras
Spiridonov, V.P.
2003-01-01
This is a brief review of the Schrodinger's factorization method and its relations to supersymmetric quantum mechanics and its nonlinear (parastatistical, etc) modifications, self-similar infinite soliton potentials, quantum algebras, coherent states, Ising chains, discretized random matrices and 2D lattice Coulomb gases.
Initialization independent clustering with actively self-training method.
Nie, Feiping; Xu, Dong; Li, Xuelong
2012-02-01
The results of traditional clustering methods are usually unreliable as there is not any guidance from the data labels, while the class labels can be predicted more reliable by the semisupervised learning if the labels of partial data are given. In this paper, we propose an actively self-training clustering method, in which the samples are actively selected as training set to minimize an estimated Bayes error, and then explore semisupervised learning to perform clustering. Traditional graph-based semisupervised learning methods are not convenient to estimate the Bayes error; we develop a specific regularization framework on graph to perform semisupervised learning, in which the Bayes error can be effectively estimated. In addition, the proposed clustering algorithm can be readily applied in a semisupervised setting with partial class labels. Experimental results on toy data and real-world data sets demonstrate the effectiveness of the proposed clustering method on the unsupervised and the semisupervised setting. It is worthy noting that the proposed clustering method is free of initialization, while traditional clustering methods are usually dependent on initialization.
Calculation of MP2 and coupled-cluster molecular properties using the q-integral method.
de Oliveira, H C B; Rangel, F C; Esteves, C S; Vieira, F M C; Mundim, K C
2009-12-31
The main purpose of this paper is to report results of quantum mechanical calculation of the H(2) system using the q-Integral method with correlation corrections to the SCF (Self Consistent Field) wave functions included through the Møller-Plesset second-order perturbation (MP(2)) and Coupled-Cluster (CC) theory. Using the q-Integral method, we evaluated potential energy curves, rovibrational spectroscopy constants, rovibrational spectra, interatomic equilibrium distance and longitudinal static hyper(polarizability). All calculations were carried out through the STO-3G, STO-6G, and double-zeta (DZV) atomic basis set. The q-Integral method was implemented in the source code of the general ab initio quantum chemistry package GAMESS.
Quantum Monte Carlo programming for atoms, molecules, clusters, and solids
Schattke, Wolfgang
2013-01-01
In one source, this textbook provides quick and comprehensive access to quantitative calculations in materials science. The authors address both newcomers as well as researchers who would like to become familiar with QMC in order to apply to their research. As such, they cover the basic theory required for applying the method, and describe how to transfer this knowledge into calculation. The book includes a series of problems of increasing difficulty with associated stand-alone programs which will be available for free download.
Perturbative dynamics of open quantum systems by renormalization group method
Kukita, Shingo
2017-10-01
We analyze perturbative dynamics of a composite system consisting of a quantum mechanical system and an environment by the renormalization group (RG) method. The solution obtained from the RG method has no secular terms and approximates the exact solution for a long time interval. Moreover, the RG method causes a reduction of the dynamics of the composite system under some assumptions. We show that this reduced dynamics is closely related to a quantum master equation for the quantum mechanical system. We compare this dynamics with the exact dynamics in an exactly solvable spin-boson model.
Quantum dynamic imaging theoretical and numerical methods
Ivanov, Misha
2011-01-01
Studying and using light or "photons" to image and then to control and transmit molecular information is among the most challenging and significant research fields to emerge in recent years. One of the fastest growing areas involves research in the temporal imaging of quantum phenomena, ranging from molecular dynamics in the femto (10-15s) time regime for atomic motion to the atto (10-18s) time scale of electron motion. In fact, the attosecond "revolution" is now recognized as one of the most important recent breakthroughs and innovations in the science of the 21st century. A major participant in the development of ultrafast femto and attosecond temporal imaging of molecular quantum phenomena has been theory and numerical simulation of the nonlinear, non-perturbative response of atoms and molecules to ultrashort laser pulses. Therefore, imaging quantum dynamics is a new frontier of science requiring advanced mathematical approaches for analyzing and solving spatial and temporal multidimensional partial differ...
A graph-based clustering method applied to protein sequences.
Mishra, Pooja; Pandey, Paras Nath
2011-01-01
The number of amino acid sequences is increasing very rapidly in the protein databases like Swiss-Prot, Uniprot, PIR and others, but the structure of only some amino acid sequences are found in the Protein Data Bank. Thus, an important problem in genomics is automatically clustering homologous protein sequences when only sequence information is available. Here, we use graph theoretic techniques for clustering amino acid sequences. A similarity graph is defined and clusters in that graph correspond to connected subgraphs. Cluster analysis seeks grouping of amino acid sequences into subsets based on distance or similarity score between pairs of sequences. Our goal is to find disjoint subsets, called clusters, such that two criteria are satisfied: homogeneity: sequences in the same cluster are highly similar to each other; and separation: sequences in different clusters have low similarity to each other. We tested our method on several subsets of SCOP (Structural Classification of proteins) database, a gold standard for protein structure classification. The results show that for a given set of proteins the number of clusters we obtained is close to the superfamilies in that set; there are fewer singeltons; and the method correctly groups most remote homologs.
Methods for Quantum Circuit Design and Simulation
2010-03-01
THIS PAGE INTENTIONALLY LEFT BLANK 30 CHAPTER 4: Quantum Algorithms Introduction Richard Feynman suggested the notion of a quantum computer in 1982...pp. 777–780, May 1935. [25] R. P. Feynman , Feynman Lectures on Computation, A. J. Hey and R. W. Allen, Eds. Cam- bridge, MA, USA: Perseus Books, 2000...Frederic T. 7, 49, 99, 100 Chuang, Isaac L. 7, 11, 24, 31, 35, 38, 46 Cirac, J. I. 48 Cleve, Richard 46 Cova, Sergio 9 Cowie, James 48, 49 de Matos, Clovis J
Fano, Guido
2017-01-01
This book is designed to make accessible to nonspecialists the still evolving concepts of quantum mechanics and the terminology in which these are expressed. The opening chapters summarize elementary concepts of twentieth century quantum mechanics and describe the mathematical methods employed in the field, with clear explanation of, for example, Hilbert space, complex variables, complex vector spaces and Dirac notation, and the Heisenberg uncertainty principle. After detailed discussion of the Schrödinger equation, subsequent chapters focus on isotropic vectors, used to construct spinors, and on conceptual problems associated with measurement, superposition, and decoherence in quantum systems. Here, due attention is paid to Bell’s inequality and the possible existence of hidden variables. Finally, progression toward quantum computation is examined in detail: if quantum computers can be made practicable, enormous enhancements in computing power, artificial intelligence, and secure communication will result...
Numerical linked cluster expansions for quantum quenches in one-dimensional lattices.
Mallayya, Krishnanand; Rigol, Marcos
2017-03-01
We discuss the application of numerical linked cluster expansions (NLCEs) to study one dimensional lattice systems in thermal equilibrium and after quantum quenches from thermal equilibrium states. For the former, we calculate observables in the grand canonical ensemble, and for the latter we calculate observables in the diagonal ensemble. When converged, NLCEs provide results in the thermodynamic limit. We use two different NLCEs: a maximally connected expansion introduced in previous works and a site-based expansion. We compare the effectiveness of both NLCEs. The site-based NLCE is found to work best for systems in thermal equilibrium. However, in thermal equilibrium and after quantum quenches, the site-based NLCE can diverge when the maximally connected one converges. We relate this divergence to the exponentially large number of clusters in the site-based NLCE and the behavior of the weights of observables in those clusters. We discuss the effectiveness of resummations to cure the divergence. Our NLCE calculations are compared to exact diagonalization ones in lattices with periodic boundary conditions. NLCEs are found to outperform exact diagonalization in periodic systems for all quantities studied.
Tan, Huatang; Wei, Yanghua; Li, Gaoxiang
2017-11-01
Greenberger-Horne-Zeilinger (GHZ) and cluster states are two typical kinds of multipartite entangled states and can respectively be used for realizing quantum networks and one-way computation. We propose a feasible scheme for generating Gaussian GHZ and cluster states of multiple mechanical oscillators by pulsed cavity optomechanics. In our scheme, each optomechanical cavity is driven by a blue-detuned pulse to establish quantum steerable correlations between the cavity output field and the mechanical oscillator, and the cavity outputs are combined at a beam-splitter array with given transmissivity and reflectivity for each beam splitter. We show that by harnessing the light-mechanical steerable correlations, the mechanical GHZ and cluster states can be realized via homodyne detection on the amplitude and phase quadratures of the output fields from the beam-splitter array. These achieved mechanical entangled states can be viewed as the output states of an effective mechanical beam-splitter array with the mechanical inputs prepared in squeezed states with the light-mechanical steering. The effects of detection efficiency and thermal noise on the achieved mechanical states are investigated. The present scheme does not require externally injected squeezing and it can also be applicable to other systems such as light-atomic-ensemble interface, apart from optomechanical systems.
Matisz, G; Kelterer, A-M; Fabian, W M F; Kunsági-Máté, S
2015-04-07
Density functional theory (B3LYP-D3, M06-2X) has been used to calculate the structures, interaction energies and vibrational frequencies of a set of 93 methanol-water clusters of different type (cubic, ring, spiro, lasso, bicyclic), size and composition. These interaction energies have been used within the framework of the Quantum Cluster Equilibrium Theory (QCE) to calculate cluster populations as well as thermodynamic properties of binary methanol-water mixtures spanning the whole range from pure water to pure methanol. The necessary parameters amf and bxv of the QCE model were obtained by fitting to experimental isobars of MeOH-H2O mixtures with different MeOH content. The cubic and spiro motifs dominate the distribution of methanol-water clusters in the mixtures with a maximum of mixed clusters at x(MeOH) = 0.365. Reasonable agreement with experimental data as well as earlier molecular dynamics simulations was found for excess enthalpies H(E), entropies S(E) as well as Gibbs free energies of mixing G(E). In contrast, heat capacities Cp and C showed only poor agreement with experimental data.
A Quantum-Based Similarity Method in Virtual Screening
Directory of Open Access Journals (Sweden)
Mohammed Mumtaz Al-Dabbagh
2015-10-01
Full Text Available One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB. The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR, maximum unbiased validation (MUV and Directory of Useful Decoys (DUD data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure.
Quantum Cloning of an Unknown 2-Atom State via Entangled Cluster States
Yu, L.-z.; Zhong, F.
2016-06-01
This paper presented a scheme for cloning a 2-atom state in the QED cavity with the help of Victor who is the state's preparer. The cloning scheme has two steps. In the first step, the scheme requires probabilistic teleportation of a 2-atom state that is unknown in advance, and uses a 4-atom cluster state as quantum channel. In the second step, perfect copies of the 2-atom entangled state may be realized with the assistance of Victor. The finding is that our scheme has two outstanding advantages: it is not sensitive to the cavity decay, and Bell state is easy to identify.
Entanglement percolation on a quantum internet with scale-free and clustering characters
Wu, Liang; Zhu, Shiqun
2011-11-01
The applicability of entanglement percolation protocol to real Internet structure is investigated. If the current Internet can be used directly in the quantum regime, the protocol can provide a way to establish long-distance entanglement when the links are pure nonmaximally entangled states. This applicability is primarily due to the combination of scale-free degree distribution and a high level of clustering, both of which are widely observed in many natural and artificial networks including the current Internet. It suggests that the topology of real Internet may play an important role in entanglement establishment.
Energy Technology Data Exchange (ETDEWEB)
Jose, Meera, E-mail: gunasekaran@karunya.edu; Sakthivel, T., E-mail: gunasekaran@karunya.edu; Chandran, Hrisheekesh T., E-mail: gunasekaran@karunya.edu; Nivea, R., E-mail: gunasekaran@karunya.edu; Gunasekaran, V., E-mail: gunasekaran@karunya.edu [Nanomaterials Research Lab, Department of Nanoscience and Technology, Karunya University, Coimbatore - 641 114, Tamil Nadu (India)
2014-10-15
In this work, undoped and Ag-doped ZnS quantum dots were synthesized using various chemical methods. The products were characterized using X-ray diffraction (XRD), UV-visible spectroscopy and Photoluminescence spectroscopy. Our results revealed that the size of the as-prepared samples range from 1–6 nm in diameter and have a cubic zinc-blende structure. Also, we observed the emission of different wavelength of light from different sized quantum dots of the same material due to quantum confinement effect. The results will be presented in detail and ZnS can be a potential candidate for optical device development and applications.
A new method to prepare colloids of size-controlled clusters from a matrix assembly cluster source
Cai, Rongsheng; Jian, Nan; Murphy, Shane; Bauer, Karl; Palmer, Richard E.
2017-05-01
A new method for the production of colloidal suspensions of physically deposited clusters is demonstrated. A cluster source has been used to deposit size-controlled clusters onto water-soluble polymer films, which are then dissolved to produce colloidal suspensions of clusters encapsulated with polymer molecules. This process has been demonstrated using different cluster materials (Au and Ag) and polymers (polyvinylpyrrolidone, polyvinyl alcohol, and polyethylene glycol). Scanning transmission electron microscopy of the clusters before and after colloidal dispersion confirms that the polymers act as stabilizing agents. We propose that this method is suitable for the production of biocompatible colloids of ultraprecise clusters.
Maritime clusters productivity and competitiveness evaluation methods: Systematic approach
Directory of Open Access Journals (Sweden)
Viederytė Rasa
2014-01-01
Full Text Available Many scientists underline the importance of the clusters as agglomerated industries, working for the same purpose with joined resources and potential. This article analyses the basic assumptions which turn organizations to be clustered: the Productivity and the Competitiveness. For the evaluation of those assumptions in Maritime Clusters, many of the methods practically are applied without systematic approach - some are focused to the port efficiency, others provide quantity of resources growth dynamics, infrastructure parameters or even explain productivity and competitiveness as the same assumption. This article presents the analysis of Maritime Clusters' Productivity and Competitiveness evaluation methods in systematic approach, providing the analysis on the mostly-used variables and parameters of the evaluation the assumptions to be examined.
Methods for sample size determination in cluster randomized trials.
Rutterford, Clare; Copas, Andrew; Eldridge, Sandra
2015-06-01
The use of cluster randomized trials (CRTs) is increasing, along with the variety in their design and analysis. The simplest approach for their sample size calculation is to calculate the sample size assuming individual randomization and inflate this by a design effect to account for randomization by cluster. The assumptions of a simple design effect may not always be met; alternative or more complicated approaches are required. We summarise a wide range of sample size methods available for cluster randomized trials. For those familiar with sample size calculations for individually randomized trials but with less experience in the clustered case, this manuscript provides formulae for a wide range of scenarios with associated explanation and recommendations. For those with more experience, comprehensive summaries are provided that allow quick identification of methods for a given design, outcome and analysis method. We present first those methods applicable to the simplest two-arm, parallel group, completely randomized design followed by methods that incorporate deviations from this design such as: variability in cluster sizes; attrition; non-compliance; or the inclusion of baseline covariates or repeated measures. The paper concludes with methods for alternative designs. There is a large amount of methodology available for sample size calculations in CRTs. This paper gives the most comprehensive description of published methodology for sample size calculation and provides an important resource for those designing these trials. © The Author 2015. Published by Oxford University Press on behalf of the International Epidemiological Association.
Low-energy spectrum of iron-sulfur clusters directly from many-particle quantum mechanics
Sharma, Sandeep; Neese, Frank; Chan, Garnet Kin-Lic
2014-01-01
FeS clusters are a universal biological motif. They carry out electron transfer, redox chemistry, and even oxygen sensing, in diverse processes including nitrogen fixation, respiration, and photosynthesis. The low-lying electronic states are key to their remarkable reactivity, but cannot be directly observed. Here we present the first ever quantum calculation of the electronic levels of [2Fe-2S] and [4Fe-4S] clusters free from any model assumptions. Our results highlight limitations of long-standing models of their electronic structure. In particular, we demonstrate that the widely used Heisenberg-Double-Exchange model underestimates the number of states by 1-2 orders of magnitude, which can conclusively be traced to the absence of Fe d$\\rightarrow$d excitations, thought to be important in these clusters. Further, the electronic energy levels of even the same spin are dense on the scale of vibrational fluctuations, and this provides a natural explanation for the ubiquity of these clusters in nature for cataly...
Mani, Devendra; Can, Cihad; Pal, Nitish; Schwaab, Gerhard; Havenith, Martina
2017-06-01
Imidazole ring is a part of many biologically important molecules and drugs. Imidazole monomer, dimer and its complexes with water have earlier been studied using infrared spectroscopy in helium droplets^{1,2} and molecular beams^{3}. These studies were focussed on the N-H and O-H stretch regions, covering the spectral region of 3200-3800 \\wn. We have extended the studies on imidazole clusters into the ring vibration region. The imidazole clusters were isolated in helium droplets and were probed using a combination of infrared spectroscopy and mass spectrometry. The spectra in the region of 1000-1100 \\wn and 1300-1460 \\wn were recorded using quantum cascade lasers. Some of the observed bands could be assigned to imidazole monomer and higher order imidazole clusters, using pickup curve analysis and ab initio calculations. Work is still in progress. The results will be discussed in detail in the talk. References: 1) M.Y. Choi and R.E. Miller, J. Phys. Chem. A, 110, 9344 (2006). 2) M.Y. Choi and R.E. Miller, Chem. Phys. Lett., 477, 276 (2009). 3) J. Zischang, J. J. Lee and M. Suhm, J. Chem. Phys., 135, 061102 (2011). Note: This work was supported by the Cluster of Excellence RESOLV (Ruhr-Universitat EXC1069) funded by the Deutsche Forschungsgemeinschaft.
Introduction to modern methods of quantum many-body theory and their applications
Fantoni, Stefano; Krotscheck, Eckhard S
2002-01-01
This invaluable book contains pedagogical articles on the dominant nonstochastic methods of microscopic many-body theories - the methods of density functional theory, coupled cluster theory, and correlated basis functions - in their widest sense. Other articles introduce students to applications of these methods in front-line research, such as Bose-Einstein condensates, the nuclear many-body problem, and the dynamics of quantum liquids. These keynote articles are supplemented by experimental reviews on intimately connected topics that are of current relevance. The book addresses the striking l
Multiparameter estimation along quantum trajectories with sequential Monte Carlo methods
Ralph, Jason F.; Maskell, Simon; Jacobs, Kurt
2017-11-01
This paper proposes an efficient method for the simultaneous estimation of the state of a quantum system and the classical parameters that govern its evolution. This hybrid approach benefits from efficient numerical methods for the integration of stochastic master equations for the quantum system, and efficient parameter estimation methods from classical signal processing. The classical techniques use sequential Monte Carlo (SMC) methods, which aim to optimize the selection of points within the parameter space, conditioned by the measurement data obtained. We illustrate these methods using a specific example, an SMC sampler applied to a nonlinear system, the Duffing oscillator, where the evolution of the quantum state of the oscillator and three Hamiltonian parameters are estimated simultaneously.
Image Registration Using Single Cluster PHD Methods
Campbell, M.; Schlangen, I.; Delande, E.; Clark, D.
Cadets in the Department of Physics at the United States Air Force Academy are using the technique of slitless spectroscopy to analyze the spectra from geostationary satellites during glint season. The equinox periods of the year are particularly favorable for earth-based observers to detect specular reflections off satellites (glints), which have been observed in the past using broadband photometry techniques. Three seasons of glints were observed and analyzed for multiple satellites, as measured across the visible spectrum using a diffraction grating on the Academy’s 16-inch, f/8.2 telescope. It is clear from the results that the glint maximum wavelength decreases relative to the time periods before and after the glint, and that the spectral reflectance during the glint is less like a blackbody. These results are consistent with the presumption that solar panels are the predominant source of specular reflection. The glint spectra are also quantitatively compared to different blackbody curves and the solar spectrum by means of absolute differences and standard deviations. Our initial analysis appears to indicate a potential method of determining relative power capacity.
Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution
Satish Gajawada; Durga Toshniwal
2012-01-01
Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...
Agent-based method for distributed clustering of textual information
Potok, Thomas E [Oak Ridge, TN; Reed, Joel W [Knoxville, TN; Elmore, Mark T [Oak Ridge, TN; Treadwell, Jim N [Louisville, TN
2010-09-28
A computer method and system for storing, retrieving and displaying information has a multiplexing agent (20) that calculates a new document vector (25) for a new document (21) to be added to the system and transmits the new document vector (25) to master cluster agents (22) and cluster agents (23) for evaluation. These agents (22, 23) perform the evaluation and return values upstream to the multiplexing agent (20) based on the similarity of the document to documents stored under their control. The multiplexing agent (20) then sends the document (21) and the document vector (25) to the master cluster agent (22), which then forwards it to a cluster agent (23) or creates a new cluster agent (23) to manage the document (21). The system also searches for stored documents according to a search query having at least one term and identifying the documents found in the search, and displays the documents in a clustering display (80) of similarity so as to indicate similarity of the documents to each other.
Lehmann, S B C; Spickermann, C; Kirchner, B
2009-06-09
With the aid of the quantum cluster equilibrium method, we calculate thermodynamic properties for a new water cluster set containing 2-fold and additional tetrahedrally hydrogen-bonded water molecules on the basis of accurate correlated electronic structure calculations. The addition of clusters with 4-fold coordinated water molecules leads to an improved thermodynamical description of the liquid phase in comparison to experimental values. The comparison of the obtained isobars from the pure 2-fold cluster set with the mixed cluster set shows improved results for the mixed set. Furthermore, the results of the liquid-phase entropy calculation compare excellently with experiment if the mixed cluster set is applied. The calculated populations allow us to determine hydrogen bond numbers, resulting in a temperature-dependent average hydrogen bond number. We observe a decreasing average hydrogen bond number of 2.77 at 274 K to 2.26 at 373 K and a dominance of 75% 2-fold hydrogen-bonded water molecules at room temperature for the mixed cluster set.
Non perturbative methods in two dimensional quantum field theory
Abdalla, Elcio; Rothe, Klaus D
1991-01-01
This book is a survey of methods used in the study of two-dimensional models in quantum field theory as well as applications of these theories in physics. It covers the subject since the first model, studied in the fifties, up to modern developments in string theories, and includes exact solutions, non-perturbative methods of study, and nonlinear sigma models.
Statistical mechanics of self-gravitating system: Cluster expansion method
Iguchi, O.; Kurokawa, T.; Morikawa, M.; Nakamichi, A.; Sota, Y.; Tatekawa, T.; Maeda, K.-I.
1999-09-01
We study statistical mechanics of the self-gravitating system applying the cluster expansion method developed in solid state physics. By summing infinite series of diagrams, we derive a complex free energy whose imaginary part is related to the relaxation time of the system, and a two-point correlation function.
On the factorization method in quantum mechanics
Rosas-Ortiz, J. Oscar
1998-01-01
New exactly solvable problems have already been studied by using a modification of the factorization method introduced by Mielnik. We review this method and its connection with the traditional factorization method. The survey includes the discussion on a generalization of the factorization energies used in the traditional Infeld and Hull method.
Henry, David; Dymnicki, Allison B.; Mohatt, Nathaniel; Allen, James; Kelly, James G.
2016-01-01
Qualitative methods potentially add depth to prevention research, but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data, but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-Means clustering, and latent class analysis produced similar levels of accuracy with binary data, and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a “real-world” example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities. PMID:25946969
Henry, David; Dymnicki, Allison B; Mohatt, Nathaniel; Allen, James; Kelly, James G
2015-10-01
Qualitative methods potentially add depth to prevention research but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed-methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed-methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-means clustering, and latent class analysis produced similar levels of accuracy with binary data and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a "real-world" example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities.
Mathematical methods of many-body quantum field theory
Lehmann, Detlef
2004-01-01
Mathematical Methods of Many-Body Quantum Field Theory offers a comprehensive, mathematically rigorous treatment of many-body physics. It develops the mathematical tools for describing quantum many-body systems and applies them to the many-electron system. These tools include the formalism of second quantization, field theoretical perturbation theory, functional integral methods, bosonic and fermionic, and estimation and summation techniques for Feynman diagrams. Among the physical effects discussed in this context are BCS superconductivity, s-wave and higher l-wave, and the fractional quantum Hall effect. While the presentation is mathematically rigorous, the author does not focus solely on precise definitions and proofs, but also shows how to actually perform the computations.Presenting many recent advances and clarifying difficult concepts, this book provides the background, results, and detail needed to further explore the issue of when the standard approximation schemes in this field actually work and wh...
Theoretical physics 7 quantum mechanics : methods and applications
Nolting, Wolfgang
2017-01-01
This textbook offers a clear and comprehensive introduction to methods and applications in quantum mechanics, one of the core components of undergraduate physics courses. It follows on naturally from the previous volumes in this series, thus developing the understanding of quantized states further on. The first part of the book introduces the quantum theory of angular momentum and approximation methods. More complex themes are covered in the second part of the book, which describes multiple particle systems and scattering theory. Ideally suited to undergraduate students with some grounding in the basics of quantum mechanics, the book is enhanced throughout with learning features such as boxed inserts and chapter summaries, with key mathematical derivations highlighted to aid understanding. The text is supported by numerous worked examples and end of chapter problem sets. About the Theoretical Physics series Translated from the renowned and highly successful German editions, the eight volumes of this seri...
The Born–Oppenheimer method, quantum gravity and matter
Kamenshchik, Alexander Yu; Tronconi, Alessandro; Venturi, Giovanni
2018-01-01
We illustrate and examine diverse approaches to the quantum matter–gravity system which refer to the Born–Oppenheimer (BO) method. In particular we first examine a quantum geometrodynamical approach introduced by other authors in a manner analogous to that previously employed by us, so as to include back reaction and non-adiabatic contributions. On including such effects it is seen that the unitarity violating effects previously found disappear. A quantum loop space formulation (based on a hybrid quantisation, polymer for gravitation and canonical for matter) also refers to the BO method. It does not involve the classical limit for gravitation and has a highly peaked initial scalar field state. We point out that it does not resemble in any way our traditional BO approach. Instead it does resemble an alternative, canonically quantised, non BO approach which we have also previously discussed.
Analytic continuation average spectrum method for transport in quantum liquids
Energy Technology Data Exchange (ETDEWEB)
Kletenik-Edelman, Orly [School of Chemistry, Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978 (Israel); Rabani, Eran, E-mail: rabani@tau.ac.il [School of Chemistry, Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978 (Israel); Reichman, David R. [Department of Chemistry, Columbia University, 3000 Broadway, New York, NY 10027 (United States)
2010-05-12
Recently, we have applied the analytic continuation averaged spectrum method (ASM) to calculate collective density fluctuations in quantum liquid . Unlike the maximum entropy (MaxEnt) method, the ASM approach is capable of revealing resolved modes in the dynamic structure factor in agreement with experiments. In this work we further develop the ASM to study single-particle dynamics in quantum liquids with dynamical susceptibilities that are characterized by a smooth spectrum. Surprisingly, we find that for the power spectrum of the velocity autocorrelation function there are pronounced differences in comparison with the MaxEnt approach, even for this simple case of smooth unimodal dynamic response. We show that for liquid para-hydrogen the ASM is closer to the centroid molecular dynamics (CMD) result while for normal liquid helium it agrees better with the quantum mode coupling theory (QMCT) and with the MaxEnt approach.
a Probabilistic Embedding Clustering Method for Urban Structure Detection
Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.
2017-09-01
Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.
A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION
Directory of Open Access Journals (Sweden)
X. Lin
2017-09-01
Full Text Available Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM to find latent features from high dimensional urban sensing data by “learning” via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.
Method of preparing size-selected metal clusters
Elam, Jeffrey W.; Pellin, Michael J.; Stair, Peter C.
2010-05-11
The invention provides a method for depositing catalytic clusters on a surface, the method comprising confining the surface to a controlled atmosphere; contacting the surface with catalyst containing vapor for a first period of time; removing the vapor from the controlled atmosphere; and contacting the surface with a reducing agent for a second period of time so as to produce catalyst-containing nucleation sites.
Directory of Open Access Journals (Sweden)
Yevgeniy M. Sgibnev
2016-11-01
Full Text Available Subject of Study.The paper deals with novel research of ion exchange duration influence on spectral-luminescent properties of silver clusters formed in photo-thermo-refractive glass. Method. Photo-thermo-refractive matrix glass based on Na2O–Al2O3–ZnO–SiO2–F (% mol. system doped with 0,002% mol. of Sb2O3 was synthesized for further research. Silver ions were introduced with low temperature ion exchange method. The glass samples were immersed in the mixture of sodium and silver nitrates 5AgNO3/95NaNO3 (% mol. at the temperature of 320 °C. Ion exchange duration varied from 5 minutes to 21 hours. Luminescent silver clusters were formed in surface layers of photo-thermo-refractive glass by subsequent heat treatment at the temperature of 450 °C. Main Results. Embedding of silver ions in photo-thermo-refractive glass with ion exchange method led to long-wavelength shift of the UV edge of strong absorption. Location of the UV edge of strong absorption and emission peak of silver clusters depends on ion exchange duration and shifts to the greater wavelengthswith increasing the ion exchange process time. Quantum yield of luminescence decreases significantly according to Stern-Volmer equation with the rising of ion exchange duration. Practical Relevance. Research results can be used for developing white LEDs and down-convertors of solar radiation.
Monte Carlo methods in AB initio quantum chemistry quantum Monte Carlo for molecules
Lester, William A; Reynolds, PJ
1994-01-01
This book presents the basic theory and application of the Monte Carlo method to the electronic structure of atoms and molecules. It assumes no previous knowledge of the subject, only a knowledge of molecular quantum mechanics at the first-year graduate level. A working knowledge of traditional ab initio quantum chemistry is helpful, but not essential.Some distinguishing features of this book are: Clear exposition of the basic theory at a level to facilitate independent study. Discussion of the various versions of the theory: diffusion Monte Carlo, Green's function Monte Carlo, and release n
A multiscale quantum mechanics/electromagnetics method for device simulations.
Yam, ChiYung; Meng, Lingyi; Zhang, Yu; Chen, GuanHua
2015-04-07
Multiscale modeling has become a popular tool for research applying to different areas including materials science, microelectronics, biology, chemistry, etc. In this tutorial review, we describe a newly developed multiscale computational method, incorporating quantum mechanics into electronic device modeling with the electromagnetic environment included through classical electrodynamics. In the quantum mechanics/electromagnetics (QM/EM) method, the regions of the system where active electron scattering processes take place are treated quantum mechanically, while the surroundings are described by Maxwell's equations and a semiclassical drift-diffusion model. The QM model and the EM model are solved, respectively, in different regions of the system in a self-consistent manner. Potential distributions and current densities at the interface between QM and EM regions are employed as the boundary conditions for the quantum mechanical and electromagnetic simulations, respectively. The method is illustrated in the simulation of several realistic systems. In the case of junctionless field-effect transistors, transfer characteristics are obtained and a good agreement between experiments and simulations is achieved. Optical properties of a tandem photovoltaic cell are studied and the simulations demonstrate that multiple QM regions are coupled through the classical EM model. Finally, the study of a carbon nanotube-based molecular device shows the accuracy and efficiency of the QM/EM method.
Bayesian methods, maximum entropy, and quantum Monte Carlo
Energy Technology Data Exchange (ETDEWEB)
Gubernatis, J.E.; Silver, R.N. (Los Alamos National Lab., NM (United States)); Jarrell, M. (Cincinnati Univ., OH (United States))
1991-01-01
We heuristically discuss the application of the method of maximum entropy to the extraction of dynamical information from imaginary-time, quantum Monte Carlo data. The discussion emphasizes the utility of a Bayesian approach to statistical inference and the importance of statistically well-characterized data. 14 refs.
Use of ab initio quantum chemical methods in battery technology
Energy Technology Data Exchange (ETDEWEB)
Deiss, E. [Paul Scherrer Inst. (PSI), Villigen (Switzerland)
1997-06-01
Ab initio quantum chemistry can nowadays predict physical and chemical properties of molecules and solids. An attempt should be made to use this tool more widely for predicting technologically favourable materials. To demonstrate the use of ab initio quantum chemistry in battery technology, the theoretical energy density (energy per volume of active electrode material) and specific energy (energy per mass of active electrode material) of a rechargeable lithium-ion battery consisting of a graphite electrode and a nickel oxide electrode has been calculated with this method. (author) 1 fig., 1 tab., 7 refs.
An orientation analysis method for protein immobilized on quantum dot particles
Energy Technology Data Exchange (ETDEWEB)
Aoyagi, Satoka, E-mail: aoyagi@life.shimane-u.ac.jp [Faculty of Life and Environmental Science, Shimane University, 1060 Matsue-shi, Shimane 690-8504 (Japan); Inoue, Masae [Toyota Central R and D Labs., Inc., Nagakute, Aichi 480-1192 (Japan)
2009-11-30
The evaluation of orientation of biomolecules immobilized on nanodevices is crucial for the development of high performance devices. Such analysis requires ultra high sensitivity so as to be able to detect less than one molecular layer on a device. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has sufficient sensitivity to evaluate the uppermost surface structure of a single molecular layer. The objective of this study is to develop an orientation analysis method for proteins immobilized on nanomaterials such as quantum dot particles, and to evaluate the orientation of streptavidin immobilized on quantum dot particles by means of TOF-SIMS. In order to detect fragment ions specific to the protein surface, a monoatomic primary ion source (Ga{sup +}) and a cluster ion source (Au{sub 3}{sup +}) were employed. Streptavidin-immobilized quantum dot particles were immobilized on aminosilanized ITO glass plates at amino groups by covalent bonding. The reference samples streptavidin directly immobilized on ITO plates were also prepared. All samples were dried with a freeze dryer before TOF-SIMS measurement. The positive secondary ion spectra of each sample were obtained using TOF-SIMS with Ga{sup +} and Au{sub 3}{sup +}, respectively, and then they were compared so as to characterize each sample and detect the surface structure of the streptavidin immobilized with the biotin-immobilized quantum dots. The chemical structures of the upper surface of the streptavidin molecules immobilized on the quantum dot particles were evaluated with TOF-SIMS spectra analysis. The indicated surface side of the streptavidin molecules immobilized on the quantum dots includes the biotin binding site.
Karimi, Hamed; Rosenberg, Gili; Katzgraber, Helmut G.
2017-10-01
We present and apply a general-purpose, multistart algorithm for improving the performance of low-energy samplers used for solving optimization problems. The algorithm iteratively fixes the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are smaller and less connected, and samplers tend to give better low-energy samples for these problems. The algorithm is trivially parallelizable since each start in the multistart algorithm is independent, and could be applied to any heuristic solver that can be run multiple times to give a sample. We present results for several classes of hard problems solved using simulated annealing, path-integral quantum Monte Carlo, parallel tempering with isoenergetic cluster moves, and a quantum annealer, and show that the success metrics and the scaling are improved substantially. When combined with this algorithm, the quantum annealer's scaling was substantially improved for native Chimera graph problems. In addition, with this algorithm the scaling of the time to solution of the quantum annealer is comparable to the Hamze-de Freitas-Selby algorithm on the weak-strong cluster problems introduced by Boixo et al. Parallel tempering with isoenergetic cluster moves was able to consistently solve three-dimensional spin glass problems with 8000 variables when combined with our method, whereas without our method it could not solve any.
Exploring function prediction in protein interaction networks via clustering methods.
Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco
2014-01-01
Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.
Exploring function prediction in protein interaction networks via clustering methods.
Directory of Open Access Journals (Sweden)
Kire Trivodaliev
Full Text Available Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.
Translationally-invariant coupled-cluster method for finite systems
Energy Technology Data Exchange (ETDEWEB)
Guardiola, R.; Moliner, I. [Valencia Univ., Burjassot (Spain). Dept. de Fisica Atomica Molecular i Nuclear; Navarro, J.; Portesi, M. [IFIC (Centre Mixt CSIC -Universitat de Valencia), Avda. Dr. Moliner 50, E-46.100 Burjassot (Spain)
1998-01-12
The translational invariant formulation of the coupled-cluster method is presented here at the complete SUB(2) level for a system of nucleons treated as bosons. The correlation amplitudes are solutions of a non-linear coupled system of equations. These equations have been solved for light and medium systems, considering the central but still semi-realistic nucleon-nucleon S3 interaction. (orig.). 16 refs.
Quantum Imaging: New Methods and Applications
2012-01-23
higher-nonlinearity, periodically- poled 2nd-order nonlinear crystals (lithium niobate and potassium titanyl phosphate) together with off-the-shelf high...imaging for multi- layered and scattering media. Dispersion cancellation has been demonstrated experimentally. In conjunction with this, a method for
Segmentation of MRI Volume Data Based on Clustering Method
Directory of Open Access Journals (Sweden)
Ji Dongsheng
2016-01-01
Full Text Available Here we analyze the difficulties of segmentation without tag line of left ventricle MR images, and propose an algorithm for automatic segmentation of left ventricle (LV internal and external profiles. Herein, we propose an Incomplete K-means and Category Optimization (IKCO method. Initially, using Hough transformation to automatically locate initial contour of the LV, the algorithm uses a simple approach to complete data subsampling and initial center determination. Next, according to the clustering rules, the proposed algorithm finishes MR image segmentation. Finally, the algorithm uses a category optimization method to improve segmentation results. Experiments show that the algorithm provides good segmentation results.
Vysotsky, Yu B; Fomina, E S; Belyaeva, E A; Aksenenko, E V; Vollhardt, D; Miller, R
2009-12-31
The semiempirical quantum-chemical PM3 method is used to calculate the thermodynamic parameters of clusterization of the S-form of alpha-amino acids with the general composition C(n)H(2n+1)CHNH(2)COOH (n = 5-15) at 278 and 298 K. It is shown that six stable conformations of monomers exist, for which the thermodynamic parameters (enthalpy and Gibbs' energy) of the formation and absolute entropy are calculated. The correlation dependencies of the calculated parameters on the alkyl chain length are found to be linear. The structures of the monomers are used to build larger clusters (dimers, tetramers, hexamers). For all small clusters (comprised of two to six molecules), the thermodynamic parameters of formation and clusterization are calculated. It is shown that for tetramers and hexamers the enthalpy, entropy, and Gibbs' energy of clusterization are linearly dependent on the alkyl chain length, whereas for the dimers these dependencies are stepwise. The thermodynamic characteristics of clusterization of associates tilted by angles of 9 and 30 degrees with respect to the normal to the interface are calculated. It is shown that the 30 degrees angle orientation is more energetically advantageous for this class of compounds. The geometric parameters of the unit cell characteristic for the infinite 2D film which corresponds to the most advantageous conformation of the monomer were calculated using the PM3 parametrization to be a = 4.57-4.71 A and b = 5.67-5.75 A, with the angle between the axes theta = 100-103 degrees . These values agree well with the available experimental data. Spontaneous clusterization of alpha-amino acids at the air/water interface at 278 K takes place if the alkyl chain length exceeds 11-12 carbon atoms, whereas for 298 K this clusterization threshold corresponds to 13-14 carbon atoms in the alkyl chain, also in agreement with the experimental data.
A novel quantum LSB-based steganography method using the Gray code for colored quantum images
Heidari, Shahrokh; Farzadnia, Ehsan
2017-10-01
As one of the prevalent data-hiding techniques, steganography is defined as the act of concealing secret information in a cover multimedia encompassing text, image, video and audio, imperceptibly, in order to perform interaction between the sender and the receiver in which nobody except the receiver can figure out the secret data. In this approach a quantum LSB-based steganography method utilizing the Gray code for quantum RGB images is investigated. This method uses the Gray code to accommodate two secret qubits in 3 LSBs of each pixel simultaneously according to reference tables. Experimental consequences which are analyzed in MATLAB environment, exhibit that the present schema shows good performance and also it is more secure and applicable than the previous one currently found in the literature.
Effective numerical method of spectral analysis of quantum graphs
Barrera-Figueroa, Víctor; Rabinovich, Vladimir S.
2017-05-01
We present in the paper an effective numerical method for the determination of the spectra of periodic metric graphs equipped by Schrödinger operators with real-valued periodic electric potentials as Hamiltonians and with Kirchhoff and Neumann conditions at the vertices. Our method is based on the spectral parameter power series method, which leads to a series representation of the dispersion equation, which is suitable for both analytical and numerical calculations. Several important examples demonstrate the effectiveness of our method for some periodic graphs of interest that possess potentials usually found in quantum mechanics.
Free Energies of Quantum Particles: The Coupled-Perturbed Quantum Umbrella Sampling Method.
Glover, William J; Casey, Jennifer R; Schwartz, Benjamin J
2014-10-14
We introduce a new simulation method called Coupled-Perturbed Quantum Umbrella Sampling that extends the classical umbrella sampling approach to reaction coordinates involving quantum mechanical degrees of freedom. The central idea in our method is to solve coupled-perturbed equations to find the response of the quantum system's wave function along a reaction coordinate of interest. This allows for propagation of the system's dynamics under the influence of a quantum biasing umbrella potential and provides a method to rigorously undo the effects of the bias to compute equilibrium ensemble averages. In this way, one can drag electrons into regions of high free energy where they would otherwise not go, thus enabling chemistry by fiat. We demonstrate the applicability of our method for two condensed-phase systems of interest. First, we consider the interaction of a hydrated electron with an aqueous sodium cation, and we calculate a potential of mean force that shows that an e(-):Na(+) contact pair is the thermodynamically favored product starting from either a neutral sodium atom or the separate cation and electron species. Second, we present the first determination of a hydrated electron's free-energy profile relative to an air/water interface. For the particular model parameters used, we find that the hydrated electron is more thermodynamically stable in the bulk rather than at the interface. Our analysis suggests that the primary driving force keeping the electron away from the interface is the long-range electron-solvent polarization interaction rather than the short-range details of the chosen pseudopotential.
Analysis of protein profiles using fuzzy clustering methods
DEFF Research Database (Denmark)
Karemore, Gopal Raghunath; Ukendt, Sujatha; Rai, Lavanya
The tissue protein profiles of healthy volunteers and volunteers with cervical cancer were recorded using High Performance Liquid Chromatography combined with Laser Induced Fluorescence technique (HPLC-LIF) developed in our lab. We analyzed the protein profile data using different...... clustering methods for their classification followed by various validation measures. The clustering algorithms used for the study were K- means, K- medoid, Fuzzy C-means, Gustafson-Kessel, and Gath-Geva. The results presented in this study conclude that the protein profiles of tissue...... samples recorded by using the HPLC- LIF system and the data analyzed by clustering algorithms quite successfully classifies them as belonging from normal and malignant conditions....
Quantum Private Comparison of Equality Based on Five-Particle Cluster State
Chang, Yan; Zhang, Wen-Bo; Zhang, Shi-Bin; Wang, Hai-Chun; Yan, Li-Li; Han, Gui-Hua; Sheng, Zhi-Wei; Huang, Yuan-Yuan; Suo, Wang; Xiong, Jin-Xin
2016-12-01
A protocol for quantum private comparison of equality (QPCE) is proposed based on five-particle cluster state with the help of a semi-honest third party (TP). In our protocol, TP is allowed to misbehave on its own but can not conspire with either of two parties. Compared with most two-user QPCE protocols, our protocol not only can compare two groups of private information (each group has two users) in one execution, but also compare just two private information. Compared with the multi-user QPCE protocol proposed, our protocol is safer with more reasonable assumptions of TP. The qubit efficiency is computed and analyzed. Our protocol can also be generalized to the case of 2N participants with one TP. The 2N-participant protocol can compare two groups (each group has N private information) in one execution or just N private information. Supported by NSFC under Grant Nos. 61402058, 61572086, the Fund for Middle and Young Academic Leaders of CUIT under Grant No. J201511, the Science and Technology Support Project of Sichuan Province of China under Grant No. 2013GZX0137, the Fund for Young Persons Project of Sichuan Province of China under Grant No. 12ZB017, and the Foundation of Cyberspace Security Key Laboratory of Sichuan Higher Education Institutions under Grant No. szjj2014-074
Wang, Tai-Chi; Phoa, Frederick Kin Hing
2016-03-01
Community/cluster is one of the most important features in social networks. Many cluster detection methods were proposed to identify such an important pattern, but few were able to identify the statistical significance of the clusters by considering the likelihood of network structure and its attributes. Based on the definition of clustering, we propose a scanning method, originated from analyzing spatial data, for identifying clusters in social networks. Since the properties of network data are more complicated than those of spatial data, we verify our method's feasibility via simulation studies. The results show that the detection powers are affected by cluster sizes and connection probabilities. According to our simulation results, the detection accuracy of structure clusters and both structure and attribute clusters detected by our proposed method is better than that of other methods in most of our simulation cases. In addition, we apply our proposed method to some empirical data to identify statistically significant clusters.
Determining wood chip size: image analysis and clustering methods
Directory of Open Access Journals (Sweden)
Paolo Febbi
2013-09-01
Full Text Available One of the standard methods for the determination of the size distribution of wood chips is the oscillating screen method (EN 15149- 1:2010. Recent literature demonstrated how image analysis could return highly accurate measure of the dimensions defined for each individual particle, and could promote a new method depending on the geometrical shape to determine the chip size in a more accurate way. A sample of wood chips (8 litres was sieved through horizontally oscillating sieves, using five different screen hole diameters (3.15, 8, 16, 45, 63 mm; the wood chips were sorted in decreasing size classes and the mass of all fractions was used to determine the size distribution of the particles. Since the chip shape and size influence the sieving results, Wang’s theory, which concerns the geometric forms, was considered. A cluster analysis on the shape descriptors (Fourier descriptors and size descriptors (area, perimeter, Feret diameters, eccentricity was applied to observe the chips distribution. The UPGMA algorithm was applied on Euclidean distance. The obtained dendrogram shows a group separation according with the original three sieving fractions. A comparison has been made between the traditional sieve and clustering results. This preliminary result shows how the image analysis-based method has a high potential for the characterization of wood chip size distribution and could be further investigated. Moreover, this method could be implemented in an online detection machine for chips size characterization. An improvement of the results is expected by using supervised multivariate methods that utilize known class memberships. The main objective of the future activities will be to shift the analysis from a 2-dimensional method to a 3- dimensional acquisition process.
The direct midpoint method as a quantum mechanical integrator
Mutze, U
2006-01-01
A computational implementation of quantum dynamics for an arbitrary time-independent Hamilton operator is defined and analyzed. The proposed evolution algorithm for a time step needs three additions of state vectors, three multiplications of state vectors with real numbers, and one application of the square of the Hamilton operator to a state vector. A trajectory starting from a unit-vector remains totally within the unit-sphere in Hilbert space if the time step is smaller than 2 divided by the norm of the Hamilton operator.If the time step is larger than this bound, the trajectory grows exponentially over all limits. The method is exemplified with a computational quantum system which models collision and inelastic scattering of two particles. Each of these particles lives in a discrete finite space which is a subset of a line. The two lines thus associated with the particles cross each other at right angle.
Element-free Galerkin method applied to quantum dot and quantum well nanostructures
Energy Technology Data Exchange (ETDEWEB)
Sperotto, Lucas Kriesel [Instituto Tecnologico de Aeronautica (ITA/IEAv), Sao Jose dos Campos, SP (Brazil). Instituto de Estudos Avancados; Passaro, Angelo; Tanaka, Roberto Y. [Instituto de Estudos Avancados (IEAv), Sao Jose dos Campos, SP (Brazil); Marques, Gleber N. [Universidade do Estado de Mato Grosso (UNEMAT), MT (Brazil)
2012-07-01
Full text: The development of native technologies for the fabrication of infrared photodetectors based on quantum wells and quantum dots is the goal of a set of Brazilian Research Institutes and Universities gathered in a National Institute for Science and Technology. The research covers all phases of the production of such devices in Brazil, from the design to the growing of nanostructured semiconductors, processing and characterization of samples. In this context, a set of computer programs have been developed in the recent years in order to assist the design of such structures, some of them based on the Finite Element Methods (FEM). The Element-Free Galerkin Method (EFGM) is an attractive numerical alternative to the FEM. To perform an EFGM approximation it is required a set of nodal points and the shape functions associated to each node. In this sense its similar to FEM. In the EFGM, the Moving Least Squares (MLS) is used to build highly continuous shape functions, which also result in approximations (solutions) highly continuous. The assembling of the final linear system requires support for numerical integration, which in this work is the same triangular mesh generated for the FEM. One of the main drawbacks of the EFGM is the reproduction of the physical discontinuities inherent to each phenomenon, which means discontinuities of the state variable and/or of its spatial derivatives. If no additional numerical treatment is adopted, spurious oscillations arise in the approximation nearby the discontinuity lines. For instance, some aid techniques such as the domain truncation have been successfully applied for the treatment of material interfaces in the computation of electrostatic and electromagnetic fields. Although the EFGM has been successfully tested for one-dimensional quantum well structures, additional techniques are required for ensuring the Dirichlet boundary conditions, e.g. Lagrange multipliers, which spoil the symmetrical character of the final
A quantitative method for clustering size distributions of elements
Dillner, Ann M.; Schauer, James J.; Christensen, William F.; Cass, Glen R.
A quantitative method was developed to group similarly shaped size distributions of particle-phase elements in order to ascertain sources of the elements. This method was developed and applied using data from two sites in Houston, TX; one site surrounded by refineries, chemical plants and vehicular and commercial shipping traffic, and the other site, 25 miles inland surrounded by residences, light industrial facilities and vehicular traffic. Twenty-four hour size-segregated (0.056fluid catalytic cracking unit catalysts, fuel oil burning, a coal-fired power plant, and high-temperature metal working. The clustered elements were generally attributed to different sources at the two sites during each sampling day indicating the diversity of local sources that impact heavy metals concentrations in the region.
Directory of Open Access Journals (Sweden)
Fu Yuhua
2016-08-01
Full Text Available By using Neutrosophy and Quad-stage Method, the expansions of comparative literature include: comparative social sciences clusters, comparative natural sciences clusters, comparative interdisciplinary sciences clusters, and so on. Among them, comparative social sciences clusters include: comparative literature, comparative history, comparative philosophy, and so on; comparative natural sciences clusters include: comparative mathematics, comparative physics, comparative chemistry, comparative medicine, comparative biology, and so on.
One-way quantum computing in the optical frequency comb.
Menicucci, Nicolas C; Flammia, Steven T; Pfister, Olivier
2008-09-26
One-way quantum computing allows any quantum algorithm to be implemented easily using just measurements. The difficult part is creating the universal resource, a cluster state, on which the measurements are made. We propose a scalable method that uses a single, multimode optical parametric oscillator (OPO). The method is very efficient and generates a continuous-variable cluster state, universal for quantum computation, with quantum information encoded in the quadratures of the optical frequency comb of the OPO.
Lestari, D.; Raharjo, D.; Bustamam, A.; Abdillah, B.; Widhianto, W.
2017-07-01
Dengue virus consists of 10 different constituent proteins and are classified into 4 major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and then we analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. Protein interactions that are connected in a tissue, form a complex protein that serves as a specific biological process unit. The analysis of result shows the R-MCL clustering produces clusters of dengue virus family based on the similarity role of their constituent protein, regardless of serotypes.
Quantum Conductivity of Nanosystems
Pozhar, Liudmila
2004-03-01
Quantum statistical mechanical method of Bogoliubov-Tyablikov two-time Green's functions (TTGFs) suggested by Zubarev and Tserkovnikov is generalized to include spatially inhomogeneous systems, such as small semiconductor quantum dots, artificial atoms, etc. The developed formalism is applied to derive a fundamental quantum theory of conductivity of spatially inhomogeneous systems in weak external electromagnetic fields. Conservation equations for the charge and current densities are derived and analyzed. Explicit expressions for the linear (in the field potentials) longitudal and transverse quantum conductivity, dielectric and magnetic susceptibilities are also derived in terms of the equilibrium/steady state charge density - charge density and microcurrent-microcurrent retarded TTGFs. The obtained results are used in conjunction with quantum computations of electronic energy spectra of small clusters of In, Ga and As atoms to predict the quantum conductivity of such clusters.
The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis
Directory of Open Access Journals (Sweden)
Chen Yidong
2004-01-01
Full Text Available An unsupervised data clustering method, called the local maximum clustering (LMC method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999.
Optimal pulse design in quantum control: a unified computational method.
Li, Jr-Shin; Ruths, Justin; Yu, Tsyr-Yan; Arthanari, Haribabu; Wagner, Gerhard
2011-02-01
Many key aspects of control of quantum systems involve manipulating a large quantum ensemble exhibiting variation in the value of parameters characterizing the system dynamics. Developing electromagnetic pulses to produce a desired evolution in the presence of such variation is a fundamental and challenging problem in this research area. We present such robust pulse designs as an optimal control problem of a continuum of bilinear systems with a common control function. We map this control problem of infinite dimension to a problem of polynomial approximation employing tools from geometric control theory. We then adopt this new notion and develop a unified computational method for optimal pulse design using ideas from pseudospectral approximations, by which a continuous-time optimal control problem of pulse design can be discretized to a constrained optimization problem with spectral accuracy. Furthermore, this is a highly flexible and efficient numerical method that requires low order of discretization and yields inherently smooth solutions. We demonstrate this method by designing effective broadband π/2 and π pulses with reduced rf energy and pulse duration, which show significant sensitivity enhancement at the edge of the spectrum over conventional pulses in 1D and 2D NMR spectroscopy experiments.
McCloskey, Rosemary M; Poon, Art F Y
2017-11-01
Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP), which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85%) and specificity (91%) than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46%) as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where it is critical to
Directory of Open Access Journals (Sweden)
Xiurong Zhu
2017-05-01
Full Text Available In the present paper, the liquid–liquid micro extraction of 4-tert-butylphenol from aqueous solution to trioctyl phosphate organic phase in carbon paste electrode was studied by potentiometry and semi-empirical quantum chemistry with MOPAC2009. The extraction dynamic process was monitored by open circuit potential method, which follows an exponential association function with the apparent first extraction kinetic rate constant of 0.01685 s−1. The Nernstian plot of potential difference of the open circuit potentials against logarithm of 4-tert-butylphenol concentration at 500 s extraction time gives a slope of 0.01382, and indicates that 3 or 4 of 4-tert-butylphenol molecules can be extracted by one cluster of trioctyl phosphate dimer. This equation can also serve as working curve for the determination of 4-tert-butylphenol in the concentration range of 1.0 × 10−4–5.0 × 10−7 M with detection limit of 5.0 × 10−7 M (n = 3, ratio of signal/noise = 3. The semi-empirical quantum chemical calculation offers a thermodynamic evidence for the molecular mechanism of the liquid–liquid micro extraction of 4-tert-butylphenol from aqueous solution to trioctyl phosphate cluster.
Spotlight: assembly of protein complexes by integrating graph clustering methods.
Chin, Chia-Hao; Chen, Shu-Hwa; Chen, Chun-Yu; Hsiung, Chao A; Ho, Chin-Wen; Ko, Ming-Tat; Lin, Chung-Yen
2013-04-10
As is generally assumed, clusters in protein-protein interaction (PPI) networks perform specific, crucial functions in biological systems. Various network community detection methods have been developed to exploit PPI networks in order to identify protein complexes and functional modules. Due to the potential role of various regulatory modes in biological networks, a single method may just apply a single graph property and neglect communities highlighted by other network properties. This work presents a novel integration method to capture protein modules/protein complexes by multiple network features detected by different algorithms. The integration method is further implemented in a web-based platform with a highly effective interactive network analyzer. Conventionally adopted methods with different perspectives on network community detection (e.g., CPM, FastGreedy, HUNTER, MCL, LE, SpinGlass, and WalkTrap) are also executed simultaneously. Analytical results indicate that the proposed method performs better than the conventional ones. The proposed approach can capture the transcription and RNA splicing machineries from the yeast protein network. Meanwhile, proteins that are highly associated with each other, yet not described in both machineries are also identified. In sum, a protein that is closely connected to components of a known module or a complex in the network view implies the functional association among them. Importantly, our method can detect these unique network features, thus facilitating efforts to discover unknown components of functional modules/protein complexes. Spotlight is freely accessible at http://hub.iis.sinica.edu.tw/spotlight. Video clips for a quick view of usage are available in the website online help page. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.
Computational Quantum Mechanics for Materials Engineers The EMTO Method and Applications
Vitos, L
2007-01-01
Traditionally, new materials have been developed by empirically correlating their chemical composition, and the manufacturing processes used to form them, with their properties. Until recently, metallurgists have not used quantum theory for practical purposes. However, the development of modern density functional methods means that today, computational quantum mechanics can help engineers to identify and develop novel materials. Computational Quantum Mechanics for Materials Engineers describes new approaches to the modelling of disordered alloys that combine the most efficient quantum-level th
A comparison of heuristic and model-based clustering methods for dietary pattern analysis.
Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia
2016-02-01
Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.
Projector Quantum Monte Carlo Method for Nonlinear Wave Functions
Schwarz, Lauretta R.; Alavi, A.; Booth, George H.
2017-04-01
We reformulate the projected imaginary-time evolution of the full configuration interaction quantum Monte Carlo method in terms of a Lagrangian minimization. This naturally leads to the admission of polynomial complex wave function parametrizations, circumventing the exponential scaling of the approach. While previously these functions have traditionally inhabited the domain of variational Monte Carlo approaches, we consider recent developments for the identification of deep-learning neural networks to optimize this Lagrangian, which can be written as a modification of the propagator for the wave function dynamics. We demonstrate this approach with a form of tensor network state, and use it to find solutions to the strongly correlated Hubbard model, as well as its application to a fully periodic ab initio graphene sheet. The number of variables which can be simultaneously optimized greatly exceeds alternative formulations of variational Monte Carlo methods, allowing for systematic improvability of the wave function flexibility towards exactness for a number of different forms, while blurring the line between traditional variational and projector quantum Monte Carlo approaches.
Brown, Sandra E.; Mandelshtam, Vladimir A.
2016-12-01
The self-consistent phonons (SCP) method is a practical approach for computing structural and dynamical properties of a general quantum or classical many-body system while incorporating anharmonic effects. However, a convincing demonstration of the accuracy of SCP and its advantages over the standard harmonic approximation is still lacking. Here we apply SCP to classical Lennard-Jones (LJ) clusters and compare with numerically exact results. The close agreement between the two reveals that SCP accurately describes structural properties of the classical LJ clusters from zero-temperature (where the method is exact) up to the temperatures at which the chosen cluster conformation becomes unstable. Given the similarities between thermal and quantum fluctuations, both physically and within the SCP ansatz, the accuracy of classical SCP over a range of temperatures suggests that quantum SCP is also accurate over a range of quantum de Boer parameter Λ = ℏ / (σ√{ mε }) , which describes the degree of quantum character of the system.
Quantum realization of the bilinear interpolation method for NEQR.
Zhou, Ri-Gui; Hu, Wenwen; Fan, Ping; Ian, Hou
2017-05-31
In recent years, quantum image processing is one of the most active fields in quantum computation and quantum information. Image scaling as a kind of image geometric transformation has been widely studied and applied in the classical image processing, however, the quantum version of which does not exist. This paper is concerned with the feasibility of the classical bilinear interpolation based on novel enhanced quantum image representation (NEQR). Firstly, the feasibility of the bilinear interpolation for NEQR is proven. Then the concrete quantum circuits of the bilinear interpolation including scaling up and scaling down for NEQR are given by using the multiply Control-Not operation, special adding one operation, the reverse parallel adder, parallel subtractor, multiplier and division operations. Finally, the complexity analysis of the quantum network circuit based on the basic quantum gates is deduced. Simulation result shows that the scaled-up image using bilinear interpolation is clearer and less distorted than nearest interpolation.
High-Efficient Arbitrated Quantum Signature Scheme Based on Cluster States
Fatahi, Negin; Naseri, Mosayeb; Gong, Li-Hua; Liao, Qing-Hong
2017-02-01
The arbitrated quantum signature characteristics including the security and the efficiency are investigated and a new efficient and secure arbitrated quantum signature is proposed. It is shown that the proposed scheme exhibits an efficiency of 64 %. Furthermore, to gain a higher security, the decoy photons security checking is employed.
The initial conditions of observed star clusters - I. Method description and validation
Pijloo, J. T.; Portegies Zwart, S. F.; Alexander, P. E. R.; Gieles, M.; Larsen, S. S.; Groot, P. J.; Devecchi, B.
2015-10-01
We have coupled a fast, parametrized star cluster evolution code to a Markov Chain Monte Carlo code to determine the distribution of probable initial conditions of observed star clusters, that may serve as a starting point for future N-body calculations. In this paper, we validate our method by applying it to a set of star clusters which have been studied in detail numerically with N-body simulations and Monte Carlo methods: the Galactic globular clusters M4, 47 Tucanae, NGC 6397, M22, ω Centauri, Palomar 14 and Palomar 4, the Galactic open cluster M67, and the M31 globular cluster G1. For each cluster, we derive a distribution of initial conditions that, after evolution up to the cluster's current age, evolves to the currently observed conditions. We find that there is a connection between the morphology of the distribution of initial conditions and the dynamical age of a cluster and that a degeneracy in the initial half-mass radius towards small radii is present for clusters that have undergone a core collapse during their evolution. We find that the results of our method are in agreement with N-body and Monte Carlo studies for the majority of clusters. We conclude that our method is able to find reliable posteriors for the determined initial mass and half-mass radius for observed star clusters, and thus forms an suitable starting point for modelling an observed cluster's evolution.
Energy Technology Data Exchange (ETDEWEB)
Balzer, Matthias
2008-07-01
The central goal of this thesis is the examination of strongly correlated electron systems on the basis of the two-dimensional Hubbard model. We analyze how the properties of the Mott insulator change upon doping and with interaction strength. The numerical evaluation is done using quantum cluster approximations, which allow for a thermodynamically consistent description of the ground state properties. The framework of self-energy-functional theory offers great flexibility for the construction of cluster approximations. A detailed analysis sheds light on the quality and the convergence properties of different cluster approximations within the self-energy-functional theory. We use the one-dimensional Hubbard model for these examinations and compare our results with the exact solution. In two dimensions the ground state of the particle-hole symmetric model at half-filling is an antiferromagnetic insulator, independent of the interaction strength. The inclusion of short-range spatial correlations by our cluster approach leads to a considerable improvement of the antiferromagnetic order parameter as compared to dynamical mean-field theory. In the paramagnetic phase we furthermore observe a metal-insulator transition as a function of the interaction strength, which qualitatively differs from the pure mean-field scenario. Starting from the antiferromagnetic Mott insulator a filling-controlled metal-insulator transition in a paramagnetic metallic phase can be observed. Depending on the cluster approximation used an antiferromagnetic metallic phase may occur at first. In addition to long-range antiferromagnetic order, we also considered superconductivity in our calculations. The superconducting order parameter as a function of doping is in good agreement with other numerical methods, as well as with experimental results. (orig.)
Sun, Weiwei; Ma, Jun; Yang, Gang; Du, Bo; Zhang, Liangpei
2017-06-01
A new Bayesian method named Poisson Nonnegative Matrix Factorization with Parameter Subspace Clustering Constraint (PNMF-PSCC) has been presented to extract endmembers from Hyperspectral Imagery (HSI). First, the method integrates the liner spectral mixture model with the Bayesian framework and it formulates endmember extraction into a Bayesian inference problem. Second, the Parameter Subspace Clustering Constraint (PSCC) is incorporated into the statistical program to consider the clustering of all pixels in the parameter subspace. The PSCC could enlarge differences among ground objects and helps finding endmembers with smaller spectrum divergences. Meanwhile, the PNMF-PSCC method utilizes the Poisson distribution as the prior knowledge of spectral signals to better explain the quantum nature of light in imaging spectrometer. Third, the optimization problem of PNMF-PSCC is formulated into maximizing the joint density via the Maximum A Posterior (MAP) estimator. The program is finally solved by iteratively optimizing two sub-problems via the Alternating Direction Method of Multipliers (ADMM) framework and the FURTHESTSUM initialization scheme. Five state-of-the art methods are implemented to make comparisons with the performance of PNMF-PSCC on both the synthetic and real HSI datasets. Experimental results show that the PNMF-PSCC outperforms all the five methods in Spectral Angle Distance (SAD) and Root-Mean-Square-Error (RMSE), and especially it could identify good endmembers for ground objects with smaller spectrum divergences.
Directory of Open Access Journals (Sweden)
Cooper James B
2010-03-01
Full Text Available Abstract Background Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. Results We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four. Conclusions By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome.
Quantum Simulations of Solvated Biomolecules Using Hybrid Methods
Hodak, Miroslav
2009-03-01
One of the most important challenges in quantum simulations on biomolecules is efficient and accurate inclusion of the solvent, because the solvent atoms usually outnumber those in the biomolecule of interest. We have developed a hybrid method that allows for explicit quantum-mechanical treatment of the solvent at low computational cost. In this method, Kohn-Sham (KS) density functional theory (DFT) is combined with an orbital-free (OF) DFT. Kohn-Sham (KS) DFT is used to describe the biomolecule and its first solvation shells, while the orbital-free (OF) DFT is employed for the rest of the solvent. The OF part is fully O(N) and capable of handling 10^5 solvent molecules on current parallel supercomputers, while taking only ˜ 10 % of the total time. The compatibility between the KS and OF DFT methods enables seamless integration between the two. In particular, the flow of solvent molecules across the KS/OF interface is allowed and the total energy is conserved. As the first large-scale applications, the hybrid method has been used to investigate the binding of copper ions to proteins involved in prion (PrP) and Parkinson's diseases. Our results for the PrP, which causes mad cow disease when misfolded, resolve a contradiction found in experiments, in which a stronger binding mode is replaced by a weaker one when concentration of copper ions is increased, and show how it can act as a copper buffer. Furthermore, incorporation of copper stabilizes the structure of the full-length PrP, suggesting its protective role in prion diseases. For alpha-synuclein, a Parkinson's disease (PD) protein, we show that Cu binding modifies the protein structurally, making it more susceptible to misfolding -- an initial step in the onset of PD. In collaboration with W. Lu, F. Rose and J. Bernholc.
Non-unitary probabilistic quantum computing circuit and method
Williams, Colin P. (Inventor); Gingrich, Robert M. (Inventor)
2009-01-01
A quantum circuit performing quantum computation in a quantum computer. A chosen transformation of an initial n-qubit state is probabilistically obtained. The circuit comprises a unitary quantum operator obtained from a non-unitary quantum operator, operating on an n-qubit state and an ancilla state. When operation on the ancilla state provides a success condition, computation is stopped. When operation on the ancilla state provides a failure condition, computation is performed again on the ancilla state and the n-qubit state obtained in the previous computation, until a success condition is obtained.
An introduction to quantum chemical methods applied to drug design.
Stenta, Marco; Dal Peraro, Matteo
2011-06-01
The advent of molecular medicine allowed identifying the malfunctioning of subcellular processes as the source of many diseases. Since then, drugs are not only discovered, but actually designed to fulfill a precise task. Modern computational techniques, based on molecular modeling, play a relevant role both in target identification and drug lead development. By flanking and integrating standard experimental techniques, modeling has proven itself as a powerful tool across the drug design process. The success of computational methods depends on a balance between cost (computation time) and accuracy. Thus, the integration of innovative theories and more powerful hardware architectures allows molecular modeling to be used as a reliable tool for rationalizing the results of experiments and accelerating the development of new drug design strategies. We present an overview of the most common quantum chemistry computational approaches, providing for each one a general theoretical introduction to highlight limitations and strong points. We then discuss recent developments in software and hardware resources, which have allowed state-of-the-art of computational quantum chemistry to be applied to drug development.
Application of single-linkage clustering method in the analysis of ...
African Journals Online (AJOL)
Single-linkage is one of the methods in cluster analysis, which is used, for determining natural groupings in multi-variate data. Given a data set with one or more characteristics, singlelinkage system classifies the data into clusters so that they are as similar as possible within each cluster and as different as possible between ...
Directory of Open Access Journals (Sweden)
Susan Worner
2013-09-01
Full Text Available For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the co-occurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM, a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k
High quantum yield ZnO quantum dots synthesizing via an ultrasonication microreactor method.
Yang, Weimin; Yang, Huafang; Ding, Wenhao; Zhang, Bing; Zhang, Le; Wang, Lixi; Yu, Mingxun; Zhang, Qitu
2016-11-01
Green emission ZnO quantum dots were synthesized by an ultrasonic microreactor. Ultrasonic radiation brought bubbles through ultrasonic cavitation. These bubbles built microreactor inside the microreactor. The photoluminescence properties of ZnO quantum dots synthesized with different flow rate, ultrasonic power and temperature were discussed. Flow rate, ultrasonic power and temperature would influence the type and quantity of defects in ZnO quantum dots. The sizes of ZnO quantum dots would be controlled by those conditions as well. Flow rate affected the reaction time. With the increasing of flow rate, the sizes of ZnO quantum dots decreased and the quantum yields first increased then decreased. Ultrasonic power changed the ultrasonic cavitation intensity, which affected the reaction energy and the separation of the solution. With the increasing of ultrasonic power, sizes of ZnO quantum dots first decreased then increased, while the quantum yields kept increasing. The effect of ultrasonic temperature on the photoluminescence properties of ZnO quantum dots was influenced by the flow rate. Different flow rate related to opposite changing trend. Moreover, the quantum yields of ZnO QDs synthesized by ultrasonic microreactor could reach 64.7%, which is higher than those synthesized only under ultrasonic radiation or only by microreactor. Copyright © 2016 Elsevier B.V. All rights reserved.
Christensen, Anders S.; Kromann, Jimmy C.; Jensen, Jan H.; Cui, Qiang
2017-10-01
To facilitate further development of approximate quantum mechanical methods for condensed phase applications, we present a new benchmark dataset of intermolecular interaction energies in the solution phase for a set of 15 dimers, each containing one charged monomer. The reference interaction energy in solution is computed via a thermodynamic cycle that integrates dimer binding energy in the gas phase at the coupled cluster level and solute-solvent interaction with density functional theory; the estimated uncertainty of such calculated interaction energy is ±1.5 kcal/mol. The dataset is used to benchmark the performance of a set of semi-empirical quantum mechanical (SQM) methods that include DFTB3-D3, DFTB3/CPE-D3, OM2-D3, PM6-D3, PM6-D3H+, and PM7 as well as the HF-3c method. We find that while all tested SQM methods tend to underestimate binding energies in the gas phase with a root-mean-squared error (RMSE) of 2-5 kcal/mol, they overestimate binding energies in the solution phase with an RMSE of 3-4 kcal/mol, with the exception of DFTB3/CPE-D3 and OM2-D3, for which the systematic deviation is less pronounced. In addition, we find that HF-3c systematically overestimates binding energies in both gas and solution phases. As most approximate QM methods are parametrized and evaluated using data measured or calculated in the gas phase, the dataset represents an important first step toward calibrating QM based methods for application in the condensed phase where polarization and exchange repulsion need to be treated in a balanced fashion.
An Improved Filtering Method for Quantum Color Image in Frequency Domain
Li, Panchi; Xiao, Hong
2017-10-01
In this paper we investigate the use of quantum Fourier transform (QFT) in the field of image processing. We consider QFT-based color image filtering operations and their applications in image smoothing, sharpening, and selective filtering using quantum frequency domain filters. The underlying principle used for constructing the proposed quantum filters is to use the principle of the quantum Oracle to implement the filter function. Compared with the existing methods, our method is not only suitable for color images, but also can flexibly design the notch filters. We provide the quantum circuit that implements the filtering task and present the results of several simulation experiments on color images. The major advantages of the quantum frequency filtering lies in the exploitation of the efficient implementation of the quantum Fourier transform.
The Cluster Variation Method: A Primer for Neuroscientists
Directory of Open Access Journals (Sweden)
Alianna J. Maren
2016-09-01
Full Text Available Effective Brain–Computer Interfaces (BCIs require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns. The equilibrium distribution of local patterns, or configuration variables, is defined in terms of a single interaction enthalpy parameter (h for the case of an equiprobable distribution of bistate (neural/neural ensemble units. Thus, either one enthalpy parameter (or two, for the case of non-equiprobable distribution yields equilibrium configuration variable values. Modeling 2-D neural activation distribution patterns with the representational layer of a computational engine, we can thus correlate variational free energy minimization with specific configuration variable distributions. The CVM triplet configuration variables also map well to the notion of a M = 3 functional motif. This paper addresses the special case of an equiprobable unit distribution, for which an analytic solution can be found.
Higher-Order Equation-of-Motion Coupled-Cluster Methods for Ionization Processes
Energy Technology Data Exchange (ETDEWEB)
Kamiya, Muneaki; Hirata, So
2006-08-21
Compact algebraic equations defining the equation-of-motion coupled-cluster (EOM-CC) methods for ionization potentials (IP-EOM-CC) have been derived and computer implemented by virtue of a symbolic algebra system largely automating these processes. Models with connected cluster excitation operators truncated after double, triple, or quadruple level and with linear ionization operators truncated after two-hole-one-particle (2h1p), three-hole-two-particle (3h2p), or four-hole-three-particle (4h3p) level (abbreviated as IP-EOM-CCSD, CCSDT, and CCSDTQ, respectively) have been realized into parallel algorithms taking advantage of spin, spatial, and permutation symmetries with optimal size dependence of the computational costs. They are based on spin-orbital formalisms and can describe both {alpha} and {beta} and ionizations from open-shell (doublet, triplet, etc.) reference states into ionized states with various spin magnetic quantum numbers. The application of these methods to Koopmans and satellite ionizations of N{sub 2} and CO (with the ambiguity due to finite basis sets eliminated by extrapolation) has shown that IP-EOM-CCSD frequently accounts for orbital relaxation inadequately and displays errors exceeding a couple of eV. However, these errors can be systematically reduced to tenths or even hundredths of an eV by IP-EOM-CCSDT or CCSDTQ. Comparison of spectroscopic parameters of the FH{sup +} and NH{sup +} radicals between IP-EOM-CC and experiments has also underscored the importance of higher-order IP-EOM-CC treatments. For instance, the harmonic frequencies of the {tilde A} {sup 2}{Sigma}{sup -} state of NH{sup +}+ are predicted to be 1285, 1723, and 1705 cm{sup -1} by IP-EOM-CCSD, CCSDT, and CCSDTQ, respectively, as compared to the observed value of 1707 cm{sup -1}. The small adiabatic energy separation (observed 0.04 eV) between the {tilde X} {sup 2}II and {tilde a} {sup 4}{sigma}{sup -} states of NH{sup +} also requires IP-EOM-CCSDTQ for a quantitative
Atomistic Modeling of Nanostructures via the BFS Quantum Approximate Method
Bozzolo, Guillermo; Garces, Jorge E.; Noebe, Ronald D.; Farias, D.
2003-01-01
Ideally, computational modeling techniques for nanoscopic physics would be able to perform free of limitations on the type and number of elements, while providing comparable accuracy when dealing with bulk or surface problems. Computational efficiency is also desirable, if not mandatory, for properly dealing with the complexity of typical nano-strucured systems. A quantum approximate technique, the BFS method for alloys, which attempts to meet these demands, is introduced for the calculation of the energetics of nanostructures. The versatility of the technique is demonstrated through analysis of diverse systems, including multi-phase precipitation in a five element Ni-Al-Ti-Cr-Cu alloy and the formation of mixed composition Co-Cu islands on a metallic Cu(III) substrate.
Modified semi-classical methods for nonlinear quantum oscillations problems
Energy Technology Data Exchange (ETDEWEB)
Moncrief, Vincent [Department of Physics and Department of Mathematics, Yale University, P.O. Box 208120, New Haven, Connecticut 06520 (United States); Marini, Antonella [Department of Mathematics, Yeshiva University, 500 West 185th Street, New York, New York 10033, USA and Department of Mathematics, University of L' Aquila, Via Vetoio, 67010 L' Aquila, AQ (Italy); Maitra, Rachel [Department of Physics, Albion College, 611 E. Porter Street, Albion, Michigan 49224 (United States)
2012-10-15
We develop a modified semi-classical approach to the approximate solution of Schroedinger's equation for certain nonlinear quantum oscillations problems. In our approach, at lowest order, the Hamilton-Jacobi equation of the conventional semi-classical formalism is replaced by an inverted-potential-vanishing-energy variant thereof. With suitable smoothness, convexity and coercivity properties imposed on its potential energy function, we prove, using methods drawn from the calculus of variations together with the (Banach space) implicit function theorem, the existence of a global, smooth 'fundamental solution' to this equation. Higher order quantum corrections thereto, for both ground and excited states, can then be computed through the integration of associated systems of linear transport equations, derived from Schroedinger's equation, and formal expansions for the corresponding energy eigenvalues obtained therefrom by imposing the natural demand for smoothness on the (successively computed) quantum corrections to the eigenfunctions. For the special case of linear oscillators our expansions naturally truncate, reproducing the well-known exact solutions for the energy eigenfunctions and eigenvalues. As an explicit application of our methods to computable nonlinear problems, we calculate a number of terms in the corresponding expansions for the one-dimensional anharmonic oscillators of quartic, sectic, octic, and dectic types and compare the results obtained with those of conventional Rayleigh/Schroedinger perturbation theory. To the orders considered (and, conjecturally, to all orders) our eigenvalue expansions agree with those of Rayleigh/Schroedinger theory whereas our wave functions more accurately capture the more-rapid-than-gaussian decay known to hold for the exact solutions to these problems. For the quartic oscillator in particular our results strongly suggest that both the ground state energy eigenvalue expansion and its associated wave
An eigenvalue approach to quantum plasmonics based on a self-consistent hydrodynamics method.
Ding, Kun; Chan, Che Ting
2017-12-28
Plasmonics has attracted much attention not only because it has useful properties such as strong field enhancement, but also because it reveals the quantum nature of matter. To handle quantum plasmonics effects, ab initio packages or empirical Feibelman d-parameters have been used to explore the quantum correction of plasmonic resonances. However, most of these methods are formulated within the quasi-static framework. The self-consistent hydrodynamics model offers a reliable approach to study quantum plasmonics because it can incorporate the quantum effect of the electron gas into classical electrodynamics in a consistent manner. Instead of the standard scattering method, we formulate the self-consistent hydrodynamics method as an eigenvalue problem to study quantum plasmonics with electrons and photons treated on the same footing. We find that the eigenvalue approach must involve a global operator, which originates from the energy functional of the electron gas. This manifests the intrinsic nonlocality of the response of quantum plasmonic resonances. Our model gives the analytical forms of quantum corrections to plasmonic modes, incorporating quantum electron spill-out effects and electrodynamical retardation. We apply our method to study the quantum surface plasmon polariton for a single flat interface. © 2017 IOP Publishing Ltd.
Unsupervised Image Steganalysis Method Using Self-Learning Ensemble Discriminant Clustering
National Research Council Canada - National Science Library
CAO, Bing; FENG, Guorui; YIN, Zhaoxia; FAN, Lingyan
2017-01-01
... and detecting steganographic method. In this paper, we just attempt to process unsupervised learning problem and propose a detection model called self-learning ensemble discriminant clustering (SEDC...
Methods and Patterns for User-Friendly Quantum Programming.
Singh, Alexandros; Giannakis, Konstantinos; Kastampolidou, Kalliopi; Papalitsas, Christos
2017-01-01
The power and efficiency of particular quantum algorithms over classical ones has been proved. The rise of quantum computing and algorithms has highlighted the need for appropriate programming means and tools. Here, we present a brief overview of some techniques and a proposed methodology in writing quantum programs and designing languages. Our approach offers "user-friendly" features to ease the development of such programs. We also give indicative snippets in an untyped fragment of the Qumin language, describing well-known quantum algorithms.
Hartono, Albert; Lu, Qingda; Henretty, Thomas; Krishnamoorthy, Sriram; Zhang, Huaijian; Baumgartner, Gerald; Bernholdt, David E.; Nooijen, Marcel; Pitzer, Russell; Ramanujam, J.; Sadayappan, P.
2009-09-01
Complex tensor contraction expressions arise in accurate electronic structure models in quantum chemistry, such as the coupled cluster method. This paper addresses two complementary aspects of performance optimization of such tensor contraction expressions. Transformations using algebraic properties of commutativity and associativity can be used to significantly decrease the number of arithmetic operations required for evaluation of these expressions. The identification of common subexpressions among a set of tensor contraction expressions can result in a reduction of the total number of operations required to evaluate the tensor contractions. The first part of the paper describes an effective algorithm for operation minimization with common subexpression identification and demonstrates its effectiveness on tensor contraction expressions for coupled cluster equations. The second part of the paper highlights the importance of data layout transformation in the optimization of tensor contraction computations on modern processors. A number of considerations, such as minimization of cache misses and utilization of multimedia vector instructions, are discussed. A library for efficient index permutation of multidimensional tensors is described, and experimental performance data is provided that demonstrates its effectiveness.
Shepherd, James J; Scuseria, Gustavo E
2016-01-01
Over the past few years pair coupled cluster doubles (pCCD) has shown promise for the description of strong correlation. This promise is related to its apparent ability to match results from doubly occupied configuration interaction (DOCI), even though the latter method has exponential computational cost. Here, by modifying the full configuration interaction quantum Monte Carlo (FCIQMC) algorithm to sample only the seniority zero sector of Hilbert space, we show that the DOCI and pCCD energies are in agreement for a variety of 2D Hubbard models, including for systems well out of reach for conventional configuration interaction algorithms. Our calculations are aided by the sign problem being much reduced in the seniority zero space compared with the full space. We present evidence for this, and then discuss the sign problem in terms of the wave function of the system which appears to have a simplified sign structure.
Indian Academy of Sciences (India)
electron transfer chains involved in a number of biologi- cal systems including respiration and photosynthesis.1. The most common iron–sulphur clusters found as active centres in iron–sulphur proteins are [Fe2S2], [Fe3S4] and [Fe4S4], in which Fe(III) ions are coordinated to cysteines from the peptide and are linked to each ...
Liu, Zhihao; Chen, Hanwu; Liu, Wenjie
2016-10-01
A new attack strategy, the so-called intercept-selectively-measure-resend attack is put forward. It shows that there are some security issues in the controlled quantum secure direct communication (CQSDC) and authentication protocol based on five-particle cluster states and quantum one-time pad. Firstly, an eavesdropper (Eve) can use this attack to eavesdrop on 0.656 bit of every bit of the identity string of the receiver and 1.406 bits of every couple of the corresponding bits of the secret message without being detected. Also, she can eavesdrop on 0.311 bit of every bit of the identity string of the controller. Secondly, the receiver can also take this attack to obtain 1.311 bits of every couple of the corresponding bits of the secret message without the permission of the controller, which is not allowed in the CQSDC protocols. In fact, there is another security issue in this protocol, that is, one half of the information about the secret is leaked out unconsciously. In addition, an alternative attack strategy which is called as the selective-CNOT-operation attack strategy to attack this protocol is discussed.
Al-Khalili, Jim
2003-01-01
In this lively look at quantum science, a physicist takes you on an entertaining and enlightening journey through the basics of subatomic physics. Along the way, he examines the paradox of quantum mechanics--beautifully mathematical in theory but confoundingly unpredictable in the real world. Marvel at the Dual Slit experiment as a tiny atom passes through two separate openings at the same time. Ponder the peculiar communication of quantum particles, which can remain in touch no matter how far apart. Join the genius jewel thief as he carries out a quantum measurement on a diamond without ever touching the object in question. Baffle yourself with the bizzareness of quantum tunneling, the equivalent of traveling partway up a hill, only to disappear then reappear traveling down the opposite side. With its clean, colorful layout and conversational tone, this text will hook you into the conundrum that is quantum mechanics.
Mikelsons, Karlis; Khatami, Ehsan; Galanakis, Dimitrios; Macridin, Alexandru; Moreno, Juana; Jarrell, Mark
2010-03-01
We study the thermodynamics of the two-dimensional Hubbard model within the dynamical cluster approximation. We use continuous time quantum Monte Carlo as a cluster solver to avoid the systematic error which complicates the calculation of the entropy and potential energy (double occupancy). We find that at a critical filling, there is a pronounced peak in the entropy divided by temperature, S/T, and in the normalized double occupancy as a function of doping. At this filling, we find that specific heat divided by temperature, C/T, increases strongly with decreasing temperature and kinetic and potential energies vary like T^2 T. These are all characteristics of quantum critical behavior.
A clustering method of Chinese medicine prescriptions based on modified firefly algorithm.
Yuan, Feng; Liu, Hong; Chen, Shou-Qiang; Xu, Liang
2016-12-01
This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.
Energy Technology Data Exchange (ETDEWEB)
Lafata, K; Ren, L; Wu, Q; Kelsey, C; Hong, J; Cai, J; Yin, F [Duke University Medical Center, Durham, NC (United States)
2016-06-15
Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong
A Synthetic Approach to the Transfer Matrix Method in Classical and Quantum Physics
Pujol, O.; Perez, J. P.
2007-01-01
The aim of this paper is to propose a synthetic approach to the transfer matrix method in classical and quantum physics. This method is an efficient tool to deal with complicated physical systems of practical importance in geometrical light or charged particle optics, classical electronics, mechanics, electromagnetics and quantum physics. Teaching…
Directory of Open Access Journals (Sweden)
Peixin Zhao
2013-01-01
Full Text Available Community detection in social networks plays an important role in cluster analysis. Many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy. In this paper we propose a graph-based clustering method for multidimensional datasets. This novel method has two distinguished features: nonbinary hierarchical tree and the multi-membership clusters. The nonbinary hierarchical tree clearly highlights meaningful clusters, while the multimembership feature may provide more useful service strategies. Experimental results on the customer relationship management confirm the effectiveness of the new method.
Barrier heights of hydrogen-transfer reactions with diffusion quantum monte carlo method.
Zhou, Xiaojun; Wang, Fan
2017-04-30
Hydrogen-transfer reactions are an important class of reactions in many chemical and biological processes. Barrier heights of H-transfer reactions are underestimated significantly by popular exchange-correlation functional with density functional theory (DFT), while coupled-cluster (CC) method is quite expensive and can be applied only to rather small systems. Quantum Monte-Carlo method can usually provide reliable results for large systems. Performance of fixed-node diffusion quantum Monte-Carlo method (FN-DMC) on barrier heights of the 19 H-transfer reactions in the HTBH38/08 database is investigated in this study with the trial wavefunctions of the single-Slater-Jastrow form and orbitals from DFT using local density approximation. Our results show that barrier heights of these reactions can be calculated rather accurately using FN-DMC and the mean absolute error is 1.0 kcal/mol in all-electron calculations. Introduction of pseudopotentials (PP) in FN-DMC calculations improves efficiency pronouncedly. According to our results, error of the employed PPs is smaller than that of the present CCSD(T) and FN-DMC calculations. FN-DMC using PPs can thus be applied to investigate H-transfer reactions involving larger molecules reliably. In addition, bond dissociation energies of the involved molecules using FN-DMC are in excellent agreement with reference values and they are even better than results of the employed CCSD(T) calculations using the aug-cc-pVQZ basis set. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Šubelj, Lovro; Waltman, Ludo
2015-01-01
Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between di...
Grinding Wheel Condition Monitoring with Hidden Markov Model-Based Clustering Methods
Energy Technology Data Exchange (ETDEWEB)
Liao, T. W. [Louisiana State University; Hua, G [Louisiana State University; Qu, Jun [ORNL; Blau, Peter Julian [ORNL
2006-01-01
Hidden Markov model (HMM) is well known for sequence modeling and has been used for condition monitoring. However, HMM-based clustering methods are developed only recently. This article proposes a HMM-based clustering method for monitoring the condition of grinding wheel used in grinding operations. The proposed method first extract features from signals based on discrete wavelet decomposition using a moving window approach. It then generates a distance (dissimilarity) matrix using HMM. Based on this distance matrix several hierarchical and partitioning-based clustering algorithms are applied to obtain clustering results. The proposed methodology was tested with feature sequences extracted from acoustic emission signals. The results show that clustering accuracy is dependent upon cutting condition. Higher material removal rate seems to produce more discriminatory signals/features than lower material removal rate. The effect of window size, wavelet decomposition level, wavelet basis, clustering algorithm, and data normalization were also studied.
Continuous-variable quantum computing in optical time-frequency modes using quantum memories.
Humphreys, Peter C; Kolthammer, W Steven; Nunn, Joshua; Barbieri, Marco; Datta, Animesh; Walmsley, Ian A
2014-09-26
We develop a scheme for time-frequency encoded continuous-variable cluster-state quantum computing using quantum memories. In particular, we propose a method to produce, manipulate, and measure two-dimensional cluster states in a single spatial mode by exploiting the intrinsic time-frequency selectivity of Raman quantum memories. Time-frequency encoding enables the scheme to be extremely compact, requiring a number of memories that are a linear function of only the number of different frequencies in which the computational state is encoded, independent of its temporal duration. We therefore show that quantum memories can be a powerful component for scalable photonic quantum information processing architectures.
Blanchard, Philippe
2015-01-01
The second edition of this textbook presents the basic mathematical knowledge and skills that are needed for courses on modern theoretical physics, such as those on quantum mechanics, classical and quantum field theory, and related areas. The authors stress that learning mathematical physics is not a passive process and include numerous detailed proofs, examples, and over 200 exercises, as well as hints linking mathematical concepts and results to the relevant physical concepts and theories. All of the material from the first edition has been updated, and five new chapters have been added on such topics as distributions, Hilbert space operators, and variational methods. The text is divided into three main parts. Part I is a brief introduction to distribution theory, in which elements from the theories of ultradistributions and hyperfunctions are considered in addition to some deeper results for Schwartz distributions, thus providing a comprehensive introduction to the theory of generalized functions. P...
Numerical Simulation of Bubble Cluster Induced Flow by Three-Dimensional Vortex-in-Cell Method.
Chen, Bin; Wang, Zhiwei; Uchiyama, Tomomi
2014-08-01
The behavior of air bubble clusters rising in water and the induced flow field are numerically studied using a three-dimensional two-way coupling algorithm based on a vortex-in-cell (VIC) method. In this method, vortex elements are convected in the Lagrangian frame and the liquid velocity field is solved from the Poisson equation of potential on the Eulerian grid. Two-way coupling is implemented by introducing a vorticity source term induced by the gradient of void fraction. Present simulation results are favorably compared with the measured results of bubble plume, which verifies the validity of the proposed VIC method. The rising of a single bubble cluster as well as two tandem bubble clusters are simulated. The mechanism of the aggregation effect in the rising process of bubble cluster is revealed and the transient processes of the generation, rising, strengthening, and separation of a vortex ring structure with bubble clusters are illustrated and analyzed in detail. Due to the aggregation, the average rising velocity increases with void fraction and is larger than the terminal rising velocity of single bubble. For the two tandem bubble cluster cases, the aggregation effect is stronger for smaller initial cluster distance, and both the strength of the induced vortex structure and the average bubble rising velocity are larger. For the 20 mm cluster distance case, the peak velocity of the lower cluster is about 2.7 times that of the terminal velocity of the single bubble and the peak average velocity of two clusters is about 2 times larger. While for the 30 mm cluster distance case, both the peak velocity of the lower cluster and two clusters are about 1.7 times that of the terminal velocity of the single bubble.
Quantum image pseudocolor coding based on the density-stratified method
Jiang, Nan; Wu, Wenya; Wang, Luo; Zhao, Na
2015-05-01
Pseudocolor processing is a branch of image enhancement. It dyes grayscale images to color images to make the images more beautiful or to highlight some parts on the images. This paper proposes a quantum image pseudocolor coding scheme based on the density-stratified method which defines a colormap and changes the density value from gray to color parallel according to the colormap. Firstly, two data structures: quantum image GQIR and quantum colormap QCR are reviewed or proposed. Then, the quantum density-stratified algorithm is presented. Based on them, the quantum realization in the form of circuits is given. The main advantages of the quantum version for pseudocolor processing over the classical approach are that it needs less memory and can speed up the computation. Two kinds of examples help us to describe the scheme further. Finally, the future work are analyzed.
Analytic methods for field induced tunneling in quantum wells with ...
Indian Academy of Sciences (India)
Status Solidi. B156, 259 (1989). [8] A K Ghatak, I C Goyal and R L Gallawa, IEEE J. Quantum Electron. 26, 305 (1990). [9] D Ahn and S L Chuang, Phys. Rev. B34, 9034 (1986). [10] A K Ghatak, K Thyagarajan and M R Shenoy, IEEE J. Quantum Electron. 24, 1524 (1988). [11] F Borondo and J S醤chez-Dehesa, Phys. Rev.
Particle number projecting method for description of pairing effects in metal clusters
Energy Technology Data Exchange (ETDEWEB)
Kuzmenko, N. [Khlopin Radium Institute, St. Peterburg (Russian Federation); Nesterenko, V.; Pashkevich, V. [Joint Institute for Nuclear Research, Dubna (Russian Federation). Lab. of Theoretical Physics; Frauendorf, S. [Institut fuer Kern- und Hadronenphysik, Forshungszentrum Rossendorf, Dresden (Germany)
1996-05-01
The particle number projecting method for the description of pairing effects in metal clusters is proposed. In contrast with the Bardeen-Cooper-Schrieffer method (BCS) which does not conserve the particle number (thus not providing the necessary accuracy of calculations for small clusters) and has no solutions at sufficiently weak pairing, the projecting method can be applied to both small and large clusters with any pairing strength. As an example, the projection method is used to check the assertion on the pairing origin of the odd-even staggering (OES) in the ionization potentials (IP) of sodium clusters. Both effects of pairing and shape deformation are taken into account simultaneously. In general, the results obtained show that the existence of pairing in sodium clusters is doubtful.
An empirical method to cluster objective nebulizer adherence data among adults with cystic fibrosis.
Hoo, Zhe H; Campbell, Michael J; Curley, Rachael; Wildman, Martin J
2017-01-01
The purpose of using preventative inhaled treatments in cystic fibrosis is to improve health outcomes. Therefore, understanding the relationship between adherence to treatment and health outcome is crucial. Temporal variability, as well as absolute magnitude of adherence affects health outcomes, and there is likely to be a threshold effect in the relationship between adherence and outcomes. We therefore propose a pragmatic algorithm-based clustering method of objective nebulizer adherence data to better understand this relationship, and potentially, to guide clinical decisions. This clustering method consists of three related steps. The first step is to split adherence data for the previous 12 months into four 3-monthly sections. The second step is to calculate mean adherence for each section and to score the section based on mean adherence. The third step is to aggregate the individual scores to determine the final cluster ("cluster 1" = very low adherence; "cluster 2" = low adherence; "cluster 3" = moderate adherence; "cluster 4" = high adherence), and taking into account adherence trend as represented by sequential individual scores. The individual scores should be displayed along with the final cluster for clinicians to fully understand the adherence data. We present three cases to illustrate the use of the proposed clustering method. This pragmatic clustering method can deal with adherence data of variable duration (ie, can be used even if 12 months' worth of data are unavailable) and can cluster adherence data in real time. Empirical support for some of the clustering parameters is not yet available, but the suggested classifications provide a structure to investigate parameters in future prospective datasets in which there are accurate measurements of nebulizer adherence and health outcomes.
A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis
Directory of Open Access Journals (Sweden)
Huanhuan Li
2017-08-01
Full Text Available The Shipboard Automatic Identification System (AIS is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW, a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our
Quantum Monte Carlo Methods for First Principles Simulation of Liquid Water
Gergely, John Robert
2009-01-01
Obtaining an accurate microscopic description of water structure and dynamics is of great interest to molecular biology researchers and in the physics and quantum chemistry simulation communities. This dissertation describes efforts to apply quantum Monte Carlo methods to this problem with the goal of making progress toward a fully "ab initio"…
Li, Junling; Zhang, Yonghua; Ai, Junjie; Gao, Qiang; Qi, Honglan; Zhang, Chengxiao; Cheng, Zhiliang
2016-01-01
A simple assay for phospholipase A2 (PLA2) enzyme was developed based on a fluorescence resonance energy transfer (FRET) probe using the quantum dot cluster (QDC)-loaded phospholipid micelles. The probe was prepared by encapsulating many small hydrophobic quantum dots (QDs) within the hydrophobic core of micelles that were formed from the coassembly of hydrogenated soy phosphatidylcholine phospholipids (HSPC) and fluorescent lipids (NBD-PC). QDCs formed within the micelle core served as the substrate for NBD fluorescence quenching through FRET. The QDC-loaded micelles showed very low background fluorescence. As the PLA2 enzyme selectively digested lipids, the NBD fluorescence was recovered from its quenched state, leading to the sensitive detection of PLA2. This assay provided a limit of detection (at a signal-to-noise ratio of 3) of 3 U/L for PLA2. In the presence of a PLA2 inhibitor, the fluorescent response of the sensor for PLA2 decreased, indicating that the assay could also be used for screening the PLA2 inhibitors.
An extended affinity propagation clustering method based on different data density types.
Zhao, XiuLi; Xu, WeiXiang
2015-01-01
Affinity propagation (AP) algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers) equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.
An Extended Affinity Propagation Clustering Method Based on Different Data Density Types
Directory of Open Access Journals (Sweden)
XiuLi Zhao
2015-01-01
Full Text Available Affinity propagation (AP algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.
Consensus of satellite cluster flight using an energy-matching optimal control method
Luo, Jianjun; Zhou, Liang; Zhang, Bo
2017-11-01
This paper presents an optimal control method for consensus of satellite cluster flight under a kind of energy matching condition. Firstly, the relation between energy matching and satellite periodically bounded relative motion is analyzed, and the satellite energy matching principle is applied to configure the initial conditions. Then, period-delayed errors are adopted as state variables to establish the period-delayed errors dynamics models of a single satellite and the cluster. Next a novel satellite cluster feedback control protocol with coupling gain is designed, so that the satellite cluster periodically bounded relative motion consensus problem (period-delayed errors state consensus problem) is transformed to the stability of a set of matrices with the same low dimension. Based on the consensus region theory in the research of multi-agent system consensus issues, the coupling gain can be obtained to satisfy the requirement of consensus region and decouple the satellite cluster information topology and the feedback control gain matrix, which can be determined by Linear quadratic regulator (LQR) optimal method. This method can realize the consensus of satellite cluster period-delayed errors, leading to the consistency of semi-major axes (SMA) and the energy-matching of satellite cluster. Then satellites can emerge the global coordinative cluster behavior. Finally the feasibility and effectiveness of the present energy-matching optimal consensus for satellite cluster flight is verified through numerical simulations.
A NEW METHOD TO QUANTIFY X-RAY SUBSTRUCTURES IN CLUSTERS OF GALAXIES
Energy Technology Data Exchange (ETDEWEB)
Andrade-Santos, Felipe; Lima Neto, Gastao B.; Lagana, Tatiana F. [Departamento de Astronomia, Instituto de Astronomia, Geofisica e Ciencias Atmosfericas, Universidade de Sao Paulo, Geofisica e Ciencias Atmosfericas, Rua do Matao 1226, Cidade Universitaria, 05508-090 Sao Paulo, SP (Brazil)
2012-02-20
We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model ({beta}-model or Sersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range z in [0.02, 0.2] and have a signal-to-noise ratio (S/N) greater than 100. We present the calibration of the method and the relations between the substructure level with physical quantities, such as the mass, X-ray luminosity, temperature, and cluster redshift. We use our method to separate the clusters in two sub-samples of high- and low-substructure levels. We conclude, using Monte Carlo simulations, that the method recuperates very well the true amount of substructure for small angular core radii clusters (with respect to the whole image size) and good S/N observations. We find no evidence of correlation between the substructure level and physical properties of the clusters such as gas temperature, X-ray luminosity, and redshift; however, analysis suggest a trend between the substructure level and cluster mass. The scaling relations for the two sub-samples (high- and low-substructure level clusters) are different (they present an offset, i.e., given a fixed mass or temperature, low-substructure clusters tend to be more X-ray luminous), which is an important result for cosmological tests using the mass-luminosity relation to obtain the cluster mass function, since they rely on the assumption that clusters do not present different scaling relations according to their dynamical state.
DEFF Research Database (Denmark)
Bernard, S.; Kutter, J. P.; Mogensen, K. B.
2014-01-01
photostability than organic fluorophores [2]. In this work AgQCs are embedded into the oligoaniline porous matrix and is tested for indirect fluorescence detection of cyanide in a simple microfluidic device (Fig. 1). Imaging of individual silver clusters inside the channel (Fig. 1) is made possible by using 100x...
Refined tropical curve counts and canonical bases for quantum cluster algebras
DEFF Research Database (Denmark)
Mandel, Travis
We express the (quantizations of the) Gross-Hacking-Keel-Kontsevich canonical bases for cluster algebras in terms of certain (Block-Göttsche) weighted counts of tropical curves. In the process, we obtain via scattering diagram techniques a new invariance result for these Block-Göttsche counts....
A quantum chemical cluster study of hydrated halide adsorption on the cathodic Al(111) surface
Kairys, Visvaldas; Head, John D.
1999-10-01
Ab-initio cluster calculations are used to simulate water, fluorine and iodine adsorption on a negatively charged Al(111) surface. In contrast to our earlier work using neutral Al clusters, we determine the water to be only weakly adsorbed above the negatively charged Al clusters, with the water H atoms being closest to the metal surface. A H-bond network is readily formed when more than one water molecule is adsorbed on the Al cluster surface. Analogous to the recent in-situ surface X-ray scattering experiments on Ag(111) surfaces, we find the separation between the water and the cathodic surface to be approximately 1.5 times greater than that found previously for the neutral Al(111) surface. In addition, there is a strong repulsion preventing the water molecules from being closer than 3.0 Å to the negatively charged surface. For the halides, in line with gas-phase adsorption experiments and other calculations, we find that fluorine is much more strongly bound to the Al clusters than iodine, with the Al(111) atop site being the most favored surface site for both halides. By performing calculations on Al clusters with a halide ion and one or more water molecules coadsorbed, we are able to develop an explanation as to why solvated iodine is more readily able to specifically adsorb on a cathodic surface than fluorine. The larger atomic size of iodine enables it to adsorb on the cathodic Al(111) surface at a higher vertical height than fluorine. Water molecules can then bond to iodine without being drawn into the region of repulsive interaction from the negatively charged surface. Thus we find the adsorption energy for I -·(H 2O) 3 adsorbed on Al -19 to be very similar to the I - adsorption energy, suggesting that iodine can be specifically adsorbed on the cathodic Al(111) surface without destabilizing any coadsorbed water molecules, whereas any water molecules hydrogen-bonding to fluorine are pulled towards the Al(111) surface and destabilized when the fluorine
Alvionita; Sutikno; Suharsono, A.
2017-03-01
Cluster analysis is a technique in multivariate analysis methods that reduces (classifying) data. This analysis has the main purpose to classify the objects of observation into groups based on characteristics. In the process, a cluster analysis is not only used for numerical data or categorical data but also developed for mixed data. There are several methods in analyzing the mixed data as ensemble methods and methods Similarity Weight and Filter Methods (SWFM). There is a lot of research on these methods, but the study did not compare the performance given by both of these methods. Therefore, this paper will be compared the performance between the clustering ensemble ROCK methods and ensemble SWFM methods. These methods will be used in clustering cross citrus accessions based on the characteristics of fruit and leaves that involve variables that are a mixture of numerical and categorical. Clustering methods with the best performance determined by looking at the ratio of standard deviation values within groups (SW) with a standard deviation between groups (SB). Methods with the best performance has the smallest ratio. From the result, we get that the performance of ensemble ROCK methods is better than ensemble SWFM methods.
Investigation of the cluster formation in lithium niobate crystals by computer modeling method
Energy Technology Data Exchange (ETDEWEB)
Voskresenskii, V. M.; Starodub, O. R., E-mail: ol-star@mail.ru; Sidorov, N. V.; Palatnikov, M. N. [Russian Academy of Sciences, Tananaev Institute of Chemistry and Technology of Rare Earth Elements and Mineral Raw Materials, Kola Science Centre (Russian Federation)
2017-03-15
The processes occurring upon the formation of energetically equilibrium oxygen-octahedral clusters in the ferroelectric phase of a stoichiometric lithium niobate (LiNbO{sub 3}) crystal have been investigated by the computer modeling method within the semiclassical atomistic model. An energetically favorable cluster size (at which a structure similar to that of a congruent crystal is organized) is shown to exist. A stoichiometric cluster cannot exist because of the electroneutrality loss. The most energetically favorable cluster is that with a Li/Nb ratio of about 0.945, a value close to the lithium-to-niobium ratio for a congruent crystal.
Communication: Improved pair approximations in local coupled-cluster methods
Energy Technology Data Exchange (ETDEWEB)
Schwilk, Max; Werner, Hans-Joachim [Institut für Theoretische Chemie, Universität Stuttgart, Pfaffenwaldring 55, D-70569 Stuttgart (Germany); Usvyat, Denis [Institute for Physical and Theoretical Chemistry, Universität Regensburg, Universitätsstrasse 31, D-93040 Regensburg (Germany)
2015-03-28
In local coupled cluster treatments the electron pairs can be classified according to the magnitude of their energy contributions or distances into strong, close, weak, and distant pairs. Different approximations are introduced for the latter three classes. In this communication, an improved simplified treatment of close and weak pairs is proposed, which is based on long-range cancellations of individually slowly decaying contributions in the amplitude equations. Benchmark calculations for correlation, reaction, and activation energies demonstrate that these approximations work extremely well, while pair approximations based on local second-order Møller-Plesset theory can lead to errors that are 1-2 orders of magnitude larger.
Indium clustering in a-plane InGaN quantum wells as evidenced by atom probe tomography
Energy Technology Data Exchange (ETDEWEB)
Tang, Fengzai; Zhu, Tongtong; Oehler, Fabrice; Fu, Wai Yuen; Griffiths, James T.; Massabuau, Fabien C.-P.; Kappers, Menno J.; Oliver, Rachel A., E-mail: rao28@cam.ac.uk [Department of Materials Science and Metallurgy, University of Cambridge, 27 Charles Babbage Road, Cambridge CB3 0FS (United Kingdom); Martin, Tomas L.; Bagot, Paul A. J.; Moody, Michael P., E-mail: michael.moody@materials.ox.ac.uk [Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH (United Kingdom)
2015-02-16
Atom probe tomography (APT) has been used to characterize the distribution of In atoms within non-polar a-plane InGaN quantum wells (QWs) grown on a GaN pseudo-substrate produced using epitaxial lateral overgrowth. Application of the focused ion beam microscope enabled APT needles to be prepared from the low defect density regions of the grown sample. A complementary analysis was also undertaken on QWs having comparable In contents grown on polar c-plane sample pseudo-substrates. Both frequency distribution and modified nearest neighbor analyses indicate a statistically non-randomized In distribution in the a-plane QWs, but a random distribution in the c-plane QWs. This work not only provides insights into the structure of non-polar a-plane QWs but also shows that APT is capable of detecting as-grown nanoscale clustering in InGaN and thus validates the reliability of earlier APT analyses of the In distribution in c-plane InGaN QWs which show no such clustering.
A simple and fast method to determine the parameters for fuzzy c-means cluster analysis
DEFF Research Database (Denmark)
Schwämmle, Veit; Jensen, Ole Nørregaard
2010-01-01
MOTIVATION: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values...
Clustering of hydrological data: a review of methods for runoff predictions in ungauged basins
Dogulu, Nilay; Kentel, Elcin
2017-04-01
There is a great body of research that has looked into the challenge of hydrological predictions in ungauged basins as driven by the Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS). Transfer of hydrological information (e.g. model parameters, flow signatures) from gauged to ungauged catchment, often referred as "regionalization", is the main objective and benefits from identification of hydrologically homogenous regions. Within this context, indirect representation of hydrologic similarity for ungauged catchments, which is not a straightforward task due to absence of streamflow measurements and insufficient knowledge of hydrologic behavior, has been explored in the literature. To this aim, clustering methods have been widely adopted. While most of the studies employ hard clustering techniques such as hierarchical (divisive or agglomerative) clustering, there have been more recent attempts taking advantage of fuzzy set theory (fuzzy clustering) and nonlinear methods (e.g. self-organizing maps). The relevant research findings from this fundamental task of hydrologic sciences have revealed the value of different clustering methods for improved understanding of catchment hydrology. However, despite advancements there still remains challenges and yet opportunities for research on clustering for regionalization purposes. The present work provides an overview of clustering techniques and their applications in hydrology with focus on regionalization for the PUB problem. Identifying their advantages and disadvantages, we discuss the potential of innovative clustering methods and reflect on future challenges in view of the research objectives of the PUB initiative.
Energy Technology Data Exchange (ETDEWEB)
Wahlen-Strothman, J. M. [Rice Univ., Houston, TX (United States); Henderson, T. H. [Rice Univ., Houston, TX (United States); Hermes, M. R. [Rice Univ., Houston, TX (United States); Degroote, M. [Rice Univ., Houston, TX (United States); Qiu, Y. [Rice Univ., Houston, TX (United States); Zhao, J. [Rice Univ., Houston, TX (United States); Dukelsky, J. [Consejo Superior de Investigaciones Cientificas (CSIC), Madrid (Spain). Inst. de Estructura de la Materia; Scuseria, G. E. [Rice Univ., Houston, TX (United States)
2018-01-03
Coupled cluster and symmetry projected Hartree-Fock are two central paradigms in electronic structure theory. However, they are very different. Single reference coupled cluster is highly successful for treating weakly correlated systems, but fails under strong correlation unless one sacrifices good quantum numbers and works with broken-symmetry wave functions, which is unphysical for finite systems. Symmetry projection is effective for the treatment of strong correlation at the mean-field level through multireference non-orthogonal configuration interaction wavefunctions, but unlike coupled cluster, it is neither size extensive nor ideal for treating dynamic correlation. We here examine different scenarios for merging these two dissimilar theories. We carry out this exercise over the integrable Lipkin model Hamiltonian, which despite its simplicity, encompasses non-trivial physics for degenerate systems and can be solved via diagonalization for a very large number of particles. We show how symmetry projection and coupled cluster doubles individually fail in different correlation limits, whereas models that merge these two theories are highly successful over the entire phase diagram. Despite the simplicity of the Lipkin Hamiltonian, the lessons learned in this work will be useful for building an ab initio symmetry projected coupled cluster theory that we expect to be accurate in the weakly and strongly correlated limits, as well as the recoupling regime.
Wahlen-Strothman, Jacob M; Henderson, Thomas M; Hermes, Matthew R; Degroote, Matthias; Qiu, Yiheng; Zhao, Jinmo; Dukelsky, Jorge; Scuseria, Gustavo E
2017-02-07
Coupled cluster and symmetry projected Hartree-Fock are two central paradigms in electronic structure theory. However, they are very different. Single reference coupled cluster is highly successful for treating weakly correlated systems but fails under strong correlation unless one sacrifices good quantum numbers and works with broken-symmetry wave functions, which is unphysical for finite systems. Symmetry projection is effective for the treatment of strong correlation at the mean-field level through multireference non-orthogonal configuration interaction wavefunctions, but unlike coupled cluster, it is neither size extensive nor ideal for treating dynamic correlation. We here examine different scenarios for merging these two dissimilar theories. We carry out this exercise over the integrable Lipkin model Hamiltonian, which despite its simplicity, encompasses non-trivial physics for degenerate systems and can be solved via diagonalization for a very large number of particles. We show how symmetry projection and coupled cluster doubles individually fail in different correlation limits, whereas models that merge these two theories are highly successful over the entire phase diagram. Despite the simplicity of the Lipkin Hamiltonian, the lessons learned in this work will be useful for building an ab initio symmetry projected coupled cluster theory that we expect to be accurate in the weakly and strongly correlated limits, as well as the recoupling regime.
Directory of Open Access Journals (Sweden)
D. A. Viattchenin
2009-01-01
Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible clusterization with partial training is proposed in the paper. The method is based on data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.
Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles.
Williams, N J; Nasuto, S J; Saddy, J D
2015-07-30
The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. We propose a complete pipeline for the cluster analysis of ERP data. To increase the signal-to-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA) to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). After validating the pipeline on simulated data, we tested it on data from two experiments - a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership. Our analysis operates on denoised single-trials, the number of clusters are determined in a principled manner and the results are presented through an intuitive visualisation. Given the cluster structure in some experimental conditions, we suggest application of cluster analysis as a preliminary step before ensemble averaging. Copyright © 2015 Elsevier B.V. All rights reserved.
Analysis of a Gibbs sampler method for model-based clustering of gene expression data.
Joshi, Anagha; Van de Peer, Yves; Michoel, Tom
2008-01-15
Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model-based clustering approaches have emerged as statistically well-grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set. We have extended an existing algorithm for model-based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for Saccharomyces cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression. GaneSh, a Java package for coclustering, is available under the terms of the GNU General Public License from our website at http://bioinformatics.psb.ugent.be/software
A two-stage method for microcalcification cluster segmentation in mammography by deformable models
Energy Technology Data Exchange (ETDEWEB)
Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.; Karahaliou, A.; Costaridou, L., E-mail: costarid@upatras.gr [Department of Medical Physics, School of Medicine, University of Patras, Patras 26504 (Greece); Vassiou, K. [Department of Anatomy, School of Medicine, University of Thessaly, Larissa 41500 (Greece)
2015-10-15
Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods are applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST{sub cluster}, average of minimum distance—AMINDIST{sub cluster}) and the area overlap measure (AOM{sub cluster}). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error) utilizing
Vysotsky, Yu B; Kartashynska, E S; Belyaeva, E A; Fainerman, V B; Vollhardt, D; Miller, R
2015-11-21
Using the quantum chemical semi-empirical PM3 method it is shown that aliphatic alcohols favor the spontaneous clusterization of vaporous alkanes at the water surface due to the change of adsorption from the barrier to non-barrier mechanism. A theoretical model of the non-barrier mechanism for monolayer formation is developed. In the framework of this model alcohols (or any other surfactants) act as 'floats', which interact with alkane molecules of the vapor phase using their hydrophobic part, whereas the hydrophilic part is immersed into the water phase. This results in a significant increase of contact effectiveness of alkanes with the interface during the adsorption and film formation. The obtained results are in good agreement with the existing experimental data. To test the model the thermodynamic and structural parameters of formation and clusterization are calculated for vaporous alkanes C(n)H(2n+2) (n(CH3) = 6-16) at the water surface in the presence of aliphatic alcohols C(n)H(2n+1)OH (n(OH) = 8-16) at 298 K. It is shown that the values of clusterization enthalpy, entropy and Gibbs' energy per one monomer of the cluster depend on the chain lengths of corresponding alcohols and alkanes, the alcohol molar fraction in the monolayers formed, and the shift of the alkane molecules with respect to the alcohol molecules Δn. Two possible competitive structures of mixed 2D film alkane-alcohol are considered: 2D films 1 with single alcohol molecules enclosed by alkane molecules (the alcohols do not form domains) and 2D films 2 that contain alcohol domains enclosed by alkane molecules. The formation of the alkane films of the first type is nearly independent of the surfactant type present at the interface, but depends on their molar fraction in the monolayer formed and the chain length of the compounds participating in the clusterization, whereas for the formation of the films of the second type the interaction between the hydrophilic parts of the surfactant is
Dynamic Quantum Clustering: A Tool for Unsupervised Exploration of Structures in Data
Energy Technology Data Exchange (ETDEWEB)
Weinstein, Marvin; /SLAC; Horn, David; /Tel Aviv U.
2008-10-30
A given set of data-points in some feature space may be associated with a Schroedinger equation whose potential is determined by the data. This is known to lead to good clustering solutions. Here we extend this approach into a full-fledged dynamical scheme using a time-dependent Schroedinger equation with a small diffusion component. Moreover, we approximate this Hamiltonian formalism by a truncated calculation within a set of Gaussian wave functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition or feature filtering.
Dynamic quantum clustering: a tool for visual exploration of structures in data
Energy Technology Data Exchange (ETDEWEB)
Weinstein, Marvin; /SLAC; Horn, David; /Tel Aviv U.
2009-10-17
A given set of data-points in some feature space may be associated with a Schroedinger equation whose potential is determined by the data. This is known to lead to good clustering solutions. Here we extend this approach into a full-fledged dynamical scheme using a time-dependent Schroedinger equation. Moreover, we approximate this Hamiltonian formalism by a truncated calculation within a set of Gaussian wave functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition or feature filtering.
Directory of Open Access Journals (Sweden)
Mário Mestria
2013-08-01
Full Text Available The Clustered Traveling Salesman Problem (CTSP is a generalization of the Traveling Salesman Problem (TSP in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously. The CTSP is NP-hard and, in this context, we are proposed heuristic methods for the CTSP using GRASP, Path Relinking and Variable Neighborhood Descent (VND. The heuristic methods were tested using Euclidean instances with up to 2000 vertices and clusters varying between 4 to 150 vertices. The computational tests were performed to compare the performance of the heuristic methods with an exact algorithm using the Parallel CPLEX software. The computational results showed that the hybrid heuristic method using VND outperforms other heuristic methods.
Device and Method of Scintillating Quantum Dots for Radiation Imaging
Burke, Eric R. (Inventor); DeHaven, Stanton L. (Inventor); Williams, Phillip A. (Inventor)
2017-01-01
A radiation imaging device includes a radiation source and a micro structured detector comprising a material defining a surface that faces the radiation source. The material includes a plurality of discreet cavities having openings in the surface. The detector also includes a plurality of quantum dots disclosed in the cavities. The quantum dots are configured to interact with radiation from the radiation source, and to emit visible photons that indicate the presence of radiation. A digital camera and optics may be used to capture images formed by the detector in response to exposure to radiation.
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo
2016-01-01
Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.
Directory of Open Access Journals (Sweden)
Lovro Šubelj
Full Text Available Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.
Introduction to the quantum trajectory method and to Fermi molecular dynamics
Lagattuta, K J
2003-01-01
The quantum trajectory method (QTM) will be introduced, and an approximation to the QTM known as Fermi molecular dynamics (FMD) will be described. Results of simulations based on FMD will be mentioned for specific nonequilibrium systems dominated by Coulomb interactions.
The Modeling Library of Eavesdropping Methods in Quantum Cryptography Protocols by Model Checking
Yang, Fan; Yang, Guowu; Hao, Yujie
2016-07-01
The most crucial issue of quantum cryptography protocols is its security. There exists many ways to attack the quantum communication process. In this paper, we present a model checking method for modeling the eavesdropping in quantum information protocols. So when the security properties of a certain protocol are needed to be verified, we can directly use the models which are already built. Here we adopt the probabilistic model checking tool—PRISM to model these attack methods. The verification results show that the detection rate of eavesdropping is approximately close to 1 when enough photons are transmitted.
Sarro, L. M.; Bouy, H.; Berihuete, A.; Bertin, E.; Moraux, E.; Bouvier, J.; Cuillandre, J.-C.; Barrado, D.; Solano, E.
2014-03-01
Context. With the advent of deep wide surveys, large photometric and astrometric catalogues of literally all nearby clusters and associations have been produced. The unprecedented accuracy and sensitivity of these data sets and their broad spatial, temporal and wavelength coverage make obsolete the classical membership selection methods that were based on a handful of colours and luminosities. We present a new technique designed to take full advantage of the high dimensionality (photometric, astrometric, temporal) of such a survey to derive self-consistent and robust membership probabilities of the Pleiades cluster. Aims: We aim at developing a methodology to infer membership probabilities to the Pleiades cluster from the DANCe multidimensional astro-photometric data set in a consistent way throughout the entire derivation. The determination of the membership probabilities has to be applicable to censored data and must incorporate the measurement uncertainties into the inference procedure. Methods: We use Bayes' theorem and a curvilinear forward model for the likelihood of the measurements of cluster members in the colour-magnitude space, to infer posterior membership probabilities. The distribution of the cluster members proper motions and the distribution of contaminants in the full multidimensional astro-photometric space is modelled with a mixture-of-Gaussians likelihood. Results: We analyse several representation spaces composed of the proper motions plus a subset of the available magnitudes and colour indices. We select two prominent representation spaces composed of variables selected using feature relevance determination techniques based in Random Forests, and analyse the resulting samples of high probability candidates. We consistently find lists of high probability (p > 0.9975) candidates with ≈1000 sources, 4 to 5 times more than obtained in the most recent astro-photometric studies of the cluster. Conclusions: Multidimensional data sets require
Directory of Open Access Journals (Sweden)
Mário Mestria
2014-11-01
Full Text Available In this paper, we propose new heuristic methods for solver the Clustered Traveling Salesman Problem (CTSP. The CTSP is a generalization of the Traveling Salesman Problem (TSP in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously. We develop two Variable Neighborhood Random Descent with Iterated Local for solver the CTSP. The heuristic methods proposed were tested in types of instances with data at different level of granularity for the number of vertices and clusters. The computational results showed that the heuristic methods outperform recent existing methods in the literature and they are competitive with an exact algorithm using the Parallel CPLEX software.
Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes
Directory of Open Access Journals (Sweden)
Kok Gerdalize
2009-04-01
Full Text Available Abstract Background Mpumalanga Province, South Africa is a low malaria transmission area that is subject to malaria epidemics. SaTScan methodology was used by the malaria control programme to detect local malaria clusters to assist disease control planning. The third season for case cluster identification overlapped with the first season of implementing an outbreak identification and response system in the area. Methods SaTScan™ software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using definitively confirmed individual cases in seven towns over three malaria seasons. Following passive case reporting at health facilities during the 2002 to 2005 seasons, active case detection was carried out in the communities, this assisted with determining the probable source of infection. The distribution and statistical significance of the clusters were explored by means of Monte Carlo replication of data sets under the null hypothesis with replications greater than 999 to ensure adequate power for defining clusters. Results and discussion SaTScan detected five space-clusters and two space-time clusters during the study period. There was strong concordance between recognized local clustering of cases and outbreak declaration in specific towns. Both Albertsnek and Thambokulu reported malaria outbreaks in the same season as space-time clusters. This synergy may allow mutual validation of the two systems in confirming outbreaks demanding additional resources and cluster identification at local level to better target resources. Conclusion Exploring the clustering of cases assisted with the planning of public health activities, including mobilizing health workers and resources. Where appropriate additional indoor residual spraying, focal larviciding and health promotion activities, were all also carried out.
Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T
2015-01-01
We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].
Metal Clustering and Solvation in Hydrothermal Steam: FT-MS, IRMPD, and Quantum Chemical Studies.
Lemke, K.
2016-12-01
FT-mass spectrometry in combination with wave tunable IRMPD spectroscopy can be used to probe the speciation of metals in aqueous media [1], and, in particular, shed light on clustering and solvation processes that occur in low-density aqueous fluids close to and on the Earth's surface [2]. In order to probe the structure of the solvation envelope around geochemicially important molecular ions, we have begun to survey a range of representative metal clusters of the form [Mn(ClO4)2n-1]+(H2O)m, (M=Mn, Ni, Cu, Co, Zn) using a combination of SWIFT mass isolation and wave tunable IR techniques. Ion cluster experiments have been conducted on a modified FT-ICR mass spectrometer coupled to a Nd:YAG pumped OPO/OPA laser; for example, ESI FT-MS of dilute (0.5 mM) aqueous Ni(ClO4)2 yields ion signals at m/z 452 (m=2), 470 (m=3), 488 (m=4) and 506 (m=5) due to clusters of the form [Ni2(ClO4)3]+(H2O)m with up to 5 waters, and larger trinuclear clusters at m/z 726, i.e. [Ni3(ClO4)5]+(H2O)3 and m/z 744, i.e.[Ni3(ClO4)5]+(H2O)4. Following mass isolation and IRMPD of Ni2(ClO4)3]+(H2O)4 at m/z 488, there is clear spectroscopic evidence for strongly red-shifted water OH stretching modes because of hydrogen bonding. Assignment of individual IR bands in Ni2(ClO4)3]+(H2O)4 was achieved by using a swarm based optimization algorithm and anharmonic DFT and MP2 calculations. Infrared spectral assignment could be made by assuming that two H2O molecules bind to each Ni center in two structurally distinct but isoenergetic Ni2(ClO4)3]+(H2O)4 clusters. In the case of Ni2(ClO4)3]+(H2O)4, the two lowest energy structures predicted by theory have all 4 water molecules bound into the first solvation shell, in other words, the strongly red-shifted OH stretching bands below 3590 cm-1 are due to asym. and sym. OH stretching modes of water moleculess directly bound to nickel and perchlorate O. These new FT-MS and IRMPD data together with results from IR spectral simulations of [Nin(ClO4)2n-1]+(H2O
Stochastic methods for the fermion determinant in lattice quantum chromodynamics
Energy Technology Data Exchange (ETDEWEB)
Finkenrath, Jacob Friedrich
2015-02-17
In this thesis, algorithms in lattice quantum chromodynamics are presented by developing and using stochastic methods for fermion determinant ratios. For that an integral representation is proved which can be used also for non hermitian matrices. The stochastic estimation or the Monte Carlo integration of this integral representation introduces stochastic fluctuations which are controlled by using Domain Decomposition of the Dirac operator and introducing interpolation techniques. Determinant ratios of the lattice fermion operator, here the Wilson Dirac operator, are needed for corrections of the Boltzmann weight. These corrections have interesting applications e.g. in the mass by using mass reweighting. It will be shown that mass reweighting can be used e.g. to improve extrapolation in the light quark mass towards the chiral or physical point or to introduce an isospin breaking by splitting up the mass of the light quark. Furthermore the extraction of the light quark masses will be shown by using dynamical 2 flavor CLS ensembles. Stochastic estimation of determinant ratios can be used in Monte Carlo algorithms, e.g. in the Partial Stochastic Multi Step algorithm which can sample two mass-degenerate quarks. The idea is to propose a new configuration weighted by the pure gauge weight and including afterwards the fermion weight by using Metropolis accept-reject steps. It is shown by using an adequate interpolation with relative gauge fixing and a hierarchical filter structure that it is possible to simulate moderate lattices up to (2.1 fm){sup 4}. Furthermore the iteration of the pure gauge update can be increased which can decouple long autocorrelation times from the weighting with the fermions. Moreover a novel Hybrid Monte Carlo algorithm based on Domain Decomposition and combined with mass reweighting is presented. By using Domain Decomposition it is possible to split up the mass term in the Schur complement and the block operators. By introducing a higher mass
Directory of Open Access Journals (Sweden)
Yun Mo
2014-01-01
Full Text Available Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect.
Mo, Yun; Zhang, Zhongzhao; Meng, Weixiao; Ma, Lin; Wang, Yao
2014-01-22
Indoor positioning systems based on the fingerprint method are widely used due to the large number of existing devices with a wide range of coverage. However, extensive positioning regions with a massive fingerprint database may cause high computational complexity and error margins, therefore clustering methods are widely applied as a solution. However, traditional clustering methods in positioning systems can only measure the similarity of the Received Signal Strength without being concerned with the continuity of physical coordinates. Besides, outage of access points could result in asymmetric matching problems which severely affect the fine positioning procedure. To solve these issues, in this paper we propose a positioning system based on the Spatial Division Clustering (SDC) method for clustering the fingerprint dataset subject to physical distance constraints. With the Genetic Algorithm and Support Vector Machine techniques, SDC can achieve higher coarse positioning accuracy than traditional clustering algorithms. In terms of fine localization, based on the Kernel Principal Component Analysis method, the proposed positioning system outperforms its counterparts based on other feature extraction methods in low dimensionality. Apart from balancing online matching computational burden, the new positioning system exhibits advantageous performance on radio map clustering, and also shows better robustness and adaptability in the asymmetric matching problem aspect.
WEIGHING GALAXY CLUSTERS WITH GAS. I. ON THE METHODS OF COMPUTING HYDROSTATIC MASS BIAS
Energy Technology Data Exchange (ETDEWEB)
Lau, Erwin T.; Nagai, Daisuke [Department of Physics, Yale University, New Haven, CT 06520 (United States); Nelson, Kaylea, E-mail: erwin.lau@yale.edu [Department of Astronomy, Yale University, New Haven, CT 06520 (United States)
2013-11-10
Mass estimates of galaxy clusters from X-ray and Sunyeav-Zel'dovich observations assume the intracluster gas is in hydrostatic equilibrium with their gravitational potential. However, since galaxy clusters are dynamically active objects whose dynamical states can deviate significantly from the equilibrium configuration, the departure from the hydrostatic equilibrium assumption is one of the largest sources of systematic uncertainties in cluster cosmology. In the literature there have been two methods for computing the hydrostatic mass bias based on the Euler and the modified Jeans equations, respectively, and there has been some confusion about the validity of these two methods. The word 'Jeans' was a misnomer, which incorrectly implies that the gas is collisionless. To avoid further confusion, we instead refer these methods as 'summation' and 'averaging' methods respectively. In this work, we show that these two methods for computing the hydrostatic mass bias are equivalent by demonstrating that the equation used in the second method can be derived from taking spatial averages of the Euler equation. Specifically, we identify the correspondences of individual terms in these two methods mathematically and show that these correspondences are valid to within a few percent level using hydrodynamical simulations of galaxy cluster formation. In addition, we compute the mass bias associated with the acceleration of gas and show that its contribution is small in the virialized regions in the interior of galaxy clusters, but becomes non-negligible in the outskirts of massive galaxy clusters. We discuss future prospects of understanding and characterizing biases in the mass estimate of galaxy clusters using both hydrodynamical simulations and observations and their implications for cluster cosmology.
Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods
DEFF Research Database (Denmark)
Sokolowski, Ineta; Ørnbøl, Eva; Rosendal, Marianne
is commonly used for skewed distributions. For health care data, however, we need to recover the total cost in a given patient population. Thus, we focus, on making inferences on population means. Furthermore, a problem of clustered data is added as data related to patients in primary care are organized...... in clusters of general practices. There have been suggestions to apply different methods, e.g., the non-parametric bootstrap, to highly skewed data from pragmatic randomized trials without clusters, but there is very little information about how to analyse skewed data from cluster-randomized trials. Many...... We consider health care data from a cluster-randomized intervention study in primary care to test whether the average health care costs among study patients differ between the two groups. The problems of analysing cost data are that most data are severely skewed. Median instead of mean...
Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.
2011-01-01
Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.
Method of making an improved superconducting quantum interference device
Wu, Cheng-Teh; Falco, Charles M.; Kampwirth, Robert T.
1977-01-01
An improved superconducting quantum interference device is made by sputtering a thin film of an alloy of three parts niobium to one part tin in a pattern comprising a closed loop with a narrow region, depositing a thin film of a radiation shield such as copper over the niobium-tin, scribing a narrow line in the copper over the narrow region, exposing the structure at the scribed line to radiation and removing the deposited copper.
On the skeleton method and an application to a quantum scissor
DEFF Research Database (Denmark)
Cornean, Horia; Duclos, P.; Ricaud, B.
2008-01-01
In the spectral analysis of few one dimensional quantum particles interacting through delta potentials it is well known that one can recast the problem into the spectral analysis of an integral operator (the skeleton) living on the submanifold which supports the delta interactions. We shall present...... several tools which allow direct insight into the spectral structure of this skeleton. We shall illustrate the method on a model of a two dimensional quantum particle interacting with two infinitely long straight wires which cross one anonter at angle \\theta: the quantum scissor....
A new method to search for high-redshift clusters using photometric redshifts
Energy Technology Data Exchange (ETDEWEB)
Castignani, G.; Celotti, A. [SISSA, Via Bonomea 265, I-34136 Trieste (Italy); Chiaberge, M.; Norman, C., E-mail: castigna@sissa.it [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States)
2014-09-10
We describe a new method (Poisson probability method, PPM) to search for high-redshift galaxy clusters and groups by using photometric redshift information and galaxy number counts. The method relies on Poisson statistics and is primarily introduced to search for megaparsec-scale environments around a specific beacon. The PPM is tailored to both the properties of the FR I radio galaxies in the Chiaberge et al. sample, which are selected within the COSMOS survey, and to the specific data set used. We test the efficiency of our method of searching for cluster candidates against simulations. Two different approaches are adopted. (1) We use two z ∼ 1 X-ray detected cluster candidates found in the COSMOS survey and we shift them to higher redshift up to z = 2. We find that the PPM detects the cluster candidates up to z = 1.5, and it correctly estimates both the redshift and size of the two clusters. (2) We simulate spherically symmetric clusters of different size and richness, and we locate them at different redshifts (i.e., z = 1.0, 1.5, and 2.0) in the COSMOS field. We find that the PPM detects the simulated clusters within the considered redshift range with a statistical 1σ redshift accuracy of ∼0.05. The PPM is an efficient alternative method for high-redshift cluster searches that may also be applied to both present and future wide field surveys such as SDSS Stripe 82, LSST, and Euclid. Accurate photometric redshifts and a survey depth similar or better than that of COSMOS (e.g., I < 25) are required.
A Temperature Sensor Clustering Method for Thermal Error Modeling of Heavy Milling Machine Tools
Directory of Open Access Journals (Sweden)
Fengchun Li
2017-01-01
Full Text Available A clustering method is an effective way to select the proper temperature sensor location for thermal error modeling of machine tools. In this paper, a new temperature sensor clustering method is proposed. By analyzing the characteristics of the temperature of the sensors in a heavy floor-type milling machine tool, an indicator involving both the Euclidean distance and the correlation coefficient was proposed to reflect the differences between temperature sensors, and the indicator was expressed by a distance matrix to be used for hierarchical clustering. Then, the weight coefficient in the distance matrix and the number of the clusters (groups were optimized by a genetic algorithm (GA, and the fitness function of the GA was also rebuilt by establishing the thermal error model at one rotation speed, then deriving its accuracy at two different rotation speeds with a temperature disturbance. Thus, the parameters for clustering, as well as the final selection of the temperature sensors, were derived. Finally, the method proposed in this paper was verified on a machine tool. According to the selected temperature sensors, a thermal error model of the machine tool was established and used to predict the thermal error. The results indicate that the selected temperature sensors can accurately predict thermal error at different rotation speeds, and the proposed temperature sensor clustering method for sensor selection is expected to be used for the thermal error modeling for other machine tools.
Cool Cluster Correctly Correlated
Energy Technology Data Exchange (ETDEWEB)
Varganov, Sergey Aleksandrovich [Iowa State Univ., Ames, IA (United States)
2005-01-01
Atomic clusters are unique objects, which occupy an intermediate position between atoms and condensed matter systems. For a long time it was thought that physical and chemical properties of atomic dusters monotonically change with increasing size of the cluster from a single atom to a condensed matter system. However, recently it has become clear that many properties of atomic clusters can change drastically with the size of the clusters. Because physical and chemical properties of clusters can be adjusted simply by changing the cluster's size, different applications of atomic clusters were proposed. One example is the catalytic activity of clusters of specific sizes in different chemical reactions. Another example is a potential application of atomic clusters in microelectronics, where their band gaps can be adjusted by simply changing cluster sizes. In recent years significant advances in experimental techniques allow one to synthesize and study atomic clusters of specified sizes. However, the interpretation of the results is often difficult. The theoretical methods are frequently used to help in interpretation of complex experimental data. Most of the theoretical approaches have been based on empirical or semiempirical methods. These methods allow one to study large and small dusters using the same approximations. However, since empirical and semiempirical methods rely on simple models with many parameters, it is often difficult to estimate the quantitative and even qualitative accuracy of the results. On the other hand, because of significant advances in quantum chemical methods and computer capabilities, it is now possible to do high quality ab-initio calculations not only on systems of few atoms but on clusters of practical interest as well. In addition to accurate results for specific clusters, such methods can be used for benchmarking of different empirical and semiempirical approaches. The atomic clusters studied in this work contain from a few atoms
Mattioli, Marco
2016-12-01
In this mini-review, we report results from M. Mattioli, et al. [Phys. Rev. Lett. 111, 165302 (2013)], M. Dalmonte, et al. [Phys. Rev. B 92, 045106 (2015)] and M. Mattioli, et al. [New J. Phys. 17, 113039 (2015)], where it is shown that Rydberg atoms trapped in one-dimensional optical lattices are a useful tool to investigate the equilibrium phase diagram and the non-equilibrium dynamics of extended Hubbard models and Kinetically Constrained Models, respectively. Atoms weakly-dressed to an high-lying Rydberg state, which interact with a constant potential extended over several lattice sites, can be in an exotic quantum liquid state, the cluster Luttinger liquid phase [42, 43]. Furthermore, we show how a many-body model of interacting three-level atoms in the V-shaped configuration, where one of the level is a Rydberg state, might relax to equilibrium according to the same rules, so-called kinetic constraints, which are known to reproduce the characteristic dynamical arrest and separation of timescales of real glass-forming materials [62].
A Spatial Shape Constrained Clustering Method for Mammographic Mass Segmentation
Directory of Open Access Journals (Sweden)
Jian-Yong Lou
2015-01-01
error of 7.18% for well-defined masses (or 8.06% for ill-defined masses was obtained by using DACF on MiniMIAS database, with 5.86% (or 5.55% and 6.14% (or 5.27% improvements as compared to the standard DA and fuzzy c-means methods.
Adaptive cluster sampling: An efficient method for assessing inconspicuous species
Andrea M. Silletti; Joan Walker
2003-01-01
Restorationistis typically evaluate the success of a project by estimating the population sizes of species that have been planted or seeded. Because total census is raely feasible, they must rely on sampling methods for population estimates. However, traditional random sampling designs may be inefficient for species that, for one reason or another, are challenging to...
A Comparison of Methods for Player Clustering via Behavioral Telemetry
DEFF Research Database (Denmark)
Drachen, Anders; Thurau, Christian; Sifa, Rafet
2013-01-01
can be exceptionally complex, with features recorded for a varying population of users over a temporal segment that can reach years in duration. Categorization of behaviors, whether through descriptive methods (e.g. segmentation) or unsupervised/supervised learning techniques, is valuable for finding...
La Cour, Brian R.; Ostrove, Corey I.
2017-01-01
This paper describes a novel approach to solving unstructured search problems using a classical, signal-based emulation of a quantum computer. The classical nature of the representation allows one to perform subspace projections in addition to the usual unitary gate operations. Although bandwidth requirements will limit the scale of problems that can be solved by this method, it can nevertheless provide a significant computational advantage for problems of limited size. In particular, we find that, for the same number of noisy oracle calls, the proposed subspace projection method provides a higher probability of success for finding a solution than does an single application of Grover's algorithm on the same device.
A new approximation method for time-dependent problems in quantum mechanics
Energy Technology Data Exchange (ETDEWEB)
Amore, Paolo [Facultad de Ciencias, Universidad de Colima, Bernal Diaz del Castillo 340, Colima, Colima (Mexico)]. E-mail: paolo@ucol.mx; Aranda, Alfredo [Facultad de Ciencias, Universidad de Colima, Bernal Diaz del Castillo 340, Colima, Colima (Mexico)]. E-mail: fefo@ucol.mx; Fernandez, Francisco M. [INIFTA (Conicet, UNLP), Diag. 113 y 64 S/N, Sucursal 4, Casilla de Correo 16, 1900 La Plata (Argentina)]. E-mail: fernande@quimica.unlp.edu.ar; Jones, Hugh [Department of Physics, Imperial College, London SW7 2AZ (United Kingdom)]. E-mail: h.f.jones@imperial.ac.uk
2005-06-06
We propose an approximate solution of the time-dependent Schroedinger equation using the method of stationary states combined with a variational matrix method for finding the energies and eigenstates. We illustrate the effectiveness of the method by applying it to the time development of the wave-function in the quantum-mechanical version of the inflationary slow-roll transition.
Directory of Open Access Journals (Sweden)
Z Darizin
2016-06-01
Full Text Available Electronic transport has been investigated in four-quantum-dot combination coupled to metal electrodes using the non-equilibrium Green’s function method, and curves I-V and conductance (dI/dV were analyzed for special combination. We have showed that the emergence of negative differential conductivity is due to asymmetric distribution of quantum dots in the central region, existence of non-coupled dots (side quantum dots, and the interference effect. We found that more side quantum dots lead to more negative differential conductance.
K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering
Lv Tao; Yongtao Hao; Hao Yijie; Shen Chunfeng
2017-01-01
Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem ...
Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis
Sen Zhang; Yongquan Zhou
2015-01-01
One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO), inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving cluster...
Determination of the transmission coefficients for quantum structures using FDTD method.
Peng, Yangyang; Wang, Xiaoying; Sui, Wenquan
2011-12-01
The purpose of this work is to develop a simple method to incorporate quantum effect in traditional finite-difference time-domain (FDTD) simulators. Witch could make it possible to co-simulate systems include quantum structures and traditional components. In this paper, tunneling transmission coefficient is calculated by solving time-domain Schrödinger equation with a developed FDTD technique, called FDTD-S method. To validate the feasibility of the method, a simple resonant tunneling diode (RTD) structure model has been simulated using the proposed method. The good agreement between the numerical and analytical results proves its accuracy. The effectness and accuracy of this approach makes it a potential method for analysis and design of hybrid systems includes quantum structures and traditional components.
Transport Studies of Quantum Magnetism: Physics and Methods
Energy Technology Data Exchange (ETDEWEB)
Lee, Minhyea [Univ. of Colorado, Boulder, CO (United States)
2017-03-30
The main goal of this project was to understand novel ground states of spin systems probed by thermal and electrical transport measurements. They are well-suited to characterize the nature of low-energy excitations as unique property of the ground state. More specifically, it was aimed to study the transverse electrical conductivity in the presence of non-collinear and non-coplanar spin ordering and the effects of gauge field as well as novel spin excitations as a coherent heat transport channel in insulating quantum magnets. Most of works done during the grant period focused on these topics. As a natural extension of the project's initial goals, the scope was broadened to include transport studies on the spin systems with strong spin-orbit coupling. One particular focus was an exploration of systems with strong magnetic anisotropy combined with non-trivial spin configuration. Magnetic anisotropy is directly related to implement the non-collinear spin ordering to the existing common geometry of planar devices and thus poses a significant potential. Work in this direction includes the comparison of the topological Hall signal under hydrostatic pressure and chemical doping, as well as the angular dependence dependence of the non-collinear spin ordered phase and their evolution up on temperature and field strength. Another focus was centered around the experimental identification of spin-originated heat carrying excitation in quasi two dimensional honeycomb lattice, where Kitaev type of quantum spin liquid phase is expected to emerge. In fact, when its long range magnetic order is destroyed by the applied field, we discovered anomalously large enhancement of thermal conductivity, for which proximate Kitaev excitations in field-induced spin liquid state are responsible for. This work, combined with further investigations in materials in the similar class may help establish the experimental characterization of new quantum spin liquid and their unique low energy
Pruning method for a cluster-based neural network
Ranney, Kenneth I.; Khatri, Hiralal; Nguyen, Lam H.; Sichina, Jeffrey
2000-08-01
Many radar automatic target detection (ATD) algorithms operate on a set of data statistics or features rather than on the raw radar sensor data. These features are selected based on their ability to separate target data samples from background clutter samples. The ATD algorithms often operate on the features through a set of parameters that must be determined from a set of training data that are statistically similar to the data set to be encountered in practice. The designer usually attempts to minimize the number of features used by the algorithm -- a process commonly referred to as pruning. This not only reduces the computational demands of the algorithm, but it also prevents overspecialization to the samples from the training data set. Thus, the algorithm will perform better on a set of test data samples it has not encountered during training. The Optimal Brain Surgeon (OBS) and Divergence Method provide two different approaches to pruning. We apply the two methods to a set of radar data features to determine a new, reduced set of features. We then evaluate the resulting feature sets and discuss the differences between the two methods.
A semantics-based method for clustering of Chinese web search results
Zhang, Hui; Wang, Deqing; Wang, Li; Bi, Zhuming; Chen, Yong
2014-01-01
Information explosion is a critical challenge to the development of modern information systems. In particular, when the application of an information system is over the Internet, the amount of information over the web has been increasing exponentially and rapidly. Search engines, such as Google and Baidu, are essential tools for people to find the information from the Internet. Valuable information, however, is still likely submerged in the ocean of search results from those tools. By clustering the results into different groups based on subjects automatically, a search engine with the clustering feature allows users to select most relevant results quickly. In this paper, we propose an online semantics-based method to cluster Chinese web search results. First, we employ the generalised suffix tree to extract the longest common substrings (LCSs) from search snippets. Second, we use the HowNet to calculate the similarities of the words derived from the LCSs, and extract the most representative features by constructing the vocabulary chain. Third, we construct a vector of text features and calculate snippets' semantic similarities. Finally, we improve the Chameleon algorithm to cluster snippets. Extensive experimental results have shown that the proposed algorithm has outperformed over the suffix tree clustering method and other traditional clustering methods.
An application of the KNND method for detecting nearby open clusters based on Gaia-DR1
Gao, Xin-Hua
2017-05-01
This paper presents a preliminary test of the k-th nearest neighbor distance (KNND) method for detecting nearby open clusters based on Gaia-DR1. We select 38 386 nearby stars (Ber) open clusters), and obtain 57 reliable cluster members. Based on these cluster members, the distances to the Hyades and Coma Ber clusters are determined to be 46.0±0.2 and 83.5±0.3 pc, respectively. Our results demonstrate that the KNND method can be used to detect open clusters based on a large volume of astrometry data.
Robustness of serial clustering of extratropical cyclones to the choice of tracking method
Directory of Open Access Journals (Sweden)
Joaquim G. Pinto
2016-07-01
Full Text Available Cyclone clusters are a frequent synoptic feature in the Euro-Atlantic area. Recent studies have shown that serial clustering of cyclones generally occurs on both flanks and downstream regions of the North Atlantic storm track, while cyclones tend to occur more regulary on the western side of the North Atlantic basin near Newfoundland. This study explores the sensitivity of serial clustering to the choice of cyclone tracking method using cyclone track data from 15 methods derived from ERA-Interim data (1979–2010. Clustering is estimated by the dispersion (ratio of variance to mean of winter [December – February (DJF] cyclone passages near each grid point over the Euro-Atlantic area. The mean number of cyclone counts and their variance are compared between methods, revealing considerable differences, particularly for the latter. Results show that all different tracking methods qualitatively capture similar large-scale spatial patterns of underdispersion and overdispersion over the study region. The quantitative differences can primarily be attributed to the differences in the variance of cyclone counts between the methods. Nevertheless, overdispersion is statistically significant for almost all methods over parts of the eastern North Atlantic and Western Europe, and is therefore considered as a robust feature. The influence of the North Atlantic Oscillation (NAO on cyclone clustering displays a similar pattern for all tracking methods, with one maximum near Iceland and another between the Azores and Iberia. The differences in variance between methods are not related with different sensitivities to the NAO, which can account to over 50% of the clustering in some regions. We conclude that the general features of underdispersion and overdispersion of extratropical cyclones over the North Atlantic and Western Europe are robust to the choice of tracking method. The same is true for the influence of the NAO on cyclone dispersion.
Nakamura, Shin; Noguchi, Takumi
2016-10-11
During photosynthesis, the light-driven oxidation of water performed by photosystem II (PSII) provides electrons necessary to fix CO 2 , in turn supporting life on Earth by liberating molecular oxygen. Recent high-resolution X-ray images of PSII show that the water-oxidizing center (WOC) is composed of an Mn 4 CaO 5 cluster with six carboxylate, one imidazole, and four water ligands. FTIR difference spectroscopy has shown significant structural changes of the WOC during the S-state cycle of water oxidation, especially within carboxylate groups. However, the roles that these carboxylate groups play in water oxidation as well as how they should be properly assigned in spectra are unresolved. In this study, we performed a normal mode analysis of the WOC using the quantum mechanics/molecular mechanics (QM/MM) method to simulate FTIR difference spectra on the S 1 to S 2 transition in the carboxylate stretching region. By evaluating WOC models with different oxidation and protonation states, we determined that models of high-oxidation states, Mn(III) 2 Mn(IV) 2 , satisfactorily reproduced experimental spectra from intact and Ca-depleted PSII compared with low-oxidation models. It is further suggested that the carboxylate groups bridging Ca and Mn ions within this center tune the reactivity of water ligands bound to Ca by shifting charge via their π conjugation.
A New Method for the Detection of Galaxy Clusters in X-Ray Surveys
Energy Technology Data Exchange (ETDEWEB)
Piacentine, J.M.; Marshall, P.J.; Peterson, J.R.; Andersson, K.E.
2005-01-01
For many years the power of counting clusters of galaxies as a function of their mass has been recognized as a powerful cosmological probe; however, we are only now beginning to acquire data from dedicated surveys with sufcient sky coverage and sensitivity to measure the cluster population out to distances where the dark energy came to dominate the Universe’s evolution. One such survey uses the XMM X-ray telescope to scan a large area of sky, detecting the X-ray photons from the hot plasma that lies in the deep potential wells of massive clusters of galaxies. These clusters appear as extended (not point-like) objects, each providing just a few hundred photons in a typical observation. The detection of extended sources in such a low signal-to-noise situation is an important problem in astrophysics: we attempt to solve it by using as much prior information as possible, translating our experience with wellmeasured clusters to define a “template” cluster that can be varied and matched to the features seen in the XMM images. In this work we adapt an existing Monte Carlo analysis code for this problem. Two detection templates were dened and their suitability explored using simulated data; the method was then applied to a publically avalable XMM observation of a “blank” field. Presented are the encouraging results of this series of experiments, suggesting that this approach continue to be developed for future cluster-identication endeavours.
Application of AI methods in the clustering of architecture interior forms
Directory of Open Access Journals (Sweden)
Maryam Banaei
2017-09-01
Full Text Available Form or shape is one of the main aspects of architecture design. A gap exists in scientific studies on categorizing different architecture interior forms according to design. This paper presents a methodology for categorizing interior forms of built places. The main innovation of this study was to evaluate the architecture interior forms of real built places as a base for any analysis on form. We proposed a clustering method by selecting 343 images of living rooms from residential places according to their history and interior design style. We labeled all the images in AutoCAD software depending on form features. The labeling results showed that images had 1104 distinct form features, including sloped, vertical and horizontal linear solids, and edges. Regarding the high dimension of data, we used Graphical Clustering Toolkit software for clustering, which involved the use of correlation coefficients and internal similarity among clusters. The clustering analysis grouped all the images into 25 clusters with the highest internal and the lowest external similarities. The descriptive features of each cluster could show its formal characteristics.
Chemical Reactivity as Described by Quantum Chemical Methods
Directory of Open Access Journals (Sweden)
F. De Proft
2002-04-01
Full Text Available Abstract: Density Functional Theory is situated within the evolution of Quantum Chemistry as a facilitator of computations and a provider of new, chemical insights. The importance of the latter branch of DFT, conceptual DFT is highlighted following Parr's dictum "to calculate a molecule is not to understand it". An overview is given of the most important reactivity descriptors and the principles they are couched in. Examples are given on the evolution of the structure-property-wave function triangle which can be considered as the central paradigm of molecular quantum chemistry to (for many purposes a structure-property-density triangle. Both kinetic as well as thermodynamic aspects can be included when further linking reactivity to the property vertex. In the field of organic chemistry, the ab initio calculation of functional group properties and their use in studies on acidity and basicity is discussed together with the use of DFT descriptors to study the kinetics of SN2 reactions and the regioselectivity in Diels Alder reactions. Similarity in reactivity is illustrated via a study on peptide isosteres. In the field of inorganic chemistry non empirical studies of adsorption of small molecules in zeolite cages are discussed providing Henry constants and separation constants, the latter in remarkable good agreement with experiments. Possible refinements in a conceptual DFT context are presented. Finally an example from biochemistry is discussed : the influence of point mutations on the catalytic activity of subtilisin.
Appropriate statistical methods were infrequently used in cluster-randomized crossover trials.
Arnup, Sarah J; Forbes, Andrew B; Kahan, Brennan C; Morgan, Katy E; McKenzie, Joanne E
2016-06-01
To assess the design and statistical methods used in cluster-randomized crossover (CRXO) trials. We undertook a systematic review of CRXO trials. Searches of MEDLINE, EMBASE, and CINAHL Plus; and citation searches of CRXO methodological articles were conducted to December 2014. We extracted data on design characteristics and statistical methods for sample size, data analysis, and handling of missing data. Ninety-one trials including 139 end point analyses met the inclusion criteria. Trials had a median of nine clusters [interquartile range (IQR), 4-21] and median cluster-period size of 30 individuals (IQR, 14-77); 58 (69%) trials had two periods, and 27 trials (30%) included the same individuals in all periods. A rationale for the design was reported in only 25 trials (27%). A sample size justification was provided in 53 (58%) trials. Only nine (10%) trials accounted appropriately for the design in their sample size calculation. Ten of the 12 cluster-level analyses used a method that accounted for the clustering and multiple-period aspects of the design. In contrast, only 4 of the 127 individual-level analyses used a potentially appropriate method. There is a need for improved application of appropriate analysis and sample size methods, and reporting, in CRXO trials. Copyright © 2015 Elsevier Inc. All rights reserved.
An effective trust-based recommendation method using a novel graph clustering algorithm
Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin
2015-10-01
Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.
Dong, Yumin; Xiao, Shufen; Ma, Hongyang; Chen, Libo
2016-12-01
Cloud computing and big data have become the developing engine of current information technology (IT) as a result of the rapid development of IT. However, security protection has become increasingly important for cloud computing and big data, and has become a problem that must be solved to develop cloud computing. The theft of identity authentication information remains a serious threat to the security of cloud computing. In this process, attackers intrude into cloud computing services through identity authentication information, thereby threatening the security of data from multiple perspectives. Therefore, this study proposes a model for cloud computing protection and management based on quantum authentication, introduces the principle of quantum authentication, and deduces the quantum authentication process. In theory, quantum authentication technology can be applied in cloud computing for security protection. This technology cannot be cloned; thus, it is more secure and reliable than classical methods.
Dreuw, Andreas
2006-11-13
With the advent of modern computers and advances in the development of efficient quantum chemical computer codes, the meaningful computation of large molecular systems at a quantum mechanical level became feasible. Recent experimental effort to understand photoinitiated processes in biological systems, for instance photosynthesis or vision, at a molecular level also triggered theoretical investigations in this field. In this Minireview, standard quantum chemical methods are presented that are applicable and recently used for the calculation of excited states of photoinitiated processes in biological molecular systems. These methods comprise configuration interaction singles, the complete active space self-consistent field method, and time-dependent density functional theory and its variants. Semiempirical approaches are also covered. Their basic theoretical concepts and mathematical equations are briefly outlined, and their properties and limitations are discussed. Recent successful applications of the methods to photoinitiated processes in biological systems are described and theoretical tools for the analysis of excited states are presented.
Energy Technology Data Exchange (ETDEWEB)
Senthilkumar, K., E-mail: senovi2007@gmail.com [Department of Physics, AMET University, Chennai 603112 (India); Department of Physics, SRM University, Chennai 603203 (India); Kalaivani, T. [Department of Physics, SRM University, Chennai 603203 (India); Kanagesan, S. [Materials Synthesis and Characterization Laboratory (MSCL), Institute of Advanced Technology, Universiti Putra Malaysia, Serdang 43400, Selangor (Malaysia); Balasubramanian, V. [Department of Chemistry, AMET University, Chennai 603112 (India)
2013-01-01
Highlights: Black-Right-Pointing-Pointer We prepared zinc sulfide (ZnS) quantum dots of sizes 2.68-4.8 nm. Black-Right-Pointing-Pointer It is embedded on polyvinyl alcohol (PVA) matrix, have been synthesized at 70 Degree-Sign C by wet chemical method. Black-Right-Pointing-Pointer Optical absorption spectra showed strong blue shift, which is an indication of strong quantum confinement. Black-Right-Pointing-Pointer ZnS quantum dots exhibit strong quantum confinement effect as the optical band gap increases significantly, from 3.96 eV to 4.06 eV, compared to bulk value 3.68 eV. - Abstract: Zinc Sulfide (ZnS) quantum dots of sizes 2.68-4.8 nm, embedded on polyvinyl alcohol (PVA) matrix, have been synthesized at 70 Degree-Sign C by wet chemical method. X-ray diffraction (XRD), high resolution transmission electron microscopy (HRTEM), scanning electron microscopy (SEM), UV-vis spectroscopy and photoluminescence (PL) Spectroscopy has been adopted for sample characterization. Optical absorption spectra showed strong blue shift, which is an indication of strong quantum confinement. Photoluminescence spectra of the sample have been recorded at room temperature and observed two peaks centred around 415 nm and 440 nm. We have assigned the first peak due to band gap transitions while the later due to sulfur vacancy in the sample.
System and Method for Outlier Detection via Estimating Clusters
Iverson, David J. (Inventor)
2016-01-01
An efficient method and system for real-time or offline analysis of multivariate sensor data for use in anomaly detection, fault detection, and system health monitoring is provided. Models automatically derived from training data, typically nominal system data acquired from sensors in normally operating conditions or from detailed simulations, are used to identify unusual, out of family data samples (outliers) that indicate possible system failure or degradation. Outliers are determined through analyzing a degree of deviation of current system behavior from the models formed from the nominal system data. The deviation of current system behavior is presented as an easy to interpret numerical score along with a measure of the relative contribution of each system parameter to any off-nominal deviation. The techniques described herein may also be used to "clean" the training data.
Self-learning quantum Monte Carlo method in interacting fermion systems
Xu, Xiao Yan; Qi, Yang; Liu, Junwei; Fu, Liang; Meng, Zi Yang
2017-07-01
The self-learning Monte Carlo method is a powerful general-purpose numerical method recently introduced to simulate many-body systems. In this work, we extend it to an interacting fermion quantum system in the framework of the widely used determinant quantum Monte Carlo. This method can generally reduce the computational complexity and moreover can greatly suppress the autocorrelation time near a critical point. This enables us to simulate an interacting fermion system on a 100 ×100 lattice even at the critical point and obtain critical exponents with high precision.
Non-Hierarchical Clustering as a method to analyse an open-ended ...
African Journals Online (AJOL)
Non-Hierarchical Clustering as a method to analyse an open-ended questionnaire on algebraic thinking. ... Student responses to written questions and multiple-choice tests have been characterised and studied using several qualitative and/or quantitative analysis methods. However, there are inherent difficulties in the ...
Alexandroni, Guy; Zimmerman Moreno, Gali; Sochen, Nir; Greenspan, Hayit
2016-03-01
Recent advances in Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) of white matter in conjunction with improved tractography produce impressive reconstructions of White Matter (WM) pathways. These pathways (fiber sets) often contain hundreds of thousands of fibers, or more. In order to make fiber based analysis more practical, the fiber set needs to be preprocessed to eliminate redundancies and to keep only essential representative fibers. In this paper we demonstrate and compare two distinctive frameworks for selecting this reduced set of fibers. The first framework entails pre-clustering the fibers using k-means, followed by Hierarchical Clustering and replacing each cluster with one representative. For the second clustering stage seven distance metrics were evaluated. The second framework is based on an efficient geometric approximation paradigm named coresets. Coresets present a new approach to optimization and have huge success especially in tasks requiring large computation time and/or memory. We propose a modified version of the coresets algorithm, Density Coreset. It is used for extracting the main fibers from dense datasets, leaving a small set that represents the main structures and connectivity of the brain. A novel approach, based on a 3D indicator structure, is used for comparing the frameworks. This comparison was applied to High Angular Resolution Diffusion Imaging (HARDI) scans of 4 healthy individuals. We show that among the clustering based methods, that cosine distance gives the best performance. In comparing the clustering schemes with coresets, Density Coreset method achieves the best performance.
Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis
Directory of Open Access Journals (Sweden)
Sen Zhang
2015-01-01
Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.
Identification of rural landscape classes through a GIS clustering method
Directory of Open Access Journals (Sweden)
Irene Diti
2013-09-01
Full Text Available The paper presents a methodology aimed at supporting the rural planning process. The analysis of the state of the art of local and regional policies focused on rural and suburban areas, and the study of the scientific literature in the field of spatial analysis methodologies, have allowed the definition of the basic concept of the research. The proposed method, developed in a GIS, is based on spatial metrics selected and defined to cover various agricultural, environmental, and socio-economic components. The specific goal of the proposed methodology is to identify homogeneous extra-urban areas through their objective characterization at different scales. Once areas with intermediate urban-rural characters have been identified, the analysis is then focused on the more detailed definition of periurban agricultural areas. The synthesis of the results of the analysis of the various landscape components is achieved through an original interpretative key which aims to quantify the potential impacts of rural areas on the urban system. This paper presents the general framework of the methodology and some of the main results of its first implementation through an Italian case study.
Crawford, I.; Ruske, S.; Topping, D. O.; Gallagher, M. W.
2015-11-01
In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs) by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio-hydro-atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen-Rocky Mountain Biogenic Aerosol Study) ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of
Directory of Open Access Journals (Sweden)
I. Crawford
2015-11-01
Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the
A method for determining the radius of an open cluster from stellar proper motions
Sánchez, Néstor; Alfaro, Emilio J.; López-Martínez, Fátima
2018-01-01
We propose a method for calculating the radius of an open cluster in an objective way from an astrometric catalogue containing, at least, positions and proper motions. It uses the minimum spanning tree (hereinafter MST) in the proper motion space to discriminate cluster stars from field stars and it quantifies the strength of the cluster-field separation by means of a statistical parameter defined for the first time in this paper. This is done for a range of different sampling radii from where the cluster radius is obtained as the size at which the best cluster-field separation is achieved. The novelty of this strategy is that the cluster radius is obtained independently of how its stars are spatially distributed. We test the reliability and robustness of the method with both simulated and real data from a well-studied open cluster (NGC 188), and apply it to UCAC4 data for five other open clusters with different catalogued radius values. NGC 188, NGC 1647, NGC 6603 and Ruprecht 155 yielded unambiguous radius values of 15.2 ± 1.8, 29.4 ± 3.4, 4.2 ± 1.7 and 7.0 ± 0.3 arcmin, respectively. ASCC 19 and Collinder 471 showed more than one possible solution but it is not possible to know whether this is due to the involved uncertainties or to the presence of complex patterns in their proper motion distributions, something that could be inherent to the physical object or due to the way in which the catalogue was sampled.
Fast Search the Density Peaks and Clustering Method for Check-in Data
Directory of Open Access Journals (Sweden)
LIU Meng
2017-04-01
Full Text Available Check-in data obtained from Location-based Social Network (LBSN is a sort of crowd geographic data which will reveal daily activities of urban residents. Different check-in behaviors with the same check-in location will produce the phenomenon of location duplication because of location candidate function in LBSN system. The current density-based spatial clustering algorithms have the following problems: ①difficulty to find density peak point. ②clustering error caused by check-in point objects with duplicate positions. In order to solve these problems, we proposed a fast search density peaks and clustering method for check-in data, based on clustering by fast search and find of density peaks (CFSFDP. Firstly, position repetition frequency was introduced and calculated to illustrate the number of the check-in position duplications data. Secondly, a new type of point feature was constructed by adding position repetition frequency of the original check-in position data, which was used as study object to search density peaks. At last, clustering algorithm based on density peak point was constructed in which density connectivity was taken into account to ensure the continuity and integrity of density clusters. Taking check-in data obtained from Sina Microblog as an example, an experiment was designed and implemented. The results demonstrates:①Clustering method can effectively avoid the problem that the outlier location object with high repeatability is chosen as the peak and clustering, and has excellent spatial adaptability as well when comparing with check-in data from other area. ②Extracted density peak points can not only be used to represent the center of the hot zone, but also reflect the concentration trend of the hot zone, which can help to explore the dynamic change of the hot zone.
Orsi, Rebecca
2017-02-01
Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
A new method to assign galaxy cluster membership using photometric redshifts
Castignani, G.; Benoist, C.
2016-11-01
We introduce a new effective strategy to assign group and cluster membership probabilities Pmem to galaxies using photometric redshift information. Large dynamical ranges both in halo mass and cosmic time are considered. The method takes into account the magnitude distribution of both cluster and field galaxies as well as the radial distribution of galaxies in clusters using a non-parametric formalism, and relies on Bayesian inference to take photometric redshift uncertainties into account. We successfully test the method against 1208 galaxy clusters within redshifts z = 0.05-2.58 and masses 1013.29-14.80M⊙ drawn from wide field simulated galaxy mock catalogs mainly developed for the forthcoming Euclid mission. Median purity and completeness values of and are reached for galaxies brighter than 0.25 L∗ within r200 of each simulated halo and for a statistical photometric redshift accuracy σ((zs-zp)/(1 + zs)) = 0.03. The mean values p̅=56% and c̅=93% are consistent with the median and have negligible sub-percent uncertainties. Accurate photometric redshifts (σ((zs-zp)/(1 + zs)) ≲ 0.05) and robust estimates for the cluster redshift and cluster center coordinates are required. The dependence of the assignments on photometric redshift accuracy, galaxy magnitude and distance from the halo center, and halo properties such as mass, richness, and redshift are investigated. Variations in the mean values of both purity and completeness are globally limited to a few percent. The largest departures from the mean values are found for galaxies associated with distant z ≳ 1.5 halos, faint ( 0.25 L∗) galaxies, and those at the outskirts of the halo (at cluster-centric projected distances r200) for which the purity is decreased, Δp ≃ 20% at most, with respect to the mean value. The proposed method is applied to derive accurate richness estimates. A statistical comparison between the true (Ntrue) vs. estimated richness (λ = ∑ Pmem) yields on average to unbiased
2D Quantum Simulation of MOSFET Using the Non Equilibrium Green's Function Method
Svizhenko, Alexel; Anantram, M. P.; Govindan, T. R.; Yan, Jerry (Technical Monitor)
2000-01-01
The objectives this viewgraph presentation summarizes include: (1) the development of a quantum mechanical simulator for ultra short channel MOSFET simulation, including theory, physical approximations, and computer code; (2) explore physics that is not accessible by semiclassical methods; (3) benchmarking of semiclassical and classical methods; and (4) study other two-dimensional devices and molecular structure, from discretized Hamiltonian to tight-binding Hamiltonian.
Gangarapu, S.; Marcelis, A.T.M.; Zuilhof, H.
2013-01-01
The influence of electronic and steric effects on the stabilities of carbamates formed from the reaction of CO2 with a wide range of alkanolamines was investigated by quantum chemical methods. For the calculations, B3LYP, M11-L, MP2, and spin-component-scaled MP2 (SCS-MP2) methods were used, coupled
Kovac, Philip A.; Cina, Jeffrey A.
2017-12-01
We report the successful application of a recently developed mixed quantum/semiclassical wave-packet dynamical theory to the calculation of a spectroscopic signal, the linear absorption spectrum of a realistic small-molecule chromophore in a cryogenic environment. This variational fixed vibrational basis/Gaussian bath (FVB/GB) theory avails itself of an assumed time scale separation between a few, mostly intramolecular, high-frequency nuclear motions and a larger number of slower degrees of freedom primarily associated with an extended host medium. The more rapid, large-amplitude system dynamics is treated with conventional basis-set methods, while the slower time-evolution of the weakly coupled bath is subject to a semiclassical, thawed Gaussian trial form that honors the overall vibrational ground state, and hence the initial state prepared by its Franck-Condon transfer to an excited electronic state. We test this general approach by applying it to a small, symmetric iodine-krypton cluster suggestive of molecular iodine embedded in a low-temperature matrix. Because of the relative simplicity of this model complex, we are able to compare the absorption spectrum calculated via FVB/GB dynamics using Heller's time-dependent formula with one obtained from rigorously calculated eigenenergies and Franck-Condon factors. The FVB/GB treatment proves to be accurate at approximately 15-cm -1 resolution, despite the presence of several thousand spectral lines and a sequence of various-order system-bath resonances culminating at the highest absorption frequencies in an inversion of the relative system and bath time scales.
Torsional diffusion Monte Carlo: A method for quantum simulations of proteins
Clary, David C.
2001-06-01
The quantum diffusion Monte Carlo (DMC) method is extended to the treatment of coupled torsional motions in proteins. A general algorithm and computer program has been developed by interfacing this torsional-DMC method with all-atom force-fields for proteins. The method gives the zero-point energy and atomic coordinates averaged over the coupled torsional motions in the quantum ground state of the protein. Application of the new algorithm is made to the proteins gelsolin (356 atoms and 142 torsions) and gp41-HIV (1101 atoms and 452 torsions). The results indicate that quantum-dynamical effects are important for the energies and geometries of typical proteins such as these.
Energy Technology Data Exchange (ETDEWEB)
Anderson, Ryan B., E-mail: randerson@astro.cornell.edu [Cornell University Department of Astronomy, 406 Space Sciences Building, Ithaca, NY 14853 (United States); Bell, James F., E-mail: Jim.Bell@asu.edu [Arizona State University School of Earth and Space Exploration, Bldg.: INTDS-A, Room: 115B, Box 871404, Tempe, AZ 85287 (United States); Wiens, Roger C., E-mail: rwiens@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States); Morris, Richard V., E-mail: richard.v.morris@nasa.gov [NASA Johnson Space Center, 2101 NASA Parkway, Houston, TX 77058 (United States); Clegg, Samuel M., E-mail: sclegg@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States)
2012-04-15
We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO{sub 2} at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by {approx} 3 wt.%. The statistical significance of these improvements was {approx} 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and
Dong, Skye T; Costa, Daniel S J; Butow, Phyllis N; Lovell, Melanie R; Agar, Meera; Velikova, Galina; Teckle, Paulos; Tong, Allison; Tebbutt, Niall C; Clarke, Stephen J; van der Hoek, Kim; King, Madeleine T; Fayers, Peter M
2016-01-01
Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.
Clustering method and representative feeder selection for the California solar initiative
Energy Technology Data Exchange (ETDEWEB)
Broderick, Robert Joseph; Williams, Joseph R.; Munoz-Ramos, Karina
2014-02-01
The screening process for DG interconnection procedures needs to be improved in order to increase the PV deployment level on the distribution grid. A significant improvement in the current screening process could be achieved by finding a method to classify the feeders in a utility service territory and determine the sensitivity of particular groups of distribution feeders to the impacts of high PV deployment levels. This report describes the utility distribution feeder characteristics in California for a large dataset of 8,163 feeders and summarizes the California feeder population including the range of characteristics identified and most important to hosting capacity. The report describes the set of feeders that are identified for modeling and analysis as well as feeders identified for the control group. The report presents a method for separating a utilitys distribution feeders into unique clusters using the k-means clustering algorithm. An approach for determining the feeder variables of interest for use in a clustering algorithm is also described. The report presents an approach for choosing the feeder variables to be utilized in the clustering process and a method is identified for determining the optimal number of representative clusters.
Directory of Open Access Journals (Sweden)
Qiao Wei
2017-01-01
Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.
AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA
Directory of Open Access Journals (Sweden)
A. Alizade Naeini
2014-10-01
Full Text Available K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman–Pearson detection theory based eigen-thresholding method (i.e., the HFC method has been employed to estimate the number of endmembers in the image. Afterwards, the spectral signatures of the endmembers are obtained using the Minimum Volume Enclosing Simplex (MVES algorithm. Eventually, these spectral signatures are used to initialize the k-means clustering algorithm. The proposed method is implemented on a hyperspectral dataset acquired by ROSIS sensor with 103 spectral bands over the Pavia University campus, Italy. For comparative evaluation, two other commonly used initialization methods (i.e., Bradley & Fayyad (BF and Random methods are implemented and compared. The confusion matrix, overall accuracy and Kappa coefficient are employed to assess the methods’ performance. The evaluations demonstrate that the proposed solution outperforms the other initialization methods and can be applied for unsupervised classification of hyperspectral imagery for landcover mapping.
Quantum Monte Carlo methods and strongly correlated electrons on honeycomb structures
Energy Technology Data Exchange (ETDEWEB)
Lang, Thomas C.
2010-12-16
In this thesis we apply recently developed, as well as sophisticated quantum Monte Carlo methods to numerically investigate models of strongly correlated electron systems on honeycomb structures. The latter are of particular interest owing to their unique properties when simulating electrons on them, like the relativistic dispersion, strong quantum fluctuations and their resistance against instabilities. This work covers several projects including the advancement of the weak-coupling continuous time quantum Monte Carlo and its application to zero temperature and phonons, quantum phase transitions of valence bond solids in spin-1/2 Heisenberg systems using projector quantum Monte Carlo in the valence bond basis, and the magnetic field induced transition to a canted antiferromagnet of the Hubbard model on the honeycomb lattice. The emphasis lies on two projects investigating the phase diagram of the SU(2) and the SU(N)-symmetric Hubbard model on the hexagonal lattice. At sufficiently low temperatures, condensed-matter systems tend to develop order. An exception are quantum spin-liquids, where fluctuations prevent a transition to an ordered state down to the lowest temperatures. Previously elusive in experimentally relevant microscopic two-dimensional models, we show by means of large-scale quantum Monte Carlo simulations of the SU(2) Hubbard model on the honeycomb lattice, that a quantum spin-liquid emerges between the state described by massless Dirac fermions and an antiferromagnetically ordered Mott insulator. This unexpected quantum-disordered state is found to be a short-range resonating valence bond liquid, akin to the one proposed for high temperature superconductors. Inspired by the rich phase diagrams of SU(N) models we study the SU(N)-symmetric Hubbard Heisenberg quantum antiferromagnet on the honeycomb lattice to investigate the reliability of 1/N corrections to large-N results by means of numerically exact QMC simulations. We study the melting of phases
Vibrational Energy Relaxation: A Benchmark for Mixed Quantum-Classical Methods.
Jain, Amber; Subotnik, Joseph E
2018-01-11
We investigate the ability of mixed quantum-classical methods to capture the dynamics of vibrational energy relaxation. Several methods, including surface hopping, and Ehrenfest and symmetrical quasiclassical (SQC) dynamics, are benchmarked for the exactly solvable model problem of a harmonic oscillator bilinearly coupled to a bath of harmonic oscillators. Results show that, very often, one can recover accurate vibrational relaxation rates and detailed balance using simple mixed quantum-classical approaches. A few anomalous results do appear, however, especially regarding Ehrenfest and SQC dynamics.
Open-Source Sequence Clustering Methods Improve the State Of the Art.
Kopylova, Evguenia; Navas-Molina, Jose A; Mercier, Céline; Xu, Zhenjiang Zech; Mahé, Frédéric; He, Yan; Zhou, Hong-Wei; Rognes, Torbjørn; Caporaso, J Gregory; Knight, Rob
2016-01-01
Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http
Watanabe, Hiroshi C; Banno, Misa; Sakurai, Minoru
2016-03-14
Quantum effects in solute-solvent interactions, such as the many-body effect and the dipole-induced dipole, are known to be critical factors influencing the infrared spectra of species in the liquid phase. For accurate spectrum evaluation, the surrounding solvent molecules, in addition to the solute of interest, should be treated using a quantum mechanical method. However, conventional quantum mechanics/molecular mechanics (QM/MM) methods cannot handle free QM solvent molecules during molecular dynamics (MD) simulation because of the diffusion problem. To deal with this problem, we have previously proposed an adaptive QM/MM "size-consistent multipartitioning (SCMP) method". In the present study, as the first application of the SCMP method, we demonstrate the reproduction of the infrared spectrum of liquid-phase water, and evaluate the quantum effect in comparison with conventional QM/MM simulations.
Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification
Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang
2017-12-01
To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.
Form gene clustering method about pan-ethnic-group products based on emotional semantic
Chen, Dengkai; Ding, Jingjing; Gao, Minzhuo; Ma, Danping; Liu, Donghui
2016-09-01
The use of pan-ethnic-group products form knowledge primarily depends on a designer's subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers' perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.
Directory of Open Access Journals (Sweden)
Xiaoyong Zhang
2013-01-01
Full Text Available The presence of microcalcification clusters (MCs in mammogram is a major indicator of breast cancer. Detection of an MC is one of the key issues for breast cancer control. In this paper, we present a highly accurate method based on a morphological image processing and wavelet transform technique to detect the MCs in mammograms. The microcalcifications are firstly enhanced by using multistructure elements morphological processing. Then, the candidates of microcalcifications are refined by a multilevel wavelet reconstruction approach. Finally, MCs are detected based on their distributions feature. Experiments are performed on 138 clinical mammograms. The proposed method is capable of detecting 92.9% of true microcalcification clusters with an average of 0.08 false microcalcification clusters detected per image.
Energy Technology Data Exchange (ETDEWEB)
Dallaire-Demers, Pierre-Luc
2016-10-07
Quantum computers are the ideal platform for quantum simulations. Given enough coherent operations and qubits, such machines can be leveraged to simulate strongly correlated materials, where intricate quantum effects give rise to counter-intuitive macroscopic phenomena such as high-temperature superconductivity. Many phenomena of strongly correlated materials are encapsulated in the Fermi-Hubbard model. In general, no closed-form solution is known for lattices of more than one spatial dimension, but they can be numerically approximated using cluster methods. To model long-range effects such as order parameters, a powerful method to compute the cluster's Green's function consists in finding its self-energy through a variational principle. As is shown in this thesis, this allows the possibility of studying various phase transitions at finite temperature in the Fermi-Hubbard model. However, a classical cluster solver quickly hits an exponential wall in the memory (or computation time) required to store the computation variables. We show theoretically that the cluster solver can be mapped to a subroutine on a quantum computer whose quantum memory usage scales linearly with the number of orbitals in the simulated cluster and the number of measurements scales quadratically. We also provide a gate decomposition of the cluster Hamiltonian and a simple planar architecture for a quantum simulator that can also be used to simulate more general fermionic systems. We briefly analyze the Trotter-Suzuki errors and estimate the scaling properties of the algorithm for more complex applications. A quantum computer with a few tens of qubits could therefore simulate the thermodynamic properties of complex fermionic lattices inaccessible to classical supercomputers.
Directory of Open Access Journals (Sweden)
Hansoo Lee
2017-01-01
Full Text Available Several types of non-precipitation echoes appear in radar images and disrupt the weather forecasting process. An anomalous propagation echo is an unwanted observation result similar to a precipitation echo. It occurs through radar-beam ducting because of the temperature, humidity distribution, and other complicated atmospheric conditions. Anomalous propagation echoes should be removed because they make weather forecasting difficult. In this paper, we suggest an ensemble classification method based on an artificial neural network and a clustering-based subset-selection method. This method allows us to implement an efficient classification method when a feature space has complicated distributions. By separating the input data into atomic and non-atomic clusters, each derived cluster will receive its own base classifier. In the experiments, we compared our method with a standalone artificial neural network classifier. The suggested ensemble classifier showed 84.14% performance, which was about 2% higher than that of the k-means clustering-based ensemble classifier and about 4% higher than the standalone artificial neural network classifier.
Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method
DEFF Research Database (Denmark)
Andersen, Mikkel Meyer; Eriksen, Poul Svante; Morling, Niels
2014-01-01
The European Y-chromosomal short tandem repeat (STR) haplotype distribution has previously been analysed in various ways. Here, we introduce a new way of analysing population substructure using a new method based on clustering within the discrete Laplace exponential family that models the probabi......The European Y-chromosomal short tandem repeat (STR) haplotype distribution has previously been analysed in various ways. Here, we introduce a new way of analysing population substructure using a new method based on clustering within the discrete Laplace exponential family that models...... the probability distribution of the Y-STR haplotypes. Creating a consistent statistical model of the haplotypes enables us to perform a wide range of analyses. Previously, haplotype frequency estimation using the discrete Laplace method has been validated. In this paper we investigate how the discrete Laplace...... method can be used for cluster analysis to further validate the discrete Laplace method. A very important practical fact is that the calculations can be performed on a normal computer. We identified two sub-clusters of the Eastern and Western European Y-STR haplotypes similar to results of previous...
Energy Technology Data Exchange (ETDEWEB)
Baur, H.
2006-07-01
In the first part of this work we extract the algebraic structure behind the method of the influence functional in the context of dissipative quantum mechanics. Special emphasis was put on the transition from a quantum mechanical description to a classical one, since it allows a deeper understanding of the measurement-process. This is tightly connected with the transition from a microscopic to a macroscopic world where the former one is described by the rules of quantum mechanics whereas the latter follows the rules of classical mechanics. In addition we show how the results of the influence functional method can be interpreted as a stochastical process, which in turn allows an easy comparison with the well known time development of a quantum mechanical system by use of the Schroedinger equation. In the following we examine the tight-binding approximation of models of which their hamiltionian shows discrete eigenstates in position space and where transitions between those states are suppressed so that propagation either is described by tunneling or by thermal activation. In the framework of dissipative quantum mechanics this leads to a tremendous simplification of the effective description of the system since instead of looking at the full history of all paths in the path integral description, we only have to look at all possible jump times and the possible corresponding set of weights for the jump direction, which is much easier to handle both analytically and numerically. In addition we deal with the mapping and the connection of dissipative quantum mechanical models with ones in quantum field theory and in particular models in statistical field theory. As an example we mention conformal invariance in two dimensions which always becomes relevant if a statistical system only has local interaction and is invariant under scaling. (orig.)
Quantum Monte Carlo diagonalization method as a variational calculation
Energy Technology Data Exchange (ETDEWEB)
Mizusaki, Takahiro; Otsuka, Takaharu [Tokyo Univ. (Japan). Dept. of Physics; Honma, Michio
1997-05-01
A stochastic method for performing large-scale shell model calculations is presented, which utilizes the auxiliary field Monte Carlo technique and diagonalization method. This method overcomes the limitation of the conventional shell model diagonalization and can extremely widen the feasibility of shell model calculations with realistic interactions for spectroscopic study of nuclear structure. (author)
Directory of Open Access Journals (Sweden)
Leuze Michael
2009-07-01
Full Text Available Abstract Background The Centers for Disease Control and Prevention's (CDC's BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Our study focused on finding useful anomalies at manageable alert rates according to available BioSense data history. Methods The study dataset included more than 3 years of daily counts of military outpatient clinic visits for respiratory and rash syndrome groupings. We applied four spatial estimation methods in implementations of space-time scan statistics cross-checked in Matlab and C. We compared the utility of these methods according to the resultant background cluster rate (a false alarm surrogate and sensitivity to injected cluster signals. The comparison runs used a spatial resolution based on the facility zip code in the patient record and a finer resolution based on the residence zip code. Results Simple estimation methods that account for day-of-week (DOW data patterns yielded a clear advantage both in background cluster rate and in signal sensitivity. A 28-day baseline gave the most robust results for this estimation; the preferred baseline is long enough to remove daily fluctuations but short enough to reflect recent disease trends and data representation. Background cluster rates were lower for the rash syndrome counts than for the respiratory counts, likely because of seasonality and the large scale of the respiratory counts. Conclusion The spatial estimation method should be chosen according to characteristics of the selected data streams. In this dataset with strong day-of-week effects, the overall best detection performance was achieved using subregion averages over a 28-day baseline stratified by weekday or weekend/holiday behavior. Changing the estimation method for particular scenarios involving
Dong, Geng; Phung, Quan Manh; Hallaert, Simon D; Pierloot, Kristine; Ryde, Ulf
2017-04-19
[NiFe] hydrogenases catalyse the reversible conversion of molecular hydrogen to protons and electrons. This seemingly simple reaction has attracted much attention because of the prospective use of H2 as a clean fuel. In this paper, we have studied how H2 binds to the active site of this enzyme. Combined quantum mechanical and molecular mechanics (QM/MM) optimisation was performed to obtain the geometries, using both the TPSS and B3LYP density-functional theory (DFT) methods and considering both the singlet and triplet states of the Ni(ii) ion. To get more accurate energies and obtain a detailed account of the surroundings, we performed calculations with 819 atoms in the QM region. Moreover, coupled-cluster calculations with singles, doubles, and perturbatively treated triples (CCSD(T)) and cumulant-approximated second-order perturbation theory based on the density-matrix renormalisation group (DMRG-CASPT2) were carried out using three models to decide which DFT methods give the most accurate structures and energies. Our calculations show that H2 binding to Ni in the singlet state is the most favourable by at least 47 kJ mol(-1). In addition, the TPSS functional gives more accurate energies than B3LYP for this system.
An accurate potential energy surface for the F + H2 → HF + H reaction by the coupled-cluster method.
Chen, Jun; Sun, Zhigang; Zhang, Dong H
2015-01-14
A three dimensional potential energy surface for the F + H2 → HF + H reaction has been computed by the spin unrestricted coupled cluster method with singles, doubles, triples, and perturbative quadruples [UCCSDT(2)Q] using the augmented correlation-consistent polarised valence quadruple zeta basis set for the fluorine atom and the correlation-consistent polarised valence quadruple zeta basis set for the hydrogen atom. All the calculations are based on the restricted open-shell Hartree-Fock orbitals, together with the frozen core approximations, and the UCCSD(T)/complete basis set (CBS) correction term was included. The global potential energy surface was calculated by fitting the sampled ab initio points without any scaling factor for the correlation energy part using a neutral network function method. Extensive dynamics calculations have been carried out on the potential energy surface. The reaction rate constants, integral cross sections, product rotational states distribution, and forward and backward scattering as a function of collision energy of the F + HD → HF + D, F + HD → DF + H, and F + H2 reaction, were calculated by the time-independent quantum dynamics scattering theory using the new surface. The satisfactory agreement with the reported experimental observations previously demonstrates the accuracy of the new potential energy surface.
Directory of Open Access Journals (Sweden)
Lee Yun-Shien
2008-03-01
Full Text Available Abstract Background The hierarchical clustering tree (HCT with a dendrogram 1 and the singular value decomposition (SVD with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.
Statistical method on nonrandom clustering with application to somatic mutations in cancer
Directory of Open Access Journals (Sweden)
Rejto Paul A
2010-01-01
Full Text Available Abstract Background Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention. Results We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and β-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors. Conclusions Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.
Non-Hierarchical Clustering as a method to analyse an open-ended ...
African Journals Online (AJOL)
Apple
South African Journal of Education, Volume 36, Number 1, February 2016. 1. Art. # 1142, 13 pages, doi: 10.15700/saje.v36n1a1142. Non-Hierarchical Clustering as a method to analyse an open-ended questionnaire on algebraic thinking. Benedetto Di Paola. Department of Mathematics and Informatics, University of ...
Tandem: A Context-Aware Method for Spontaneous Clustering of Dynamic Wireless Sensor Nodes
Marin Perianu, Raluca; Lombriser, C.; Havinga, Paul J.M.; Scholten, Johan; Tröster, G.
Wireless sensor nodes attached to everyday objects and worn by people are able to collaborate and actively assist users in their activities. We propose a method through which wireless sensor nodes organize spontaneously into clusters based on a common context. Provided that the confidence of sharing
Application of the cluster variation method to ordering in an interstitital solid solution
DEFF Research Database (Denmark)
Pekelharing, Marjon I.; Böttger, Amarante; Somers, Marcel A. J.
1999-01-01
The tetrahedron approximation of the cluster variation method (CVM) was applied to describe the ordering on the fcc interstitial sublattice of gamma-Fe[N] and gamma'-Fe4N1-x. A Lennard-Jones potential was used to describe the dominantly strain-induced interactions, caused by misfitting of the N a...
Borm, G.F.; Melis, R.J.F.; Teerenstra, S.; Peer, P.G.M.
2005-01-01
In some clinical trials, treatment allocation on a patient level is not feasible, and whole groups or clusters of patients are allocated to the same treatment. If, for example, a clinical trial is investigating the efficacy of various patient coaching methods and randomization is done on a patient
Bustamam, A.; Aldila, D.; Fatimah, Arimbi, M. D.
2017-07-01
One of the most widely used clustering method, since it has advantage on its robustness, is Self-Organizing Maps (SOM) method. This paper discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is the main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA-based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV.
Quantum-Mechanical Methods for Quantifying Incorporation of Contaminants in Proximal Minerals
Directory of Open Access Journals (Sweden)
Lindsay C. Shuller-Nickles
2014-07-01
Full Text Available Incorporation reactions play an important role in dictating immobilization and release pathways for chemical species in low-temperature geologic environments. Quantum-mechanical investigations of incorporation seek to characterize the stability and geometry of incorporated structures, as well as the thermodynamics and kinetics of the reactions themselves. For a thermodynamic treatment of incorporation reactions, a source of the incorporated ion and a sink for the released ion is necessary. These sources/sinks in a real geochemical system can be solids, but more commonly, they are charged aqueous species. In this contribution, we review the current methods for ab initio calculations of incorporation reactions, many of which do not consider incorporation from aqueous species. We detail a recently-developed approach for the calculation of incorporation reactions and expand on the part that is modeling the interaction of periodic solids with aqueous source and sink phases and present new research using this approach. To model these interactions, a systematic series of calculations must be done to transform periodic solid source and sink phases to aqueous-phase clusters. Examples of this process are provided for three case studies: (1 neptunyl incorporation into studtite and boltwoodite: for the layered boltwoodite, the incorporation energies are smaller (more favorable for reactions using environmentally relevant source and sink phases (i.e., ΔErxn(oxides > ΔErxn(silicates > ΔErxn(aqueous. Estimates of the solid-solution behavior of Np5+/P5+- and U6+/Si4+-boltwoodite and Np5+/Ca2+- and U6+/K+-boltwoodite solid solutions are used to predict the limit of Np-incorporation into boltwoodite (172 and 768 ppm at 300 °C, respectively; (2 uranyl and neptunyl incorporation into carbonates and sulfates: for both carbonates and sulfates, it was found that actinyl incorporation into a defect site is more favorable than incorporation into defect-free periodic
Lubombo, C; Curotto, E; Janeiro Barral, Paula E; Mella, Massimo
2009-07-21
Classical and quantum simulations of ammonia clusters in the dimer through the hendecamer range are performed using the stereographic projection path integral. Employing the most recent polarizable potential to describe intermolecular interactions, energetic and structural data obtained with our simulations provide support for a more fluxional or flexible nature at low temperature of the ammonia dimer, pentamer, and hexamer than in the other investigated species. The octamer and the hendecamer display a relatively strong melting peak in the classical heat capacity and a less intense but significant melting peak in the quantum heat capacity. The latter are shifted to lower temperature (roughly 15 and 40 K lower, respectively) by the quantum effects. The features present in both classical and quantum constant volume heat capacity are interpreted as an indication of melting even in the octamer case, where a large energy gap is present between its global minimum and second most stable species. We develop a first order finite difference algorithm to integrate the geodesic equations in the inertia ellipsoid generated by n rigid nonlinear bodies mapped with stereographic projections. We use the technique to optimize configurations and to explore the potential surface of the hendecamer.
A Combinational Clustering Based Method for cDNA Microarray Image Segmentation.
Shao, Guifang; Li, Tiejun; Zuo, Wangda; Wu, Shunxiang; Liu, Tundong
2015-01-01
Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi's individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is
A Combinational Clustering Based Method for cDNA Microarray Image Segmentation.
Directory of Open Access Journals (Sweden)
Guifang Shao
Full Text Available Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1 Stanford Microarray Database (SMD, 2 Gene Expression Omnibus (GEO, 3 Baylor College of Medicine (BCM, 4 Swiss Institute of Bioinformatics (SIB, 5 Joe DeRisi's individual tiff files (DeRisi, and 6 University of California, San Francisco (UCSF, indicate that the improved
A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data
DEFF Research Database (Denmark)
Kent, Peter; Jensen, Rikke K; Kongsted, Alice
2014-01-01
intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing......BACKGROUND: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA...
Xing, Jian; Burkom, Howard; Moniz, Linda; Edgerton, James; Leuze, Michael; Tokars, Jerome
2009-07-17
The Centers for Disease Control and Prevention's (CDC's) BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Our study focused on finding useful anomalies at manageable alert rates according to available BioSense data history. The study dataset included more than 3 years of daily counts of military outpatient clinic visits for respiratory and rash syndrome groupings. We applied four spatial estimation methods in implementations of space-time scan statistics cross-checked in Matlab and C. We compared the utility of these methods according to the resultant background cluster rate (a false alarm surrogate) and sensitivity to injected cluster signals. The comparison runs used a spatial resolution based on the facility zip code in the patient record and a finer resolution based on the residence zip code. Simple estimation methods that account for day-of-week (DOW) data patterns yielded a clear advantage both in background cluster rate and in signal sensitivity. A 28-day baseline gave the most robust results for this estimation; the preferred baseline is long enough to remove daily fluctuations but short enough to reflect recent disease trends and data representation. Background cluster rates were lower for the rash syndrome counts than for the respiratory counts, likely because of seasonality and the large scale of the respiratory counts. The spatial estimation method should be chosen according to characteristics of the selected data streams. In this dataset with strong day-of-week effects, the overall best detection performance was achieved using subregion averages over a 28-day baseline stratified by weekday or weekend/holiday behavior. Changing the estimation method for particular scenarios involving different spatial resolution or other syndromes can yield further
Teaching Basic Quantum Mechanics in Secondary School Using Concepts of Feynman Path Integrals Method
Fanaro, Maria de los Angeles; Otero, Maria Rita; Arlego, Marcelo
2012-01-01
This paper discusses the teaching of basic quantum mechanics in high school. Rather than following the usual formalism, our approach is based on Feynman's path integral method. Our presentation makes use of simulation software and avoids sophisticated mathematical formalism. (Contains 3 figures.)
Antiferromagnetism in the Hubbard model using a cluster slave-spin method
Lee, Wei-Cheng; Lee, Ting-Kuo
2017-09-01
The cluster slave-spin method is introduced to systematically investigate the solutions of the Hubbard model including the symmetry-broken phases. In this method, the electron operator is factorized into a fermionic spinon describing the physical spin and a slave-spin describing the charge fluctuations. Following the U (1 ) formalism derived by Yu and Si [Phys. Rev. B 86, 085104 (2012), 10.1103/PhysRevB.86.085104], it is shown that the self-consistent equations to explore various symmetry-broken density wave states can be constructed in general with a cluster of multiple slave-spin sites. We employ this method to study the antiferromagnetic (AFM) state in the single band Hubbard model with the two- and four-site clusters of slave spins. While the Hubbard gap, the charge gap due to the doubly occupied states, scales with the Hubbard interaction U as expected, the AFM gap Δ , the gap in the spinon dispersion in the AFM state, exhibits a crossover from the weak- to strong-coupling behaviors as U increases. Our cluster slave-spin method reproduces not only the traditional mean-field behavior of Δ ˜U in the weak-coupling limit, but also the behavior of Δ ˜t2/U predicted by the superexchange mechanism in the strong-coupling limit. In addition, the holon-doublon correlator as functions of U and doping x is also computed, which exhibits a strong tendency toward the holon-doublon binding in the strong coupling regime. We further show that the quasiparticle weight obtained by the cluster slave-spin method is in a good agreement with the generalized Gutzwiller approximation in both AFM and paramagnetic states, and the results can be improved beyond the generalized Gutzwiller approximation as the cluster is enlarged from a single site to four sites. Our results demonstrate that the cluster slave-spin method can be a powerful tool to systematically investigate the strongly correlated system.
IP2P K-means: an efficient method for data clustering on sensor networks
Directory of Open Access Journals (Sweden)
Peyman Mirhadi
2013-03-01
Full Text Available Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2 and the preliminary results show that the algorithm works effectively and relatively more precisely.
Using cluster analysis as a method of classification of the genus Salix L. representatives
Directory of Open Access Journals (Sweden)
М. В. Роїк
2015-06-01
Full Text Available Purpose. To study interactions among the representatives of the genus Salix L. through the cluster analysis, form groups of closely related species and hybrid forms basing on differences of morphological parameters of leaves. Methods. Field, cluster analysis and tree graphics. Results. Willow species were grouped according to absolute parameters of leaf, and three groups of clusters were identified. The degree of affinity between species were assessed using values of an Euclidean distance. Distinctive features of leaf parameters were defined: length of a leaf blade (Ll, distance (cm between the leaf tip and its maximum width (SDmxT and the distance between the leaf tip (cm and the line of its width that corresponds to the length of petiole (SLpT. Conclusions. Using the willow species collection as an example, diagnostically valuable quantitative parameters of leaves were revealed, the use of which allows to identify willow species and hybrid forms through PC applications.
Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models
Elsheikh, Ahmed H.
2013-05-01
A novel multi-modal parameter estimation algorithm is introduced. Parameter estimation is an ill-posed inverse problem that might admit many different solutions. This is attributed to the limited amount of measured data used to constrain the inverse problem. The proposed multi-modal model calibration algorithm uses an iterative stochastic ensemble method (ISEM) for parameter estimation. ISEM employs an ensemble of directional derivatives within a Gauss-Newton iteration for nonlinear parameter estimation. ISEM is augmented with a clustering step based on k-means algorithm to form sub-ensembles. These sub-ensembles are used to explore different parts of the search space. Clusters are updated at regular intervals of the algorithm to allow merging of close clusters approaching the same local minima. Numerical testing demonstrates the potential of the proposed algorithm in dealing with multi-modal nonlinear parameter estimation for subsurface flow models. © 2013 Elsevier B.V.
Symanzik's method applied to fractional quantum Hall edge states
Energy Technology Data Exchange (ETDEWEB)
Blasi, A.; Ferraro, D.; Maggiore, N.; Magnoli, N. [Dipartimento di Fisica, Universita di Genova (Italy); LAMIA-INFM-CNR, Genova (Italy); Sassetti, M.
2008-11-15
The method of separability, introduced by Symanzik, is applied in order to describe the effect of a boundary for a fractional quantum Hall liquid in the Laughlin series. An Abelian Chern-Simons theory with plane boundary is considered and the Green functions both in the bulk and on the edge are constructed, following a rigorous, perturbative, quantum field theory treatment. We show that the conserved boundary currents find an explicit interpretation in terms of the continuity equation with the electron density satisfying the Tomonaga-Luttinger commutation relation. (Abstract Copyright [2008], Wiley Periodicals, Inc.)
Directory of Open Access Journals (Sweden)
Qunyi Xie
2016-01-01
Full Text Available Content-based image retrieval has recently become an important research topic and has been widely used for managing images from repertories. In this article, we address an efficient technique, called MNGS, which integrates multiview constrained nonnegative matrix factorization (NMF and Gaussian mixture model- (GMM- based spectral clustering for image retrieval. In the proposed methodology, the multiview NMF scheme provides competitive sparse representations of underlying images through decomposition of a similarity-preserving matrix that is formed by fusing multiple features from different visual aspects. In particular, the proposed method merges manifold constraints into the standard NMF objective function to impose an orthogonality constraint on the basis matrix and satisfy the structure preservation requirement of the coefficient matrix. To manipulate the clustering method on sparse representations, this paper has developed a GMM-based spectral clustering method in which the Gaussian components are regrouped in spectral space, which significantly improves the retrieval effectiveness. In this way, image retrieval of the whole database translates to a nearest-neighbour search in the cluster containing the query image. Simultaneously, this study investigates the proof of convergence of the objective function and the analysis of the computational complexity. Experimental results on three standard image datasets reveal the advantages that can be achieved with the proposed retrieval scheme.
A Method for Context-Based Adaptive QRS Clustering in Real Time.
Castro, Daniel; Félix, Paulo; Presedo, Jesús
2015-09-01
Continuous followup of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This paper presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the QRS complexes sequentially by grouping them into a dynamic set of clusters based on the information content of the temporal context. The clusters are represented by templates which evolve over time and adapt to the QRS morphology changes. Rules to create, merge, and remove clusters are defined along with techniques for noise detection in order to avoid their proliferation. To cope with beat misalignment, derivative dynamic time warping is used. The proposed method has been validated against the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global purity of 98.56% and 99.56%, respectively. Results show that our proposal not only provides better results than previous offline solutions but also fulfills real-time requirements.
Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling
Energy Technology Data Exchange (ETDEWEB)
Peterson, John R.; Marshall, P.J.; /KIPAC, Menlo Park; Andersson, K.; /Stockholm U. /SLAC
2005-08-05
We propose an ambitious new method that models the intracluster medium in clusters of galaxies as a set of X-ray emitting smoothed particles of plasma. Each smoothed particle is described by a handful of parameters including temperature, location, size, and elemental abundances. Hundreds to thousands of these particles are used to construct a model cluster of galaxies, with the appropriate complexity estimated from the data quality. This model is then compared iteratively with X-ray data in the form of adaptively binned photon lists via a two-sample likelihood statistic and iterated via Markov Chain Monte Carlo. The complex cluster model is propagated through the X-ray instrument response using direct sampling Monte Carlo methods. Using this approach the method can reproduce many of the features observed in the X-ray emission in a less assumption-dependent way that traditional analyses, and it allows for a more detailed characterization of the density, temperature, and metal abundance structure of clusters. Multi-instrument X-ray analyses and simultaneous X-ray, Sunyaev-Zeldovich (SZ), and lensing analyses are a straight-forward extension of this methodology. Significant challenges still exist in understanding the degeneracy in these models and the statistical noise induced by the complexity of the models.
A method for context-based adaptive QRS clustering in real-time
Castro, Daniel; Félix Lamas, Paulo; Rodríguez Presedo, Jesús María
2014-01-01
Continuous follow-up of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This work presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the Q...
Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.
Weber, Lukas M; Robinson, Mark D
2016-12-01
Recent technological developments in high-dimensional flow cytometry and mass cytometry (CyTOF) have made it possible to detect expression levels of dozens of protein markers in thousands of cells per second, allowing cell populations to be characterized in unprecedented detail. Traditional data analysis by "manual gating" can be inefficient and unreliable in these high-dimensional settings, which has led to the development of a large number of automated analysis methods. Methods designed for unsupervised analysis use specialized clustering algorithms to detect and define cell populations for further downstream analysis. Here, we have performed an up-to-date, extensible performance comparison of clustering methods for high-dimensional flow and mass cytometry data. We evaluated methods using several publicly available data sets from experiments in immunology, containing both major and rare cell populations, with cell population identities from expert manual gating as the reference standard. Several methods performed well, including FlowSOM, X-shift, PhenoGraph, Rclusterpp, and flowMeans. Among these, FlowSOM had extremely fast runtimes, making this method well-suited for interactive, exploratory analysis of large, high-dimensional data sets on a standard laptop or desktop computer. These results extend previously published comparisons by focusing on high-dimensional data and including new methods developed for CyTOF data. R scripts to reproduce all analyses are available from GitHub (https://github.com/lmweber/cytometry-clustering-comparison), and pre-processed data files are available from FlowRepository (FR-FCM-ZZPH), allowing our comparisons to be extended to include new clustering methods and reference data sets. © 2016 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC. © 2016 The Authors. Cytometry Part A Published by Wiley Periodicals, Inc. on behalf of ISAC.
Dogulu, Nilay; Solomatine, Dimitri; Lal Shrestha, Durga
2014-05-01
Within the context of flood forecasting, assessment of predictive uncertainty has become a necessity for most of the modelling studies in operational hydrology. There are several uncertainty analysis and/or prediction methods available in the literature; however, most of them rely on normality and homoscedasticity assumptions for model residuals occurring in reproducing the observed data. This study focuses on a statistical method analyzing model residuals without having any assumptions and based on a clustering approach: Uncertainty Estimation based on local Errors and Clustering (UNEEC). The aim of this work is to provide a comprehensive evaluation of the UNEEC method's performance in view of clustering approach employed within its methodology. This is done by analyzing normality of model residuals and comparing uncertainty analysis results (for 50% and 90% confidence level) with those obtained from uniform interval and quantile regression methods. An important part of the basis by which the methods are compared is analysis of data clusters representing different hydrometeorological conditions. The validation measures used are PICP, MPI, ARIL and NUE where necessary. A new validation measure linking prediction interval to the (hydrological) model quality - weighted mean prediction interval (WMPI) - is also proposed for comparing the methods more effectively. The case study is Brue catchment, located in the South West of England. A different parametrization of the method than its previous application in Shrestha and Solomatine (2008) is used, i.e. past error values in addition to discharge and effective rainfall is considered. The results show that UNEEC's notable characteristic in its methodology, i.e. applying clustering to data of predictors upon which catchment behaviour information is encapsulated, contributes increased accuracy of the method's results for varying flow conditions. Besides, classifying data so that extreme flow events are individually
Chengling Zhu; Shenmin Zhu; Kai Zhang; Zeyu Hui; Hui Pan; Zhixin Chen; Yao Li; Di Zhang; Da-Wei Wang
2016-01-01
Construction of metal oxide nanoparticles as anodes is of special interest for next-generation lithium-ion batteries. The main challenge lies in their rapid capacity fading caused by the structural degradation and instability of solid-electrolyte interphase (SEI) layer during charge/discharge process. Herein, we address these problems by constructing a novel-structured SnO2-based anode. The novel structure consists of mesoporous clusters of SnO2 quantum dots (SnO2 QDs), which are wrapped with...
Gao, Jiali; Major, Dan T; Fan, Yao; Lin, Yen-Lin; Ma, Shuhua; Wong, Kin-Yiu
2008-01-01
A method for incorporating quantum mechanics into enzyme kinetics modeling is presented. Three aspects are emphasized: 1) combined quantum mechanical and molecular mechanical methods are used to represent the potential energy surface for modeling bond forming and breaking processes, 2) instantaneous normal mode analyses are used to incorporate quantum vibrational free energies to the classical potential of mean force, and 3) multidimensional tunneling methods are used to estimate quantum effects on the reaction coordinate motion. Centroid path integral simulations are described to make quantum corrections to the classical potential of mean force. In this method, the nuclear quantum vibrational and tunneling contributions are not separable. An integrated centroid path integral-free energy perturbation and umbrella sampling (PI-FEP/UM) method along with a bisection sampling procedure was summarized, which provides an accurate, easily convergent method for computing kinetic isotope effects for chemical reactions in solution and in enzymes. In the ensemble-averaged variational transition state theory with multidimensional tunneling (EA-VTST/MT), these three aspects of quantum mechanical effects can be individually treated, providing useful insights into the mechanism of enzymatic reactions. These methods are illustrated by applications to a model process in the gas phase, the decarboxylation reaction of N-methyl picolinate in water, and the proton abstraction and reprotonation process catalyzed by alanine racemase. These examples show that the incorporation of quantum mechanical effects is essential for enzyme kinetics simulations.
a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis
Huang, W.; Li, S.; Xu, S.
2016-06-01
How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the
A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS
Directory of Open Access Journals (Sweden)
W. Huang
2016-06-01
Full Text Available How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time to four dimensions (space, time and semantics. More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The
A Novel Algorithm of Quantum Random Walk in Server Traffic Control and Task Scheduling
Directory of Open Access Journals (Sweden)
Dong Yumin
2014-01-01
Full Text Available A quantum random walk optimization model and algorithm in network cluster server traffic control and task scheduling is proposed. In order to solve the problem of server load balancing, we research and discuss the distribution theory of energy field in quantum mechanics and apply it to data clustering. We introduce the method of random walk and illuminate what the quantum random walk is. Here, we mainly research the standard model of one-dimensional quantum random walk. For the data clustering problem of high dimensional space, we can decompose one m-dimensional quantum random walk into m one-dimensional quantum random walk. In the end of the paper, we compare the quantum random walk optimization method with GA (genetic algorithm, ACO (ant colony optimization, and SAA (simulated annealing algorithm. In the same time, we prove its validity and rationality by the experiment of analog and simulation.
Cui, Jia; Hong, Bei; Jiang, Xuepeng; Chen, Qinghua
2017-05-01
With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.
Directory of Open Access Journals (Sweden)
Cui Jia
2017-05-01
Full Text Available With the purpose of reinforcing correlation analysis of risk assessment threat factors, a dynamic assessment method of safety risks based on particle filtering is proposed, which takes threat analysis as the core. Based on the risk assessment standards, the method selects threat indicates, applies a particle filtering algorithm to calculate influencing weight of threat indications, and confirms information system risk levels by combining with state estimation theory. In order to improve the calculating efficiency of the particle filtering algorithm, the k-means cluster algorithm is introduced to the particle filtering algorithm. By clustering all particles, the author regards centroid as the representative to operate, so as to reduce calculated amount. The empirical experience indicates that the method can embody the relation of mutual dependence and influence in risk elements reasonably. Under the circumstance of limited information, it provides the scientific basis on fabricating a risk management control strategy.
Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm
Karaca, Yeliz; Cattani, Carlo
Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.
Directory of Open Access Journals (Sweden)
Deepa Devasenapathy
2015-01-01
Full Text Available The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.
An Efficient High Dimensional Cluster Method and its Application in Global Climate Sets
Directory of Open Access Journals (Sweden)
Ke Li
2007-10-01
Full Text Available Because of the development of modern-day satellites and other data acquisition systems, global climate research often involves overwhelming volume and complexity of high dimensional datasets. As a data preprocessing and analysis method, the clustering method is playing a more and more important role in these researches. In this paper, we propose a spatial clustering algorithm that, to some extent, cures the problem of dimensionality in high dimensional clustering. The similarity measure of our algorithm is based on the number of top-k nearest neighbors that two grids share. The neighbors of each grid are computed based on the time series associated with each grid, and computing the nearest neighbor of an object is the most time consuming step. According to Tobler's "First Law of Geography," we add a spatial window constraint upon each grid to restrict the number of grids considered and greatly improve the efficiency of our algorithm. We apply this algorithm to a 100-year global climate dataset and partition the global surface into sub areas under various spatial granularities. Experiments indicate that our spatial clustering algorithm works well.
Pre-crash scenarios at road junctions: A clustering method for car crash data.
Nitsche, Philippe; Thomas, Pete; Stuetz, Rainer; Welsh, Ruth
2017-10-01
Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth "On-the-Spot" database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.
Janetzko, Florian; Köster, Andreas M.; Salahub, Dennis R.
2008-01-01
The development of the cyclic cluster model (CCM) formalism for Kohn-Sham auxiliary density functional theory (KS-ADFT) methods is presented. The CCM is a direct space approach for the calculation of perfect and defective systems under periodic boundary conditions. Translational symmetry is introduced in the CCM by integral weighting. A consistent weighting scheme for all two-center and three-center interactions appearing in the KS-ADFT method is presented. For the first time, an approach for the numerical integration of the exchange-correlation potential within the cyclic cluster formalism is derived. The presented KS-ADFT CCM implementation was applied to covalent periodic systems. The results of cyclic and molecular cluster model (MCM) calculations for trans-polyacetylene, graphene, and diamond are discussed as examples for systems periodic in one, two, and three dimensions, respectively. All structures were optimized. It is shown that the CCM results represent the results of MCM calculations in the limit of infinite molecular clusters. By analyzing the electronic structure, we demonstrate that the symmetry of the corresponding periodic systems is retained in CCM calculations. The obtained geometric and electronic structures are compared with available data from the literature.
Threshold selection for classification of MR brain images by clustering method
Energy Technology Data Exchange (ETDEWEB)
Moldovanu, Simona [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania); Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi (Romania); Obreja, Cristian; Moraru, Luminita, E-mail: luminita.moraru@ugal.ro [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania)
2015-12-07
Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.
Finite-size scaling for quantum criticality using the finite-element method.
Antillon, Edwin; Wehefritz-Kaufmann, Birgit; Kais, Sabre
2012-03-01
Finite size scaling for the Schrödinger equation is a systematic approach to calculate the quantum critical parameters for a given Hamiltonian. This approach has been shown to give very accurate results for critical parameters by using a systematic expansion with global basis-type functions. Recently, the finite-element method was shown to be a powerful numerical method for ab initio electronic-structure calculations with a variable real-space resolution. In this work, we demonstrate how to obtain quantum critical parameters by combining the finite-element method (FEM) with finite size scaling (FSS) using different ab initio approximations and exact formulations. The critical parameters could be atomic nuclear charges, internuclear distances, electron density, disorder, lattice structure, and external fields for stability of atomic, molecular systems and quantum phase transitions of extended systems. To illustrate the effectiveness of this approach we provide detailed calculations of applying FEM to approximate solutions for the two-electron atom with varying nuclear charge; these include Hartree-Fock, local density approximation, and an "exact" formulation using FEM. We then use the FSS approach to determine its critical nuclear charge for stability; here, the size of the system is related to the number of elements used in the calculations. Results prove to be in good agreement with previous Slater-basis set calculations and demonstrate that it is possible to combine finite size scaling with the finite-element method by using ab initio calculations to obtain quantum critical parameters. The combined approach provides a promising first-principles approach to describe quantum phase transitions for materials and extended systems.
Energy Technology Data Exchange (ETDEWEB)
Farberovich, Oleg V. [School of Physics and Astronomy, Beverly and Raymond Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978 (Israel); Research Center for Nanoscale Structure of Matter, Southern Federal University, Zorge 5, 344090 Rostov-on-Don (Russian Federation); Voronezh State University, Voronezh 394000 (Russian Federation); Mazalova, Victoria L., E-mail: mazalova@sfedu.ru [Research Center for Nanoscale Structure of Matter, Southern Federal University, Zorge 5, 344090 Rostov-on-Don (Russian Federation); Soldatov, Alexander V. [Research Center for Nanoscale Structure of Matter, Southern Federal University, Zorge 5, 344090 Rostov-on-Don (Russian Federation)
2015-11-15
We present here the quantum model of a Ni solid-state electron spin qubit on a silicon surface with the use of a density-functional scheme for the calculation of the exchange integrals in the non-collinear spin configurations in the generalized spin Hamiltonian (GSH) with the anisotropic exchange coupling parameters linking the nickel ions with a silicon substrate. In this model the interaction of a spin qubit with substrate is considered in GSH at the calculation of exchange integrals J{sub ij} of the nanosystem Ni{sub 7}–Si in the one-electron approach taking into account chemical bonds of all Si-atoms of a substrate (environment) with atoms of the Ni{sub 7}-cluster. The energy pattern was found from the effective GSH Hamiltonian acting in the restricted spin space of the Ni ions by the application of the irreducible tensor operators (ITO) technique. In this paper we offer the model of the quantum solid-state N-spin qubit based on the studying of the spin structure and the spin-dynamics simulations of the 3d-metal Ni clusters on the silicon surface. The solution of the problem of the entanglement between spin states in the N-spin systems is becoming more interesting when considering clusters or molecules with a spectral gap in their density of states. For quantifying the distribution of the entanglement between the individual spin eigenvalues (modes) in the spin structure of the N-spin system we use the density of entanglement (DOE). In this study we have developed and used the advanced high-precision numerical techniques to accurately assess the details of the decoherence process governing the dynamics of the N-spin qubits interacting with a silicon surface. We have studied the Rabi oscillations to evaluate the N-spin qubits system as a function of the time and the magnetic field. We have observed the stabilized Rabi oscillations and have stabilized the quantum dynamical qubit state and Rabi driving after a fixed time (0.327 μs). The comparison of the energy
A simple method to generate equal-sized homogenous strata or clusters for population-based sampling.
Elliott, Michael R
2011-04-01
Statistical efficiency and cost efficiency can be achieved in population-based samples through stratification and/or clustering. Strata typically combine subgroups of the population that are similar with respect to an outcome. Clusters are often taken from preexisting units, but may be formed to minimize between-cluster variance, or to equalize exposure to a treatment or risk factor. Area probability sample design procedures for the National Children's Study required contiguous strata and clusters that maximized within-stratum and within-cluster homogeneity while maintaining approximately equal size of the strata or clusters. However, there were few methods that allowed such strata or clusters to be constructed under these contiguity and equal size constraints. A search algorithm generates equal-size cluster sets that approximately span the space of all possible clusters of equal size. An optimal cluster set is chosen based on analysis of variance and convexity criteria. The proposed algorithm is used to construct 10 strata based on demographics and air pollution measures in Kent County, MI, following census tract boundaries. A brief simulation study is also conducted. The proposed algorithm is effective at uncovering underlying clusters from noisy data. It can be used in multi-stage sampling where equal-size strata or clusters are desired. Copyright © 2011 Elsevier Inc. All rights reserved.
Event by event method for quantum interference simulation
Mutia Delina, M
2014-01-01
Event by event method is a simulation approach which is not based on the knowledge of the Schrödinger equation. This approach uses the classical wave theory and particle concept: we use particles, not waves. The data is obtained by counting the events that were detected by the detector, just as in
Non-normal Lanczos methods for quantum scattering.
Khorasani, Reza Rajaie; Dumont, Randall S
2008-07-21
This article presents a new complex absorbing potential (CAP) block Lanczos method for computing scattering eigenfunctions and reaction probabilities. The method reduces the problem of computing energy eigenfunctions to solving two energy dependent systems of equations. An energy independent block Lanczos factorization casts the system into a block tridiagonal form, which can be solved very efficiently for all energies. We show that CAP-Lanczos methods exhibit instability due to the non-normality of CAP Hamiltonians and may break down for some systems. The instability is not due to loss of orthogonality but to non-normality of the Hamiltonian matrix. While use of a Woods-Saxon exponential CAP-as opposed to a polynomial CAP-reduced non-normality, it did not always ensure convergence. Our results indicate that the Arnoldi algorithm is more robust for non-normal systems and less prone to break down. An Arnoldi version of our method is applied to a nonadiabatic tunneling Hamiltonian with excellent results, while the Lanczos algorithm breaks down for this system.
Energy Technology Data Exchange (ETDEWEB)
Wykes, M., E-mail: mikewykes@gmail.com; Parambil, R.; Gierschner, J. [Madrid Institute for Advanced Studies, IMDEA Nanoscience, Calle Faraday 9, Campus Cantoblanco, 28049 Madrid (Spain); Beljonne, D. [Laboratory for Chemistry of Novel Materials, University of Mons, Place du Parc 20, 7000 Mons (Belgium)
2015-09-21
Here, we present a general approach to treating vibronic coupling in molecular crystals based on atomistic simulations of large clusters. Such clusters comprise model aggregates treated at the quantum chemical level embedded within a realistic environment treated at the molecular mechanics level. As we calculate ground and excited state equilibrium geometries and vibrational modes of model aggregates, our approach is able to capture effects arising from coupling to intermolecular degrees of freedom, absent from existing models relying on geometries and normal modes of single molecules. Using the geometries and vibrational modes of clusters, we are able to simulate the fluorescence spectra of aggregates for which the lowest excited state bears negligible oscillator strength (as is the case, e.g., ideal H-aggregates) by including both Franck-Condon (FC) and Herzberg-Teller (HT) vibronic transitions. The latter terms allow the adiabatic excited state of the cluster to couple with vibrations in a perturbative fashion via derivatives of the transition dipole moment along nuclear coordinates. While vibronic coupling simulations employing FC and HT terms are well established for single-molecules, to our knowledge this is the first time they are applied to molecular aggregates. Here, we apply this approach to the simulation of the low-temperature fluorescence spectrum of para-distyrylbenzene single-crystal H-aggregates and draw comparisons with coarse-grained Frenkel-Holstein approaches previously extensively applied to such systems.
Wykes, M.; Parambil, R.; Beljonne, D.; Gierschner, J.
2015-09-01
Here, we present a general approach to treating vibronic coupling in molecular crystals based on atomistic simulations of large clusters. Such clusters comprise model aggregates treated at the quantum chemical level embedded within a realistic environment treated at the molecular mechanics level. As we calculate ground and excited state equilibrium geometries and vibrational modes of model aggregates, our approach is able to capture effects arising from coupling to intermolecular degrees of freedom, absent from existing models relying on geometries and normal modes of single molecules. Using the geometries and vibrational modes of clusters, we are able to simulate the fluorescence spectra of aggregates for which the lowest excited state bears negligible oscillator strength (as is the case, e.g., ideal H-aggregates) by including both Franck-Condon (FC) and Herzberg-Teller (HT) vibronic transitions. The latter terms allow the adiabatic excited state of the cluster to couple with vibrations in a perturbative fashion via derivatives of the transition dipole moment along nuclear coordinates. While vibronic coupling simulations employing FC and HT terms are well established for single-molecules, to our knowledge this is the first time they are applied to molecular aggregates. Here, we apply this approach to the simulation of the low-temperature fluorescence spectrum of para-distyrylbenzene single-crystal H-aggregates and draw comparisons with coarse-grained Frenkel-Holstein approaches previously extensively applied to such systems.
Weissker, Hans-Christian; Whetten, Robert L; López-Lozano, Xóchitl
2014-06-28
Quantum-sized silver and gold clusters show very different spectral characteristics. While silver exhibits a strong localized surface-plasmon resonance (LSPR) band down to very small sizes, the resonance is broadened beyond recognition in Au clusters below about 2 nm. In the present work, we study icosahedral hollow-shell structures, or cages, of about 1.8 nm diameter in comparison with compact clusters and show that the qualitative difference between Ag and Au remains but is reduced, as a significant increase of absorption is found for the Au cage structures. The silver shell Ag92 exhibits a resonance that is red-shifted compared to the compact Ag147, coinciding with the general result found in much larger shells that are amenable to the classical description by Mie theory. However, the electronic structure in particular of the d band is strongly changed. The spectrum of the empty Ag shell is remarkably similar to the spectrum of the respective Au55Ag92 core-shell structure. Inspection of the time-dependent electronic density does not explain this similarity. However, it shows that the overall classical picture of a collective charge oscillation remains valid, although clearly with modifications. We further show a remarkable insensitivity of the absorption spectra of both Ag and Au clusters to even rather extreme values of compression or dilatation.
Nonequilibrium Green's function method for quantum thermal transport
Wang, Jian-Sheng; Agarwalla, Bijay Kumar; Li, Huanan; Thingna, Juzar
2014-12-01
This review deals with the nonequilibrium Green's function (NEGF) method applied to the problems of energy transport due to atomic vibrations (phonons), primarily for small junction systems. We present a pedagogical introduction to the subject, deriving some of the well-known results such as the Laudauer-like formula for heat current in ballistic systems. The main aim of the review is to build the machinery of the method so that it can be applied to other situations, which are not directly treated here. In addition to the above, we consider a number of applications of NEGF, not in routine model system calculations, but in a few new aspects showing the power and usefulness of the formalism. In particular, we discuss the problems of multiple leads, coupled left-right-lead system, and system without a center. We also apply the method to the problem of full counting statistics. In the case of nonlinear systems, we make general comments on the thermal expansion effect, phonon relaxation time, and a certain class of mean-field approximations. Lastly, we examine the relationship between NEGF, reduced density matrix, and master equation approaches to thermal transport.
Cluster detection methods applied to the Upper Cape Cod cancer data
Directory of Open Access Journals (Sweden)
Ozonoff David
2005-09-01
Full Text Available Abstract Background A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. Methods We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. Results The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. Conclusion The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.
Use of a modified cluster sampling method to perform rapid needs assessment after Hurricane Andrew.
Hlady, W G; Quenemoen, L E; Armenia-Cope, R R; Hurt, K J; Malilay, J; Noji, E K; Wurm, G
1994-04-01
To rapidly obtain population-based estimates of needs in the early aftermath of Hurricane Andrew in South Florida. We used a modified cluster-sampling method (the Expanded Programme on Immunization [EPI] method) for three surveys. We selected a systematic sample of 30 quarter-mile square clusters for each survey and, beginning from a random start, interviewed members of seven consecutive occupied households in each cluster. Two surveys were of the most affected area (1990 population, 32,672) at three and ten days after the hurricane struck; one survey was of a less affected area (1990 population, 15,576) seven days after the hurricane struck. Results were available within 24 hours of beginning each survey. Initial findings emphasized the need for restoring utilities and sanitation and helped to focus medical relief on primary care and preventive services. The second survey of the most affected area showed improvement in the availability of food, water, electricity, and sanitation (P < or = .05). There was no evidence of disease outbreaks. For the first time, the EPI method provided population-based information to guide and evaluate relief operations after a sudden-impact natural disaster. An improvement over previous approaches, the EPI method warrants further evaluation as a needs assessment tool in acute disasters.
Miyamoto, S.; Nakayama, K.
1983-01-01
A method of two-stage clustering of literature based on citation frequency is applied to 5,065 articles from 57 journals in environmental and civil engineering. Results of related methods of citation analysis (hierarchical graph, clustering of journals, multidimensional scaling) applied to same set of articles are compared. Ten references are…
Smolin, John A; Gambetta, Jay M; Smith, Graeme
2012-02-17
We provide an efficient method for computing the maximum-likelihood mixed quantum state (with density matrix ρ) given a set of measurement outcomes in a complete orthonormal operator basis subject to Gaussian noise. Our method works by first changing basis yielding a candidate density matrix μ which may have nonphysical (negative) eigenvalues, and then finding the nearest physical state under the 2-norm. Our algorithm takes at worst O(d(4)) for the basis change plus O(d(3)) for finding ρ where d is the dimension of the quantum state. In the special case where the measurement basis is strings of Pauli operators, the basis change takes only O(d(3)) as well. The workhorse of the algorithm is a new linear-time method for finding the closest probability distribution (in Euclidean distance) to a set of real numbers summing to one.
Comparative study of theoretical methods for non-equilibrium quantum transport
Energy Technology Data Exchange (ETDEWEB)
Eckel, J; Thorwart, M [Freiburg Institute for Advanced Studies (FRIAS), Albert-Ludwigs-Universitaet Freiburg, 79104 Freiburg (Germany); Heidrich-Meisner, F [Department of Physics, Arnold Sommerfeld Center for Theoretical Physics and Center for NanoScience, Ludwig-Maximilians-Universitaet Muenchen, D-80333 Muenchen (Germany); Jakobs, S G; Pletyukhov, M [Institut fuer Theoretische Physik A, RWTH Aachen, 52056 Aachen (Germany); Egger, R, E-mail: eckelj@thphy.uni-duesseldorf.d [Institut fuer Theoretische Physik, Heinrich-Heine-Universitaet Duesseldorf, 40225 Duesseldorf (Germany)
2010-04-15
We present a detailed comparison of three different methods designed to tackle non-equilibrium quantum transport, namely the functional renormalization group (fRG), the time-dependent density matrix renormalization group (tDMRG) and the iterative summation of real-time path integrals (ISPI). For the non-equilibrium single-impurity Anderson model (including a Zeeman term at the impurity site), we demonstrate that the three methods are in quantitative agreement over a wide range of parameters at the particle-hole symmetric point as well as in the mixed-valence regime. We further compare these techniques with two quantum Monte Carlo approaches and the time-dependent numerical renormalization group method.
Self-guided method to search maximal Bell violations for unknown quantum states
Yang, Li-Kai; Chen, Geng; Zhang, Wen-Hao; Peng, Xing-Xiang; Yu, Shang; Ye, Xiang-Jun; Li, Chuan-Feng; Guo, Guang-Can
2017-11-01
In recent decades, a great variety of research and applications concerning Bell nonlocality have been developed with the advent of quantum information science. Providing that Bell nonlocality can be revealed by the violation of a family of Bell inequalities, finding maximal Bell violation (MBV) for unknown quantum states becomes an important and inevitable task during Bell experiments. In this paper we introduce a self-guided method to find MBVs for unknown states using a stochastic gradient ascent algorithm (SGA), by parametrizing the corresponding Bell operators. For three investigated systems (two qubit, three qubit, and two qutrit), this method can ascertain the MBV of general two-setting inequalities within 100 iterations. Furthermore, we prove SGA is also feasible when facing more complex Bell scenarios, e.g., d -setting d -outcome Bell inequality. Moreover, compared to other possible methods, SGA exhibits significant superiority in efficiency, robustness, and versatility.
Wang, Hui; Li, Xin; Xu, Bi-lian; Xu, Wei-ming
2004-05-01
Synthetical evaluation of promoting effect of some kinds of transdermal enhancers was carried through. Diclofenac sodium was used as model, and azone and l-menthol and synthetic borneol and olieic acid and essential oil from Cnidium monnieri were used as transdermal enhancers. Transdermal absorption experimentation of diclofenac sodium on the device of penetrating skins in vitro was done. Cumulation of permeation amount and penetrating rates and steady fluxes and lag times were observed, and grey relational cluster method was used to evaluate the promoting effect of some kinds of transdermal enhancers. As for promoting effect on diclofenac sodium, azone and l-menthol were the best, and synthetic borneol and olieic acid ranked behind. Grey relational cluster method can evaluate promoting effect objectively and fairly.
The measurement of human development using the Ward method of cluster analysis
Directory of Open Access Journals (Sweden)
Ingrid Majerova
2017-06-01
Full Text Available The Human Development Index is one of the methods how to measure human development. It measures the level of human development both in the economic and social field. Human development is studied at the national level in most cases, yet it might be used at the regional level of a country, too. The objective of the article is to describe the potential for human development in the NUTS II regions of the Visegrad Group Plus countries (the Czech Republic, Poland, Hungary, Slovakia, and Austria and Slovenia using the cluster analysis. The research was carried out in the period from 2004 to 2013. Initially, a research hypothesis regarding the dynamization of the human development processes in most of the regions was set, moving from a lower to a higher development potential within three groups. This hypothesis was verified by a hierarchy cluster analysis in the Ward method and was not confirmed.
Linear-response theory for Mukherjee's multireference coupled-cluster method: excitation energies.
Jagau, Thomas-C; Gauss, Jürgen
2012-07-28
The recently presented linear-response function for Mukherjee's multireference coupled-cluster method (Mk-MRCC) [T.-C. Jagau and J. Gauss, J. Chem. Phys. 137, 044115 (2012)] is employed to determine vertical excitation energies within the singles and doubles approximation (Mk-MRCCSD-LR) for ozone as well as for o-benzyne, m-benzyne, and p-benzyne, which display increasing multireference character in their ground states. In order to assess the impact of a multireference ground-state wavefunction on excitation energies, we compare all our results to those obtained at the single-reference coupled-cluster level of theory within the singles and doubles as well as within the singles, doubles, and triples approximation. Special attention is paid to the artificial splitting of certain excited states which arises from the redundancy intrinsic to Mk-MRCC theory and hinders the straightforward application of the Mk-MRCC-LR method.
Cooks, Robert Graham; Li, Anyin; Luo, Qingjie
2017-08-01
The invention generally relates to systems and methods for producing metal clusters; functionalized surfaces; and droplets including solvated metal ions. In certain aspects, the invention provides methods that involve providing a metal and a solvent. The methods additionally involve applying voltage to the solvated metal to thereby produce solvent droplets including ions of the metal containing compound, and directing the solvent droplets including the metal ions to a target. In certain embodiments, once at the target, the metal ions can react directly or catalyze reactions.
Scalable fault tolerant algorithms for linear-scaling coupled-cluster electronic structure methods.
Energy Technology Data Exchange (ETDEWEB)
Leininger, Matthew L.; Nielsen, Ida Marie B.; Janssen, Curtis L.
2004-10-01
By means of coupled-cluster theory, molecular properties can be computed with an accuracy often exceeding that of experiment. The high-degree polynomial scaling of the coupled-cluster method, however, remains a major obstacle in the accurate theoretical treatment of mainstream chemical problems, despite tremendous progress in computer architectures. Although it has long been recognized that this super-linear scaling is non-physical, the development of efficient reduced-scaling algorithms for massively parallel computers has not been realized. We here present a locally correlated, reduced-scaling, massively parallel coupled-cluster algorithm. A sparse data representation for handling distributed, sparse multidimensional arrays has been implemented along with a set of generalized contraction routines capable of handling such arrays. The parallel implementation entails a coarse-grained parallelization, reducing interprocessor communication and distributing the largest data arrays but replicating as many arrays as possible without introducing memory bottlenecks. The performance of the algorithm is illustrated by several series of runs for glycine chains using a Linux cluster with an InfiniBand interconnect.
Directory of Open Access Journals (Sweden)
Mohammad Abuei Ardakan
2010-04-01
Full Text Available The present paper offers a basic introduction to data clustering and demonstrates the application of clustering methods in drawing maps of science. All approaches towards classification and clustering of information are briefly discussed. Their application to the process of visualization of conceptual information and drawing of science maps are illustrated by reviewing similar researches in this field. By implementing aggregated hierarchical clustering algorithm, which is an algorithm based on complete-link method, the map for urban management science as an emerging, interdisciplinary scientific field is analyzed and reviewed.
Zhang, Xiaoyong; Homma, Noriyasu; Goto, Shotaro; Kawasumi, Yosuke; Ishibashi, Tadashi; Abe, Makoto; Sugita, Norihiro; Yoshizawa, Makoto
2013-01-01
The presence of microcalcification clusters (MCs) in mammogram is a major indicator of breast cancer. Detection of an MC is one of the key issues for breast cancer control. In this paper, we present a highly accurate method based on a morphological image processing and wavelet transform technique to detect the MCs in mammograms. The microcalcifications are firstly enhanced by using multistructure elements morphological processing. Then, the candidates of microcalcifications are refined by a m...
Energy Technology Data Exchange (ETDEWEB)
Brehm, Sascha
2009-02-26
Two-particle excitations, such as spin and charge excitations, play a key role in high-T{sub c} cuprate superconductors (HTSC). Due to the antiferromagnetism of the parent compound the magnetic excitations are supposed to be directly related to the mechanism of superconductivity. In particular, the so-called resonance mode is a promising candidate for the pairing glue, a bosonic excitation mediating the electronic pairing. In addition, its interactions with itinerant electrons may be responsible for some of the observed properties of HTSC. Hence, getting to the bottom of the resonance mode is crucial for a deeper understanding of the cuprate materials. To analyze the corresponding two-particle correlation functions we develop in the present thesis a new, non-perturbative and parameter-free technique for T=0 which is based on the Variational Cluster Approach (VCA, an embedded cluster method for one-particle Green's functions). Guided by the spirit of the VCA we extract an effective electron-hole vertex from an isolated cluster and use a fully renormalized bubble susceptibility {chi}{sub 0} including the VCA one-particle propagators. Within our new approach, the magnetic excitations of HTSC are shown to be reproduced for the Hubbard model within the relevant strong-coupling regime. Exceptionally, the famous resonance mode occurring in the underdoped regime within the superconductivity-induced gap of spin-flip electron-hole excitations is obtained. Its intensity and hourglass dispersion are in good overall agreement with experiments. Furthermore, characteristic features such as the position in energy of the resonance mode and the difference of the imaginary part of the susceptibility in the superconducting and the normal states are in accord with Inelastic Neutron Scattering (INS) experiments. For the first time, a strongly-correlated parameter-free calculation revealed these salient magnetic properties supporting the S=1 magnetic exciton scenario for the
Quantum confinement of lead titanate nanocrystals by wet chemical method
Energy Technology Data Exchange (ETDEWEB)
Kaviyarasu, K., E-mail: kaviyarasuloyolacollege@gmail.com [UNESCO-UNISA Africa Chair in Nanosciences/Nanotechnology Laboratories, College of Graduate Studies, University of South Africa (UNISA), Muckleneuk Ridge, P O Box 392, Pretoria (South Africa); Nanosciences African Network (NANOAFNET), Materials Research Department (MSD), iThemba LABS-National Research Foundation - NRF, 1 Old Faure Road, 7129, P O Box 722, Somerset West, Western Cape Province (South Africa); Manikandan, E., E-mail: maniphysics@gmail.com [Nanosciences African Network (NANOAFNET), Materials Research Department (MSD), iThemba LABS-National Research Foundation - NRF, 1 Old Faure Road, 7129, P O Box 722, Somerset West, Western Cape Province (South Africa); Central Research Laboratory, Sree Balaji Medical College & Hospital, Bharath University, Chrompet, Chennai, Tamil Nadu (India); Nuru, Z.Y. [UNESCO-UNISA Africa Chair in Nanosciences/Nanotechnology Laboratories, College of Graduate Studies, University of South Africa (UNISA), Muckleneuk Ridge, P O Box 392, Pretoria (South Africa); Nanosciences African Network (NANOAFNET), Materials Research Department (MSD), iThemba LABS-National Research Foundation - NRF, 1 Old Faure Road, 7129, P O Box 722, Somerset West, Western Cape Province (South Africa); Maaza, M., E-mail: likmaaz@gmail.com [UNESCO-UNISA Africa Chair in Nanosciences/Nanotechnology Laboratories, College of Graduate Studies, University of South Africa (UNISA), Muckleneuk Ridge, P O Box 392, Pretoria (South Africa); Nanosciences African Network (NANOAFNET), Materials Research Department (MSD), iThemba LABS-National Research Foundation - NRF, 1 Old Faure Road, 7129, P O Box 722, Somerset West, Western Cape Province (South Africa)
2015-11-15
Lead Titanate (PbTiO{sub 3)} is a category of the practical semiconductor metal oxides, which is widely applied in various scientific and industrial fields because of its catalytic, optical, and electrical properties. PbTiO{sub 3} nanocrystalline materials have attracted a wide attention due to their unique properties. PbTiO{sub 3} nanocrystals were investigated by X-ray diffraction (XRD) to identify the PbTiO{sub 3} nanocrystals were composed a tetragonal structure. The diameter of a single sphere was around 20 nm and the diameter reached up to 3 μm. The chemical composition of the samples and the valence states of elements were determined by X-ray photoelectron spectroscopy (XPS) in detail. - Highlights: • Single crystalline NSs of PbTiO{sub 3} fabricated by wet chemical method. • PbTiO{sub 3} NSs were uniform and continuous along the long axis. • Tetragonal perovskite structure with the diameter 20 nm and length 3 μm. • XPS spectrum was fitted with Lorentzian function respectively. • The size of the images is also 10 μm × 10 μm.
Directory of Open Access Journals (Sweden)
Reilly John J
2005-06-01
Full Text Available Abstract Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical
Farberovich, Oleg V.; Mazalova, Victoria L.; Soldatov, Alexander V.
2015-11-01
We present here the quantum model of a Ni solid-state electron spin qubit on a silicon surface with the use of a density-functional scheme for the calculation of the exchange integrals in the non-collinear spin configurations in the generalized spin Hamiltonian (GSH) with the anisotropic exchange coupling parameters linking the nickel ions with a silicon substrate. In this model the interaction of a spin qubit with substrate is considered in GSH at the calculation of exchange integrals Jij of the nanosystem Ni7-Si in the one-electron approach taking into account chemical bonds of all Si-atoms of a substrate (environment) with atoms of the Ni7-cluster. The energy pattern was found from the effective GSH Hamiltonian acting in the restricted spin space of the Ni ions by the application of the irreducible tensor operators (ITO) technique. In this paper we offer the model of the quantum solid-state N-spin qubit based on the studying of the spin structure and the spin-dynamics simulations of the 3d-metal Ni clusters on the silicon surface. The solution of the problem of the entanglement between spin states in the N-spin systems is becoming more interesting when considering clusters or molecules with a spectral gap in their density of states. For quantifying the distribution of the entanglement between the individual spin eigenvalues (modes) in the spin structure of the N-spin system we use the density of entanglement (DOE). In this study we have developed and used the advanced high-precision numerical techniques to accurately assess the details of the decoherence process governing the dynamics of the N-spin qubits interacting with a silicon surface. We have studied the Rabi oscillations to evaluate the N-spin qubits system as a function of the time and the magnetic field. We have observed the stabilized Rabi oscillations and have stabilized the quantum dynamical qubit state and Rabi driving after a fixed time (0.327 μs). The comparison of the energy pattern with the
Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology.
van der Kamp, Marc W; Mulholland, Adrian J
2013-04-23
Computational enzymology is a rapidly maturing field that is increasingly integral to understanding mechanisms of enzyme-catalyzed reactions and their practical applications. Combined quantum mechanics/molecular mechanics (QM/MM) methods are important in this field. By treating the reacting species with a quantum mechanical method (i.e., a method that calculates the electronic structure of the active site) and including the enzyme environment with simpler molecular mechanical methods, enzyme reactions can be modeled. Here, we review QM/MM methods and their application to enzyme-catalyzed reactions to investigate fundamental and practical problems in enzymology. A range of QM/MM methods is available, from cheaper and more approximate methods, which can be used for molecular dynamics simulations, to highly accurate electronic structure methods. We discuss how modeling of reactions using such methods can provide detailed insight into enzyme mechanisms and illustrate this by reviewing some recent applications. We outline some practical considerations for such simulations. Further, we highlight applications that show how QM/MM methods can contribute to the practical development and application of enzymology, e.g., in the interpretation and prediction of the effects of mutagenesis and in drug and catalyst design.
Grid-based methods for biochemical ab initio quantum chemical applications
Energy Technology Data Exchange (ETDEWEB)
Colvin, M.E.; Nelson, J.S.; Mori, E. [and others
1997-01-01
A initio quantum chemical methods are seeing increased application in a large variety of real-world problems including biomedical applications ranging from drug design to the understanding of environmental mutagens. The vast majority of these quantum chemical methods are {open_quotes}spectral{close_quotes}, that is they describe the charge distribution around the nuclear framework in terms of a fixed analytic basis set. Despite the additional complexity they bring, methods involving grid representations of the electron or solvent charge can provide more efficient schemes for evaluating spectral operators, inexpensive methods for calculating electron correlation, and methods for treating the electrostatic energy of salvation in polar solvents. The advantage of mixed or {open_quotes}pseudospectral{close_quotes} methods is that they allow individual non-linear operators in the partial differential equations, such as coulomb operators, to be calculated in the most appropriate regime. Moreover, these molecular grids can be used to integrate empirical functionals of the electron density. These so-called density functional methods (DFT) are an extremely promising alternative to conventional post-Hartree Fock quantum chemical methods. The introduction of a grid at the molecular solvent-accessible surface allows a very sophisticated treatment of a polarizable continuum solvent model (PCM). Where most PCM approaches use a truncated expansion of the solute`s electric multipole expansion, e.g. net charge (Born model) or dipole moment (Onsager model), such a grid-based boundary-element method (BEM) yields a nearly exact treatment of the solute`s electric field. This report describes the use of both DFT and BEM methods in several biomedical chemical applications.
Method and apparatus for detecting and/or imaging clusters of small scattering centers in the body
Perez-Mendez, V.; Sommer, F.G.
1982-07-13
An ultrasonic method and apparatus are provided for detecting and imaging clusters of small scattering centers in the breast wherein periodic pulses are applied to an ultrasound emitting transducer and projected into the body, thereafter being received by at least one receiving transducer positioned to receive scattering from the scattering center clusters. The signals are processed to provide an image showing cluster extent and location. 6 figs.
Jin, Li; Wang, Ying; Liu, Fangtong; Yu, Shihua; Gao, Yan; Zhang, Jianpo
2017-10-25
A method for quantitative analysis of nitrite was achieved based on fluorescence quenching of graphene quantum dots. To obtain reliable results, the effects of pH, temperature and reaction time on this fluorescence quenching system were studied. Under optimized conditions, decrease in fluorescence intensity of graphene quantum dots (F0 /F) showed a good linear relationship with nitrite concentration between 0.007692-0.38406 mmol/L and 0.03623-0.13043 μmol/L; the limits of detection were 9.8 μmol/L and 5.4 nmol/L, respectively. Variable temperature experiments, UV absorption spectra and thermodynamic calculations were used to determine the quenching mechanism, and indicated that it was an exothermic, spontaneous dynamic quenching process. This method was used to analyse urine samples, and showed that it could be applied to analyse biological samples. Copyright © 2017 John Wiley & Sons, Ltd.
A New Cluster Analysis-Marker-Controlled Watershed Method for Separating Particles of Granular Soils
Directory of Open Access Journals (Sweden)
Md Ferdous Alam
2017-10-01
Full Text Available An accurate determination of particle-level fabric of granular soils from tomography data requires a maximum correct separation of particles. The popular marker-controlled watershed separation method is widely used to separate particles. However, the watershed method alone is not capable of producing the maximum separation of particles when subjected to boundary stresses leading to crushing of particles. In this paper, a new separation method, named as Monash Particle Separation Method (MPSM, has been introduced. The new method automatically determines the optimal contrast coefficient based on cluster evaluation framework to produce the maximum accurate separation outcomes. Finally, the particles which could not be separated by the optimal contrast coefficient were separated by integrating cuboid markers generated from the clustering by Gaussian mixture models into the routine watershed method. The MPSM was validated on a uniformly graded sand volume subjected to one-dimensional compression loading up to 32 MPa. It was demonstrated that the MPSM is capable of producing the best possible separation of particles required for the fabric analysis.
Guided-wave photodiode using through-absorber quantum-well-intermixing and methods thereof
Energy Technology Data Exchange (ETDEWEB)
Skogen, Erik J.
2016-10-25
The present invention includes a high-speed, high-saturation power detector (e.g., a photodiode) compatible with a relatively simple monolithic integration process. In particular embodiments, the photodiode includes an intrinsic bulk absorption region, which is grown above a main waveguide core including a number of quantum wells (QWs) that are used as the active region of a phase modulator. The invention also includes methods of fabricating integrated photodiode and waveguide assemblies using a monolithic, simplified process.
Yevgeniy M. Sgibnev; Nikolay V. Nikonorov; Alexander I. Ignatiev; Dmitry S. Starodubov
2016-01-01
Subject of Study.The paper deals with novel research of ion exchange duration influence on spectral-luminescent properties of silver clusters formed in photo-thermo-refractive glass. Method. Photo-thermo-refractive matrix glass based on Na2O–Al2O3–ZnO–SiO2–F (% mol.) system doped with 0,002% mol. of Sb2O3 was synthesized for further research. Silver ions were introduced with low temperature ion exchange method. The glass samples were immersed in the mixture of sodium and silver nitrates 5AgNO...
A novel intrusion detection method based on OCSVM and K-means recursive clustering
Directory of Open Access Journals (Sweden)
Leandros A. Maglaras
2015-01-01
Full Text Available In this paper we present an intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition system, based on the combination of One-Class Support Vector Machine (OCSVM with RBF kernel and recursive k-means clustering. Important parameters of OCSVM, such as Gaussian width o and parameter v affect the performance of the classifier. Tuning of these parameters is of great importance in order to avoid false positives and over fitting. The combination of OCSVM with recursive k- means clustering leads the proposed intrusion detection module to distinguish real alarms from possible attacks regardless of the values of parameters o and v, making it ideal for real-time intrusion detection mechanisms for SCADA systems. Extensive simulations have been conducted with datasets extracted from small and medium sized HTB SCADA testbeds, in order to compare the accuracy, false alarm rate and execution time against the base line OCSVM method.
A Robust Blind Quantum Copyright Protection Method for Colored Images Based on Owner's Signature
Heidari, Shahrokh; Gheibi, Reza; Houshmand, Monireh; Nagata, Koji
2017-08-01
Watermarking is the imperceptible embedding of watermark bits into multimedia data in order to use for different applications. Among all its applications, copyright protection is the most prominent usage which conceals information about the owner in the carrier, so as to prohibit others from assertion copyright. This application requires high level of robustness. In this paper, a new blind quantum copyright protection method based on owners's signature in RGB images is proposed. The method utilizes one of the RGB channels as indicator and two remained channels are used for embedding information about the owner. In our contribution the owner's signature is considered as a text. Therefore, in order to embed in colored image as watermark, a new quantum representation of text based on ASCII character set is offered. Experimental results which are analyzed in MATLAB environment, exhibit that the presented scheme shows good performance against attacks and can be used to find out who the real owner is. Finally, the discussed quantum copyright protection method is compared with a related work that our analysis confirm that the presented scheme is more secure and applicable than the previous ones currently found in the literature.
A temporal precedence based clustering method for gene expression microarray data
Directory of Open Access Journals (Sweden)
Buchanan-Wollaston Vicky
2010-01-01
Full Text Available Abstract Background Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not. Results A gene-association matrix is constructed by testing temporal relationships between pairs of genes using the Granger causality test. The association matrix is further analyzed using a graph-theoretic technique to detect highly connected components representing interesting biological modules. We test our approach on synthesized datasets and real biological datasets obtained for Arabidopsis thaliana. We show the effectiveness of our approach by analyzing the results using the existing biological literature. We also report interesting structural properties of the association network commonly desired in any biological system. Conclusions Our experiments on synthesized and real microarray datasets show that our approach produces encouraging results. The method is simple in implementation and is statistically traceable at each step. The method can produce sets of functionally related genes which can be further used for reverse-engineering of gene circuits.
Energy Technology Data Exchange (ETDEWEB)
Faenov, A.Ya.; Pikuz, T.A. [Quantum Beam Science Directorate, Japan Atomic Energy Agency, Kyoto (Japan); Joint Institute for High Temperatures RAS, Moscow (Russian Federation); Fukuda, Y.; Nakamura, T.; Bulanov, S.V.; Hayashi, Y.; Kotaki, H.; Pirozhkov, A.S.; Kawachi, T.; Kando, M. [Quantum Beam Science Directorate, Japan Atomic Energy Agency, Kyoto (Japan); Skobelev, I.Yu.; Fortov, V.E. [Joint Institute for High Temperatures RAS, Moscow (Russian Federation); Chen, L.M.; Zhang, L.; Yan, W.C.; Yuan, D.W.; Mao, J.Y.; Wang, Z.H.; Ma, J.L. [Institute of Physics, Chinese Academy of Sciences, Beijing (China); Kato, Y. [The Graduate School for the Creation of New Photonics Industries, Hamamatsu, Shizuoka (Japan)
2013-02-15
A short review of our experimental studies on generation of photon and particle beams in submicron clusters irradiated by intense, high-contrast ({proportional_to} 10{sup 8}-10{sup 10}) femtosecond laser pulses is presented. It is shown that highlyefficient laser-cluster interaction allows creating bright sources of X-ray, high-energy electron and ion beams. The examples of applications of femtosecond-laser-produced cluster plasmas (FLPCP) for X-ray and ion beams radiography are presented. (copyright 2013 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
A novel Bayesian DNA motif comparison method for clustering and retrieval.
Directory of Open Access Journals (Sweden)
Naomi Habib
2008-02-01
Full Text Available Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors.
Nakamura, Shin; Ota, Kai; Shibuya, Yuichi; Noguchi, Takumi
2016-01-26
Photosynthetic water oxidation takes place at the Mn4CaO5 cluster in photosystem II. Around the Mn4CaO5 cluster, a hydrogen bond network is formed by several water molecules, including four water ligands. To clarify the role of this water network in the mechanism of water oxidation, we investigated the effects of the removal of Ca(2+) and substitution with metal ions on the vibrations of water molecules coupled to the Mn4CaO5 cluster by means of Fourier transform infrared (FTIR) difference spectroscopy and quantum mechanics/molecular mechanics (QM/MM) calculations. The OH stretching vibrations of nine water molecules forming a network between D1-D61 and YZ were calculated using the QM/MM method. On the the calculated normal modes, a broad positive feature at 3200-2500 cm(-1) in an S2-minus-S1 FTIR spectrum was attributed to the vibrations of strongly hydrogen-bonded OH bonds of water involving the vibrations of water ligands to a Mn ion and the in-phase coupled vibration of a water network connected to YZ, while bands in the 3700-3500 cm(-1) region were assigned to the coupled vibrations of weakly hydrogen-bonded OH bonds of water. All the water bands were lost upon Ca(2+) depletion and Ba(2+) substitution, which inhibit the S2 → S3 transition, indicating that a solid water network was broken by these treatments. By contrast, Sr(2+) substitution slightly altered the water bands around 3600 cm(-1), reflecting minor modification in water interactions, consistent with the retention of water oxidation activity with a decreased efficiency. These results suggest that the water network around the Mn4CaO5 cluster plays an essential role in the water oxidation mechanism particularly in a concerted process of proton transfer and water insertion during the S2 → S3 transition.
Cluster Variation Method as a Theoretical Tool for the Study of Phase Transformation
Mohri, Tetsuo
2017-06-01
Cluster variation method (CVM) has been widely employed to calculate alloy phase diagrams. The atomistic feature of the CVM is consistent with first-principles electronic structure calculations, and the combination of CVM with electronic structure calculation enables one to formulate free energy from the first-principles. CVM free energy conveys affluent information of a given system, and the second-order derivative traces the stability locus against configurational fluctuation. The kinetic extension of the CVM is the path probability method (PPM) which is utilized to calculate transformation and relaxation kinetics associated with the temperature change. Hence, the CVM and PPM are coherent methods to perform a synthetic study from initial non-equilibrium to final equilibrium states. By utilizing CVM free energy as a homogeneous free energy density term, one can calculate the time evolution of ordered domains within the phase field method. Finally, continuous displacement cluster variation method (CDCVM) is discussed as the recent development of CVM. CDCVM is capable of introducing the local lattice displacement into the free energy. Moreover, it is shown that CDCVM can be extended to study collective atomic displacements leading to displacive phase transformation.
Multiple-Features-Based Semisupervised Clustering DDoS Detection Method
Directory of Open Access Journals (Sweden)
Yonghao Gu
2017-01-01
Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.
High Performance Computing of Meshless Time Domain Method on Multi-GPU Cluster
Ikuno, Soichiro; Nakata, Susumu; Hirokawa, Yuta; Itoh, Taku
2015-01-01
High performance computing of Meshless Time Domain Method (MTDM) on multi-GPU using the supercomputer HA-PACS (Highly Accelerated Parallel Advanced system for Computational Sciences) at University of Tsukuba is investigated. Generally, the finite difference time domain (FDTD) method is adopted for the numerical simulation of the electromagnetic wave propagation phenomena. However, the numerical domain must be divided into rectangle meshes, and it is difficult to adopt the problem in a complexed domain to the method. On the other hand, MTDM can be easily adept to the problem because MTDM does not requires meshes. In the present study, we implement MTDM on multi-GPU cluster to speedup the method, and numerically investigate the performance of the method on multi-GPU cluster. To reduce the computation time, the communication time between the decomposed domain is hided below the perfect matched layer (PML) calculation procedure. The results of computation show that speedup of MTDM on 128 GPUs is 173 times faster than that of single CPU calculation.
Chattaraj, Pratim Kumar
2010-01-01
The application of quantum mechanics to many-particle systems has been an active area of research in recent years as researchers have looked for ways to tackle difficult problems in this area. The quantum trajectory method provides an efficient computational technique for solving both stationary and time-evolving states, encompassing a large area of quantum mechanics. Quantum Trajectories brings the expertise of an international panel of experts who focus on the epistemological significance of quantum mechanics through the quantum theory of motion.Emphasizing a classical interpretation of quan
Solving the inverse Ising problem by mean-field methods in a clustered phase space with many states.
Decelle, Aurélien; Ricci-Tersenghi, Federico
2016-07-01
In this work we explain how to properly use mean-field methods to solve the inverse Ising problem when the phase space is clustered, that is, many states are present. The clustering of the phase space can occur for many reasons, e.g., when a system undergoes a phase transition, but also when data are collected in different regimes (e.g., quiescent and spiking regimes in neural networks). Mean-field methods for the inverse Ising problem are typically used without taking into account the eventual clustered structure of the input configurations and may lead to very poor inference (e.g., in the low-temperature phase of the Curie-Weiss model). In this work we explain how to modify mean-field approaches when the phase space is clustered and we illustrate the effectiveness of our method on different clustered structures (low-temperature phases of Curie-Weiss and Hopfield models).
Yang, Yan-Pu; Chen, Deng-Kai; Gu, Rong; Gu, Yu-Feng; Yu, Sui-Huai
2016-01-01
Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.
Zhu, Chengling; Zhu, Shenmin; Zhang, Kai; Hui, Zeyu; Pan, Hui; Chen, Zhixin; Li, Yao; Zhang, Di; Wang, Da-Wei
2016-01-01
Construction of metal oxide nanoparticles as anodes is of special interest for next-generation lithium-ion batteries. The main challenge lies in their rapid capacity fading caused by the structural degradation and instability of solid-electrolyte interphase (SEI) layer during charge/discharge process. Herein, we address these problems by constructing a novel-structured SnO2-based anode. The novel structure consists of mesoporous clusters of SnO2 quantum dots (SnO2 QDs), which are wrapped with reduced graphene oxide (RGO) sheets. The mesopores inside the clusters provide enough room for the expansion and contraction of SnO2 QDs during charge/discharge process while the integral structure of the clusters can be maintained. The wrapping RGO sheets act as electrolyte barrier and conductive reinforcement. When used as an anode, the resultant composite (MQDC-SnO2/RGO) shows an extremely high reversible capacity of 924 mAh g−1 after 200 cycles at 100 mA g−1, superior capacity retention (96%), and outstanding rate performance (505 mAh g−1 after 1000 cycles at 1000 mA g−1). Importantly, the materials can be easily scaled up under mild conditions. Our findings pave a new way for the development of metal oxide towards enhanced lithium storage performance. PMID:27181691
Zhu, Chengling; Zhu, Shenmin; Zhang, Kai; Hui, Zeyu; Pan, Hui; Chen, Zhixin; Li, Yao; Zhang, Di; Wang, Da-Wei
2016-05-16
Construction of metal oxide nanoparticles as anodes is of special interest for next-generation lithium-ion batteries. The main challenge lies in their rapid capacity fading caused by the structural degradation and instability of solid-electrolyte interphase (SEI) layer during charge/discharge process. Herein, we address these problems by constructing a novel-structured SnO2-based anode. The novel structure consists of mesoporous clusters of SnO2 quantum dots (SnO2 QDs), which are wrapped with reduced graphene oxide (RGO) sheets. The mesopores inside the clusters provide enough room for the expansion and contraction of SnO2 QDs during charge/discharge process while the integral structure of the clusters can be maintained. The wrapping RGO sheets act as electrolyte barrier and conductive reinforcement. When used as an anode, the resultant composite (MQDC-SnO2/RGO) shows an extremely high reversible capacity of 924 mAh g(-1) after 200 cycles at 100 mA g(-1), superior capacity retention (96%), and outstanding rate performance (505 mAh g(-1) after 1000 cycles at 1000 mA g(-1)). Importantly, the materials can be easily scaled up under mild conditions. Our findings pave a new way for the development of metal oxide towards enhanced lithium storage performance.
Cerezo, Javier; Liu, Yanli; Lin, Na; Zhao, Xian; Improta, Roberto; Santoro, Fabrizio
2018-01-03
We present a novel mixed quantum classical dynamical method to include solvent effects on internal conversion (IC) processes. All the solute degrees of freedom are represented by a wavepacket moving according to nonadiabatic quantum dynamics, while the motion of an explicit solvent model is described by an ensemble of classical trajectories. The mutual coupling of the solute and solvent dynamics is included within a mean-field framework and the quantum and classical equations of motions are solved simultaneously. As a test case we apply our method to the ultrafast ππ* → nπ* decay of thymine in water. Solvent dynamical response modifies IC yield already on the 50 fs time scale. This effect is due to water librational motions that stabilize the most populated state. Pure static disorder, that is, the existence of different solvent configurations when photoexcitation takes place, also has a remarkable impact on the dynamics.
Ultrafast Method for Selective Design of Graphene Quantum Dots with Highly Efficient Blue Emission
Kang, Suk Hyun; Mhin, Sungwook; Han, Hyuksu; Kim, Kang Min; Jones, Jacob L.; Ryu, Jeong Ho; Kang, Ju Seop; Kim, Shin Hee; Shim, Kwang Bo
2016-12-01
Graphene quantum dots (GQDs) have attractive properties and potential applications. However, their various applications are limited by a current synthetic method which requires long processing time. Here, we report a facile and remarkably rapid method for production of GQDs exhibiting excellent optoelectronic properties. We employed the pulsed laser ablation (PLA) technique to exfoliate GQDs from multi-wall carbon nanotube (MWCNTs), which can be referred to as a pulsed laser exfoliation (PLE) process. Strikingly, it takes only 6 min to transform all MWCNTs precursors to GQDs by using PLE process. Furthermore, we could selectively produce either GQDs or graphene oxide quantum dots (GOQDs) by simply changing the organic solvents utilized in the PLE processing. The synthesized GQDs show distinct blue photoluminescence (PL) with excellent quantum yield (QY) up to 12% as well as sufficient brightness and resolution to be suitable for optoelectronic applications. We believe that the PLE process proposed in this work will further open up new routes for the preparation of different optoelectronic nanomaterials.
Recent Progress in Treating Protein–Ligand Interactions with Quantum-Mechanical Methods
Directory of Open Access Journals (Sweden)
Nusret Duygu Yilmazer
2016-05-01
Full Text Available We review the first successes and failures of a “new wave” of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of “enhanced”, dispersion (D, and/or hydrogen-bond (H corrected density functional theory (DFT or semi-empirical quantum mechanical (SQM methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.
A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.
Directory of Open Access Journals (Sweden)
Daniel M de Brito
Full Text Available Genomic Islands (GIs are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP--Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.
K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering
Directory of Open Access Journals (Sweden)
Lv Tao
2017-01-01
Full Text Available Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1 the predictive power of a pattern varies a great deal for different shapes and (2 each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.
Energetic analysis of conjugated hydrocarbons using the interacting quantum atoms method.
Jara-Cortés, Jesús; Hernández-Trujillo, Jesús
2017-10-26
A number of aromatic, antiaromatic, and nonaromatic organic molecules was analyzed in terms of the contributions to the electronic energy defined in the quantum theory of atoms in molecules and the interacting quantum atoms method. Regularities were found in the exchange and electrostatic interatomic energies showing trends that are closely related to those of the delocalization indices defined in the theory. In particular, the CC interaction energies between bonded atoms allow to rationalize the energetic stabilization associated with the bond length alternation in conjugated polyenes. This approach also provides support to Clar's sextet rules devised for aromatic systems. In addition, the H⋯H bonding found in some of the aromatic molecules studied was of an attractive nature, according to the stabilizing exchange interaction between the bonded H atoms. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Vortex filament method as a tool for computational visualization of quantum turbulence
Hänninen, Risto; Baggaley, Andrew W.
2014-01-01
The vortex filament model has become a standard and powerful tool to visualize the motion of quantized vortices in helium superfluids. In this article, we present an overview of the method and highlight its impact in aiding our understanding of quantum turbulence, particularly superfluid helium. We present an analysis of the structure and arrangement of quantized vortices. Our results are in agreement with previous studies showing that under certain conditions, vortices form coherent bundles, which allows for classical vortex stretching, giving quantum turbulence a classical nature. We also offer an explanation for the differences between the observed properties of counterflow and pure superflow turbulence in a pipe. Finally, we suggest a mechanism for the generation of coherent structures in the presence of normal fluid shear. PMID:24704873
Investigation of quantum phase transitions using multi-target DMRG methods
Degli Esposti Boschi, C.; Ortolani, F.
2004-10-01
In this paper we examine how the predictions of conformal invariance can be widely exploited to overcome the difficulties of the density-matrix renormalization group near quantum critical points. The main idea is to match the set of low-lying energy levels of the lattice Hamiltonian, as a function of the system’s size, with the spectrum expected for a given conformal field theory in two dimensions. As in previous studies this procedure requires an accurate targeting of various excited states. Here we discuss how this can be achieved within the DMRG algorithm by means of the recently proposed Thick-restart Lanczos method. As a nontrivial benchmark we use an anisotropic spin-1 Hamiltonian with special attention to the transitions from the Haldane phase. Nonetheless, we think that this procedure could be generally valid in the study of quantum critical phenomena.
Relaxation channels of multi-photon excited xenon clusters.
Serdobintsev, P Yu; Rakcheeva, L P; Murashov, S V; Melnikov, A S; Lyubchik, S; Timofeev, N A; Pastor, A A; Khodorkovskii, M A
2015-09-21
The relaxation processes of the xenon clusters subjected to multi-photon excitation by laser radiation with quantum energies significantly lower than the thresholds of excitation of atoms and ionization of clusters were studied. Results obtained by means of the photoelectron spectroscopy method showed that desorption processes of excited atoms play a significant role in the decay of two-photon excited xenon clusters. A number of excited states of xenon atoms formed during this process were discovered and identified.
Wentzel-Kramers-Brillouin method in the Bargmann representation. [of quantum mechanics
Voros, A.
1989-01-01
It is demonstrated that the Bargmann representation of quantum mechanics is ideally suited for semiclassical analysis, using as an example the WKB method applied to the bound-state problem in a single well of one degree of freedom. For the harmonic oscillator, this WKB method trivially gives the exact eigenfunctions in addition to the exact eigenvalues. For an anharmonic well, a self-consistent variational choice of the representation greatly improves the accuracy of the semiclassical ground state. Also, a simple change of scale illuminates the relationship of semiclassical versus linear perturbative expansions, allowing a variety of multidimensional extensions.
A study of potential energy curves from the model space quantum Monte Carlo method
Energy Technology Data Exchange (ETDEWEB)
Ohtsuka, Yuhki; Ten-no, Seiichiro, E-mail: tenno@cs.kobe-u.ac.jp [Department of Computational Sciences, Graduate School of System Informatics, Kobe University, Nada-ku, Kobe 657-8501 (Japan)
2015-12-07
We report on the first application of the model space quantum Monte Carlo (MSQMC) to potential energy curves (PECs) for the excited states of C{sub 2}, N{sub 2}, and O{sub 2} to validate the applicability of the method. A parallel MSQMC code is implemented with the initiator approximation to enable efficient sampling. The PECs of MSQMC for various excited and ionized states are compared with those from the Rydberg-Klein-Rees and full configuration interaction methods. The results indicate the usefulness of MSQMC for precise PECs in a wide range obviating problems concerning quasi-degeneracy.
Directory of Open Access Journals (Sweden)
Shireesh Srivastava
Full Text Available Among the primary goals of microarray analysis is the identification of genes that could distinguish between different phenotypes (feature selection. Previous studies indicate that incorporating prior information of the genes' function could help identify physiologically relevant features. However, current methods that incorporate prior functional information do not provide a relative estimate of the effect of different genes on the biological processes of interest.Here, we present a method that integrates gene ontology (GO information and expression data using Bayesian regression mixture models to perform unsupervised clustering of the samples and identify physiologically relevant discriminating features. As a model application, the method was applied to identify the genes that play a role in the cytotoxic responses of human hepatoblastoma cell line (HepG2 to saturated fatty acid (SFA and tumor necrosis factor (TNF-alpha, as compared to the non-toxic response to the unsaturated FFAs (UFA and TNF-alpha. Incorporation of prior knowledge led to a better discrimination of the toxic phenotypes from the others. The model identified roles of lysosomal ATPases and adenylate cyclase (AC9 in the toxicity of palmitate. To validate the role of AC in palmitate-treated cells, we measured the intracellular levels of cyclic AMP (cAMP. The cAMP levels were found to be significantly reduced by palmitate treatment and not by the other FFAs, in accordance with the model selection of AC9.A framework is presented that incorporates prior ontology information, which helped to (a perform unsupervised clustering of the phenotypes, and (b identify the genes relevant to each cluster of phenotypes. We demonstrate the proposed framework by applying it to identify physiologically-relevant feature genes that conferred differential toxicity to saturated vs. unsaturated FFAs. The framework can be applied to other problems to efficiently integrate ontology information and
A robust automatic leukocyte recognition method based on island-clustering texture
Directory of Open Access Journals (Sweden)
Xiaoshun Li
2016-01-01
Full Text Available A leukocyte recognition method for human peripheral blood smear based on island-clustering texture (ICT is proposed. By analyzing the features of the five typical classes of leukocyte images, a new ICT model is established. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift clustering to be the centers of islands. Secondly, the growing region is employed to create regions of the islands in which the seeds are just these feature points. These islands distribution can describe a new texture. Finally, a distinguished parameter vector of these islands is created as the ICT features by combining the ICT features with the geometric features of the leukocyte. Then the five typical classes of leukocytes can be recognized successfully at the correct recognition rate of more than 92.3% with a total sample of 1310 leukocytes. Experimental results show the feasibility of the proposed method. Further analysis reveals that the method is robust and results can provide important information for disease diagnosis.
Energy Technology Data Exchange (ETDEWEB)
Sudiarta, I. Wayan; Angraini, Lily Maysari, E-mail: lilyangraini@unram.ac.id [Physics Study Program, University of Mataram, Jln. Majapahit 62 Mataram, NTB (Indonesia)
2016-04-19
We have applied the finite difference time domain (FDTD) method with the supersymmetric quantum mechanics (SUSY-QM) procedure to determine excited energies of one dimensional quantum systems. The theoretical basis of FDTD, SUSY-QM, a numerical algorithm and an illustrative example for a particle in a one dimensional square-well potential were given in this paper. It was shown that the numerical results were in excellent agreement with theoretical results. Numerical errors produced by the SUSY-QM procedure was due to errors in estimations of superpotentials and supersymmetric partner potentials.
Energy Technology Data Exchange (ETDEWEB)
Li, Xiafei; Li, Jin; Kuang, Huiyan; Feng, Lei; Yi, Shoujun; Xia, Xiaodong; Huang, Haowen [School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201 (China); Key Laboratory of Theoretical Chemistry and Molecular Simulation of Ministry of Education of China, Hunan University of Science and Technology, Xiangtan 411201 (China); Chen, Yong; Tang, Chunran [School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201 (China); Zeng, Yunlong, E-mail: yunlongzeng1955@126.com [School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201 (China); Key Laboratory of Theoretical Chemistry and Molecular Simulation of Ministry of Education of China, Hunan University of Science and Technology, Xiangtan 411201 (China); State Key Laboratory for Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082 (China)
2013-11-13
Graphical abstract: Melamine takes place of the TGA on the surface of TGA-CdTe QDs with negative charge to form melamine coated QDs changing the surface charge of the QDs, resulting the fluorescence quenched as the QDs aggregation occurred by electrostatic attraction of the two opposite charged nanocrystals. -- Highlights: •An ultrasensitive and selective method for the determination of melamine was developed at pH 11.0. •The selectivity of the method was improved. •The sensitivity of the method enhanced obviously as the CdTe QDs have higher QYs at pH 11. •The sensitivity and linear range for the analysis are size dependent using QDs PL probes. •Melamine takes the place of TGA resulting fluorescence quenched of QDs. -- Abstract: An ultrasensitive and simple method for the determination of melamine was developed based on the fluorescence quenching of thioglycolic acid (TGA) capped CdTe quantum dots (QDs) at pH 11.0. In strong alkaline aqueous solution, the selectivity of the method has been greatly improved due to most heavy metal ions show no interference as they are in the precipitation form or in their anion form. Furthermore, CdTe quantum dots have higher quantum yields at higher pH. The method has a wider concentration range and lower detection limit. The influence factors on the determination of melamine were investigated and the optimum conditions were determined. Under optimum conditions, the fluorescence intensity change of TGA coated CdTe quantum dots was linearly proportional to melamine over a concentration range from 1.0 × 10{sup −11} to 1.0 × 10{sup −5} mol L{sup −1} with a correlation coefficient of 0.9943 and a detection limit of 5 × 10{sup −12} mol L{sup −1}. The mechanism of fluorescence quenching of the QDs has been proposed based on the infrared spectroscopy information and electrophoresis experiments in presence of melamine under alkaline condition. The proposed method was employed to detect trace melamine in milk powder
Method of using deuterium-cluster foils for an intense pulsed neutron source
Miley, George H.; Yang, Xiaoling
2013-09-03
A method is provided for producing neutrons, comprising: providing a converter foil comprising deuterium clusters; focusing a laser on the foil with power and energy sufficient to cause deuteron ions to separate from the foil; and striking a surface of a target with the deuteron ions from the converter foil with energy sufficient to cause neutron production by a reaction selected from the group consisting of D-D fusion, D-T fusion, D-metal nuclear spallation, and p-metal. A further method is provided for assembling a plurality of target assemblies for a target injector to be used in the previously mentioned manner. A further method is provided for producing neutrons, comprising: splitting a laser beam into a first beam and a second beam; striking a first surface of a target with the first beam, and an opposite second surface of the target with the second beam with energy sufficient to cause neutron production.
Guo, Jingyu; Tian, Dehua; McKinney, Brett A.; Hartman, John L.
2010-06-01
of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.
Diffusion behavior of Cr diluted in bcc and fcc Fe: Classical and quantum simulation methods
Energy Technology Data Exchange (ETDEWEB)
Ramunni, Viviana P., E-mail: vpram@cnea.gov.ar [CONICET, Avda. Rivadavia 1917, Cdad. de Buenos Aires C.P. 1033 (Argentina); Comisión Nacional de Energía Atómica, Gerencia Materiales, Av. Del Libertador 8250, C1429BNP Ciudad de Buenos Aires (Argentina); Rivas, Alejandro M.F. [CONICET, Avda. Rivadavia 1917, Cdad. de Buenos Aires C.P. 1033 (Argentina); Comisión Nacional de Energía Atómica, Departamento de Física Teórica, Tandar, Av. Del Libertador 8250, C1429BNP Ciudad de Buenos Aires (Argentina)
2015-07-15
We characterize the atomic mobility behavior driven by vacancies, in bcc and fcc Fe−Cr diluted alloys, using a multi-frequency model. We calculate the full set of the Onsager coefficients and the tracer self and solute diffusion coefficients in terms of the mean jump frequencies. The involved jump frequencies are calculated using a classical molecular static (CMS) technique. For the bcc case, we also perform quantum calculations based on the density functional theory (DFT). There, we show that, in accordance with Bohr's correspondence principle, as the size of the atomic cell (total number of atoms) is increased, quantum results with DFT recover the classical ones obtained with CMS calculations. This last ones, are in perfect agreement with available experimental data for both, solute and solvent diffusion coefficients. For high temperatures, in the fcc phase where no experimental data are yet available, our CMS calculations predict the expected solute and solvent diffusion coefficients. - Graphical abstract: Display Omitted - Highlights: • Comparison of diffusion coefficients obtained from classical and quantum methods. • We perform our calculations in diluted bcc/fcc Fe–Cr alloy. • Magnetic and phonon effects must be taken into account. • Classical calculations are in perfect agreement with experimental data.
Functional integral method in quantum field theory of plasmons in graphene
Duoc Phan, Nguyen Duc; Hau Tran, Van
2017-12-01
In the present work we apply the functional integral method to the study of quantum field theory of collective excitations of spinless Dirac fermion in graphene at vanishing absolute temperature and at Fermi level {{E}F}=0 . After introducing the Hermitian scalar field \\varphi (x) describing these collective excitations we establish the expression of the functional integral {{Z}\\varphi } containing a functional series I[\\varphi ] . The explicit expressions of several terms of this functional series were derived. Then we consider the functional series I[\\varphi ] in second order approximation and denote {{I}0}[\\varphi ] the corresponding approximate expression of I[\\varphi ] . We shall demonstrate that in this approximation the scalar field \\varphi (x) can be devided into two parts: a background field {{\\varphi }0}(x) corresponding to the extremum of {{I}0}[\\varphi ] and another scalar field ξ (x) describing the fluctuation of \\varphi (x) around the background {{\\varphi }0}(x) . We call ξ (x) the fluctuation field. Then we establish the relationship between this fluctuation field ξ (x) and the quantum field of plasmons in graphene. Considering some range of values of frequency (energy) and wave vector (momentum) of plasmons, when the analytical calculations can be performed, we derived the differential equation for the quantum field of graphene plasmons. From this field equation we establish the relation between frequency and wave vector of plasmons in the long wavelength limit.
Directory of Open Access Journals (Sweden)
Yongwei Zhang
2017-01-01
Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.
Kent, Peter; Jensen, Rikke K; Kongsted, Alice
2014-10-02
There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets
Quantum correlations and distinguishability of quantum states
Energy Technology Data Exchange (ETDEWEB)
Spehner, Dominique [Université Grenoble Alpes and CNRS, Institut Fourier, F-38000 Grenoble, France and Laboratoire de Physique et Modélisation des Milieux Condensés, F-38000 Grenoble (France)
2014-07-15
A survey of various concepts in quantum information is given, with a main emphasis on the distinguishability of quantum states and quantum correlations. Covered topics include generalized and least square measurements, state discrimination, quantum relative entropies, the Bures distance on the set of quantum states, the quantum Fisher information, the quantum Chernoff bound, bipartite entanglement, the quantum discord, and geometrical measures of quantum correlations. The article is intended both for physicists interested not only by collections of results but also by the mathematical methods justifying them, and for mathematicians looking for an up-to-date introductory course on these subjects, which are mainly developed in the physics literature.
Directory of Open Access Journals (Sweden)
Shiza Anand
2015-08-01
Full Text Available As the number of hypertext documents are increasing continuously day by day on world wide web. Therefore clustering methods will be required to bind documents into the clusters repositories according to the similarity lying between the documents. Various clustering methods exist such as Hierarchical Based K-means Fuzzy Logic Based Centroid Based etc. These keyword based clustering methods takes much more amount of time for creating containers and putting documents in their respective containers. These traditional methods use File Handling techniques of different programming languages for creating repositories and transferring web documents into these containers. In contrast openstack4j SDK is a new technique for creating containers and shifting web documents into these containers according to the similarity in much more less amount of time as compared to the traditional methods. Another benefit of this technique is that this SDK understands and reads all types of files such as jpg html pdf doc etc. This paper compares the time required for clustering of documents by using openstack4j and by traditional methods and suggests various search engines to adopt this technique for clustering so that they give result to the user querries in less amount of time.
Measuring Group Synchrony: A Cluster-Phase Method for Analyzing Multivariate Movement Time-Series
Directory of Open Access Journals (Sweden)
Michael eRichardson
2012-10-01
Full Text Available A new method for assessing group synchrony is introduced as being potentially useful for objectively determining degree of group cohesiveness or entitativity. The cluster-phase method of Frank and Richardson (2010 was used to analyze movement data from the rocking chair movements of six-member groups who rocked their chairs while seated in a circle facing the center. In some trials group members had no information about others’ movements (their eyes were shut or they had their eyes open and gazed at a marker in the center of the group. As predicted, the group level synchrony measure was able to distinguish between situations where synchrony would have been possible and situations where it would be impossible. Moreover, other aspects of the analysis illustrated how the cluster phase measures can be used to determine the type of patterning of group synchrony, and, when integrated with multi-level modeling, can be used to examine individual-level differences in synchrony and dyadic level synchrony as well.
LENUS (Irish Health Repository)
Singan, Vasanth R
2012-06-08
AbstractBackgroundThe localization of proteins to specific subcellular structures in eukaryotic cells provides important information with respect to their function. Fluorescence microscopy approaches to determine localization distribution have proved to be an essential tool in the characterization of unknown proteins, and are now particularly pertinent as a result of the wide availability of fluorescently-tagged constructs and antibodies. However, there are currently very few image analysis options able to effectively discriminate proteins with apparently similar distributions in cells, despite this information being important for protein characterization.FindingsWe have developed a novel method for combining two existing image analysis approaches, which results in highly efficient and accurate discrimination of proteins with seemingly similar distributions. We have combined image texture-based analysis with quantitative co-localization coefficients, a method that has traditionally only been used to study the spatial overlap between two populations of molecules. Here we describe and present a novel application for quantitative co-localization, as applied to the study of Rab family small GTP binding proteins localizing to the endomembrane system of cultured cells.ConclusionsWe show how quantitative co-localization can be used alongside texture feature analysis, resulting in improved clustering of microscopy images. The use of co-localization as an additional clustering parameter is non-biased and highly applicable to high-throughput image data sets.
Stability of maximum-likelihood-based clustering methods: exploring the backbone of classifications
Mungan, Muhittin; Ramasco, José J.
2010-04-01
Components of complex systems are often classified according to the way they interact with each other. In graph theory such groups are known as clusters or communities. Many different techniques have been recently proposed to detect them, some of which involve inference methods using either Bayesian or maximum likelihood approaches. In this paper, we study a statistical model designed for detecting clusters based on connection similarity. The basic assumption of the model is that the graph was generated by a certain grouping of the nodes and an expectation maximization algorithm is employed to infer that grouping. We show that the method admits further development to yield a stability analysis of the groupings that quantifies the extent to which each node influences its neighbors' group membership. Our approach naturally allows for the identification of the key elements responsible for the grouping and their resilience to changes in the network. Given the generality of the assumptions underlying the statistical model, such nodes are likely to play special roles in the original system. We illustrate this point by analyzing several empirical networks for which further information about the properties of the nodes is available. The search and identification of stabilizing nodes constitutes thus a novel technique to characterize the relevance of nodes in complex networks.
Dynamical Cluster Approximation
Fotso, H.; Yang, S.; Chen, K.; Pathak, S.; Moreno, J.; Jarrell, M.; Mikelsons, K.; Khatami, E.; Galanakis, D.
The dynamical cluster approximation (DCA) is a method which systematically incorporates nonlocal corrections to the dynamical mean-field approximation. Here we present a pedagogical discussion of the DCA by describing it as a Φ-derivable coarse-graining approximation in k-space, which maps an infinite lattice problem onto a periodic finite-sized cluster embedded in a self-consistently determined effective medium. We demonstrate the method by applying it to the two-dimensional Hubbard model. From this application, we show evidences of the presence of a quantum critical point (QCP) at a finite doping underneath the superconducting dome. The QCP is associated with the second-order terminus of a line of first order phase separation transitions. This critical point is driven to zero temperature by varying the band parameters, generating the QCP. The effect of the proximity of the QCP to the superconducting dome is also discussed.
Yannouleas, Constantine; Landman, Uzi
2007-12-01
Investigations of emergent symmetry breaking phenomena occurring in small finite-size systems are reviewed, with a focus on the strongly correlated regime of electrons in two-dimensional semiconductor quantum dots and trapped ultracold bosonic atoms in harmonic traps. Throughout the review we emphasize universal aspects and similarities of symmetry breaking found in these systems, as well as in more traditional fields like nuclear physics and quantum chemistry, which are characterized by very different interparticle forces. A unified description of strongly correlated phenomena in finite systems of repelling particles (whether fermions or bosons) is presented through the development of a two-step method of symmetry breaking at the unrestricted Hartree-Fock level and of subsequent symmetry restoration via post Hartree-Fock projection techniques. Quantitative and qualitative aspects of the two-step method are treated and validated by exact diagonalization calculations. Strongly-correlated phenomena emerging from symmetry breaking include the following. Chemical bonding, dissociation and entanglement (at zero and finite magnetic fields) in quantum dot molecules and in pinned electron molecular dimers formed within a single anisotropic quantum dot, with potential technological applications to solid-state quantum-computing devices. Electron crystallization, with particle localization on the vertices of concentric polygonal rings, and formation of rotating electron molecules (REMs) in circular quantum dots. Such electron molecules exhibit ro-vibrational excitation spectra, in analogy with natural molecules. At high magnetic fields, the REMs are described by parameter-free analytic wave functions, which are an alternative to the Laughlin and composite-fermion approaches, offering a new point of view of the fractional quantum Hall regime in quantum dots (with possible implications for the thermodynamic limit). Crystalline phases of strongly repelling bosons. In rotating
Spectral methods in chemistry and physics applications to kinetic theory and quantum mechanics
Shizgal, Bernard
2015-01-01
This book is a pedagogical presentation of the application of spectral and pseudospectral methods to kinetic theory and quantum mechanics. There are additional applications to astrophysics, engineering, biology and many other fields. The main objective of this book is to provide the basic concepts to enable the use of spectral and pseudospectral methods to solve problems in diverse fields of interest and to a wide audience. While spectral methods are generally based on Fourier Series or Chebychev polynomials, non-classical polynomials and associated quadratures are used for many of the applications presented in the book. Fourier series methods are summarized with a discussion of the resolution of the Gibbs phenomenon. Classical and non-classical quadratures are used for the evaluation of integrals in reaction dynamics including nuclear fusion, radial integrals in density functional theory, in elastic scattering theory and other applications. The subject matter includes the calculation of transport coefficient...
A Clustering K-Anonymity Privacy-Preserving Method for Wearable IoT Devices
Directory of Open Access Journals (Sweden)
Fang Liu
2018-01-01
Full Text Available Wearable technology is one of the greatest applications of the Internet of Things. The popularity of wearable devices has led to a massive scale of personal (user-specific data. Generally, data holders (manufacturers of wearable devices are willing to share these data with others to get benefits. However, significant privacy concerns would arise when sharing the data with the third party in an improper manner. In this paper, we first propose a specific threat model about the data sharing process of wearable devices’ data. Then we propose a K-anonymity method based on clustering to preserve privacy of wearable IoT devices’ data and guarantee the usability of the collected data. Experiment results demonstrate the effectiveness of the proposed method.
Morgenstern Horing, Norman J
2017-01-01
This book provides an introduction to the methods of coupled quantum statistical field theory and Green's functions. The methods of coupled quantum field theory have played a major role in the extensive development of nonrelativistic quantum many-particle theory and condensed matter physics. This introduction to the subject is intended to facilitate delivery of the material in an easily digestible form to advanced undergraduate physics majors at a relatively early stage of their scientific development. The main mechanism to accomplish this is the early introduction of variational calculus and the Schwinger Action Principle, accompanied by Green's functions. Important achievements of the theory in condensed matter and quantum statistical physics are reviewed in detail to help develop research capability. These include the derivation of coupled field Green's function equations-of-motion for a model electron-hole-phonon system, extensive discussions of retarded, thermodynamic and nonequilibrium Green's functions...
Energy Technology Data Exchange (ETDEWEB)
Minami, Takuya; Nakano, Masayoshi [Department of Materials Engineering Science, Graduate School of Engineering Science, Osaka University, Toyonaka, Osaka 560-8531 (Japan)
2015-01-22
Electromagnetically induced transparency (EIT), which is known as an efficient control method of optical absorption property, is investigated using the polarizability spectra and population dynamics obtained by solving the quantum Liouville equation. In order to clarify the intermolecular interaction effect on EIT, we examine several molecular aggregate models composed of three-state monomers with the dipole-dipole coupling. On the basis of the present results, we discuss the applicability of EIT in molecular aggregate systems to a new type of optical switch.
Ryabinkin, Ilya G; Nagesh, Jayashree; Izmaylov, Artur F
2015-11-05
We have developed a numerical differentiation scheme that eliminates evaluation of overlap determinants in calculating the time-derivative nonadiabatic couplings (TDNACs). Evaluation of these determinants was the bottleneck in previous implementations of mixed quantum-classical methods using numerical differentiation of electronic wave functions in the Slater determinant representation. The central idea of our approach is, first, to reduce the analytic time derivatives of Slater determinants to time derivatives of molecular orbitals and then to apply a finite-difference formula. Benchmark calculations prove the efficiency of the proposed scheme showing impressive several-order-of-magnitude speedups of the TDNAC calculation step for midsize molecules.
Directory of Open Access Journals (Sweden)
Yunfeng Dong
2017-01-01
Full Text Available The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM, which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM. In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.
Yamamoto, Takeshi M
2005-09-08
We first show that a simple scaling of fluctuation coordinates defined in terms of a given reference point gives the conventional virial estimator in discretized path integral, where different choices of the reference point lead to different forms of the estimator (e.g., centroid virial). The merit of this procedure is that it allows a finite-difference evaluation of the virial estimator with respect to temperature, which totally avoids the need of higher-order potential derivatives. We apply this procedure to energy and heat-capacity calculations of the (H(2))(22) and Ne(13) clusters at low temperature using the fourth-order Takahashi-Imada [J. Phys. Soc. Jpn. 53, 3765 (1984)] and Suzuki [Phys. Lett. A 201, 425 (1995)] propagators. This type of calculation requires up to third-order potential derivatives if analytical virial estimators are used, but in practice only first-order derivatives suffice by virtue of the finite-difference scheme above. From the application to quantum clusters, we find that the fourth-order propagators do improve upon the primitive approximation, and that the choice of the reference point plays a vital role in reducing the variance of the virial estimator.
METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS.
Tignor, Nicole; Wang, Pei; Genes, Nicholas; Rogers, Linda; Hershman, Steven G; Scott, Erick R; Zweig, Micol; Yvonne Chan, Yu-Feng; Schadt, Eric E
2017-01-01
In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important
Application of the SCC-DFTB method to hydroxide water clusters and aqueous hydroxide solutions.
Choi, Tae Hoon; Liang, Ruibin; Maupin, C Mark; Voth, Gregory A
2013-05-02
The self-consistent charge density functional tight binding (SCC-DFTB) method has been applied to hydroxide water clusters and a hydroxide ion in bulk water. To determine the impact of various implementations of SCC-DFTB on the energetics and dynamics of a hydroxide ion in gas phase and condensed phase, the DFTB2, DFTB2-γ(h), DFTB2-γ(h)+gaus, DFTB3-diag, DFTB3-diag+gaus, DFTB3-Full+gaus, and DFTB3-3OB implementations have been tested. Energetic stabilities for small hydroxide clusters, OH(-)(H2O)n, where n = 4-7, are inconsistent with the results calculated with the B3LYP and second order Møller-Plesset (MP2) levels of ab initio theory. The condensed phase simulations, OH(-)(H2O)127, using the DFTB2, DFTB2-γ(h), DFTB2-γ(h)+gaus, DFTB3-diag, DFTB3-diag+gaus, DFTB3-Full+gaus and DFTB3-3OB methods are compared to Car-Parrinello molecular dynamics (CPMD) simulations using the BLYP functional. The SCC-DFTB method including a modified O-H repulsive potential and the third order correction (DFTB3-diag/Full+gaus) is shown to poorly reproduce the CPMD computational results, while the DFTB2 and DFTB2-γ(h) method somewhat more closely describe the structural and dynamical nature of the hydroxide ion in condensed phase. The DFTB3-3OB outperforms the MIO parameter set but is no more accurate than DFTB2. It is also shown that the overcoordinated water molecules lead to an incorrect bulk water density and result in unphysical water void formation. The results presented in this paper point to serious drawbacks for various DFTB extensions and corrections for a hydroxide ion in aqueous environments.
Mauldin, F William; Zhu, Hongtu T; Behler, Russell H; Nichols, Timothy C; Gallippi, Caterina M
2008-02-01
We introduce a new method for automatic classification of acoustic radiation force impulse (ARFI) displacement profiles using what have been termed "robust" methods for principal component analysis (PCA) and clustering. Unlike classical approaches, the robust methods are less sensitive to high variance outlier profiles and require no a priori information regarding expected tissue response to ARFI excitation. We first validate our methods using synthetic data with additive noise and/or outlier curves. Second, the robust techniques are applied to classifying ARFI displacement profiles acquired in an atherosclerotic familial hypercholesterolemic (FH) pig iliac artery in vivo. The in-vivo classification results are compared with parametric ARFI images showing peak induced displacement and time to 67% recovery and to spatially correlated immunohistochemistry. Our results support that robust techniques outperform conventional PCA and clustering approaches to classification when ARFI data are inclusive of low to relatively high noise levels (up to 5 dB average signal-to-noise [SNR] to amplitude) but no outliers: for example, 99.53% correct for robust techniques vs. 97.75% correct for the classical approach. The robust techniques also perform better than conventional approaches when ARFI data are inclusive of moderately high noise levels (10 dB average SNR to amplitude) in addition to a high concentration of outlier displacement profiles (10% outlier content): for example, 99.87% correct for robust techniques vs. 33.33% correct for the classical approach. This work suggests that automatic identification of tissue structures exhibiting similar displacement responses to ARFI excitation is possible, even in the context of outlier profiles. Moreover, this work represents an important first step toward automatic correlation of ARFI data to spatially matched immunohistochemistry.
Watanabe, Hiroshi C; Kubillus, Maximilian; Kubař, Tomáš; Stach, Robert; Mizaikoff, Boris; Ishikita, Hiroshi
2017-07-21
In the condensed phase, quantum chemical properties such as many-body effects and intermolecular charge fluctuations are critical determinants of the solvation structure and dynamics. Thus, a quantum mechanical (QM) molecular description is required for both solute and solvent to incorporate these properties. However, it is challenging to conduct molecular dynamics (MD) simulations for condensed systems of sufficient scale when adapting QM potentials. To overcome this problem, we recently developed the size-consistent multi-partitioning (SCMP) quantum mechanics/molecular mechanics (QM/MM) method and realized stable and accurate MD simulations, using the QM potential to a benchmark system. In the present study, as the first application of the SCMP method, we have investigated the structures and dynamics of Na + , K + , and Ca 2+ solutions based on nanosecond-scale sampling, a sampling 100-times longer than that of conventional QM-based samplings. Furthermore, we have evaluated two dynamic properties, the diffusion coefficient and difference spectra, with high statistical certainty. Furthermore the calculation of these properties has not previously been possible within the conventional QM/MM framework. Based on our analysis, we have quantitatively evaluated the quantum chemical solvation effects, which show distinct differences between the cations.
Directory of Open Access Journals (Sweden)
Vladimir A. Fedorov
2015-01-01
Full Text Available The aim of the investigation is to justify urgency and efficiency of teachers’ collective work of the chair on formation of integrated special professional competences of trainees.Methods. Teamwork is considered as a leading method of aim realisation; it is suggested to carry this method in practice on the basis of a situational technique of intra-chair structure formation – composite staff-clusters, whose activity is based on synergetic interaction of educational process participants.Results. Described innovations will give the chance to carry out necessary integration of educational processes content; will promote natural interaction of teachers on the basis of the generated complete integrated subsystem – composite staff-clusters. The innovation vector can be connected with more dimensional field of integration, i.e. generating not only special professional competences, but also general professional and common cultural ones.Scientific novelty. Activity of pedagogical composite staff-clusters is offered to consider in the complete block of scientific-theoretical and practice-pedagogical aspects: scientifically-organizational, academic, practice-organizational, educational, etc. Scientific novelty of article is connected with expansion of some content positions which are based on scientific-theoretical conclusions of researches pedagogic-collective (by V. A. Suhomlinsky, etc. and organizational-psychological (by K. Rogers, etc. processes, and also ideas of synergetics extrapolated into organizational-pedagogical processes. It is shown that efficiency of educational process can be reached under a condition of organizational and methodical work interrelatedness; synthesis of administrative and performing activity on the basis of the whole educational process interaction. Necessity of composite pedagogical creativity is proved by synergetic processes and motivation-emotional characteristics.Practical significance. Authors give specific
Cavallo, A; Cosenza, F; De Cesare, L
2008-05-01
We extend the formalism of the thermodynamic two-time Green's functions to nonextensive quantum statistical mechanics. Working in the optimal Lagrangian multiplier representation, the q -spectral properties and the methods for a direct calculation of the two-time q Green's functions and the related q -spectral density ( q measures the nonextensivity degree) for two generic operators are presented in strict analogy with the extensive (q=1) counterpart. Some emphasis is devoted to the nonextensive version of the less known spectral density method whose effectiveness in exploring equilibrium and transport properties of a wide variety of systems has been well established in conventional classical and quantum many-body physics. To check how both the equations of motion and the spectral density methods work to study the q -induced nonextensivity effects in nontrivial many-body problems, we focus on the equilibrium properties of a second-quantized model for a high-density Bose gas with strong attraction between particles for which exact results exist in extensive conditions. Remarkably, the contributions to several thermodynamic quantities of the q -induced nonextensivity close to the extensive regime are explicitly calculated in the low-temperature regime by overcoming the calculation of the q grand-partition function.
De Raedt, H.; De Raedt, K.; Michielsen, K.
2005-01-01
We demonstrate that networks of locally connected processing units with a primitive learning capability exhibit behavior that is usually only attributed to quantum systems. We describe networks that simulate single-photon beam-splitter and Mach-Zehnder interferometer experiments on a causal,
Directory of Open Access Journals (Sweden)
Agus Perdana Windarto
2017-10-01
Full Text Available Indonesia is a country where most of its people rely on the agricultural sector as a livelihood. Indonesia's rice production is so high that it can not meet the needs of its population, consequently Indonesia still has to import rice from other food producing countries. One of the main causes is the enormous population. Statistics show that in the range of 230-237 million people, the staple food of all residents is rice so it is clear that the need for rice becomes very large. This study discusses the application of datamining on rice import by main country of origin using K-Means Clustering Method. Sources of data of this study were collected based on import import declaration documents produced by the Directorate General of Customs and Excise. In addition since 2015, import data also comes from PT. Pos Indonesia, records of other agencies at the border, and the results of cross-border maritime trade surveys. The data used in this study is the data of rice imports by country of origin from 2000-2015 consisting of 10 countries namely Vietnam, Thailand, China, India, Pakistan, United States, Taiwan, Singapore, Myanmar and Others. Variable used (1 total import of rice (net and (2 import purchase value (CIF. The data will be processed by clustering rice imports by main country of origin in 3 clusters ie high imported cluster, medium imported cluster and low import level cluster. The clustering method used in this research is K-Means method. Cetroid data for high import level clusters 7429180 and 2735452,25, Cetroid data for medium import level clusters 1046359.5 and 337703.05 and Cetroid data for low import level clusters 185559.425 and 53089.225. The result is an assessment based on rice import index with 2 high imported cluster countries namely Vietnam and Thailand, 4 medium-level clusters of moderate import countries namely China, India, Pakistan and Lainya and 4 low imported cluster countries namely USA, Taiwan, Singapore and Myanmar. The
Liu, Xiaoming; Mei, Ming; Liu, Jun; Hu, Wei
2015-12-01
Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.
Information Reconstruction Method for Improved Clustering and Diagnosis of Generic Gearbox Signals
Directory of Open Access Journals (Sweden)
Jay Lee
2011-01-01
Full Text Available Gearbox is a very complex mechanical system that can generate vibrations from its various elements such as gears, shafts, and bearings. Transmission path effect, signal coupling, and noise contamination can further induce difficulties to the development of a prognostics and health management (PHM system for a gearbox. This paper introduces a novel information reconstruction approach to clustering and diagnosis of gearbox signals in varying operating conditions. First, vibration signal is transformed from time domain to frequency domain with Fast Fourier Transform (FFT. Then, reconstruction filters are employed to sift the frequency components in FFT spectrum to retain the information of interest. Features are further extracted to calculate the coefficients of the reconstructed energy expression. Then, correlation analysis (CA and distance measurement (DM techniques are utilized to cluster signals under diverse shaft speeds and loads. Finally, energy coefficients are used as health indicators for the purpose of fault diagnosis of the rotating elements in the gearbox. The proposed method was used to solve the gearbox problem of the 2009 PHM Conference Data Analysis Competition and won with the best score in both professional and student categories.
Adham, Manal T; Bentley, Peter J
2016-08-01
This paper proposes and evaluates a solution to the truck redistribution problem prominent in London's Santander Cycle scheme. Due to the complexity of this NP-hard combinatorial optimisation problem, no efficient optimisation techniques are known to solve the problem exactly. This motivates our use of the heuristic Artificial Ecosystem Algorithm (AEA) to find good solutions in a reasonable amount of time. The AEA is designed to take advantage of highly distributed computer architectures and adapt to changing problems. In the AEA a problem is first decomposed into its relative sub-components; they then evolve solution building blocks that fit together to form a single optimal solution. Three variants of the AEA centred on evaluating clustering methods are presented: the baseline AEA, the community-based AEA which groups stations according to journey flows, and the Adaptive AEA which actively modifies clusters to cater for changes in demand. We applied these AEA variants to the redistribution problem prominent in bike share schemes (BSS). The AEA variants are empirically evaluated using historical data from Santander Cycles to validate the proposed approach and prove its potential effectiveness. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Comparison of three methods for the estimation of cross-shock electric potential using Cluster data
Directory of Open Access Journals (Sweden)
A. P. Dimmock
2011-05-01
Full Text Available Cluster four point measurements provide a comprehensive dataset for the separation of temporal and spatial variations, which is crucial for the calculation of the cross shock electrostatic potential using electric field measurements. While Cluster is probably the most suited among present and past spacecraft missions to provide such a separation at the terrestrial bow shock, it is far from ideal for a study of the cross shock potential, since only 2 components of the electric field are measured in the spacecraft spin plane. The present paper is devoted to the comparison of 3 different techniques that can be used to estimate the potential with this limitation. The first technique is the estimate taking only into account the projection of the measured components onto the shock normal. The second uses the ideal MHD condition E·B = 0 to estimate the third electric field component. The last method is based on the structure of the electric field in the Normal Incidence Frame (NIF for which only the potential component along the shock normal and the motional electric field exist. All 3 approaches are used to estimate the potential for a single crossing of the terrestrial bow shock that took place on the 31 March 2001. Surprisingly all three methods lead to the same order of magnitude for the cross shock potential. It is argued that the third method must lead to more reliable results. The effect of the shock normal inaccuracy is investigated for this particular shock crossing. The resulting electrostatic potential appears too high in comparison with the theoretical results for low Mach number shocks. This shows the variability of the potential, interpreted in the frame of the non-stationary shock model.
Comparison of three methods for the estimation of cross-shock electric potential using Cluster data
Directory of Open Access Journals (Sweden)
Y. Hobara
2011-05-01
Full Text Available Cluster four point measurements provide a comprehensive dataset for the separation of temporal and spatial variations, which is crucial for the calculation of the cross shock electrostatic potential using electric field measurements. While Cluster is probably the most suited among present and past spacecraft missions to provide such a separation at the terrestrial bow shock, it is far from ideal for a study of the cross shock potential, since only 2 components of the electric field are measured in the spacecraft spin plane. The present paper is devoted to the comparison of 3 different techniques that can be used to estimate the potential with this limitation. The first technique is the estimate taking only into account the projection of the measured components onto the shock normal. The second uses the ideal MHD condition E·B = 0 to estimate the third electric field component. The last method is based on the structure of the electric field in the Normal Incidence Frame (NIF for which only the potential component along the shock normal and the motional electric field exist. All 3 approaches are used to estimate the potential for a single crossing of the terrestrial bow shock that took place on the 31 March 2001. Surprisingly all three methods lead to the same order of magnitude for the cross shock potential. It is argued that the third method must lead to more reliable results. The effect of the shock normal inaccuracy is investigated for this particular shock crossing. The resulting electrostatic potential appears too high in comparison with the theoretical results for low Mach number shocks. This shows the variability of the potential, interpreted in the frame of the non-stationary shock model.
Ramabhadran, Raghunath O; Raghavachari, Krishnan
2014-12-16
CONSPECTUS: Quantum chemistry and electronic structure theory have proven to be essential tools to the experimental chemist, in terms of both a priori predictions that pave the way for designing new experiments and rationalizing experimental observations a posteriori. Translating the well-established success of electronic structure theory in obtaining the structures and energies of small chemical systems to increasingly larger molecules is an exciting and ongoing central theme of research in quantum chemistry. However, the prohibitive computational scaling of highly accurate ab initio electronic structure methods poses a fundamental challenge to this research endeavor. This scenario necessitates an indirect fragment-based approach wherein a large molecule is divided into small fragments and is subsequently reassembled to compute its energy accurately. In our quest to further reduce the computational expense associated with the fragment-based methods and overall enhance the applicability of electronic structure methods to large molecules, we realized that the broad ideas involved in a different area, theoretical thermochemistry, are transferable to the area of fragment-based methods. This Account focuses on the effective merger of these two disparate frontiers in quantum chemistry and how new concepts inspired by theoretical thermochemistry significantly reduce the total number of electronic structure calculations needed to be performed as part of a fragment-based method without any appreciable loss of accuracy. Throughout, the generalized connectivity based hierarchy (CBH), which we developed to solve a long-standing problem in theoretical thermochemistry, serves as the linchpin in this merger. The accuracy of our method is based on two strong foundations: (a) the apt utilization of systematic and sophisticated error-canceling schemes via CBH that result in an optimal cutting scheme at any given level of fragmentation and (b) the use of a less expensive second
Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D
2017-01-01
Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P<0.001) and GLUMM7-1 (OR: 7.88, P<0.001) with depression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
Robinson, T R; Haven, E; Fry, A M
2017-11-01
The set of natural numbers may be identified with the spectrum of eigenvalues of an operator (quantum counting), and the dynamical equations of populations of discrete, countable items may be formulated using operator methods. These equations take the form of time dependent operator equations, involving Hamiltonian operators, from which the statistical time dependence of population numbers may be determined. The quantum operator method is illustrated by a novel approach to cell population dynamics. This involves Hamiltonians that mimic the process of stimulated cell division. We evaluate two different models, one in which the stimuli are expended in the division process and one in which the stimuli act as true catalysts. While the former model exhibits only bounded cell population variations, the latter exhibits two distinct regimes; one has bounded population fluctuations about a mean level and in the other, the population can undergo growth to levels that are orders of magnitude above threshold levels, through an instability that could be interpreted as a cancerous growth phase. Copyright © 2017 Elsevier Ltd. All rights reserved.
Anitha, J.; Rangarajan, R.
2015-01-01
Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. However, the popularity and wide availability of data mining tools also raised concerns about the privacy of individuals. Thus, the burden of data privacy protection falls on the shoulder of the data holder and data disambiguation problem occurs in the data matrix, anonymized data becomes less secure. All of the existing privacy preservation clustering methods performs clustering base...
Noniterative Multireference Coupled Cluster Methods on Heterogeneous CPU-GPU Systems
Energy Technology Data Exchange (ETDEWEB)
Bhaskaran-Nair, Kiran; Ma, Wenjing; Krishnamoorthy, Sriram; Villa, Oreste; van Dam, Hubertus JJ; Apra, Edoardo; Kowalski, Karol
2013-04-09
A novel parallel algorithm for non-iterative multireference coupled cluster (MRCC) theories, which merges recently introduced reference-level parallelism (RLP) [K. Bhaskaran-Nair, J.Brabec, E. Aprà, H.J.J. van Dam, J. Pittner, K. Kowalski, J. Chem. Phys. 137, 094112 (2012)] with the possibility of accelerating numerical calculations using graphics processing unit (GPU) is presented. We discuss the performance of this algorithm on the example of the MRCCSD(T) method (iterative singles and doubles and perturbative triples), where the corrections due to triples are added to the diagonal elements of the MRCCSD (iterative singles and doubles) effective Hamiltonian matrix. The performance of the combined RLP/GPU algorithm is illustrated on the example of the Brillouin-Wigner (BW) and Mukherjee (Mk) state-specific MRCCSD(T) formulations.