Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J; Adatia, Ian
2016-01-01
We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral. PMID:27609672
Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian
2016-01-01
We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral. PMID:27609672
Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences.
Siebert, Matthias; Söding, Johannes
2016-07-27
Position weight matrices (PWMs) are the standard model for DNA and RNA regulatory motifs. In PWMs nucleotide probabilities are independent of nucleotides at other positions. Models that account for dependencies need many parameters and are prone to overfitting. We have developed a Bayesian approach for motif discovery using Markov models in which conditional probabilities of order k - 1 act as priors for those of order k This Bayesian Markov model (BaMM) training automatically adapts model complexity to the amount of available data. We also derive an EM algorithm for de-novo discovery of enriched motifs. For transcription factor binding, BaMMs achieve significantly (P = 1/16) higher cross-validated partial AUC than PWMs in 97% of 446 ChIP-seq ENCODE datasets and improve performance by 36% on average. BaMMs also learn complex multipartite motifs, improving predictions of transcription start sites, polyadenylation sites, bacterial pause sites, and RNA binding sites by 26-101%. BaMMs never performed worse than PWMs. These robust improvements argue in favour of generally replacing PWMs by BaMMs. PMID:27288444
A new graph model and algorithms for consistent superstring problems†
Na, Joong Chae; Cho, Sukhyeun; Choi, Siwon; Kim, Jin Wook; Park, Kunsoo; Sim, Jeong Seop
2014-01-01
Problems related to string inclusion and non-inclusion have been vigorously studied in diverse fields such as data compression, molecular biology and computer security. Given a finite set of positive strings and a finite set of negative strings , a string α is a consistent superstring if every positive string is a substring of α and no negative string is a substring of α. The shortest (resp. longest) consistent superstring problem is to find a string α that is the shortest (resp. longest) among all the consistent superstrings for the given sets of strings. In this paper, we first propose a new graph model for consistent superstrings for given and . In our graph model, the set of strings represented by paths satisfying some conditions is the same as the set of consistent superstrings for and . We also present algorithms for the shortest and the longest consistent superstring problems. Our algorithms solve the consistent superstring problems for all cases, including cases that are not considered in previous work. Moreover, our algorithms solve in polynomial time the consistent superstring problems for more cases than the previous algorithms. For the polynomially solvable cases, our algorithms are more efficient than the previous ones. PMID:24751868
A consistent-mode indicator for the eigensystem realization algorithm
NASA Technical Reports Server (NTRS)
Pappa, Richard S.; Elliott, Kenny B.; Schenk, Axel
1992-01-01
A new method is described for assessing the consistency of model parameters identified with the Eigensystem Realization Algorithm (ERA). Identification results show varying consistency in practice due to many sources, including high modal density, nonlinearity, and inadequate excitation. Consistency is considered to be a reliable indicator of accuracy. The new method is the culmination of many years of experience in developing a practical implementation of the Eigensystem Realization Algorithm. The effectiveness of the method is illustrated using data from NASA Langley's Controls-Structures-Interaction Evolutionary Model.
The strobe algorithms for multi-source warehouse consistency
Zhuge, Yue; Garcia-Molina, H.; Wiener, J.L.
1996-12-31
A warehouse is a data repository containing integrated information for efficient querying and analysis. Maintaining the consistency of warehouse data is challenging, especially if the data sources are autonomous and views of the data at the warehouse span multiple sources. Transactions containing multiple updates at one or more sources, e.g., batch updates, complicate the consistency problem. In this paper we identify and discuss three fundamental transaction processing scenarios for data warehousing. We define four levels of consistency for warehouse data and present a new family of algorithms, the Strobe family, that maintain consistency as the warehouse is updated, under the various warehousing scenarios. All of the algorithms are incremental and can handle a continuous and overlapping stream of updates from the sources. Our implementation shows that the algorithms are practical and realistic choices for a wide variety of update scenarios.
Formal verification of an oral messages algorithm for interactive consistency
NASA Technical Reports Server (NTRS)
Rushby, John
1992-01-01
The formal specification and verification of an algorithm for Interactive Consistency based on the Oral Messages algorithm for Byzantine Agreement is described. We compare our treatment with that of Bevier and Young, who presented a formal specification and verification for a very similar algorithm. Unlike Bevier and Young, who observed that 'the invariant maintained in the recursive subcases of the algorithm is significantly more complicated than is suggested by the published proof' and who found its formal verification 'a fairly difficult exercise in mechanical theorem proving,' our treatment is very close to the previously published analysis of the algorithm, and our formal specification and verification are straightforward. This example illustrates how delicate choices in the formulation of the problem can have significant impact on the readability of its formal specification and on the tractability of its formal verification.
Maliwichi, Madalitso; Rosenberg, Nora E.; Macfie, Rebekah; Olson, Dan; Hoffman, Irving; van der Horst, Charles M.; Kazembe, Peter N.; Hosseinipour, Mina C.; McCollum, Eric D.
2014-01-01
Objective To determine, for the WHO algorithm for point-of-care diagnosis of HIV infection, the agreement levels between pediatricians and non-physician clinicians, and to compare sensitivity and specificity profiles of the WHO algorithm and different CD4 thresholds against HIV PCR testing in hospitalized Malawian infants. Methods In 2011, hospitalized HIV-exposed infants <12 months in Lilongwe, Malawi were evaluated independently with the WHO algorithm by both a pediatrician and clinical officer. Blood was collected for CD4 and molecular HIV testing (DNA or RNA PCR). Using molecular testing as the reference, sensitivity, specificity, and positive predictive value (PPV) were determined for the WHO algorithm and CD4 count thresholds of 1500 and 2000 cells/mm3 by pediatricians and clinical officers. Results We enrolled 166 infants (50% female, 34% <2 months, 37% HIV-infected). Sensitivity was higher using CD4 thresholds (<1500, 80%; <2000, 95%) than with the algorithm (physicians, 57%; clinical officers, 71%). Specificity was comparable for CD4 thresholds (<1500, 68%, <2000, 50%) and the algorithm (pediatricians, 55%, clinical officers, 50%). The positive predictive values were slightly better using CD4 thresholds (<1500, 59%, <2000, 52%) than the algorithm (pediatricians, 43%, clinical officers 45%) at this prevalence. Conclusion Performance by the WHO algorithm and CD4 thresholds resulted in many misclassifications. Point-of-care CD4 thresholds of <1500 cells/mm3 or <2000 cells/mm3 could identify more HIV-infected infants with fewer false positives than the algorithm. However, a point-of-care option with better performance characteristics is needed for accurate, timely HIV diagnosis. PMID:24754543
Variationally consistent discretization schemes and numerical algorithms for contact problems
NASA Astrophysics Data System (ADS)
Wohlmuth, Barbara
We consider variationally consistent discretization schemes for mechanical contact problems. Most of the results can also be applied to other variational inequalities, such as those for phase transition problems in porous media, for plasticity or for option pricing applications from finance. The starting point is to weakly incorporate the constraint into the setting and to reformulate the inequality in the displacement in terms of a saddle-point problem. Here, the Lagrange multiplier represents the surface forces, and the constraints are restricted to the boundary of the simulation domain. Having a uniform inf-sup bound, one can then establish optimal low-order a priori convergence rates for the discretization error in the primal and dual variables. In addition to the abstract framework of linear saddle-point theory, complementarity terms have to be taken into account. The resulting inequality system is solved by rewriting it equivalently by means of the non-linear complementarity function as a system of equations. Although it is not differentiable in the classical sense, semi-smooth Newton methods, yielding super-linear convergence rates, can be applied and easily implemented in terms of a primal-dual active set strategy. Quite often the solution of contact problems has a low regularity, and the efficiency of the approach can be improved by using adaptive refinement techniques. Different standard types, such as residual- and equilibrated-based a posteriori error estimators, can be designed based on the interpretation of the dual variable as Neumann boundary condition. For the fully dynamic setting it is of interest to apply energy-preserving time-integration schemes. However, the differential algebraic character of the system can result in high oscillations if standard methods are applied. A possible remedy is to modify the fully discretized system by a local redistribution of the mass. Numerical results in two and three dimensions illustrate the wide range of
Cobb, J.W.; Leboeuf, J.N.
1994-10-01
The authors present a particle algorithm to extend simulation capabilities for plasma based materials processing reactors. The orbit integrator uses a syncopated leap-frog algorithm in cylindrical coordinates, which maintains second order accuracy, and minimizes computational complexity. Plasma source terms are accumulated orbit consistently directly in the frequency and azimuthal mode domains. Finally they discuss the numerical analysis of this algorithm. Orbit consistency greatly reduces the computational cost for a given level of precision. The computational cost is independent of the degree of time scale separation.
NEW MULTICATEGORY BOOSTING ALGORITHMS BASED ON MULTICATEGORY FISHER-CONSISTENT LOSSES
Zou, Hui; Zhu, Ji; Hastie, Trevor
2016-01-01
Fisher-consistent loss functions play a fundamental role in the construction of successful binary margin-based classifiers. In this paper we establish the Fisher-consistency condition for multicategory classification problems. Our approach uses the margin vector concept which can be regarded as a multicategory generalization of the binary margin. We characterize a wide class of smooth convex loss functions that are Fisher-consistent for multicategory classification. We then consider using the margin-vector-based loss functions to derive multicategory boosting algorithms. In particular, we derive two new multicategory boosting algorithms by using the exponential and logistic regression losses.
A formally verified algorithm for interactive consistency under a hybrid fault model
NASA Technical Reports Server (NTRS)
Lincoln, Patrick; Rushby, John
1993-01-01
Consistent distribution of single-source data to replicated computing channels is a fundamental problem in fault-tolerant system design. The 'Oral Messages' (OM) algorithm solves this problem of Interactive Consistency (Byzantine Agreement) assuming that all faults are worst-cass. Thambidurai and Park introduced a 'hybrid' fault model that distinguished three fault modes: asymmetric (Byzantine), symmetric, and benign; they also exhibited, along with an informal 'proof of correctness', a modified version of OM. Unfortunately, their algorithm is flawed. The discipline of mechanically checked formal verification eventually enabled us to develop a correct algorithm for Interactive Consistency under the hybrid fault model. This algorithm withstands $a$ asymmetric, $s$ symmetric, and $b$ benign faults simultaneously, using $m+1$ rounds, provided $n is greater than 2a + 2s + b + m$, and $m\\geg a$. We present this algorithm, discuss its subtle points, and describe its formal specification and verification in PVS. We argue that formal verification systems such as PVS are now sufficiently effective that their application to fault-tolerance algorithms should be considered routine.
A subgroup algorithm to identify cross-rotation peaks consistent with non-crystallographic symmetry.
Lilien, Ryan H; Bailey-Kellogg, Chris; Anderson, Amy C; Donald, Bruce R
2004-06-01
Molecular replacement (MR) often plays a prominent role in determining initial phase angles for structure determination by X-ray crystallography. In this paper, an efficient quaternion-based algorithm is presented for analyzing peaks from a cross-rotation function in order to identify model orientations consistent with proper non-crystallographic symmetry (NCS) and to generate proper NCS-consistent orientations missing from the list of cross-rotation peaks. The algorithm, CRANS, analyzes the rotation differences between each pair of cross-rotation peaks to identify finite subgroups. Sets of rotation differences satisfying the subgroup axioms correspond to orientations compatible with the correct proper NCS. The CRANS algorithm was first tested using cross-rotation peaks computed from structure-factor data for three test systems and was then used to assist in the de novo structure determination of dihydrofolate reductase-thymidylate synthase (DHFR-TS) from Cryptosporidium hominis. In every case, the CRANS algorithm runs in seconds to identify orientations consistent with the observed proper NCS and to generate missing orientations not present in the cross-rotation peak list. The CRANS algorithm has application in every molecular-replacement phasing effort with proper NCS. PMID:15159565
Two Vectorized Algorithms for the Effective Calculation of Mass-Consistent Flow Fields.
NASA Astrophysics Data System (ADS)
Moussiopoulos, N.; Flassak, Th.
1986-06-01
The purpose of this paper is the calculation of mass-consistent wind velocity fields over complex orography on the basis of existing measurements. Measured data are used to generate an initial wind velocity field that in general does not satisfy continuity. For the adjustment of this velocity field a three-dimensional elliptic differential equation is solved. A transformation of this equation to a terrain-following coordinate system ensures the proper consideration of the orography. Two numerical algorithms for the solution of the transformed equation are presented. One algorithm makes use of a fast direct elliptic solver based on Fourier analysis, the other utilizes the red-black SOR method. Both algorithms achieve full vectorization on computers like the CYBER 205. A test problem is defined to compare the two algorithms with regard to the computing time: In the case of small terrain roughness, the algorithm using the fast direct elliptic solver is recommended, in the opposite case the red-black SOR method. Adjusted mass- consistent wind fields are presented for the Athens basin. The results are discussed in view of the elevated pollution levels in Athens; they are in good agreement with observations.
NASA Astrophysics Data System (ADS)
Tretiak, Sergei; Isborn, Christine M.; Niklasson, Anders M. N.; Challacombe, Matt
2009-02-01
Four different numerical algorithms suitable for a linear scaling implementation of time-dependent Hartree-Fock and Kohn-Sham self-consistent field theories are examined. We compare the performance of modified Lanczos, Arooldi, Davidson, and Rayleigh quotient iterative procedures to solve the random-phase approximation (RPA) (non-Hermitian) and Tamm-Dancoff approximation (TDA) (Hermitian) eigenvalue equations in the molecular orbital-free framework. Semiempirical Hamiltonian models are used to numerically benchmark algorithms for the computation of excited states of realistic molecular systems (conjugated polymers and carbon nanotubes). Convergence behavior and stability are tested with respect to a numerical noise imposed to simulate linear scaling conditions. The results single out the most suitable procedures for linear scaling large-scale time-dependent perturbation theory calculations of electronic excitations.
Tretiak, Sergei
2008-01-01
Four different numerical algorithms suitable for a linear scaling implementation of time-dependent Hartree-Fock and Kohn-Sham self-consistent field theories are examined. We compare the performance of modified Lanczos, Arooldi, Davidson, and Rayleigh quotient iterative procedures to solve the random-phase approximation (RPA) (non-Hermitian) and Tamm-Dancoff approximation (TDA) (Hermitian) eigenvalue equations in the molecular orbital-free framework. Semiempirical Hamiltonian models are used to numerically benchmark algorithms for the computation of excited states of realistic molecular systems (conjugated polymers and carbon nanotubes). Convergence behavior and stability are tested with respect to a numerical noise imposed to simulate linear scaling conditions. The results single out the most suitable procedures for linear scaling large-scale time-dependent perturbation theory calculations of electronic excitations.
A JFNK-based implicit moment algorithm for self-consistent, multi-scale, plasma simulation
NASA Astrophysics Data System (ADS)
Knoll, Dana; Taitano, William; Chacon, Luis
2010-11-01
Jacobian-Free-Newton-Krylov method (JFNK) is an advanced non-linear algorithm that allows solution to a coupled systems of non-linear equations [1]. In [2] we have put forward a JFNK-based implicit, consistent, time integration algorithm and demonstrated it's ability to efficiently step over electron time scales, while retaining electron kinetic effects on the ion time scale. Here we extend this work by investigating a JFNK- based implicit-moments approach for the purpose of consistent scale-bridging between the fluid description and kinetic description in order to resolve the transition region. Our preliminary results, based on a reformulated Poisson's equation (RPE) [3], allows solution to the Vlasov-Poisson system for varying grid resolutions. In the limit of local coarse grid size (grid spacing large compared to Debye length), the RPE represents an electric field based on the moment system, while in the limit of local grid spacing resolving the Debye length, the RPE represents an electric field based on the standard Poisson equation. The technique allows smooth transition between the two regimes, consistently, in one simulation. [1] D.A. Knoll and D.E. Keyes,J. Comput. Phys., vol. 193 (2004) [2] W.T. Taitano, Masters Thesis, Nuclear Engineering, University of Idaho (2010) [3] R. Belaouar, N.Crouseilles and P. Degond,J. Sci. Comput., vol. 41 (2009)
Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm
Heymann, J.; Reuter, M.; Hilker, M.; Buchwitz, M.; Schneising, O.; Bovensmann, H.; Burrows, J. P.; Kuze, A.; Suto, H.; Deutscher, N. M.; et al
2015-02-13
Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in Januarymore » 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also
NASA Astrophysics Data System (ADS)
Dong, Suchuan
2015-11-01
This talk focuses on simulating the motion of a mixture of N (N>=2) immiscible incompressible fluids with given densities, dynamic viscosities and pairwise surface tensions. We present an N-phase formulation within the phase field framework that is thermodynamically consistent, in the sense that the formulation satisfies the conservations of mass/momentum, the second law of thermodynamics and Galilean invariance. We also present an efficient algorithm for numerically simulating the N-phase system. The algorithm has overcome the issues caused by the variable coefficient matrices associated with the variable mixture density/viscosity and the couplings among the (N-1) phase field variables and the flow variables. We compare simulation results with the Langmuir-de Gennes theory to demonstrate that the presented method produces physically accurate results for multiple fluid phases. Numerical experiments will be presented for several problems involving multiple fluid phases, large density contrasts and large viscosity contrasts to demonstrate the capabilities of the method for studying the interactions among multiple types of fluid interfaces. Support from NSF and ONR is gratefully acknowledged.
Consistent satellite XCO_{2} retrievals from SCIAMACHY and GOSAT using the BESD algorithm
Heymann, J.; Reuter, M.; Hilker, M.; Buchwitz, M.; Schneising, O.; Bovensmann, H.; Burrows, J. P.; Kuze, A.; Suto, H.; Deutscher, N. M.; Dubey, M. K.; Griffith, D. W. T.; Hase, F.; Kawakami, S.; Kivi, R.; Morino, I.; Petri, C.; Roehl, C.; Schneider, M.; Sherlock, V.; Sussmann, R.; Velazco, V. A.; Warneke, T.; Wunch, D.
2015-02-13
Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO_{2}) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO_{2}, the column-averaged dry-air mole fraction of CO_{2}. BESD has been initially developed for SCIAMACHY XCO_{2} retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO_{2} product. GOSAT BESD XCO_{2} is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO_{2} from GOSAT and present detailed comparisons with ground-based observations of XCO_{2} from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO_{2} data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO_{2}. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non
A Self Consistent Multiprocessor Space Charge Algorithm that is Almost Embarrassingly Parallel
Edward Nissen, B. Erdelyi, S.L. Manikonda
2012-07-01
We present a space charge code that is self consistent, massively parallelizeable, and requires very little communication between computer nodes; making the calculation almost embarrassingly parallel. This method is implemented in the code COSY Infinity where the differential algebras used in this code are important to the algorithm's proper functioning. The method works by calculating the self consistent space charge distribution using the statistical moments of the test particles, and converting them into polynomial series coefficients. These coefficients are combined with differential algebraic integrals to form the potential, and electric fields. The result is a map which contains the effects of space charge. This method allows for massive parallelization since its statistics based solver doesn't require any binning of particles, and only requires a vector containing the partial sums of the statistical moments for the different nodes to be passed. All other calculations are done independently. The resulting maps can be used to analyze the system using normal form analysis, as well as advance particles in numbers and at speeds that were previously impossible.
Scemama, Anthony; Renon, Nicolas; Rapacioli, Mathias
2014-06-10
We present an algorithm and its parallel implementation for solving a self-consistent problem as encountered in Hartree-Fock or density functional theory. The algorithm takes advantage of the sparsity of matrices through the use of local molecular orbitals. The implementation allows one to exploit efficiently modern symmetric multiprocessing (SMP) computer architectures. As a first application, the algorithm is used within the density-functional-based tight binding method, for which most of the computational time is spent in the linear algebra routines (diagonalization of the Fock/Kohn-Sham matrix). We show that with this algorithm (i) single point calculations on very large systems (millions of atoms) can be performed on large SMP machines, (ii) calculations involving intermediate size systems (1000-100 000 atoms) are also strongly accelerated and can run efficiently on standard servers, and (iii) the error on the total energy due to the use of a cutoff in the molecular orbital coefficients can be controlled such that it remains smaller than the SCF convergence criterion. PMID:26580754
Hermes, Matthew R; Hirata, So
2014-12-28
A stochastic algorithm based on Metropolis Monte Carlo (MC) is presented for the size-extensive vibrational self-consistent field methods (XVSCF(n) and XVSCF[n]) for anharmonic molecular vibrations. The new MC-XVSCF methods substitute stochastic evaluations of a small number of high-dimensional integrals of functions of the potential energy surface (PES), which is sampled on demand, for diagrammatic equations involving high-order anharmonic force constants. This algorithm obviates the need to evaluate and store any high-dimensional partial derivatives of the potential and can be applied to the fully anharmonic PES without any Taylor-series approximation in an intrinsically parallelizable algorithm. The MC-XVSCF methods reproduce deterministic XVSCF calculations on the same Taylor-series PES in all energies, frequencies, and geometries. Calculations using the fully anharmonic PES evaluated on the fly with electronic structure methods report anharmonic effects on frequencies and geometries of much greater magnitude than deterministic XVSCF calculations, reflecting an underestimation of anharmonic effects in a Taylor-series approximation to the PES. PMID:25554137
Hermes, Matthew R.; Hirata, So
2014-12-28
A stochastic algorithm based on Metropolis Monte Carlo (MC) is presented for the size-extensive vibrational self-consistent field methods (XVSCF(n) and XVSCF[n]) for anharmonic molecular vibrations. The new MC-XVSCF methods substitute stochastic evaluations of a small number of high-dimensional integrals of functions of the potential energy surface (PES), which is sampled on demand, for diagrammatic equations involving high-order anharmonic force constants. This algorithm obviates the need to evaluate and store any high-dimensional partial derivatives of the potential and can be applied to the fully anharmonic PES without any Taylor-series approximation in an intrinsically parallelizable algorithm. The MC-XVSCF methods reproduce deterministic XVSCF calculations on the same Taylor-series PES in all energies, frequencies, and geometries. Calculations using the fully anharmonic PES evaluated on the fly with electronic structure methods report anharmonic effects on frequencies and geometries of much greater magnitude than deterministic XVSCF calculations, reflecting an underestimation of anharmonic effects in a Taylor-series approximation to the PES.
Personalized recommendation based on unbiased consistence
NASA Astrophysics Data System (ADS)
Zhu, Xuzhen; Tian, Hui; Zhang, Ping; Hu, Zheng; Zhou, Tao
2015-08-01
Recently, in physical dynamics, mass-diffusion-based recommendation algorithms on bipartite network provide an efficient solution by automatically pushing possible relevant items to users according to their past preferences. However, traditional mass-diffusion-based algorithms just focus on unidirectional mass diffusion from objects having been collected to those which should be recommended, resulting in a biased causal similarity estimation and not-so-good performance. In this letter, we argue that in many cases, a user's interests are stable, and thus bidirectional mass diffusion abilities, no matter originated from objects having been collected or from those which should be recommended, should be consistently powerful, showing unbiased consistence. We further propose a consistence-based mass diffusion algorithm via bidirectional diffusion against biased causality, outperforming the state-of-the-art recommendation algorithms in disparate real data sets, including Netflix, MovieLens, Amazon and Rate Your Music.
ERIC Educational Resources Information Center
Zhao, Dacheng; Singh, Michael
2011-01-01
International comparative studies and cross-cultural studies of mathematics achievement indicate that Chinese students (whether living in or outside China) consistently outperform their Western counterparts. This study shows that the gap between Chinese-Australian and other Australian students is best explained by differences in motivation to…
NASA Astrophysics Data System (ADS)
Levy, R. C.; Munchak, L. A.; Mattoo, S.; Patadia, F.; Remer, L. A.; Holz, R. E.
2015-07-01
To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångstrom Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March-April-May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in
NASA Astrophysics Data System (ADS)
Levy, R. C.; Munchak, L. A.; Mattoo, S.; Patadia, F.; Remer, L. A.; Holz, R. E.
2015-10-01
To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March-April-May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in
Popov, Pavel P. Pope, Stephen B.
2014-01-15
This work addresses the issue of particle mass consistency in Large Eddy Simulation/Probability Density Function (LES/PDF) methods for turbulent reactive flows. Numerical schemes for the implicit and explicit enforcement of particle mass consistency (PMC) are introduced, and their performance is examined in a representative LES/PDF application, namely the Sandia–Sydney Bluff-Body flame HM1. A new combination of interpolation schemes for velocity and scalar fields is found to better satisfy PMC than multilinear and fourth-order Lagrangian interpolation. A second-order accurate time-stepping scheme for stochastic differential equations (SDE) is found to improve PMC relative to Euler time stepping, which is the first time that a second-order scheme is found to be beneficial, when compared to a first-order scheme, in an LES/PDF application. An explicit corrective velocity scheme for PMC enforcement is introduced, and its parameters optimized to enforce a specified PMC criterion with minimal corrective velocity magnitudes.
The Chopthin Algorithm for Resampling
NASA Astrophysics Data System (ADS)
Gandy, Axel; Lau, F. Din-Houn
2016-08-01
Resampling is a standard step in particle filters and more generally sequential Monte Carlo methods. We present an algorithm, called chopthin, for resampling weighted particles. In contrast to standard resampling methods the algorithm does not produce a set of equally weighted particles; instead it merely enforces an upper bound on the ratio between the weights. Simulation studies show that the chopthin algorithm consistently outperforms standard resampling methods. The algorithms chops up particles with large weight and thins out particles with low weight, hence its name. It implicitly guarantees a lower bound on the effective sample size. The algorithm can be implemented efficiently, making it practically useful. We show that the expected computational effort is linear in the number of particles. Implementations for C++, R (on CRAN), Python and Matlab are available.
NASA Astrophysics Data System (ADS)
Robin, C.; Pillet, N.; Peña Arteaga, D.; Berger, J.-F.
2016-02-01
Background: Although self-consistent multiconfiguration methods have been used for decades to address the description of atomic and molecular many-body systems, only a few trials have been made in the context of nuclear structure. Purpose: This work aims at the development of such an approach to describe in a unified way various types of correlations in nuclei in a self-consistent manner where the mean-field is improved as correlations are introduced. The goal is to reconcile the usually set-apart shell-model and self-consistent mean-field methods. Method: This approach is referred to as "variational multiparticle-multihole configuration mixing method." It is based on a double variational principle which yields a set of two coupled equations that determine at the same time the expansion coefficients of the many-body wave function and the single-particle states. The solution of this problem is obtained by building a doubly iterative numerical algorithm. Results: The formalism is derived and discussed in a general context, starting from a three-body Hamiltonian. Links to existing many-body techniques such as the formalism of Green's functions are established. First applications are done using the two-body D1S Gogny effective force. The numerical procedure is tested on the 12C nucleus to study the convergence features of the algorithm in different contexts. Ground-state properties as well as single-particle quantities are analyzed, and the description of the first 2+ state is examined. Conclusions: The self-consistent multiparticle-multihole configuration mixing method is fully applied for the first time to the description of a test nucleus. This study makes it possible to validate our numerical algorithm and leads to encouraging results. To test the method further, we will realize in the second article of this series a systematic description of more nuclei and observables obtained by applying the newly developed numerical procedure with the same Gogny force. As
Modified OMP Algorithm for Exponentially Decaying Signals
Kazimierczuk, Krzysztof; Kasprzak, Paweł
2015-01-01
A group of signal reconstruction methods, referred to as compressed sensing (CS), has recently found a variety of applications in numerous branches of science and technology. However, the condition of the applicability of standard CS algorithms (e.g., orthogonal matching pursuit, OMP), i.e., the existence of the strictly sparse representation of a signal, is rarely met. Thus, dedicated algorithms for solving particular problems have to be developed. In this paper, we introduce a modification of OMP motivated by nuclear magnetic resonance (NMR) application of CS. The algorithm is based on the fact that the NMR spectrum consists of Lorentzian peaks and matches a single Lorentzian peak in each of its iterations. Thus, we propose the name Lorentzian peak matching pursuit (LPMP). We also consider certain modification of the algorithm by introducing the allowed positions of the Lorentzian peaks' centers. Our results show that the LPMP algorithm outperforms other CS algorithms when applied to exponentially decaying signals. PMID:25609044
Extortion can outperform generosity in the iterated prisoner's dilemma.
Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W; Zheng, Jie; Xu, Bin
2016-01-01
Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation. PMID:27067513
Dodecylresorufin (C12R) Outperforms Resorufin in Microdroplet Bacterial Assays.
Scheler, Ott; Kaminski, Tomasz S; Ruszczak, Artur; Garstecki, Piotr
2016-05-11
This paper proves that dodecylresorufin (C12R) outperforms resorufin (the conventional form of this dye) in droplet microfluidic bacterial assays. Resorufin is a marker dye that is widely used in different fields of microbiology and has increasingly been applied in droplet microfluidic assays and experiments. The main concern associated with resorufin in droplet-based systems is dye leakage into the oil phase and neighboring droplets. The leakage decreases the performance of assays because it causes averaging of the signal between the positive (bacteria-containing) and negative (empty) droplets. Here we show that C12R is a promising alternative to conventional resorufin because it maintains higher sensitivity, specificity, and signal-to-noise ratio over time. These characteristics make C12R a suitable reagent for droplet digital assays and for monitoring of microbial growth in droplets. PMID:27100211
Better than Nature: Nicotinamide Biomimetics That Outperform Natural Coenzymes.
Knaus, Tanja; Paul, Caroline E; Levy, Colin W; de Vries, Simon; Mutti, Francesco G; Hollmann, Frank; Scrutton, Nigel S
2016-01-27
The search for affordable, green biocatalytic processes is a challenge for chemicals manufacture. Redox biotransformations are potentially attractive, but they rely on unstable and expensive nicotinamide coenzymes that have prevented their widespread exploitation. Stoichiometric use of natural coenzymes is not viable economically, and the instability of these molecules hinders catalytic processes that employ coenzyme recycling. Here, we investigate the efficiency of man-made synthetic biomimetics of the natural coenzymes NAD(P)H in redox biocatalysis. Extensive studies with a range of oxidoreductases belonging to the "ene" reductase family show that these biomimetics are excellent analogues of the natural coenzymes, revealed also in crystal structures of the ene reductase XenA with selected biomimetics. In selected cases, these biomimetics outperform the natural coenzymes. "Better-than-Nature" biomimetics should find widespread application in fine and specialty chemicals production by harnessing the power of high stereo-, regio-, and chemoselective redox biocatalysts and enabling reactions under mild conditions at low cost. PMID:26727612
Extortion can outperform generosity in the iterated prisoner's dilemma
Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W.; Zheng, Jie; Xu, Bin
2016-01-01
Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation. PMID:27067513
Lewinski, Peter
2015-01-01
Little is known about people’s accuracy of recognizing neutral faces as neutral. In this paper, I demonstrate the importance of knowing how well people recognize neutral faces. I contrasted human recognition scores of 100 typical, neutral front-up facial images with scores of an arguably objective judge – automated facial coding (AFC) software. I hypothesized that the software would outperform humans in recognizing neutral faces because of the inherently objective nature of computer algorithms. Results confirmed this hypothesis. I provided the first-ever evidence that computer software (90%) was more accurate in recognizing neutral faces than people were (59%). I posited two theoretical mechanisms, i.e., smile-as-a-baseline and false recognition of emotion, as possible explanations for my findings. PMID:26441761
Schall, Marina; Martiny, Sarah E; Goetz, Thomas; Hall, Nathan C
2016-05-01
Although expressing positive emotions is typically socially rewarded, in the present work, we predicted that people suppress positive emotions and thereby experience social benefits when outperformed others are present. We tested our predictions in three experimental studies with high school students. In Studies 1 and 2, we manipulated the type of social situation (outperformance vs. non-outperformance) and assessed suppression of positive emotions. In both studies, individuals reported suppressing positive emotions more in outperformance situations than in non-outperformance situations. In Study 3, we manipulated the social situation (outperformance vs. non-outperformance) as well as the videotaped person's expression of positive emotions (suppression vs. expression). The findings showed that when outperforming others, individuals were indeed evaluated more positively when they suppressed rather than expressed their positive emotions, and demonstrate the importance of the specific social situation with respect to the effects of suppression. PMID:27029576
Better than Nature: Nicotinamide Biomimetics That Outperform Natural Coenzymes
2016-01-01
The search for affordable, green biocatalytic processes is a challenge for chemicals manufacture. Redox biotransformations are potentially attractive, but they rely on unstable and expensive nicotinamide coenzymes that have prevented their widespread exploitation. Stoichiometric use of natural coenzymes is not viable economically, and the instability of these molecules hinders catalytic processes that employ coenzyme recycling. Here, we investigate the efficiency of man-made synthetic biomimetics of the natural coenzymes NAD(P)H in redox biocatalysis. Extensive studies with a range of oxidoreductases belonging to the “ene” reductase family show that these biomimetics are excellent analogues of the natural coenzymes, revealed also in crystal structures of the ene reductase XenA with selected biomimetics. In selected cases, these biomimetics outperform the natural coenzymes. “Better-than-Nature” biomimetics should find widespread application in fine and specialty chemicals production by harnessing the power of high stereo-, regio-, and chemoselective redox biocatalysts and enabling reactions under mild conditions at low cost. PMID:26727612
Adult vultures outperform juveniles in challenging thermal soaring conditions
Harel, Roi; Horvitz, Nir; Nathan, Ran
2016-01-01
Due to the potentially detrimental consequences of low performance in basic functional tasks, individuals are expected to improve performance with age and show the most marked changes during early stages of life. Soaring-gliding birds use rising-air columns (thermals) to reduce energy expenditure allocated to flight. We offer a framework to evaluate thermal soaring performance, and use GPS-tracking to study movements of Eurasian griffon vultures (Gyps fulvus). Because the location and intensity of thermals are variable, we hypothesized that soaring performance would improve with experience and predicted that the performance of inexperienced individuals (<2 months) would be inferior to that of experienced ones (>5 years). No differences were found in body characteristics, climb rates under low wind shear, and thermal selection, presumably due to vultures’ tendency to forage in mixed-age groups. Adults, however, outperformed juveniles in their ability to adjust fine-scale movements under challenging conditions, as juveniles had lower climb rates under intermediate wind shear, particularly on the lee-side of thermal columns. Juveniles were also less efficient along the route both in terms of time and energy. The consequences of these handicaps are probably exacerbated if juveniles lag behind adults in finding and approaching food. PMID:27291590
Adult vultures outperform juveniles in challenging thermal soaring conditions.
Harel, Roi; Horvitz, Nir; Nathan, Ran
2016-01-01
Due to the potentially detrimental consequences of low performance in basic functional tasks, individuals are expected to improve performance with age and show the most marked changes during early stages of life. Soaring-gliding birds use rising-air columns (thermals) to reduce energy expenditure allocated to flight. We offer a framework to evaluate thermal soaring performance, and use GPS-tracking to study movements of Eurasian griffon vultures (Gyps fulvus). Because the location and intensity of thermals are variable, we hypothesized that soaring performance would improve with experience and predicted that the performance of inexperienced individuals (<2 months) would be inferior to that of experienced ones (>5 years). No differences were found in body characteristics, climb rates under low wind shear, and thermal selection, presumably due to vultures' tendency to forage in mixed-age groups. Adults, however, outperformed juveniles in their ability to adjust fine-scale movements under challenging conditions, as juveniles had lower climb rates under intermediate wind shear, particularly on the lee-side of thermal columns. Juveniles were also less efficient along the route both in terms of time and energy. The consequences of these handicaps are probably exacerbated if juveniles lag behind adults in finding and approaching food. PMID:27291590
Digital image analysis outperforms manual biomarker assessment in breast cancer.
Stålhammar, Gustav; Fuentes Martinez, Nelson; Lippert, Michael; Tobin, Nicholas P; Mølholm, Ida; Kis, Lorand; Rosin, Gustaf; Rantalainen, Mattias; Pedersen, Lars; Bergh, Jonas; Grunkin, Michael; Hartman, Johan
2016-04-01
In the spectrum of breast cancers, categorization according to the four gene expression-based subtypes 'Luminal A,' 'Luminal B,' 'HER2-enriched,' and 'Basal-like' is the method of choice for prognostic and predictive value. As gene expression assays are not yet universally available, routine immunohistochemical stains act as surrogate markers for these subtypes. Thus, congruence of surrogate markers and gene expression tests is of utmost importance. In this study, 3 cohorts of primary breast cancer specimens (total n=436) with up to 28 years of survival data were scored for Ki67, ER, PR, and HER2 status manually and by digital image analysis (DIA). The results were then compared for sensitivity and specificity for the Luminal B subtype, concordance to PAM50 assays in subtype classification and prognostic power. The DIA system used was the Visiopharm Integrator System. DIA outperformed manual scoring in terms of sensitivity and specificity for the Luminal B subtype, widely considered the most challenging distinction in surrogate subclassification, and produced slightly better concordance and Cohen's κ agreement with PAM50 gene expression assays. Manual biomarker scores and DIA essentially matched each other for Cox regression hazard ratios for all-cause mortality. When the Nottingham combined histologic grade (Elston-Ellis) was used as a prognostic surrogate, stronger Spearman's rank-order correlations were produced by DIA. Prognostic value of Ki67 scores in terms of likelihood ratio χ(2) (LR χ(2)) was higher for DIA that also added significantly more prognostic information to the manual scores (LR-Δχ(2)). In conclusion, the system for DIA evaluated here was in most aspects a superior alternative to manual biomarker scoring. It also has the potential to reduce time consumption for pathologists, as many of the steps in the workflow are either automatic or feasible to manage without pathological expertise. PMID:26916072
Joint optimization of algorithmic suites for EEG analysis.
Santana, Eder; Brockmeier, Austin J; Principe, Jose C
2014-01-01
Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches used for processing space and time. Nonetheless, we treat the processing stages as a neural network and apply backpropagation to jointly optimize the parameters. This approach outperforms previous results on the BCI Competition II - dataset IV; additionally, it outperforms the common spatial patterns (CSP) algorithm on the BCI Competition III dataset IV. In addition, the optimized parameters in the architecture are still interpretable. PMID:25570621
Schiex, T.; Gaspin, C.; Regin, J.C.; Verfaillie, G.
1996-12-31
Arc consistency filtering is widely used in the framework of binary constraint satisfaction problems: with a low complexity, inconsistency may be detected and domains are filtered. In this paper, we show that when detecting inconsistency is the objective, a systematic domain filtering is useless and a lazy approach is more adequate. Whereas usual arc consistency algorithms produce the maximum arc consistent sub-domain, when it exists, we propose a method, called LAC{tau}, which only looks for any arc consistent sub-domain. The algorithm is then extended to provide the additional service of locating one variable with a minimum domain cardinality in the maximum arc consistent sub-domain, without necessarily computing all domain sizes. Finally, we compare traditional AC enforcing and lazy AC enforcing using several benchmark problems, both randomly generated CSP and real life problems.
Do new wipe materials outperform traditional lead dust cleaning methods?
Lewis, Roger D; Ong, Kee Hean; Emo, Brett; Kennedy, Jason; Brown, Christopher A; Condoor, Sridhar; Thummalakunta, Laxmi
2012-01-01
traditional methods (vacuuming and wet wiping) was greater and more consistent compared to the new methods (electrostatic dry cloth and wet Swiffer mop). Vacuuming and wet wiping achieved lead reductions of 92% ± 4% and 91%, ± 4%, respectively, while the electrostatic dry cloth and wet Swiffer mops achieved lead reductions of only 89 ± 8% and 81 ± 17%, respectively. PMID:22746281
Hargrove, Levi J; Lock, Blair A; Simon, Ann M
2013-01-01
Pattern recognition myoelectric control shows great promise as an alternative to conventional amplitude based control to control multiple degree of freedom prosthetic limbs. Many studies have reported pattern recognition classification error performances of less than 10% during offline tests; however, it remains unclear how this translates to real-time control performance. In this contribution, we compare the real-time control performances between pattern recognition and direct myoelectric control (a popular form of conventional amplitude control) for participants who had received targeted muscle reinnervation. The real-time performance was evaluated during three tasks; 1) a box and blocks task, 2) a clothespin relocation task, and 3) a block stacking task. Our results found that pattern recognition significantly outperformed direct control for all three performance tasks. Furthermore, it was found that pattern recognition was configured much quicker. The classification error of the pattern recognition systems used by the patients was found to be 16% ±(1.6%) suggesting that systems with this error rate may still provide excellent control. Finally, patients qualitatively preferred using pattern recognition control and reported the resulting control to be smoother and more consistent. PMID:24110008
Landry, Brian R. Subotnik, Joseph E.
2015-03-14
We evaluate the accuracy of Tully’s surface hopping algorithm for the spin-boson model in the limit of small to moderate reorganization energy. We calculate transition rates between diabatic surfaces in the exciton basis and compare against exact results from the hierarchical equations of motion; we also compare against approximate rates from the secular Redfield equation and Ehrenfest dynamics. We show that decoherence-corrected surface hopping performs very well in this regime, agreeing with secular Redfield theory for very weak system-bath coupling and outperforming secular Redfield theory for moderate system-bath coupling. Surface hopping can also be extended beyond the Markovian limits of standard Redfield theory. Given previous work [B. R. Landry and J. E. Subotnik, J. Chem. Phys. 137, 22A513 (2012)] that establishes the accuracy of decoherence-corrected surface-hopping in the Marcus regime, this work suggests that surface hopping may well have a very wide range of applicability.
A novel iris segmentation algorithm based on small eigenvalue analysis
NASA Astrophysics Data System (ADS)
Harish, B. S.; Aruna Kumar, S. V.; Guru, D. S.; Ngo, Minh Ngoc
2015-12-01
In this paper, a simple and robust algorithm is proposed for iris segmentation. The proposed method consists of two steps. In first step, iris and pupil is segmented using Robust Spatial Kernel FCM (RSKFCM) algorithm. RSKFCM is based on traditional Fuzzy-c-Means (FCM) algorithm, which incorporates spatial information and uses kernel metric as distance measure. In second step, small eigenvalue transformation is applied to localize iris boundary. The transformation is based on statistical and geometrical properties of the small eigenvalue of the covariance matrix of a set of edge pixels. Extensive experimentations are carried out on standard benchmark iris dataset (viz. CASIA-IrisV4 and UBIRIS.v2). We compared our proposed method with existing iris segmentation methods. Our proposed method has the least time complexity of O(n(i+p)) . The result of the experiments emphasizes that the proposed algorithm outperforms the existing iris segmentation methods.
Scalable Nearest Neighbor Algorithms for High Dimensional Data.
Muja, Marius; Lowe, David G
2014-11-01
For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching. PMID:26353063
Xia, Xuhua
2016-09-01
While pairwise sequence alignment (PSA) by dynamic programming is guaranteed to generate one of the optimal alignments, multiple sequence alignment (MSA) of highly divergent sequences often results in poorly aligned sequences, plaguing all subsequent phylogenetic analysis. One way to avoid this problem is to use only PSA to reconstruct phylogenetic trees, which can only be done with distance-based methods. I compared the accuracy of this new computational approach (named PhyPA for phylogenetics by pairwise alignment) against the maximum likelihood method using MSA (the ML+MSA approach), based on nucleotide, amino acid and codon sequences simulated with different topologies and tree lengths. I present a surprising discovery that the fast PhyPA method consistently outperforms the slow ML+MSA approach for highly diverged sequences even when all optimization options were turned on for the ML+MSA approach. Only when sequences are not highly diverged (i.e., when a reliable MSA can be obtained) does the ML+MSA approach outperforms PhyPA. The true topologies are always recovered by ML with the true alignment from the simulation. However, with MSA derived from alignment programs such as MAFFT or MUSCLE, the recovered topology consistently has higher likelihood than that for the true topology. Thus, the failure to recover the true topology by the ML+MSA is not because of insufficient search of tree space, but by the distortion of phylogenetic signal by MSA methods. I have implemented in DAMBE PhyPA and two approaches making use of multi-gene data sets to derive phylogenetic support for subtrees equivalent to resampling techniques such as bootstrapping and jackknifing. PMID:27377322
NASA Astrophysics Data System (ADS)
Gandomi, A. H.; Yang, X.-S.; Talatahari, S.; Alavi, A. H.
2013-01-01
A recently developed metaheuristic optimization algorithm, firefly algorithm (FA), mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies. In the present study, we will introduce chaos into FA so as to increase its global search mobility for robust global optimization. Detailed studies are carried out on benchmark problems with different chaotic maps. Here, 12 different chaotic maps are utilized to tune the attractive movement of the fireflies in the algorithm. The results show that some chaotic FAs can clearly outperform the standard FA.
NASA Astrophysics Data System (ADS)
Goswami, D.; Chakraborty, S.
2014-11-01
Laser machining is a promising non-contact process for effective machining of difficult-to-process advanced engineering materials. Increasing interest in the use of lasers for various machining operations can be attributed to its several unique advantages, like high productivity, non-contact processing, elimination of finishing operations, adaptability to automation, reduced processing cost, improved product quality, greater material utilization, minimum heat-affected zone and green manufacturing. To achieve the best desired machining performance and high quality characteristics of the machined components, it is extremely important to determine the optimal values of the laser machining process parameters. In this paper, fireworks algorithm and cuckoo search (CS) algorithm are applied for single as well as multi-response optimization of two laser machining processes. It is observed that although almost similar solutions are obtained for both these algorithms, CS algorithm outperforms fireworks algorithm with respect to average computation time, convergence rate and performance consistency.
Analyzing Enron Data: Bitmap Indexing Outperforms MySQL Queries bySeveral Orders of Magnitude
Stockinger, Kurt; Rotem, Doron; Shoshani, Arie; Wu, Kesheng
2006-01-28
FastBit is an efficient, compressed bitmap indexing technology that was developed in our group. In this report we evaluate the performance of MySQL and FastBit for analyzing the email traffic of the Enron dataset. The first finding shows that materializing the join results of several tables significantly improves the query performance. The second finding shows that FastBit outperforms MySQL by several orders of magnitude.
The ontogeny of human point following in dogs: When younger dogs outperform older.
Zaine, Isabela; Domeniconi, Camila; Wynne, Clive D L
2015-10-01
We investigated puppies' responsiveness to hand points differing in salience. Experiment 1 compared performance of younger (8 weeks old) and older (12 weeks) shelter pups in following pointing gestures. We hypothesized that older puppies would show better performance. Both groups followed the easy and moderate but not the difficult pointing cues. Surprisingly, the younger pups outperformed the older ones in following the moderate and difficult points. Investigation of subjects' backgrounds revealed that significantly more younger pups had experience living in human homes than did the older pups. Thus, we conducted a second experiment to isolate the variable experience. We collected additional data from older pet pups living in human homes on the same three point types and compared their performance with the shelter pups from Experiment 1. The pups living in homes accurately followed all three pointing cues. When comparing both experienced groups, the older pet pups outperformed the younger shelter ones, as predicted. When comparing the two same-age groups differing in background experience, the pups living in homes outperformed the shelter pups. A significant correlation between experience with humans and success in following less salient cues was found. The importance of ontogenetic learning in puppies' responsiveness to certain human social cues is discussed. PMID:26192336
Advanced GF(3^{2}) nonbinary LDPC coded modulation with non-uniform 9-QAM outperforming star 8-QAM.
Liu, Tao; Lin, Changyu; Djordjevic, Ivan B
2016-06-27
In this paper, we first describe a 9-symbol non-uniform signaling scheme based on Huffman code, in which different symbols are transmitted with different probabilities. By using the Huffman procedure, prefix code is designed to approach the optimal performance. Then, we introduce an algorithm to determine the optimal signal constellation sets for our proposed non-uniform scheme with the criterion of maximizing constellation figure of merit (CFM). The proposed nonuniform polarization multiplexed signaling 9-QAM scheme has the same spectral efficiency as the conventional 8-QAM. Additionally, we propose a specially designed GF(3^{2}) nonbinary quasi-cyclic LDPC code for the coded modulation system based on the 9-QAM non-uniform scheme. Further, we study the efficiency of our proposed non-uniform 9-QAM, combined with nonbinary LDPC coding, and demonstrate by Monte Carlo simulation that the proposed GF(2^{3}) nonbinary LDPC coded 9-QAM scheme outperforms nonbinary LDPC coded uniform 8-QAM by at least 0.8dB. PMID:27410549
Improved satellite image compression and reconstruction via genetic algorithms
NASA Astrophysics Data System (ADS)
Babb, Brendan; Moore, Frank; Peterson, Michael; Lamont, Gary
2008-10-01
A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.
Trait responses of invasive aquatic macrophyte congeners: colonizing diploid outperforms polyploid
Grewell, Brenda J.; Skaer Thomason, Meghan J.; Futrell, Caryn J.; Iannucci, Maria; Drenovsky, Rebecca E.
2016-01-01
Understanding traits underlying colonization and niche breadth of invasive plants is key to developing sustainable management solutions to curtail invasions at the establishment phase, when efforts are often most effective. The aim of this study was to evaluate how two invasive congeners differing in ploidy respond to high and lowresource availability following establishment from asexual fragments. Because polyploids are expected to have wider niche breadths than diploid ancestors, we predicted that a decaploid species would have superior ability to maximize resource uptake and use, and outperform a diploid congener when colonizing environments with contrasting light and nutrient availability. A mesocosm experiment was designed to test the main and interactive effects of ploidy (diploid and decaploid) and soil nutrient availability (low and high) nested within light environments (shade and sun) of two invasive aquatic plant congeners. Counter to our predictions, the diploid congener outperformed the decaploid in the early stage of growth. Although growth was similar and low in the cytotypes at low nutrient availability, the diploid species had much higher growth rate and biomass accumulation than the polyploid with nutrient enrichment, irrespective of light environment. Our results also revealed extreme differences in time to anthesis between the cytotypes. The rapid growth and earlier flowering of the diploid congener relative to the decaploid congener represent alternate strategies for establishment and success. PMID:26921139
Redersdorff, Sandrine; Martinot, Delphine
2009-06-01
The present study examines the effects of group status on self-esteem when individuals are outperformed by an in-group target (Experiments 1 and 2) or an out-group (Experiment 2). The main aim was to examine different self-protective mechanisms when the current standing of the in-group vis-à-vis another group is either unfavourable (low status) or favourable (high status). Experiment 1 showed that when outperformed by an in-group target, the members of a low status group reported higher self-esteem than members of a high status group. Moreover, this effect was mediated by group identification. Experiment 2 replicated the previous results and gave rise to similar effects on investment in the group. The perceived relevance of the comparison group appeared to protect the self-esteem of high status group members. This research demonstrates the mediating role of self-protection mechanisms such as group identification and the perceived relevance of a comparison group. PMID:18922208
Trait responses of invasive aquatic macrophyte congeners: colonizing diploid outperforms polyploid.
Grewell, Brenda J; Skaer Thomason, Meghan J; Futrell, Caryn J; Iannucci, Maria; Drenovsky, Rebecca E
2016-01-01
Understanding traits underlying colonization and niche breadth of invasive plants is key to developing sustainable management solutions to curtail invasions at the establishment phase, when efforts are often most effective. The aim of this study was to evaluate how two invasive congeners differing in ploidy respond to high and lowresource availability following establishment from asexual fragments. Because polyploids are expected to have wider niche breadths than diploid ancestors, we predicted that a decaploid species would have superior ability to maximize resource uptake and use, and outperform a diploid congener when colonizing environments with contrasting light and nutrient availability. A mesocosm experiment was designed to test the main and interactive effects of ploidy (diploid and decaploid) and soil nutrient availability (low and high) nested within light environments (shade and sun) of two invasive aquatic plant congeners. Counter to our predictions, the diploid congener outperformed the decaploid in the early stage of growth. Although growth was similar and low in the cytotypes at low nutrient availability, the diploid species had much higher growth rate and biomass accumulation than the polyploid with nutrient enrichment, irrespective of light environment. Our results also revealed extreme differences in time to anthesis between the cytotypes. The rapid growth and earlier flowering of the diploid congener relative to the decaploid congener represent alternate strategies for establishment and success. PMID:26921139
NASA Astrophysics Data System (ADS)
Joung, JinWook; Smyth, Andrew W.; Chung, Lan
2010-06-01
The active interaction control (AIC) system consisting of a primary structure, an auxiliary structure and an interaction element was proposed to protect the primary structure against earthquakes and winds. The objective of the AIC system in reducing the responses of the primary structure is fulfilled by activating or deactivating the switching between the engagement and the disengagement of the primary and auxiliary structures through the interaction element. The status of the interaction element is controlled by switching control algorithms. The previously developed switching control algorithms require an excessive amount of switching, which is inefficient. In this paper, the excessive amount of switching is restricted by imposing an appropriately designed switching boundary region, where switching is prohibited, on pre-designed engagement-disengagement conditions. Two different approaches are used in designing the newly proposed AID-off and AID-off2 algorithms. The AID-off2 algorithm is designed to affect deactivated switching regions explicitly, unlike the AID-off algorithm, which follows the same procedure of designing the engagement-disengagement conditions of the previously developed algorithms, by using the current status of the AIC system. Both algorithms are shown to be effective in reducing the amount of switching times triggered from the previously developed AID algorithm under an appropriately selected control sampling period for different earthquakes, but the AID-off2 algorithm outperforms the AID-off algorithm in reducing the number of switching times.
Split Bregman's algorithm for three-dimensional mesh segmentation
NASA Astrophysics Data System (ADS)
Habiba, Nabi; Ali, Douik
2016-05-01
Variational methods have attracted a lot of attention in the literature, especially for image and mesh segmentation. The methods aim at minimizing the energy to optimize both edge and region detections. We propose a spectral mesh decomposition algorithm to obtain disjoint but meaningful regions of an input mesh. The related optimization problem is nonconvex, and it is very difficult to find a good approximation or global optimum, which represents a challenge in computer vision. We propose an alternating split Bregman algorithm for mesh segmentation, where we extended the image-dedicated model to a three-dimensional (3-D) mesh one. By applying our scheme to 3-D mesh segmentation, we obtain fast solvers that can outperform various conventional ones, such as graph-cut and primal dual methods. A consistent evaluation of the proposed method on various public domain 3-D databases for different metrics is elaborated, and a comparison with the state-of-the-art is performed.
Low-Friction Minilaparoscopy Outperforms Regular 5-mm and 3-mm Instruments for Precise Tasks
Firme, Wood A.; Lima, Diego L.; de Paula Lopes, Vladmir Goldstein; Montandon, Isabelle D.; Filho, Flavio Santos; Shadduck, Phillip P.
2015-01-01
Background and Objectives: Therapeutic laparoscopy was incorporated into surgical practice more than 25 y ago. Several modifications have since been developed to further minimize surgical trauma and improve results. Minilaparoscopy, performed with 2- to 3-mm instruments was introduced in the mid 1990s but failed to attain mainstream use, mostly because of the limitations of the early devices. Buoyed by a renewed interest, new generations of mini instruments are being developed with improved functionality and durability. This study is an objective evaluation of a new set of mini instruments with a novel low-friction design. Method: Twenty-two medical students and 22 surgical residents served as study participants. Three designs of laparoscopic instruments were evaluated: conventional 5 mm, traditional 3 mm, and low-friction 3 mm. The instruments were evaluated with a standard surgical simulator, emulating 4 exercises of various complexities, testing grasping, precise 2-handed movements, and suturing. The metric measured was time to task completion, with 5 replicates for every combination of instrument–exercise–participant. Results: For all 4 tasks, the instrument design that performed the best was the same in both the medical student and surgical resident groups. For the gross-grasping task, the 5-mm conventional instruments performed best, followed by the low-friction mini instruments. For the 3 more complex and precise tasks, the low-friction mini instruments outperformed both of the other instrument designs. Conclusion: In standard surgical simulator exercises, low-friction minilaparoscopic instruments outperformed both conventional 3- and 5-mm laparoscopic instruments for precise tasks. PMID:26390530
Success on Algorithmic and LOCS vs. Conceptual Chemistry Exam Questions
NASA Astrophysics Data System (ADS)
Zoller, Uri; Lubezky, Aviva; Nakhleh, Mary B.; Tessier, Barbara; Dori, Yehudit J.
1995-11-01
The performance of freshman science, engineering, and in-service teacher students in three Israeli and American universities on algorithmic, lower-order cognitive skills (LOCS), and conceptual chemistry exam questions was investigated. The driving force for the study was an interest in moving chemistry instruction from an algorithm-oriented factual recall approach dominated by LOCS to a decision-making, problem-solving, and critical thinking approach dominated by higher-order cognitive skills (HOCS). Students' responses to the specially designed algorithmic, LOCS, and conceptual exam questions were scored and analyzed for correlations and for differences between the means within and across universities by the question's category. The main findings were: (1) students in all three universities performed consistently on each of the three categories in the order of algorithmic > LOCS > conceptual questions, (2) success on algorithmic does not imply success on conceptual, or even on LOCS questions, and (3) students taught in small classes outperformed by far those in large lecture sessions in all three categories. The implied paradigm shift from an algorithmic/LOCS to a conceptual/HOCS orientation should be moved from a research-based theoretical domain to actual implementation in order for a meaningful improvement of chemistry teaching to occur.
Algorithms and Algorithmic Languages.
ERIC Educational Resources Information Center
Veselov, V. M.; Koprov, V. M.
This paper is intended as an introduction to a number of problems connected with the description of algorithms and algorithmic languages, particularly the syntaxes and semantics of algorithmic languages. The terms "letter, word, alphabet" are defined and described. The concept of the algorithm is defined and the relation between the algorithm and…
The Consistent Vehicle Routing Problem
Groer, Christopher S; Golden, Bruce; Edward, Wasil
2009-01-01
In the small package shipping industry (as in other industries), companies try to differentiate themselves by providing high levels of customer service. This can be accomplished in several ways, including online tracking of packages, ensuring on-time delivery, and offering residential pickups. Some companies want their drivers to develop relationships with customers on a route and have the same drivers visit the same customers at roughly the same time on each day that the customers need service. These service requirements, together with traditional constraints on vehicle capacity and route length, define a variant of the classical capacitated vehicle routing problem, which we call the consistent VRP (ConVRP). In this paper, we formulate the problem as a mixed-integer program and develop an algorithm to solve the ConVRP that is based on the record-to-record travel algorithm. We compare the performance of our algorithm to the optimal mixed-integer program solutions for a set of small problems and then apply our algorithm to five simulated data sets with 1,000 customers and a real-world data set with more than 3,700 customers. We provide a technique for generating ConVRP benchmark problems from vehicle routing problem instances given in the literature and provide our solutions to these instances. The solutions produced by our algorithm on all problems do a very good job of meeting customer service objectives with routes that have a low total travel time.
Tao, Michael W; Su, Jong-Chyi; Wang, Ting-Chun; Malik, Jitendra; Ramamoorthi, Ravi
2016-06-01
Light-field cameras have now become available in both consumer and industrial applications, and recent papers have demonstrated practical algorithms for depth recovery from a passive single-shot capture. However, current light-field depth estimation methods are designed for Lambertian objects and fail or degrade for glossy or specular surfaces. The standard Lambertian photoconsistency measure considers the variance of different views, effectively enforcing point-consistency, i.e., that all views map to the same point in RGB space. This variance or point-consistency condition is a poor metric for glossy surfaces. In this paper, we present a novel theory of the relationship between light-field data and reflectance from the dichromatic model. We present a physically-based and practical method to estimate the light source color and separate specularity. We present a new photo consistency metric, line-consistency, which represents how viewpoint changes affect specular points. We then show how the new metric can be used in combination with the standard Lambertian variance or point-consistency measure to give us results that are robust against scenes with glossy surfaces. With our analysis, we can also robustly estimate multiple light source colors and remove the specular component from glossy objects. We show that our method outperforms current state-of-the-art specular removal and depth estimation algorithms in multiple real world scenarios using the consumer Lytro and Lytro Illum light field cameras. PMID:26372203
Multisensor data fusion algorithm development
Yocky, D.A.; Chadwick, M.D.; Goudy, S.P.; Johnson, D.K.
1995-12-01
This report presents a two-year LDRD research effort into multisensor data fusion. We approached the problem by addressing the available types of data, preprocessing that data, and developing fusion algorithms using that data. The report reflects these three distinct areas. First, the possible data sets for fusion are identified. Second, automated registration techniques for imagery data are analyzed. Third, two fusion techniques are presented. The first fusion algorithm is based on the two-dimensional discrete wavelet transform. Using test images, the wavelet algorithm is compared against intensity modulation and intensity-hue-saturation image fusion algorithms that are available in commercial software. The wavelet approach outperforms the other two fusion techniques by preserving spectral/spatial information more precisely. The wavelet fusion algorithm was also applied to Landsat Thematic Mapper and SPOT panchromatic imagery data. The second algorithm is based on a linear-regression technique. We analyzed the technique using the same Landsat and SPOT data.
NASA Astrophysics Data System (ADS)
Bhattacharyya, Jayanta; Bellucci, Joseph J.; Weitzhandler, Isaac; McDaniel, Jonathan R.; Spasojevic, Ivan; Li, Xinghai; Lin, Chao-Chieh; Chi, Jen-Tsan Ashley; Chilkoti, Ashutosh
2015-08-01
Packaging clinically relevant hydrophobic drugs into a self-assembled nanoparticle can improve their aqueous solubility, plasma half-life, tumour-specific uptake and therapeutic potential. To this end, here we conjugated paclitaxel (PTX) to recombinant chimeric polypeptides (CPs) that spontaneously self-assemble into ~60 nm near-monodisperse nanoparticles that increased the systemic exposure of PTX by sevenfold compared with free drug and twofold compared with the Food and Drug Administration-approved taxane nanoformulation (Abraxane). The tumour uptake of the CP-PTX nanoparticle was fivefold greater than free drug and twofold greater than Abraxane. In a murine cancer model of human triple-negative breast cancer and prostate cancer, CP-PTX induced near-complete tumour regression after a single dose in both tumour models, whereas at the same dose, no mice treated with Abraxane survived for >80 days (breast) and 60 days (prostate), respectively. These results show that a molecularly engineered nanoparticle with precisely engineered design features outperforms Abraxane, the current gold standard for PTX delivery.
Wolf, Max; Krause, Jens; Carney, Patricia A.; Bogart, Andy; Kurvers, Ralf H. J. M.
2015-01-01
While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules (“majority”, “quorum”, and “weighted quorum”) when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence. PMID:26267331
Murciano Martínez, Patricia; Kabel, Mirjam A; Gruppen, Harry
2016-11-20
Enzyme hydrolysed (hemi-)celluloses from oil palm empty fruit bunches (EFBs) are a source for production of bio-fuels or chemicals. In this study, after either peracetic acid delignification or alkaline extraction, EFB hemicellulose structures were described, aided by xylanase hydrolysis. Delignification of EFB facilitated the hydrolysis of EFB-xylan by a pure endo-β-1,4-xylanase. Up to 91% (w/w) of the non-extracted xylan in the delignified EFB was hydrolysed compared to less than 4% (w/w) of that in untreated EFB. Alkaline extraction of EFB, without prior delignification, yielded only 50% of the xylan. The xylan obtained was hydrolysed only for 40% by the endo-xylanase used. Hence, delignification alone outperformed alkaline extraction as pretreatment for enzymatic fingerprinting of EFB xylans. From the analysis of the oligosaccharide-fingerprint of the delignified endo-xylanase hydrolysed EFB xylan, the structure was proposed as acetylated 4-O-methylglucuronoarabinoxylan. PMID:27561506
Bhattacharyya, Jayanta; Bellucci, Joseph J.; Weitzhandler, Isaac; McDaniel, Jonathan R.; Spasojevic, Ivan; Li, Xinghai; Lin, Chao-Chieh; Chi, Jen-Tsan Ashley; Chilkoti, Ashutosh
2015-01-01
Packaging clinically relevant hydrophobic drugs into a self-assembled nanoparticle can improve their aqueous solubility, plasma half-life, tumor specific uptake and therapeutic potential. To this end, here we conjugated paclitaxel (PTX) to recombinant chimeric polypeptides (CPs) that spontaneously self-assemble into ~60-nm diameter near-monodisperse nanoparticles that increased the systemic exposure of PTX by 7-fold compared to free drug and 2-fold compared to the FDA approved taxane nanoformulation (Abraxane®). The tumor uptake of the CP-PTX nanoparticle was 5-fold greater than free drug and 2-fold greater than Abraxane. In a murine cancer model of human triple negative breast cancer and prostate cancer, CP-PTX induced near complete tumor regression after a single dose in both tumor models, whereas at the same dose, no mice treated with Abraxane survived for more than 80 days (breast) and 60 days (prostate) respectively. These results show that a molecularly engineered nanoparticle with precisely engineered design features outperforms Abraxane, the current gold standard for paclitaxel delivery. PMID:26239362
Gender differences in primary and secondary education: Are girls really outperforming boys?
NASA Astrophysics Data System (ADS)
Driessen, Geert; van Langen, Annemarie
2013-06-01
A moral panic has broken out in several countries after recent studies showed that girls were outperforming boys in education. Commissioned by the Dutch Ministry of Education, the present study examines the position of boys and girls in Dutch primary education and in the first phase of secondary education over the past ten to fifteen years. On the basis of several national and international large-scale databases, the authors examined whether one can indeed speak of a gender gap, at the expense of boys. Three domains were investigated, namely cognitive competencies, non-cognitive competencies, and school career features. The results as expressed in effect sizes show that there are hardly any differences with regard to language and mathematics proficiency. However, the position of boys in terms of educational level and attitudes and behaviour is much more unfavourable than that of girls. Girls, on the other hand, score more unfavourably with regard to sector and subject choice. While the present situation in general does not differ very much from that of a decade ago, it is difficult to predict in what way the balances might shift in the years to come.
Plants adapted to warmer climate do not outperform regional plants during a natural heat wave.
Bucharova, Anna; Durka, Walter; Hermann, Julia-Maria; Hölzel, Norbert; Michalski, Stefan; Kollmann, Johannes; Bossdorf, Oliver
2016-06-01
With ongoing climate change, many plant species may not be able to adapt rapidly enough, and some conservation experts are therefore considering to translocate warm-adapted ecotypes to mitigate effects of climate warming. Although this strategy, called assisted migration, is intuitively plausible, most of the support comes from models, whereas experimental evidence is so far scarce. Here we present data on multiple ecotypes of six grassland species, which we grew in four common gardens in Germany during a natural heat wave, with temperatures 1.4-2.0°C higher than the long-term means. In each garden we compared the performance of regional ecotypes with plants from a locality with long-term summer temperatures similar to what the plants experienced during the summer heat wave. We found no difference in performance between regional and warm-adapted plants in four of the six species. In two species, regional ecotypes even outperformed warm-adapted plants, despite elevated temperatures, which suggests that translocating warm-adapted ecotypes may not only lack the desired effect of increased performance but may even have negative consequences. Even if adaptation to climate plays a role, other factors involved in local adaptation, such as biotic interactions, may override it. Based on our results, we cannot advocate assisted migration as a universal tool to enhance the performance of local plant populations and communities during climate change. PMID:27516871
Jan, Edward; Hendricks, Jeffrey L; Husaini, Vincent; Richardson-Burns, Sarah M; Sereno, Andrew; Martin, David C; Kotov, Nicholas A
2009-12-01
The safety, function, and longevity of implantable neuroprosthetic and cardiostimulating electrodes depend heavily on the electrical properties of the electrode-tissue interface, which in many cases requires substantial improvement. While different variations of carbon nanotube materials have been shown to be suitable for neural excitation, it is critical to evaluate them versus other materials used for bioelectrical interfacing, which have not been done in any study performed so far despite strong interest to this area. In this study, we carried out this evaluation and found that composite multiwalled carbon nanotube-polyelectrolyte (MWNT-PE) multilayer electrodes substantially outperform in one way or the other state-of-the-art neural interface materials available today, namely activated electrochemically deposited iridium oxide (IrOx) and poly(3,4-ethylenedioxythiophene) (PEDOT). Our findings provide the concrete experimental proof to the much discussed possibility that carbon nanotube composites can serve as excellent new material for neural interfacing with a strong possibility to lead to a new generation of implantable electrodes. PMID:19785391
Wolf, Max; Krause, Jens; Carney, Patricia A; Bogart, Andy; Kurvers, Ralf H J M
2015-01-01
While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules ("majority", "quorum", and "weighted quorum") when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence. PMID:26267331
A Mozart is not a Pavarotti: singers outperform instrumentalists on foreign accent imitation
Christiner, Markus; Reiterer, Susanne Maria
2015-01-01
Recent findings have shown that people with higher musical aptitude were also better in oral language imitation tasks. However, whether singing capacity and instrument playing contribute differently to the imitation of speech has been ignored so far. Research has just recently started to understand that instrumentalists develop quite distinct skills when compared to vocalists. In the same vein the role of the vocal motor system in language acquisition processes has poorly been investigated as most investigations (neurobiological and behavioral) favor to examine speech perception. We set out to test whether the vocal motor system can influence an ability to learn, produce and perceive new languages by contrasting instrumentalists and vocalists. Therefore, we investigated 96 participants, 27 instrumentalists, 33 vocalists and 36 non-musicians/non-singers. They were tested for their abilities to imitate foreign speech: unknown language (Hindi), second language (English) and their musical aptitude. Results revealed that both instrumentalists and vocalists have a higher ability to imitate unintelligible speech and foreign accents than non-musicians/non-singers. Within the musician group, vocalists outperformed instrumentalists significantly. Conclusion: First, adaptive plasticity for speech imitation is not reliant on audition alone but also on vocal-motor induced processes. Second, vocal flexibility of singers goes together with higher speech imitation aptitude. Third, vocal motor training, as of singers, may speed up foreign language acquisition processes. PMID:26379537
Evaluating super resolution algorithms
NASA Astrophysics Data System (ADS)
Kim, Youn Jin; Park, Jong Hyun; Shin, Gun Shik; Lee, Hyun-Seung; Kim, Dong-Hyun; Park, Se Hyeok; Kim, Jaehyun
2011-01-01
This study intends to establish a sound testing and evaluation methodology based upon the human visual characteristics for appreciating the image restoration accuracy; in addition to comparing the subjective results with predictions by some objective evaluation methods. In total, six different super resolution (SR) algorithms - such as iterative back-projection (IBP), robust SR, maximum a posteriori (MAP), projections onto convex sets (POCS), a non-uniform interpolation, and frequency domain approach - were selected. The performance comparison between the SR algorithms in terms of their restoration accuracy was carried out through both subjectively and objectively. The former methodology relies upon the paired comparison method that involves the simultaneous scaling of two stimuli with respect to image restoration accuracy. For the latter, both conventional image quality metrics and color difference methods are implemented. Consequently, POCS and a non-uniform interpolation outperformed the others for an ideal situation, while restoration based methods appear more accurate to the HR image in a real world case where any prior information about the blur kernel is remained unknown. However, the noise-added-image could not be restored successfully by any of those methods. The latest International Commission on Illumination (CIE) standard color difference equation CIEDE2000 was found to predict the subjective results accurately and outperformed conventional methods for evaluating the restoration accuracy of those SR algorithms.
Győrffy, Balázs; Karn, Thomas; Sztupinszki, Zsófia; Weltz, Boglárka; Müller, Volkmar; Pusztai, Lajos
2015-01-01
The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n = 3,534, HR = 3.68, p = 1.67 E−56). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n = 427, HR = 3.08, p = 0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR = 3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q=Re_training. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers. PMID:25274406
Do Cultivated Varieties of Native Plants Have the Ability to Outperform Their Wild Relatives?
Schröder, Roland; Prasse, Rüdiger
2013-01-01
Vast amounts of cultivars of native plants are annually introduced into the semi-natural range of their wild relatives for re-vegetation and restoration. As cultivars are often selected towards enhanced biomass production and might transfer these traits into wild relatives by hybridization, it is suggested that cultivars and the wild × cultivar hybrids are competitively superior to their wild relatives. The release of such varieties may therefore result in unintended changes in native vegetation. In this study we examined for two species frequently used in re-vegetation (Plantago lanceolata and Lotus corniculatus) whether cultivars and artificially generated intra-specific wild × cultivar hybrids may produce a higher vegetative and generative biomass than their wilds. For that purpose a competition experiment was conducted for two growing seasons in a common garden. Every plant type was growing (a.) alone, (b.) in pairwise combination with a similar plant type and (c.) in pairwise interaction with a different plant type. When competing with wilds cultivars of both species showed larger biomass production than their wilds in the first year only and hybrids showed larger biomass production than their wild relatives in both study years. As biomass production is an important factor determining fitness and competitive ability, we conclude that cultivars and hybrids are competitively superior their wild relatives. However, cultivars of both species experienced large fitness reductions (nearly complete mortality in L. corniculatus) due to local climatic conditions. We conclude that cultivars are good competitors only as long as they are not subjected to stressful environmental factors. As hybrids seemed to inherit both the ability to cope with the local climatic conditions from their wild parents as well as the enhanced competitive strength from their cultivars, we regard them as strong competitors and assume that they are able to outperform their wilds at least over
NASA Astrophysics Data System (ADS)
Zhao, Zhanlue
This dissertation consists of two parts. The first part deals with the performance appraisal of estimation algorithms. The second part focuses on the application of estimation algorithms to target tracking. Performance appraisal is crucial for understanding, developing and comparing various estimation algorithms. In particular, with the evolvement of estimation theory and the increase of problem complexity, performance appraisal is getting more and more challenging for engineers to make comprehensive conclusions. However, the existing theoretical results are inadequate for practical reference. The first part of this dissertation is dedicated to performance measures which include local performance measures, global performance measures and model distortion measure. The second part focuses on application of the recursive best linear unbiased estimation (BLUE) or linear minimum mean square error (LIB-M-ISE) estimation to nonlinear measurement problem in target tracking. Kalman filter has been the dominant basis for dynamic state filtering for several decades. Beyond Kalman filter, a more fundamental basis for the recursive best linear unbiased filtering has been thoroughly investigated in a series of papers by my advisor Dr. X. Rong Li. Based on the so-called quasi-recursive best linear unbiased filtering technique, the constraints of the Kalman filter Linear-Gaussian assumptions can be relaxed such that a general linear filtering technique for nonlinear systems can be achieved. An approximate optimal BLUE filter is implemented for nonlinear measurements in target tracking which outperforms the existing method significantly in terms of accuracy, credibility and robustness.
ERIC Educational Resources Information Center
Southam-Gerow, Michael A.; Weisz, John R.; Chu, Brian C.; McLeod, Bryce D.; Gordis, Elana B.; Connor-Smith, Jennifer K.
2010-01-01
Objective: Most tests of cognitive behavioral therapy (CBT) for youth anxiety disorders have shown beneficial effects, but these have been efficacy trials with recruited youths treated by researcher-employed therapists. One previous (nonrandomized) trial in community clinics found that CBT did not outperform usual care (UC). The present study used…
ERIC Educational Resources Information Center
Agodini, Roberto; Harris, Barbara; Remillard, Janine; Thomas, Melissa
2013-01-01
This appendix provides the details that underlie the analyses reported in the evaluation brief, "After Two Years, Three Elementary Math Curricula Outperform a Fourth." The details are organized in six sections: Study Curricula and Design (Section A), Data Collection (Section B), Construction of the Analysis File (Section C), Curriculum Effects on…
Neighborhood inverse consistency preprocessing
Freuder, E.C.; Elfe, C.D.
1996-12-31
Constraint satisfaction consistency preprocessing methods are used to reduce search effort. Time and especially space costs limit the amount of preprocessing that will be cost effective. A new form of consistency preprocessing, neighborhood inverse consistency, can achieve more problem pruning than the usual arc consistency preprocessing in a cost effective manner. There are two basic ideas: (1) Common forms of consistency enforcement basically operate by identifying and remembering solutions to subproblems for which a consistent value cannot be found for some additional problem variable. The space required for this memory can quickly become prohibitive. Inverse consistency basically operates by removing values for variables that are not consistent with any solution to some subproblem involving additional variables. The space requirement is at worst linear. (2) Typically consistency preprocessing achieves some level of consistency uniformly throughout the problem. A subproblem solution will be tested against each additional variable that constrains any subproblem variable. Neighborhood consistency focuses attention on the subproblem formed by the variables that are all constrained by the value in question. By targeting highly relevant subproblems we hope to {open_quotes}skim the cream{close_quotes}, obtaining a high payoff for a limited cost.
Consistent model driven architecture
NASA Astrophysics Data System (ADS)
Niepostyn, Stanisław J.
2015-09-01
The goal of the MDA is to produce software systems from abstract models in a way where human interaction is restricted to a minimum. These abstract models are based on the UML language. However, the semantics of UML models is defined in a natural language. Subsequently the verification of consistency of these diagrams is needed in order to identify errors in requirements at the early stage of the development process. The verification of consistency is difficult due to a semi-formal nature of UML diagrams. We propose automatic verification of consistency of the series of UML diagrams originating from abstract models implemented with our consistency rules. This Consistent Model Driven Architecture approach enables us to generate automatically complete workflow applications from consistent and complete models developed from abstract models (e.g. Business Context Diagram). Therefore, our method can be used to check practicability (feasibility) of software architecture models.
Computations and algorithms in physical and biological problems
NASA Astrophysics Data System (ADS)
Qin, Yu
This dissertation presents the applications of state-of-the-art computation techniques and data analysis algorithms in three physical and biological problems: assembling DNA pieces, optimizing self-assembly yield, and identifying correlations from large multivariate datasets. In the first topic, in-depth analysis of using Sequencing by Hybridization (SBH) to reconstruct target DNA sequences shows that a modified reconstruction algorithm can overcome the theoretical boundary without the need for different types of biochemical assays and is robust to error. In the second topic, consistent with theoretical predictions, simulations using Graphics Processing Unit (GPU) demonstrate how controlling the short-ranged interactions between particles and controlling the concentrations optimize the self-assembly yield of a desired structure, and nonequilibrium behavior when optimizing concentrations is also unveiled by leveraging the computation capacity of GPUs. In the last topic, a methodology to incorporate existing categorization information into the search process to efficiently reconstruct the optimal true correlation matrix for multivariate datasets is introduced. Simulations on both synthetic and real financial datasets show that the algorithm is able to detect signals below the Random Matrix Theory (RMT) threshold. These three problems are representatives of using massive computation techniques and data analysis algorithms to tackle optimization problems, and outperform theoretical boundary when incorporating prior information into the computation.
Schwarz, Christopher G; Reid, Robert I; Gunter, Jeffrey L; Senjem, Matthew L; Przybelski, Scott A; Zuk, Samantha M; Whitwell, Jennifer L; Vemuri, Prashanthi; Josephs, Keith A; Kantarci, Kejal; Thompson, Paul M; Petersen, Ronald C; Jack, Clifford R
2014-07-01
Tract-Based Spatial Statistics (TBSS) is a popular software pipeline to coregister sets of diffusion tensor Fractional Anisotropy (FA) images for performing voxel-wise comparisons. It is primarily defined by its skeleton projection step intended to reduce effects of local misregistration. A white matter "skeleton" is computed by morphological thinning of the inter-subject mean FA, and then all voxels are projected to the nearest location on this skeleton. Here we investigate several enhancements to the TBSS pipeline based on recent advances in registration for other modalities, principally based on groupwise registration with the ANTS-SyN algorithm. We validate these enhancements using simulation experiments with synthetically-modified images. When used with these enhancements, we discover that TBSS's skeleton projection step actually reduces algorithm accuracy, as the improved registration leaves fewer errors to warrant correction, and the effects of this projection's compromises become stronger than those of its benefits. In our experiments, our proposed pipeline without skeleton projection is more sensitive for detecting true changes and has greater specificity in resisting false positives from misregistration. We also present comparative results of the proposed and traditional methods, both with and without the skeleton projection step, on three real-life datasets: two comparing differing populations of Alzheimer's disease patients to matched controls, and one comparing progressive supranuclear palsy patients to matched controls. The proposed pipeline produces more plausible results according to each disease's pathophysiology. PMID:24650605
Indexing Consistency and Quality.
ERIC Educational Resources Information Center
Zunde, Pranas; Dexter, Margaret E.
A measure of indexing consistency is developed based on the concept of 'fuzzy sets'. It assigns a higher consistency value if indexers agree on the more important terms than if they agree on less important terms. Measures of the quality of an indexer's work and exhaustivity of indexing are also proposed. Experimental data on indexing consistency…
Epipolar Consistency in Transmission Imaging.
Aichert, André; Berger, Martin; Wang, Jian; Maass, Nicole; Doerfler, Arnd; Hornegger, Joachim; Maier, Andreas K
2015-11-01
This paper presents the derivation of the Epipolar Consistency Conditions (ECC) between two X-ray images from the Beer-Lambert law of X-ray attenuation and the Epipolar Geometry of two pinhole cameras, using Grangeat's theorem. We motivate the use of Oriented Projective Geometry to express redundant line integrals in projection images and define a consistency metric, which can be used, for instance, to estimate patient motion directly from a set of X-ray images. We describe in detail the mathematical tools to implement an algorithm to compute the Epipolar Consistency Metric and investigate its properties with detailed random studies on both artificial and real FD-CT data. A set of six reference projections of the CT scan of a fish were used to evaluate accuracy and precision of compensating for random disturbances of the ground truth projection matrix using an optimization of the consistency metric. In addition, we use three X-ray images of a pumpkin to prove applicability to real data. We conclude, that the metric might have potential in applications related to the estimation of projection geometry. By expression of redundancy between two arbitrary projection views, we in fact support any device or acquisition trajectory which uses a cone-beam geometry. We discuss certain geometric situations, where the ECC provide the ability to correct 3D motion, without the need for 3D reconstruction. PMID:25915956
Dietz, Bart; van Knippenberg, Daan; Hirst, Giles; Restubog, Simon Lloyd D
2015-11-01
Performance-prove goal orientation affects performance because it drives people to try to outperform others. A proper understanding of the performance-motivating potential of performance-prove goal orientation requires, however, that we consider the question of whom people desire to outperform. In a multilevel analysis of this issue, we propose that the shared team identification of a team plays an important moderating role here, directing the performance-motivating influence of performance-prove goal orientation to either the team level or the individual level of performance. A multilevel study of salespeople nested in teams supports this proposition, showing that performance-prove goal orientation motivates team performance more with higher shared team identification, whereas performance-prove goal orientation motivates individual performance more with lower shared team identification. Establishing the robustness of these findings, a second study replicates them with individual and team performance in an educational context. PMID:26011723
Consistent interactions and involution
NASA Astrophysics Data System (ADS)
Kaparulin, D. S.; Lyakhovich, S. L.; Sharapov, A. A.
2013-01-01
Starting from the concept of involution of field equations, a universal method is proposed for constructing consistent interactions between the fields. The method equally well applies to the Lagrangian and non-Lagrangian equations and it is explicitly covariant. No auxiliary fields are introduced. The equations may have (or have no) gauge symmetry and/or second class constraints in Hamiltonian formalism, providing the theory admits a Hamiltonian description. In every case the method identifies all the consistent interactions.
Improved progressive TIN densification filtering algorithm for airborne LiDAR data in forested areas
NASA Astrophysics Data System (ADS)
Zhao, Xiaoqian; Guo, Qinghua; Su, Yanjun; Xue, Baolin
2016-07-01
Filtering of light detection and ranging (LiDAR) data into the ground and non-ground points is a fundamental step in processing raw airborne LiDAR data. This paper proposes an improved progressive triangulated irregular network (TIN) densification (IPTD) filtering algorithm that can cope with a variety of forested landscapes, particularly both topographically and environmentally complex regions. The IPTD filtering algorithm consists of three steps: (1) acquiring potential ground seed points using the morphological method; (2) obtaining accurate ground seed points; and (3) building a TIN-based model and iteratively densifying TIN. The IPTD filtering algorithm was tested in 15 forested sites with various terrains (i.e., elevation and slope) and vegetation conditions (i.e., canopy cover and tree height), and was compared with seven other commonly used filtering algorithms (including morphology-based, slope-based, and interpolation-based filtering algorithms). Results show that the IPTD achieves the highest filtering accuracy for nine of the 15 sites. In general, it outperforms the other filtering algorithms, yielding the lowest average total error of 3.15% and the highest average kappa coefficient of 89.53%.
3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response
NASA Astrophysics Data System (ADS)
Maiti, A.; Small, W.; Lewicki, J. P.; Weisgraber, T. H.; Duoss, E. B.; Chinn, S. C.; Pearson, M. A.; Spadaccini, C. M.; Maxwell, R. S.; Wilson, T. S.
2016-04-01
3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curves predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. This indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance.
3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response.
Maiti, A; Small, W; Lewicki, J P; Weisgraber, T H; Duoss, E B; Chinn, S C; Pearson, M A; Spadaccini, C M; Maxwell, R S; Wilson, T S
2016-01-01
3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curves predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. This indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter's improved long-term stability and mechanical performance. PMID:27117858
3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response
Maiti, A.; Small, W.; Lewicki, J.; Weisgraber, T. H.; Duoss, E. B.; Chinn, S. C.; Pearson, M. A.; Spadaccini, C. M.; Maxwell, R. S.; Wilson, T. S.
2016-04-27
3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curvesmore » predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. As a result, this indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance.« less
3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response
Maiti, A.; Small, W.; Lewicki, J. P.; Weisgraber, T. H.; Duoss, E. B.; Chinn, S. C.; Pearson, M. A.; Spadaccini, C. M.; Maxwell, R. S.; Wilson, T. S.
2016-01-01
3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curves predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. This indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance. PMID:27117858
Network Consistent Data Association.
Chakraborty, Anirban; Das, Abir; Roy-Chowdhury, Amit K
2016-09-01
Existing data association techniques mostly focus on matching pairs of data-point sets and then repeating this process along space-time to achieve long term correspondences. However, in many problems such as person re-identification, a set of data-points may be observed at multiple spatio-temporal locations and/or by multiple agents in a network and simply combining the local pairwise association results between sets of data-points often leads to inconsistencies over the global space-time horizons. In this paper, we propose a Novel Network Consistent Data Association (NCDA) framework formulated as an optimization problem that not only maintains consistency in association results across the network, but also improves the pairwise data association accuracies. The proposed NCDA can be solved as a binary integer program leading to a globally optimal solution and is capable of handling the challenging data-association scenario where the number of data-points varies across different sets of instances in the network. We also present an online implementation of NCDA method that can dynamically associate new observations to already observed data-points in an iterative fashion, while maintaining network consistency. We have tested both the batch and the online NCDA in two application areas-person re-identification and spatio-temporal cell tracking and observed consistent and highly accurate data association results in all the cases. PMID:26485472
CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes.
Evers, Bastiaan; Jastrzebski, Katarzyna; Heijmans, Jeroen P M; Grernrum, Wipawadee; Beijersbergen, Roderick L; Bernards, Rene
2016-06-01
High-throughput genetic screens have become essential tools for studying a wide variety of biological processes. Here we experimentally compare systems based on clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) or its transcriptionally repressive variant, CRISPR-interference (CRISPRi), with a traditional short hairpin RNA (shRNA)-based system for performing lethality screens. We find that the CRISPR technology performed best, with low noise, minimal off-target effects and consistent activity across reagents. PMID:27111720
Learning deterministic finite automata with a smart state labeling evolutionary algorithm.
Lucas, Simon M; Reynolds, T Jeff
2005-07-01
Learning a Deterministic Finite Automaton (DFA) from a training set of labeled strings is a hard task that has been much studied within the machine learning community. It is equivalent to learning a regular language by example and has applications in language modeling. In this paper, we describe a novel evolutionary method for learning DFA that evolves only the transition matrix and uses a simple deterministic procedure to optimally assign state labels. We compare its performance with the Evidence Driven State Merging (EDSM) algorithm, one of the most powerful known DFA learning algorithms. We present results on random DFA induction problems of varying target size and training set density. We also studythe effects of noisy training data on the evolutionary approach and on EDSM. On noise-free data, we find that our evolutionary method outperforms EDSM on small sparse data sets. In the case of noisy training data, we find that our evolutionary method consistently outperforms EDSM, as well as other significant methods submitted to two recent competitions. PMID:16013754
Complexity of the Quantum Adiabatic Algorithm
NASA Technical Reports Server (NTRS)
Hen, Itay
2013-01-01
The Quantum Adiabatic Algorithm (QAA) has been proposed as a mechanism for efficiently solving optimization problems on a quantum computer. Since adiabatic computation is analog in nature and does not require the design and use of quantum gates, it can be thought of as a simpler and perhaps more profound method for performing quantum computations that might also be easier to implement experimentally. While these features have generated substantial research in QAA, to date there is still a lack of solid evidence that the algorithm can outperform classical optimization algorithms.
Unfamiliar face matching: Pairs out-perform individuals and provide a route to training.
Dowsett, Andrew J; Burton, A Mike
2015-08-01
Matching unfamiliar faces is known to be difficult. Here, we ask whether performance can be improved by asking viewers to work in pairs, a manipulation known to increase accuracy for low-level visual discrimination tasks. Across four experiments we consistently find that face matching accuracy is higher for pairs of viewers than for individuals. This 'pairs advantage' is generally driven by adopting the response of the higher scoring partner. However, when the task becomes difficult, both partners' performance is improved by working in a pair. In two experiments, we find evidence that working in a pair can lead to subsequent improvements in individual performance, specifically for viewers whose accuracy is initially low. The pairs' technique therefore offers the opportunity for substantial improvements in face matching performance, along with an added training benefit. PMID:25393594
When is holography consistent?
NASA Astrophysics Data System (ADS)
McInnes, Brett; Ong, Yen Chin
2015-09-01
Holographic duality relates two radically different kinds of theory: one with gravity, one without. The very existence of such an equivalence imposes strong consistency conditions which are, in the nature of the case, hard to satisfy. Recently a particularly deep condition of this kind, relating the minimum of a probe brane action to a gravitational bulk action (in a Euclidean formulation), has been recognized; and the question arises as to the circumstances under which it, and its Lorentzian counterpart, is satisfied. We discuss the fact that there are physically interesting situations in which one or both versions might, in principle, not be satisfied. These arise in two distinct circumstances: first, when the bulk is not an Einstein manifold and, second, in the presence of angular momentum. Focusing on the application of holography to the quark-gluon plasma (of the various forms arising in the early Universe and in heavy-ion collisions), we find that these potential violations never actually occur. This suggests that the consistency condition is a "law of physics" expressing a particular aspect of holography.
NASA Astrophysics Data System (ADS)
Griffiths, Robert B.
2001-11-01
Quantum mechanics is one of the most fundamental yet difficult subjects in physics. Nonrelativistic quantum theory is presented here in a clear and systematic fashion, integrating Born's probabilistic interpretation with Schrödinger dynamics. Basic quantum principles are illustrated with simple examples requiring no mathematics beyond linear algebra and elementary probability theory. The quantum measurement process is consistently analyzed using fundamental quantum principles without referring to measurement. These same principles are used to resolve several of the paradoxes that have long perplexed physicists, including the double slit and Schrödinger's cat. The consistent histories formalism used here was first introduced by the author, and extended by M. Gell-Mann, J. Hartle and R. Omnès. Essential for researchers yet accessible to advanced undergraduate students in physics, chemistry, mathematics, and computer science, this book is supplementary to standard textbooks. It will also be of interest to physicists and philosophers working on the foundations of quantum mechanics. Comprehensive account Written by one of the main figures in the field Paperback edition of successful work on philosophy of quantum mechanics
2014-01-01
Noncovalent mass spectrometry (MS) is emerging as an invaluable technique to probe the structure, interactions, and dynamics of membrane proteins (MPs). However, maintaining native-like MP conformations in the gas phase using detergent solubilized proteins is often challenging and may limit structural analysis. Amphipols, such as the well characterized A8-35, are alternative reagents able to maintain the solubility of MPs in detergent-free solution. In this work, the ability of A8-35 to retain the structural integrity of MPs for interrogation by electrospray ionization-ion mobility spectrometry-mass spectrometry (ESI-IMS-MS) is compared systematically with the commonly used detergent dodecylmaltoside. MPs from the two major structural classes were selected for analysis, including two β-barrel outer MPs, PagP and OmpT (20.2 and 33.5 kDa, respectively), and two α-helical proteins, Mhp1 and GalP (54.6 and 51.7 kDa, respectively). Evaluation of the rotationally averaged collision cross sections of the observed ions revealed that the native structures of detergent solubilized MPs were not always retained in the gas phase, with both collapsed and unfolded species being detected. In contrast, ESI-IMS-MS analysis of the amphipol solubilized MPs studied resulted in charge state distributions consistent with less gas phase induced unfolding, and the presence of lowly charged ions which exhibit collision cross sections comparable with those calculated from high resolution structural data. The data demonstrate that A8-35 can be more effective than dodecylmaltoside at maintaining native MP structure and interactions in the gas phase, permitting noncovalent ESI-IMS-MS analysis of MPs from the two major structural classes, while gas phase dissociation from dodecylmaltoside micelles leads to significant gas phase unfolding, especially for the α-helical MPs studied. PMID:25495802
Consistent quantum measurements
NASA Astrophysics Data System (ADS)
Griffiths, Robert B.
2015-11-01
In response to recent criticisms by Okon and Sudarsky, various aspects of the consistent histories (CH) resolution of the quantum measurement problem(s) are discussed using a simple Stern-Gerlach device, and compared with the alternative approaches to the measurement problem provided by spontaneous localization (GRW), Bohmian mechanics, many worlds, and standard (textbook) quantum mechanics. Among these CH is unique in solving the second measurement problem: inferring from the measurement outcome a property of the measured system at a time before the measurement took place, as is done routinely by experimental physicists. The main respect in which CH differs from other quantum interpretations is in allowing multiple stochastic descriptions of a given measurement situation, from which one (or more) can be selected on the basis of its utility. This requires abandoning a principle (termed unicity), central to classical physics, that at any instant of time there is only a single correct description of the world.
YAMPA: Yet Another Matching Pursuit Algorithm for compressive sensing
NASA Astrophysics Data System (ADS)
Lodhi, Muhammad A.; Voronin, Sergey; Bajwa, Waheed U.
2016-05-01
State-of-the-art sparse recovery methods often rely on the restricted isometry property for their theoretical guarantees. However, they cannot explicitly incorporate metrics such as restricted isometry constants within their recovery procedures due to the computational intractability of calculating such metrics. This paper formulates an iterative algorithm, termed yet another matching pursuit algorithm (YAMPA), for recovery of sparse signals from compressive measurements. YAMPA differs from other pursuit algorithms in that: (i) it adapts to the measurement matrix using a threshold that is explicitly dependent on two computable coherence metrics of the matrix, and (ii) it does not require knowledge of the signal sparsity. Performance comparisons of YAMPA against other matching pursuit and approximate message passing algorithms are made for several types of measurement matrices. These results show that while state-of-the-art approximate message passing algorithms outperform other algorithms (including YAMPA) in the case of well-conditioned random matrices, they completely break down in the case of ill-conditioned measurement matrices. On the other hand, YAMPA and comparable pursuit algorithms not only result in reasonable performance for well-conditioned matrices, but their performance also degrades gracefully for ill-conditioned matrices. The paper also shows that YAMPA uniformly outperforms other pursuit algorithms for the case of thresholding parameters chosen in a clairvoyant fashion. Further, when combined with a simple and fast technique for selecting thresholding parameters in the case of ill-conditioned matrices, YAMPA outperforms other pursuit algorithms in the regime of low undersampling, although some of these algorithms can outperform YAMPA in the regime of high undersampling in this setting.
Chelli, Riccardo; Signorini, Giorgio F
2012-03-13
Serial generalized ensemble simulations, such as simulated tempering, enhance phase space sampling through non-Boltzmann weighting protocols. The most critical aspect of these methods with respect to the popular replica exchange schemes is the difficulty in determining the weight factors which enter the criterion for accepting replica transitions between different ensembles. Recently, a method, called BAR-SGE, was proposed for estimating optimal weight factors by resorting to a self-consistent procedure applied during the simulation (J. Chem. Theory Comput.2010, 6, 1935-1950). Calculations on model systems have shown that BAR-SGE outperforms other approaches proposed for determining optimal weights in serial generalized ensemble simulations. However, extensive tests on real systems and on convergence features with respect to the replica exchange method are lacking. Here, we report on a thorough analysis of BAR-SGE by performing molecular dynamics simulations of a solvated alanine dipeptide, a system often used as a benchmark to test new computational methodologies, and comparing results to the replica exchange method. To this aim, we have supplemented the ORAC program, a FORTRAN suite for molecular dynamics simulations (J. Comput. Chem.2010, 31, 1106-1116), with several variants of the BAR-SGE technique. An illustration of the specific BAR-SGE algorithms implemented in the ORAC program is also provided. PMID:26593345
Arbab, Alvira Ayoub; Sun, Kyung Chul; Sahito, Iftikhar Ali; Qadir, Muhammad Bilal; Choi, Yun Seon; Jeong, Sung Hoon
2016-03-23
Highly conductive mesoporous carbon structures based on multiwalled carbon nanotubes (MWCNTs) and activated charcoal (AC) were synthesized by an enzymatic dispersion method. The synthesized carbon configuration consists of synchronized structures of highly conductive MWCNT and porous activated charcoal morphology. The proposed carbon structure was used as counter electrode (CE) for quasi-solid-state dye-sensitized solar cells (DSSCs). The AC-doped MWCNT hybrid showed much enhanced electrocatalytic activity (ECA) toward polymer gel electrolyte and revealed a charge transfer resistance (RCT) of 0.60 Ω, demonstrating a fast electron transport mechanism. The exceptional electrocatalytic activity and high conductivity of the AC-doped MWCNT hybrid CE are associated with its synchronized features of high surface area and electronic conductivity, which produces higher interfacial reaction with the quasi-solid electrolyte. Morphological studies confirm the forms of amorphous and conductive 3D carbon structure with high density of CNT colloid. The excessive oxygen surface groups and defect-rich structure can entrap an excessive volume of quasi-solid electrolyte and locate multiple sites for iodide/triiodide catalytic reaction. The resultant D719 DSSC composed of this novel hybrid CE fabricated with polymer gel electrolyte demonstrated an efficiency of 10.05% with a high fill factor (83%), outperforming the Pt electrode. Such facile synthesis of CE together with low cost and sustainability supports the proposed DSSCs' structure to stand out as an efficient next-generation photovoltaic device. PMID:26911208
O'Reilly, Joseph E; Puttick, Mark N; Parry, Luke; Tanner, Alastair R; Tarver, James E; Fleming, James; Pisani, Davide; Donoghue, Philip C J
2016-04-01
Different analytical methods can yield competing interpretations of evolutionary history and, currently, there is no definitive method for phylogenetic reconstruction using morphological data. Parsimony has been the primary method for analysing morphological data, but there has been a resurgence of interest in the likelihood-based Mk-model. Here, we test the performance of the Bayesian implementation of the Mk-model relative to both equal and implied-weight implementations of parsimony. Using simulated morphological data, we demonstrate that the Mk-model outperforms equal-weights parsimony in terms of topological accuracy, and implied-weights performs the most poorly. However, the Mk-model produces phylogenies that have less resolution than parsimony methods. This difference in the accuracy and precision of parsimony and Bayesian approaches to topology estimation needs to be considered when selecting a method for phylogeny reconstruction. PMID:27095266
Puttick, Mark N.; Parry, Luke; Tanner, Alastair R.; Tarver, James E.; Fleming, James
2016-01-01
Different analytical methods can yield competing interpretations of evolutionary history and, currently, there is no definitive method for phylogenetic reconstruction using morphological data. Parsimony has been the primary method for analysing morphological data, but there has been a resurgence of interest in the likelihood-based Mk-model. Here, we test the performance of the Bayesian implementation of the Mk-model relative to both equal and implied-weight implementations of parsimony. Using simulated morphological data, we demonstrate that the Mk-model outperforms equal-weights parsimony in terms of topological accuracy, and implied-weights performs the most poorly. However, the Mk-model produces phylogenies that have less resolution than parsimony methods. This difference in the accuracy and precision of parsimony and Bayesian approaches to topology estimation needs to be considered when selecting a method for phylogeny reconstruction. PMID:27095266
Waaktaar, Trine; Torgersen, Svenn
2010-04-01
This study's aim was to determine whether resilience scales could predict adjustment over and above that predicted by the five-factor model (FFM). A sample of 1,345 adolescents completed paper-and-pencil scales on FFM personality (Hierarchical Personality Inventory for Children), resilience (Ego-Resiliency Scale [ER89] by Block & Kremen, the Resilience Scale [RS] by Wagnild & Young) and adaptive behaviors (California Healthy Kids Survey, UCLA Loneliness Scale and three measures of school adaptation). The results showed that the FFM scales accounted for the highest proportion of variance in disturbance. For adaptation, the resilience scales contributed as much as the FFM. In no case did the resilience scales outperform the FFM by increasing the explained variance. The results challenge the validity of the resilience concept as an indicator of human adaptation and avoidance of disturbance, although the concept may have heuristic value in combining favorable aspects of a person's personality endowment. PMID:19961558
Wang, Jih-Terng; Hsu, Chia-Min; Kuo, Chao-Yang; Meng, Pei-Jie; Kao, Shuh-Ji; Chen, Chaolun Allen
2015-01-01
Terpios hoshinota, an encrusting cyanosponge, is known as a strong substrate competitor of reef-building corals that kills encountered coral by overgrowth. Terpios outbreaks cause significant declines in living coral cover in Indo-Pacific coral reefs, with the damage usually lasting for decades. Recent studies show that there are morphological transformations at a sponge's growth front when confronting corals. Whether these morphological transformations at coral contacts are involved with physiological outperformance (e.g., higher metabolic activity or nutritional status) over other portions of Terpios remains equivocal. In this study, we compared the indicators of photosynthetic capability and nitrogen status of a sponge-cyanobacteria association at proximal, middle, and distal portions of opponent corals. Terpios tissues in contact with corals displayed significant increases in photosynthetic oxygen production (ca. 61%), the δ13C value (ca. 4%), free proteinogenic amino acid content (ca. 85%), and Gln/Glu ratio (ca. 115%) compared to middle and distal parts of the sponge. In contrast, the maximum quantum yield (Fv/Fm), which is the indicator usually used to represent the integrity of photosystem II, of cyanobacteria photosynthesis was low (0.256~0.319) and showed an inverse trend of higher values in the distal portion of the sponge that might be due to high and variable levels of cyanobacterial phycocyanin. The inconsistent results between photosynthetic oxygen production and Fv/Fm values indicated that maximum quantum yields might not be a suitable indicator to represent the photosynthetic function of the Terpios-cyanobacteria association. Our data conclusively suggest that Terpios hoshinota competes with opponent corals not only by the morphological transformation of the sponge-cyanobacteria association but also by physiological outperformance in accumulating resources for the battle. PMID:26110525
Wang, Jih-Terng; Hsu, Chia-Min; Kuo, Chao-Yang; Meng, Pei-Jie; Kao, Shuh-Ji; Chen, Chaolun Allen
2015-01-01
Terpios hoshinota, an encrusting cyanosponge, is known as a strong substrate competitor of reef-building corals that kills encountered coral by overgrowth. Terpios outbreaks cause significant declines in living coral cover in Indo-Pacific coral reefs, with the damage usually lasting for decades. Recent studies show that there are morphological transformations at a sponge’s growth front when confronting corals. Whether these morphological transformations at coral contacts are involved with physiological outperformance (e.g., higher metabolic activity or nutritional status) over other portions of Terpios remains equivocal. In this study, we compared the indicators of photosynthetic capability and nitrogen status of a sponge-cyanobacteria association at proximal, middle, and distal portions of opponent corals. Terpios tissues in contact with corals displayed significant increases in photosynthetic oxygen production (ca. 61%), the δ13C value (ca. 4%), free proteinogenic amino acid content (ca. 85%), and Gln/Glu ratio (ca. 115%) compared to middle and distal parts of the sponge. In contrast, the maximum quantum yield (Fv/Fm), which is the indicator usually used to represent the integrity of photosystem II, of cyanobacteria photosynthesis was low (0.256~0.319) and showed an inverse trend of higher values in the distal portion of the sponge that might be due to high and variable levels of cyanobacterial phycocyanin. The inconsistent results between photosynthetic oxygen production and Fv/Fm values indicated that maximum quantum yields might not be a suitable indicator to represent the photosynthetic function of the Terpios-cyanobacteria association. Our data conclusively suggest that Terpios hoshinota competes with opponent corals not only by the morphological transformation of the sponge-cyanobacteria association but also by physiological outperformance in accumulating resources for the battle. PMID:26110525
Sorting on STAR. [CDC computer algorithm timing comparison
NASA Technical Reports Server (NTRS)
Stone, H. S.
1978-01-01
Timing comparisons are given for three sorting algorithms written for the CDC STAR computer. One algorithm is Hoare's (1962) Quicksort, which is the fastest or nearly the fastest sorting algorithm for most computers. A second algorithm is a vector version of Quicksort that takes advantage of the STAR's vector operations. The third algorithm is an adaptation of Batcher's (1968) sorting algorithm, which makes especially good use of vector operations but has a complexity of N(log N)-squared as compared with a complexity of N log N for the Quicksort algorithms. In spite of its worse complexity, Batcher's sorting algorithm is competitive with the serial version of Quicksort for vectors up to the largest that can be treated by STAR. Vector Quicksort outperforms the other two algorithms and is generally preferred. These results indicate that unusual instruction sets can introduce biases in program execution time that counter results predicted by worst-case asymptotic complexity analysis.
Mutation-Based Artificial Fish Swarm Algorithm for Bound Constrained Global Optimization
NASA Astrophysics Data System (ADS)
Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.
2011-09-01
The herein presented mutation-based artificial fish swarm (AFS) algorithm includes mutation operators to prevent the algorithm to falling into local solutions, diversifying the search, and to accelerate convergence to the global optima. Three mutation strategies are introduced into the AFS algorithm to define the trial points that emerge from random, leaping and searching behaviors. Computational results show that the new algorithm outperforms other well-known global stochastic solution methods.
Why envy outperforms admiration.
van de Ven, Niels; Zeelenberg, Marcel; Pieters, Rik
2011-06-01
Four studies tested the hypothesis that the emotion of benign envy, but not the emotions of admiration or malicious envy, motivates people to improve themselves. Studies 1 to 3 found that only benign envy was related to the motivation to study more (Study 1) and to actual performance on the Remote Associates Task (which measures intelligence and creativity; Studies 2 and 3). Study 4 found that an upward social comparison triggered benign envy and subsequent better performance only when people thought self-improvement was attainable. When participants thought self-improvement was hard, an upward social comparison led to more admiration and no motivation to do better. Implications of these findings for theories of social emotions such as envy, social comparisons, and for understanding the influence of role models are discussed. PMID:21383070
MEDUSAHEAD OUTPERFORMS SQUIRRETAIL
Technology Transfer Automated Retrieval System (TEKTRAN)
Understanding the ecological processes fostering invasion and dominance by medusahead is central to its management. The objectives of this study were 1) to quantify and compare interference between medusahead and squirreltail under different concentrations of soil N and P and 2) to compare growth r...
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
Technology Transfer Automated Retrieval System (TEKTRAN)
The primary advantage of Dynamically Dimensioned Search algorithm (DDS) is that it outperforms many other optimization techniques in both convergence speed and the ability in searching for parameter sets that satisfy statistical guidelines while requiring only one algorithm parameter (perturbation f...
A SAT Based Effective Algorithm for the Directed Hamiltonian Cycle Problem
NASA Astrophysics Data System (ADS)
Jäger, Gerold; Zhang, Weixiong
The Hamiltonian cycle problem (HCP) is an important combinatorial problem with applications in many areas. While thorough theoretical and experimental analyses have been made on the HCP in undirected graphs, little is known for the HCP in directed graphs (DHCP). The contribution of this work is an effective algorithm for the DHCP. Our algorithm explores and exploits the close relationship between the DHCP and the Assignment Problem (AP) and utilizes a technique based on Boolean satisfiability (SAT). By combining effective algorithms for the AP and SAT, our algorithm significantly outperforms previous exact DHCP algorithms including an algorithm based on the award-winning Concorde TSP algorithm.
Production scheduling and rescheduling with genetic algorithms.
Bierwirth, C; Mattfeld, D C
1999-01-01
A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs. PMID:10199993
A novel surface defect inspection algorithm for magnetic tile
NASA Astrophysics Data System (ADS)
Xie, Luofeng; Lin, Lijun; Yin, Ming; Meng, Lintao; Yin, Guofu
2016-07-01
In this paper, we propose a defect extraction method for magnetic tile images based on the shearlet transform. The shearlet transform is a method of multi-scale geometric analysis. Compared with similar methods, the shearlet transform offers higher directional sensitivity and this is useful to accurately extract geometric characteristics from data. In general, a magnetic tile image captured by CCD camera mainly consists of target area, background. Our strategy for extracting the surface defects of magnetic tile comprises two steps: image preprocessing and defect extraction. Both steps are critical. After preprocessing the image, we extract the target area. Due to the low contrast in the magnetic tile image, we apply the discrete shearlet transform to enhance the contrast between the defect area and the normal area. Next, we apply a threshold method to generate a binary image. To validate our algorithm, we compare our experimental results with Otsu method, the curvelet transform and the nonsubsampled contourlet transform. Results show that our algorithm outperforms the other methods considered and can very effectively extract defects.
Consistent Data Distribution Over Optical Links
NASA Technical Reports Server (NTRS)
Palumbo, Daniel L.
1988-01-01
Fiber optics combined with IDE's provide consistent data communication between fault-tolerant computers. Data-transmission-checking system designed to provide consistent and reliable data communications for fault-tolerant and highly reliable computers. New technique performs variant of algorithm for fault-tolerant computers and uses fiber optics and independent decision elements (IDE's) to require fewer processors and fewer transmissions of messages. Enables fault-tolerant computers operating at different levels of redundancy to communicate with each other over triply redundant bus. Level of redundancy limited only by maximum number of wavelengths active on bus.
A systematic comparison of genome-scale clustering algorithms
2012-01-01
Background A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. Methods For each clustering method under study, a variety of parameters were tested. Jaccard similarity was used to measure each cluster's agreement with every GO and KEGG annotation set, and the highest Jaccard score was assigned to the cluster. Clusters were grouped into small, medium, and large bins, and the Jaccard score of the top five scoring clusters in each bin were averaged and reported as the best average top 5 (BAT5) score for the particular method. Results Clusters produced by each method were evaluated based upon the positive match to known pathways. This produces a readily interpretable ranking of the relative effectiveness of clustering on the genes. Methods were also tested to determine whether they were able to identify clusters consistent with those identified by other clustering methods. Conclusions Validation of clusters against known gene classifications demonstrate that for this data, graph-based techniques outperform conventional clustering approaches, suggesting that further
Recent ATR and fusion algorithm improvements for multiband sonar imagery
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Fernández, Manuel
2009-05-01
An improved automatic target recognition processing string has been developed. The overall processing string consists of pre-processing, subimage adaptive clutter filtering, normalization, detection, data regularization, feature extraction, optimal subset feature selection, feature orthogonalization and classification processing blocks. The objects that are classified by the 3 distinct ATR strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new high-resolution three-frequency band sonar imagery. The ATR processing strings were individually tuned to the corresponding three-frequency band data, making use of the new processing improvement, data regularization; this improvement entails computing the input data mean, clipping the data to a multiple of its mean and scaling it, prior to feature extraction and resulted in a 3:1 reduction in false alarms. Two significant fusion algorithm improvements were made. First, a nonlinear exponential Box-Cox expansion (consisting of raising data to a to-be-determined power) feature LLRT fusion algorithm was developed. Second, a repeated application of a subset Box-Cox feature selection / feature orthogonalization / LLRT fusion block was utilized. It was shown that cascaded Box-Cox feature LLRT fusion of the ATR processing strings outperforms baseline "summing" and single-stage Box-Cox feature LLRT algorithms, yielding significant improvements over the best single ATR processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate.
NASA Astrophysics Data System (ADS)
Kim, Juhye; Nam, Haewon; Lee, Rena
2015-07-01
CT (computed tomography) images, metal materials such as tooth supplements or surgical clips can cause metal artifact and degrade image quality. In severe cases, this may lead to misdiagnosis. In this research, we developed a new MAR (metal artifact reduction) algorithm by using an edge preserving filter and the MATLAB program (Mathworks, version R2012a). The proposed algorithm consists of 6 steps: image reconstruction from projection data, metal segmentation, forward projection, interpolation, applied edge preserving smoothing filter, and new image reconstruction. For an evaluation of the proposed algorithm, we obtained both numerical simulation data and data for a Rando phantom. In the numerical simulation data, four metal regions were added into the Shepp Logan phantom for metal artifacts. The projection data of the metal-inserted Rando phantom were obtained by using a prototype CBCT scanner manufactured by medical engineering and medical physics (MEMP) laboratory research group in medical science at Ewha Womans University. After these had been adopted the proposed algorithm was performed, and the result were compared with the original image (with metal artifact without correction) and with a corrected image based on linear interpolation. Both visual and quantitative evaluations were done. Compared with the original image with metal artifacts and with the image corrected by using linear interpolation, both the numerical and the experimental phantom data demonstrated that the proposed algorithm reduced the metal artifact. In conclusion, the evaluation in this research showed that the proposed algorithm outperformed the interpolation based MAR algorithm. If an optimization and a stability evaluation of the proposed algorithm can be performed, the developed algorithm is expected to be an effective tool for eliminating metal artifacts even in commercial CT systems.
Ensemble algorithms in reinforcement learning.
Wiering, Marco A; van Hasselt, Hado
2008-08-01
This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms. PMID:18632380
Efficient training algorithms for a class of shunting inhibitory convolutional neural networks.
Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam
2005-05-01
This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures. PMID:15940985
Zarecki, Raphy; Oberhardt, Matthew A; Yizhak, Keren; Wagner, Allon; Shtifman Segal, Ella; Freilich, Shiri; Henry, Christopher S; Gophna, Uri; Ruppin, Eytan
2014-01-01
Growth rate has long been considered one of the most valuable phenotypes that can be measured in cells. Aside from being highly accessible and informative in laboratory cultures, maximal growth rate is often a prime determinant of cellular fitness, and predicting phenotypes that underlie fitness is key to both understanding and manipulating life. Despite this, current methods for predicting microbial fitness typically focus on yields [e.g., predictions of biomass yield using GEnome-scale metabolic Models (GEMs)] or notably require many empirical kinetic constants or substrate uptake rates, which render these methods ineffective in cases where fitness derives most directly from growth rate. Here we present a new method for predicting cellular growth rate, termed SUMEX, which does not require any empirical variables apart from a metabolic network (i.e., a GEM) and the growth medium. SUMEX is calculated by maximizing the SUM of molar EXchange fluxes (hence SUMEX) in a genome-scale metabolic model. SUMEX successfully predicts relative microbial growth rates across species, environments, and genetic conditions, outperforming traditional cellular objectives (most notably, the convention assuming biomass maximization). The success of SUMEX suggests that the ability of a cell to catabolize substrates and produce a strong proton gradient enables fast cell growth. Easily applicable heuristics for predicting growth rate, such as what we demonstrate with SUMEX, may contribute to numerous medical and biotechnological goals, ranging from the engineering of faster-growing industrial strains, modeling of mixed ecological communities, and the inhibition of cancer growth. PMID:24866123
Vaidya, Vishal S.; Ozer, Josef S.; Frank, Dieterle; Collings, Fitz B.; Ramirez, Victoria; Troth, Sean; Muniappa, Nagaraja; Thudium, Douglas; Gerhold, David; Holder, Daniel J.; Bobadilla, Norma A.; Marrer, Estelle; Perentes, Elias; Cordier, André; Vonderscher, Jacky; Maurer, Gérard; Goering, Peter L.; Sistare, Frank D.; Bonventre, Joseph V.
2010-01-01
Kidney toxicity accounts for a significant percentage of morbidity and drug candidate failure. Serum creatinine (SCr) and blood urea nitrogen (BUN) have been used to monitor kidney dysfunction for over a century but these markers are insensitive and non-specific. In multi-site preclinical rat toxicology studies the diagnostic performance of urinary kidney injury molecule-1 (Kim-1) was compared to traditional biomarkers as predictors of kidney tubular histopathologic changes, currently considered the “gold standard” of nephrotoxicity. In multiple models of kidney injury, urinary Kim-1 significantly outperformed SCr and BUN. The area under the receiver operating characteristic curve for Kim-1 was between 0.91 and 0.99 as compared to 0.79 to 0.9 for BUN and 0.73 to 0.85 for SCr. Thus urinary Kim-1 is the first injury biomarker of kidney toxicity qualified by the FDA and EMEA and is expected to significantly improve kidney safety monitoring. PMID:20458318
Hu, Jin; Wang, Guilin; Zhao, Wenguo; Liu, Xinyu; Zhang, Libin; Gao, Weiping
2016-07-01
Conjugating poly(ethylene glycol) (PEG), PEGylation, to therapeutic proteins is widely used as a means to improve their pharmacokinetics and therapeutic potential. One prime example is PEGylated interferon-alpha (PEGASYS). However, PEGylation usually leads to a heterogeneous mixture of positional isomers with reduced bioactivity and low yield. Herein, we report site-specific in situ growth (SIG) of a PEG-like polymer, poly(oligo(ethylene glycol) methyl ether methacrylate) (POEGMA), from the C-terminus of interferon-alpha to form a site-specific (C-terminal) and stoichiometric (1:1) POEGMA conjugate of interferon-alpha in high yield. The POEGMA conjugate showed significantly improved pharmacokinetics, tumor accumulation and anticancer efficacy as compared to interferon-alpha. Notably, the POEGMA conjugate possessed a 7.2-fold higher in vitro antiproliferative bioactivity than PEGASYS. More importantly, in a murine cancer model, the POEGMA conjugate completely inhibited tumor growth and eradicated tumors of 75% mice without appreciable systemic toxicity, whereas at the same dose, no mice treated with PEGASYS survived for over 58 days. The outperformance of a site-specific POEGMA conjugate prepared by SIG over PEGASYS that is the current gold standard for interferon-alpha delivery suggests that SIG is of interest for the development of next-generation protein therapeutics. PMID:27152679
Staiger, Christine; Cadot, Sidney; Györffy, Balázs; Wessels, Lodewyk F. A.; Klau, Gunnar W.
2013-01-01
Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this aggregation. Previous studies were limited to few data sets with a small number of patients. Moreover, each study used different data and evaluation procedures. This makes it difficult to objectively assess the gain in classification performance. Here we introduce the Amsterdam Classification Evaluation Suite (ACES). ACES is a Python package to objectively evaluate classification and feature-selection methods and contains methods for pooling and normalizing Affymetrix microarrays from different studies. It is simple to use and therefore facilitates the comparison of new approaches to best-in-class approaches. In addition to the methods described in our earlier study (Staiger et al., 2012), we have included two prominent prognostic gene signatures specific for breast cancer outcome, one more composite feature selection method and two network-based gene ranking methods. Employing the evaluation pipeline we show that current composite-feature classification methods do not outperform simple single-genes classifiers in predicting outcome in breast cancer. Furthermore, we find that also the stability of features across different data sets is not higher for composite features. Most stunningly, we observe that prediction performances are not affected when extracting features from randomized PPI networks. PMID:24391662
Surface Consistent Finite Frequency Phase Corrections
NASA Astrophysics Data System (ADS)
Kimman, W. P.
2016-04-01
Static time-delay corrections are frequency independent and ignore velocity variations away from the assumed vertical ray-path through the subsurface. There is therefore a clear potential for improvement if the finite frequency nature of wave propagation can be properly accounted for. Such a method is presented here based on the Born approximation, the assumption of surface consistency, and the misfit of instantaneous phase. The concept of instantaneous phase lends itself very well for sweep-like signals, hence these are the focus of this study. Analytical sensitivity kernels are derived that accurately predict frequency dependent phase shifts due to P-wave anomalies in the near surface. They are quick to compute and robust near the source and receivers. An additional correction is presented that re-introduces the non-linear relation between model perturbation and phase delay, which becomes relevant for stronger velocity anomalies. The phase shift as function of frequency is a slowly varying signal, its computation therefore doesn't require fine sampling even for broadband sweeps. The kernels reveal interesting features of the sensitivity of seismic arrivals to the near surface: small anomalies can have a relative large impact resulting from the medium field term that is dominant near the source and receivers. Furthermore, even simple velocity anomalies can produce a distinct frequency dependent phase behaviour. Unlike statics, the predicted phase corrections are smooth in space. Verification with spectral element simulations shows an excellent match for the predicted phase shifts over the entire seismic frequency band. Applying the phase shift to the reference sweep corrects for wavelet distortion, making the technique akin to surface consistent deconvolution, even though no division in the spectral domain is involved. As long as multiple scattering is mild, surface consistent finite frequency phase corrections outperform traditional statics for moderately large
Surface consistent finite frequency phase corrections
NASA Astrophysics Data System (ADS)
Kimman, W. P.
2016-07-01
Static time-delay corrections are frequency independent and ignore velocity variations away from the assumed vertical ray path through the subsurface. There is therefore a clear potential for improvement if the finite frequency nature of wave propagation can be properly accounted for. Such a method is presented here based on the Born approximation, the assumption of surface consistency and the misfit of instantaneous phase. The concept of instantaneous phase lends itself very well for sweep-like signals, hence these are the focus of this study. Analytical sensitivity kernels are derived that accurately predict frequency-dependent phase shifts due to P-wave anomalies in the near surface. They are quick to compute and robust near the source and receivers. An additional correction is presented that re-introduces the nonlinear relation between model perturbation and phase delay, which becomes relevant for stronger velocity anomalies. The phase shift as function of frequency is a slowly varying signal, its computation therefore does not require fine sampling even for broad-band sweeps. The kernels reveal interesting features of the sensitivity of seismic arrivals to the near surface: small anomalies can have a relative large impact resulting from the medium field term that is dominant near the source and receivers. Furthermore, even simple velocity anomalies can produce a distinct frequency-dependent phase behaviour. Unlike statics, the predicted phase corrections are smooth in space. Verification with spectral element simulations shows an excellent match for the predicted phase shifts over the entire seismic frequency band. Applying the phase shift to the reference sweep corrects for wavelet distortion, making the technique akin to surface consistent deconvolution, even though no division in the spectral domain is involved. As long as multiple scattering is mild, surface consistent finite frequency phase corrections outperform traditional statics for moderately large
Consistent detection of global predicates
NASA Technical Reports Server (NTRS)
Cooper, Robert; Marzullo, Keith
1991-01-01
A fundamental problem in debugging and monitoring is detecting whether the state of a system satisfies some predicate. If the system is distributed, then the resulting uncertainty in the state of the system makes such detection, in general, ill-defined. Three algorithms are presented for detecting global predicates in a well-defined way. These algorithms do so by interpreting predicates with respect to the communication that has occurred in the system.
Consistency-based rectification of nonrigid registrations
Gass, Tobias; Székely, Gábor; Goksel, Orcun
2015-01-01
Abstract. We present a technique to rectify nonrigid registrations by improving their group-wise consistency, which is a widely used unsupervised measure to assess pair-wise registration quality. While pair-wise registration methods cannot guarantee any group-wise consistency, group-wise approaches typically enforce perfect consistency by registering all images to a common reference. However, errors in individual registrations to the reference then propagate, distorting the mean and accumulating in the pair-wise registrations inferred via the reference. Furthermore, the assumption that perfect correspondences exist is not always true, e.g., for interpatient registration. The proposed consistency-based registration rectification (CBRR) method addresses these issues by minimizing the group-wise inconsistency of all pair-wise registrations using a regularized least-squares algorithm. The regularization controls the adherence to the original registration, which is additionally weighted by the local postregistration similarity. This allows CBRR to adaptively improve consistency while locally preserving accurate pair-wise registrations. We show that the resulting registrations are not only more consistent, but also have lower average transformation error when compared to known transformations in simulated data. On clinical data, we show improvements of up to 50% target registration error in breathing motion estimation from four-dimensional MRI and improvements in atlas-based segmentation quality of up to 65% in terms of mean surface distance in three-dimensional (3-D) CT. Such improvement was observed consistently using different registration algorithms, dimensionality (two-dimensional/3-D), and modalities (MRI/CT). PMID:26158083
Duan, Qian-Qian; Yang, Gen-Ke; Pan, Chang-Chun
2014-01-01
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. PMID:24772031
Pasquini, Sarah C; Wright, S Joseph; Santiago, Louis S
2015-07-01
always outperform trees, in terms of photosynthetic processes and under contrasting rates of resource supply of macronutrients, will allow lianas to increase in abundance if disturbance and tree turnover rates are increasing in Neotropical forests as has been suggested. PMID:26378309
2011-01-01
Background To identify the determinants of invasiveness, comparisons of traits of invasive and native species are commonly performed. Invasiveness is generally linked to higher values of reproductive, physiological and growth-related traits of the invasives relative to the natives in the introduced range. Phenotypic plasticity of these traits has also been cited to increase the success of invasive species but has been little studied in invasive tree species. In a greenhouse experiment, we compared ecophysiological traits between an invasive species to Europe, Acer negundo, and early- and late-successional co-occurring native species, under different light, nutrient availability and disturbance regimes. We also compared species of the same species groups in situ, in riparian forests. Results Under non-limiting resources, A. negundo seedlings showed higher growth rates than the native species. However, A. negundo displayed equivalent or lower photosynthetic capacities and nitrogen content per unit leaf area compared to the native species; these findings were observed both on the seedlings in the greenhouse experiment and on adult trees in situ. These physiological traits were mostly conservative along the different light, nutrient and disturbance environments. Overall, under non-limiting light and nutrient conditions, specific leaf area and total leaf area of A. negundo were substantially larger. The invasive species presented a higher plasticity in allocation to foliage and therefore in growth with increasing nutrient and light availability relative to the native species. Conclusions The higher level of plasticity of the invasive species in foliage allocation in response to light and nutrient availability induced a better growth in non-limiting resource environments. These results give us more elements on the invasiveness of A. negundo and suggest that such behaviour could explain the ability of A. negundo to outperform native tree species, contributes to its spread
Williams, Paul H.; Vaissière, Bernard E.; Zhou, Zhiyong; Gai, Qinbao; Dong, Jie; An, Jiandong
2015-01-01
Peach Prunus persica (L.) Batsch is self-compatible and largely self-fertile, but under greenhouse conditions pollinators must be introduced to achieve good fruit set and quality. Because little work has been done to assess the effectiveness of different pollinators on peach trees under greenhouse conditions, we studied ‘Okubo’ peach in greenhouse tunnels near Beijing between 2012 and 2014. We measured pollen deposition, pollen-tube growth rates, ovary development, and initial fruit set after the flowers were visited by either of two managed pollinators: bumblebees, Bombus patagiatus Nylander, and honeybees, Apis mellifera L. The results show that B. patagiatus is more effective than A. mellifera as a pollinator of peach in greenhouses because of differences in two processes. First, B. patagiatus deposits more pollen grains on peach stigmas than A. mellifera, both during a single visit and during a whole day of open pollination. Second, there are differences in the fertilization performance of the pollen deposited. Half of the flowers visited by B. patagiatus are fertilized 9–11 days after bee visits, while for flowers visited by A. mellifera, half are fertilized 13–15 days after bee visits. Consequently, fruit development is also accelerated by bumblebees, showing that the different pollinators have not only different pollination efficiency, but also influence the subsequent time course of fertilization and fruit set. Flowers visited by B. patagiatus show faster ovary growth and ultimately these flowers produce more fruit. Our work shows that pollinators may influence fruit production beyond the amount of pollen delivered. We show that managed indigenous bumblebees significantly outperform introduced honeybees in increasing peach initial fruit set under greenhouse conditions. PMID:25799170
Allon, Aliza A.; Butcher, Kristin; Schneider, Richard A.; Lotz, Jeffrey C.
2011-01-01
Study Design This study explores the use of bilaminar coculture pellets of mesenchymal stem cells (MSC) and Nucleus Pulposus cells (NPC) as a cell-based therapy for intervertebral disc regeneration. The pellets were tested under conditions that mimic the degenerative disc. Objective Our goal is to optimize our cell-based therapy in vitro under conditions representative of the eventual diseased tissue. Summary of Background Data Harnessing the potential of stem cells is an important strategy for regenerative medicine. Our approach seeks to direct the behavior of stem cells by mimicking embryonic processes underlying cartilage and intervertebral disc development. Prior experiments have shown that bilaminar co-culture can help differentiate MSC and substantially improve new matrix deposition. Methods We have designed a novel spherical bilaminar cell pellet (BCP) where MSC are enclosed in a shell of NPC. There were three groups: MSC, NPC, and BCP. The pellets were tested under three different culture conditions: in a bioreactor that provides pressure & hypoxia (mimicking normal disc conditions), with inflammatory cytokines (IL-1b and TNF-a), and a bioreactor with inflammation (mimicking painful disc conditions). Results When cultured in the bioreactor, the NPC pellets produced significantly more glycosaminoglycan (GAG)/cell than the other groups: 70-80% more than the BCP and MSC alone. When cultured in an inflammatory environment, the MSC and BCP groups produced 30-34% more GAG/cell than NPC (p<0.05). When the pellets were cultured in a bioreactor with inflammation, the BCP made 25% more GAG/cell than MSC and 57% more than NPC (p<0.05). Conclusion This study shows that BCP outperform controls in a simulated degenerated disc environment. Adapting inductive mechanisms from development to trigger differentiation and restore diseased tissue has many advantages. As opposed to strategies that require growth factor supplements or genetic manipulations, our method is self
Fontana, W.
1990-12-13
In this paper complex adaptive systems are defined by a self- referential loop in which objects encode functions that act back on these objects. A model for this loop is presented. It uses a simple recursive formal language, derived from the lambda-calculus, to provide a semantics that maps character strings into functions that manipulate symbols on strings. The interaction between two functions, or algorithms, is defined naturally within the language through function composition, and results in the production of a new function. An iterated map acting on sets of functions and a corresponding graph representation are defined. Their properties are useful to discuss the behavior of a fixed size ensemble of randomly interacting functions. This function gas'', or Turning gas'', is studied under various conditions, and evolves cooperative interaction patterns of considerable intricacy. These patterns adapt under the influence of perturbations consisting in the addition of new random functions to the system. Different organizations emerge depending on the availability of self-replicators.
An Algorithm Combining for Objective Prediction with Subjective Forecast Information
NASA Astrophysics Data System (ADS)
Choi, JunTae; Kim, SooHyun
2016-04-01
As direct or post-processed output from numerical weather prediction (NWP) models has begun to show acceptable performance compared with the predictions of human forecasters, many national weather centers have become interested in automatic forecasting systems based on NWP products alone, without intervention from human forecasters. The Korea Meteorological Administration (KMA) is now developing an automatic forecasting system for dry variables. The forecasts are automatically generated from NWP predictions using a post processing model (MOS). However, MOS cannot always produce acceptable predictions, and sometimes its predictions are rejected by human forecasters. In such cases, a human forecaster should manually modify the prediction consistently at points surrounding their corrections, using some kind of smart tool to incorporate the forecaster's opinion. This study introduces an algorithm to revise MOS predictions by adding a forecaster's subjective forecast information at neighbouring points. A statistical relation between two forecast points - a neighbouring point and a dependent point - was derived for the difference between a MOS prediction and that of a human forecaster. If the MOS prediction at a neighbouring point is updated by a human forecaster, the value at a dependent point is modified using a statistical relationship based on linear regression, with parameters obtained from a one-year dataset of MOS predictions and official forecast data issued by KMA. The best sets of neighbouring points and dependent point are statistically selected. According to verification, the RMSE of temperature predictions produced by the new algorithm was slightly lower than that of the original MOS predictions, and close to the RMSE of subjective forecasts. For wind speed and relative humidity, the new algorithm outperformed human forecasters.
A novel bit-quad-based Euler number computing algorithm.
Yao, Bin; He, Lifeng; Kang, Shiying; Chao, Yuyan; Zhao, Xiao
2015-01-01
The Euler number of a binary image is an important topological property in computer vision and pattern recognition. This paper proposes a novel bit-quad-based Euler number computing algorithm. Based on graph theory and analysis on bit-quad patterns, our algorithm only needs to count two bit-quad patterns. Moreover, by use of the information obtained during processing the previous bit-quad, the average number of pixels to be checked for processing a bit-quad is only 1.75. Experimental results demonstrated that our method outperforms significantly conventional Euler number computing algorithms. PMID:26636023
Parallel processors and nonlinear structural dynamics algorithms and software
NASA Technical Reports Server (NTRS)
Belytschko, Ted; Gilbertsen, Noreen D.; Neal, Mark O.; Plaskacz, Edward J.
1989-01-01
The adaptation of a finite element program with explicit time integration to a massively parallel SIMD (single instruction multiple data) computer, the CONNECTION Machine is described. The adaptation required the development of a new algorithm, called the exchange algorithm, in which all nodal variables are allocated to the element with an exchange of nodal forces at each time step. The architectural and C* programming language features of the CONNECTION Machine are also summarized. Various alternate data structures and associated algorithms for nonlinear finite element analysis are discussed and compared. Results are presented which demonstrate that the CONNECTION Machine is capable of outperforming the CRAY XMP/14.
An efficient cuckoo search algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Ong, Pauline; Zainuddin, Zarita
2013-04-01
Cuckoo search algorithm which reproduces the breeding strategy of the best known brood parasitic bird, the cuckoos has demonstrated its superiority in obtaining the global solution for numerical optimization problems. However, the involvement of fixed step approach in its exploration and exploitation behavior might slow down the search process considerably. In this regards, an improved cuckoo search algorithm with adaptive step size adjustment is introduced and its feasibility on a variety of benchmarks is validated. The obtained results show that the proposed scheme outperforms the standard cuckoo search algorithm in terms of convergence characteristic while preserving the fascinating features of the original method.
NASA Astrophysics Data System (ADS)
Lee, Hui Jung; Choi, Dong-Yoon; Song, Byung Cheol
2015-11-01
This paper proposes a learning-based superresolution algorithm using text characteristics for text images. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, a sufficient number of low-resolution (LR) to high-resolution (HR) block pairs are first extracted from various LR-HR image pairs that are composed of texts. Then, we classify those block pairs into 512 clusters and, for each cluster, calculate the optimal two-dimensional (2-D) finite impulse response (FIR) filter to synthesize a high-quality HR block from an LR block and store the block-adaptive 2-D FIR filters in a dictionary with their associated index. In the inference stage, we find the best-matched candidate to each input LR block from the dictionary and synthesize the HR block using the optimal 2-D FIR filter. Finally, an HR image is produced via proper postprocessing. Experimental results show that the proposed algorithm provides superior visual quality to images from previous works and outperforms previous processes in terms of computational complexity.
Single-Cell Tracking with PET using a Novel Trajectory Reconstruction Algorithm
Lee, Keum Sil; Kim, Tae Jin
2015-01-01
Virtually all biomedical applications of positron emission tomography (PET) use images to represent the distribution of a radiotracer. However, PET is increasingly used in cell tracking applications, for which the “imaging” paradigm may not be optimal. Here we investigate an alternative approach, which consists in reconstructing the time-varying position of individual radiolabeled cells directly from PET measurements. As a proof of concept, we formulate a new algorithm for reconstructing the trajectory of one single moving cell directly from list-mode PET data. We model the trajectory as a 3D B-spline function of the temporal variable and use non-linear optimization to minimize the mean-square distance between the trajectory and the recorded list-mode coincidence events. Using Monte Carlo simulations (GATE), we show that this new algorithm can track a single source moving within a small-animal PET system with <3 mm accuracy provided that the activity of the cell [Bq] is greater than four times its velocity [mm/s]. The algorithm outperforms conventional ML-EM as well as the “minimum distance” method used for positron emission particle tracking (PEPT). The new method was also successfully validated using experimentally acquired PET data. In conclusion, we demonstrated the feasibility of a new method for tracking a single moving cell directly from PET list-mode data, at the whole-body level, for physiologically relevant activities and velocities. PMID:25423651
Efficient sequential and parallel algorithms for record linkage
Mamun, Abdullah-Al; Mi, Tian; Aseltine, Robert; Rajasekaran, Sanguthevar
2014-01-01
Background and objective Integrating data from multiple sources is a crucial and challenging problem. Even though there exist numerous algorithms for record linkage or deduplication, they suffer from either large time needs or restrictions on the number of datasets that they can integrate. In this paper we report efficient sequential and parallel algorithms for record linkage which handle any number of datasets and outperform previous algorithms. Methods Our algorithms employ hierarchical clustering algorithms as the basis. A key idea that we use is radix sorting on certain attributes to eliminate identical records before any further processing. Another novel idea is to form a graph that links similar records and find the connected components. Results Our sequential and parallel algorithms have been tested on a real dataset of 1 083 878 records and synthetic datasets ranging in size from 50 000 to 9 000 000 records. Our sequential algorithm runs at least two times faster, for any dataset, than the previous best-known algorithm, the two-phase algorithm using faster computation of the edit distance (TPA (FCED)). The speedups obtained by our parallel algorithm are almost linear. For example, we get a speedup of 7.5 with 8 cores (residing in a single node), 14.1 with 16 cores (residing in two nodes), and 26.4 with 32 cores (residing in four nodes). Conclusions We have compared the performance of our sequential algorithm with TPA (FCED) and found that our algorithm outperforms the previous one. The accuracy is the same as that of this previous best-known algorithm. PMID:24154837
Bayer, Immanuel; Groth, Philip; Schneckener, Sebastian
2013-01-01
Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections and applied several machine learning algorithms for predicting a variety of endpoints for drug response. We compared all possible models for combinations of sample collections, algorithm, drug, and labeling to an identically generated null model. The predictability of treatment effects varies among compounds, i.e. response could be predicted for some but not for all. The choice of sample collection plays a major role towards lowering the prediction error, as does sample size. However, we found that no algorithm was able to consistently outperform the other and there was no significant difference between regression and two- or three class predictors in this experimental setting. These results indicate that response-modeling projects should direct efforts mainly towards sample collection and data quality, rather than method adjustment. PMID:23894636
Ansari, Elnaz Saberi; Eslahchi, Changiz; Pezeshk, Hamid; Sadeghi, Mehdi
2014-09-01
Decomposition of structural domains is an essential task in classifying protein structures, predicting protein function, and many other proteomics problems. As the number of known protein structures in PDB grows exponentially, the need for accurate automatic domain decomposition methods becomes more essential. In this article, we introduce a bottom-up algorithm for assigning protein domains using a graph theoretical approach. This algorithm is based on a center-based clustering approach. For constructing initial clusters, members of an independent dominating set for the graph representation of a protein are considered as the centers. A distance matrix is then defined for these clusters. To obtain final domains, these clusters are merged using the compactness principle of domains and a method similar to the neighbor-joining algorithm considering some thresholds. The thresholds are computed using a training set consisting of 50 protein chains. The algorithm is implemented using C++ language and is named ProDomAs. To assess the performance of ProDomAs, its results are compared with seven automatic methods, against five publicly available benchmarks. The results show that ProDomAs outperforms other methods applied on the mentioned benchmarks. The performance of ProDomAs is also evaluated against 6342 chains obtained from ASTRAL SCOP 1.71. ProDomAs is freely available at http://www.bioinf.cs.ipm.ir/software/prodomas. PMID:24596179
Da Chen; Cohen, Laurent D
2015-08-01
In this paper, we propose a new interactive retinal vessels extraction method with anisotropic fast marching (AFM) based on the observation that one vessel may have the property of local intensities consistency. Our goal is to extract both the centrelines and boundaries between two given points. The proposed method consists of two stages: the first stage aims to finding the vessel centrelines using AFM and local intensities consistency roughly, while the second stage is to refine the centrelines from the previous stage using constrained Riemannian metric based AFM, and get the boundaries of the vessels simultaneously. Experiments show that results of our method outperform the classical minimal path method [1]. PMID:26737257
Robustness of Tree Extraction Algorithms from LIDAR
NASA Astrophysics Data System (ADS)
Dumitru, M.; Strimbu, B. M.
2015-12-01
Forest inventory faces a new era as unmanned aerial systems (UAS) increased the precision of measurements, while reduced field effort and price of data acquisition. A large number of algorithms were developed to identify various forest attributes from UAS data. The objective of the present research is to assess the robustness of two types of tree identification algorithms when UAS data are combined with digital elevation models (DEM). The algorithms use as input photogrammetric point cloud, which are subsequent rasterized. The first type of algorithms associate tree crown with an inversed watershed (subsequently referred as watershed based), while the second type is based on simultaneous representation of tree crown as an individual entity, and its relation with neighboring crowns (subsequently referred as simultaneous representation). A DJI equipped with a SONY a5100 was used to acquire images over an area from center Louisiana. The images were processed with Pix4D, and a photogrammetric point cloud with 50 points / m2 was attained. DEM was obtained from a flight executed in 2013, which also supplied a LIDAR point cloud with 30 points/m2. The algorithms were tested on two plantations with different species and crown class complexities: one homogeneous (i.e., a mature loblolly pine plantation), and one heterogeneous (i.e., an unmanaged uneven-aged stand with mixed species pine -hardwoods). Tree identification on photogrammetric point cloud reveled that simultaneous representation algorithm outperforms watershed algorithm, irrespective stand complexity. Watershed algorithm exhibits robustness to parameters, but the results were worse than majority sets of parameters needed by the simultaneous representation algorithm. The simultaneous representation algorithm is a better alternative to watershed algorithm even when parameters are not accurately estimated. Similar results were obtained when the two algorithms were run on the LIDAR point cloud.
NASA Astrophysics Data System (ADS)
Abrams, Daniel S.
This thesis describes several new quantum algorithms. These include a polynomial time algorithm that uses a quantum fast Fourier transform to find eigenvalues and eigenvectors of a Hamiltonian operator, and that can be applied in cases (commonly found in ab initio physics and chemistry problems) for which all known classical algorithms require exponential time. Fast algorithms for simulating many body Fermi systems are also provided in both first and second quantized descriptions. An efficient quantum algorithm for anti-symmetrization is given as well as a detailed discussion of a simulation of the Hubbard model. In addition, quantum algorithms that calculate numerical integrals and various characteristics of stochastic processes are described. Two techniques are given, both of which obtain an exponential speed increase in comparison to the fastest known classical deterministic algorithms and a quadratic speed increase in comparison to classical Monte Carlo (probabilistic) methods. I derive a simpler and slightly faster version of Grover's mean algorithm, show how to apply quantum counting to the problem, develop some variations of these algorithms, and show how both (apparently distinct) approaches can be understood from the same unified framework. Finally, the relationship between physics and computation is explored in some more depth, and it is shown that computational complexity theory depends very sensitively on physical laws. In particular, it is shown that nonlinear quantum mechanics allows for the polynomial time solution of NP-complete and #P oracle problems. Using the Weinberg model as a simple example, the explicit construction of the necessary gates is derived from the underlying physics. Nonlinear quantum algorithms are also presented using Polchinski type nonlinearities which do not allow for superluminal communication. (Copies available exclusively from MIT Libraries, Rm. 14- 0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Electricity Load Forecasting Using Support Vector Regression with Memetic Algorithms
Hu, Zhongyi; Xiong, Tao
2013-01-01
Electricity load forecasting is an important issue that is widely explored and examined in power systems operation literature and commercial transactions in electricity markets literature as well. Among the existing forecasting models, support vector regression (SVR) has gained much attention. Considering the performance of SVR highly depends on its parameters; this study proposed a firefly algorithm (FA) based memetic algorithm (FA-MA) to appropriately determine the parameters of SVR forecasting model. In the proposed FA-MA algorithm, the FA algorithm is applied to explore the solution space, and the pattern search is used to conduct individual learning and thus enhance the exploitation of FA. Experimental results confirm that the proposed FA-MA based SVR model can not only yield more accurate forecasting results than the other four evolutionary algorithms based SVR models and three well-known forecasting models but also outperform the hybrid algorithms in the related existing literature. PMID:24459425
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
2014-07-01
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins. PMID:25069136
Fast ordering algorithm for exact histogram specification.
Nikolova, Mila; Steidl, Gabriele
2014-12-01
This paper provides a fast algorithm to order in a meaningful, strict way the integer gray values in digital (quantized) images. It can be used in any exact histogram specification-based application. Our algorithm relies on the ordering procedure based on the specialized variational approach. This variational method was shown to be superior to all other state-of-the art ordering algorithms in terms of faithful total strict ordering but not in speed. Indeed, the relevant functionals are in general difficult to minimize because their gradient is nearly flat over vast regions. In this paper, we propose a simple and fast fixed point algorithm to minimize these functionals. The fast convergence of our algorithm results from known analytical properties of the model. Our algorithm is equivalent to an iterative nonlinear filtering. Furthermore, we show that a particular form of the variational model gives rise to much faster convergence than other alternative forms. We demonstrate that only a few iterations of this filter yield almost the same pixel ordering as the minimizer. Thus, we apply only few iteration steps to obtain images, whose pixels can be ordered in a strict and faithful way. Numerical experiments confirm that our algorithm outperforms by far its main competitors. PMID:25347881
Sobel, E.; Lange, K.; O`Connell, J.R.
1996-12-31
Haplotyping is the logical process of inferring gene flow in a pedigree based on phenotyping results at a small number of genetic loci. This paper formalizes the haplotyping problem and suggests four algorithms for haplotype reconstruction. These algorithms range from exhaustive enumeration of all haplotype vectors to combinatorial optimization by simulated annealing. Application of the algorithms to published genetic analyses shows that manual haplotyping is often erroneous. Haplotyping is employed in screening pedigrees for phenotyping errors and in positional cloning of disease genes from conserved haplotypes in population isolates. 26 refs., 6 figs., 3 tabs.
Consistent integration of geo-information
NASA Astrophysics Data System (ADS)
Hansen, T. M.; Cordua, K. S.
2014-12-01
Probabilistically formulated inverse problems can be seen as an application of data integration. Two types of information are (almost) always available: 1) geophysical data, and 2) information about geology and geologically plausible structures. The inverse problem consists of integrating the information available from geophysical data and geological information. In recent years inversion algorithms have emerged that allow integration of such different information. However such methods only provides useful results if the geological and geophysical information provided are consistent. Using weakly informed prior models and/or sparse uncertain geophysical data typically no problems with consistency arise. However, as data coverage and quality increase and still more complex and detailed prior information can be quantified (using e.g multiple point based statistics) then the risk of problems with consistency increases. Inconsistency between two independent sources of information about the same subsurface model, means that either one or both sources of information must be wrong.We will demonstrate that using cross hole GPR tomographic data, that such consistency problems exist, and that they can dramatically affect inversion results. The problem is two folded: 1) One will typically underestimate the error associated with geophysical data, and 2) Multiple-point based prior models often provide such detailed a priori information that it will not be possible to find a priori acceptable models that lead to a data fit within measurement uncertainties. We demonstrate that if inversion is forced on inconsistent information, then the solution to the inverse problem may be earth models that neither fit the data within their uncertainty, nor represent realistic geologically features. In the worst case such models will show artefacts that appear well resolved, and that can have severe effect on subsequent flow modeling. We will demonstrate how such inconsistencies can be
Marginal Consistency: Upper-Bounding Partition Functions over Commutative Semirings.
Werner, Tomás
2015-07-01
Many inference tasks in pattern recognition and artificial intelligence lead to partition functions in which addition and multiplication are abstract binary operations forming a commutative semiring. By generalizing max-sum diffusion (one of convergent message passing algorithms for approximate MAP inference in graphical models), we propose an iterative algorithm to upper bound such partition functions over commutative semirings. The iteration of the algorithm is remarkably simple: change any two factors of the partition function such that their product remains the same and their overlapping marginals become equal. In many commutative semirings, repeating this iteration for different pairs of factors converges to a fixed point when the overlapping marginals of every pair of factors coincide. We call this state marginal consistency. During that, an upper bound on the partition function monotonically decreases. This abstract algorithm unifies several existing algorithms, including max-sum diffusion and basic constraint propagation (or local consistency) algorithms in constraint programming. We further construct a hierarchy of marginal consistencies of increasingly higher levels and show than any such level can be enforced by adding identity factors of higher arity (order). Finally, we discuss instances of the framework for several semirings, including the distributive lattice and the max-sum and sum-product semirings. PMID:26352452
Consistency, Markedness and Language Change: On the Notion 'Consistent Language.'
ERIC Educational Resources Information Center
Smith, N. V.
1981-01-01
Explores markedness of languages and language change in relation to their roles in the consistency of language. Concludes typology provides no explanations in itself, but rather through data which need explanations and form a testing ground for linguistic theories. (Author/BK)
A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems
NASA Astrophysics Data System (ADS)
Thammano, Arit; Teekeng, Wannaporn
2015-05-01
The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.
Generalized arc consistency for global cardinality constraint
Regin, J.C.
1996-12-31
A global cardinality constraint (gcc) is specified in terms of a set of variables X = (x{sub 1},..., x{sub p}) which take their values in a subset of V = (v{sub 1},...,v{sub d}). It constrains the number of times a value v{sub i} {epsilon} V is assigned to a variable in X to be in an interval [l{sub i}, c{sub i}]. Cardinality constraints have proved very useful in many real-life problems, such as scheduling, timetabling, or resource allocation. A gcc is more general than a constraint of difference, which requires each interval to be. In this paper, we present an efficient way of implementing generalized arc consistency for a gcc. The algorithm we propose is based on a new theorem of flow theory. Its space complexity is O({vert_bar}X{vert_bar} {times} {vert_bar}V{vert_bar}) and its time complexity is O({vert_bar}X{vert_bar}{sup 2} {times} {vert_bar}V{vert_bar}). We also show how this algorithm can efficiently be combined with other filtering techniques.
Exploration of new multivariate spectral calibration algorithms.
Van Benthem, Mark Hilary; Haaland, David Michael; Melgaard, David Kennett; Martin, Laura Elizabeth; Wehlburg, Christine Marie; Pell, Randy J.; Guenard, Robert D.
2004-03-01
A variety of multivariate calibration algorithms for quantitative spectral analyses were investigated and compared, and new algorithms were developed in the course of this Laboratory Directed Research and Development project. We were able to demonstrate the ability of the hybrid classical least squares/partial least squares (CLSIPLS) calibration algorithms to maintain calibrations in the presence of spectrometer drift and to transfer calibrations between spectrometers from the same or different manufacturers. These methods were found to be as good or better in prediction ability as the commonly used partial least squares (PLS) method. We also present the theory for an entirely new class of algorithms labeled augmented classical least squares (ACLS) methods. New factor selection methods are developed and described for the ACLS algorithms. These factor selection methods are demonstrated using near-infrared spectra collected from a system of dilute aqueous solutions. The ACLS algorithm is also shown to provide improved ease of use and better prediction ability than PLS when transferring calibrations between near-infrared calibrations from the same manufacturer. Finally, simulations incorporating either ideal or realistic errors in the spectra were used to compare the prediction abilities of the new ACLS algorithm with that of PLS. We found that in the presence of realistic errors with non-uniform spectral error variance across spectral channels or with spectral errors correlated between frequency channels, ACLS methods generally out-performed the more commonly used PLS method. These results demonstrate the need for realistic error structure in simulations when the prediction abilities of various algorithms are compared. The combination of equal or superior prediction ability and the ease of use of the ACLS algorithms make the new ACLS methods the preferred algorithms to use for multivariate spectral calibrations.
A sparse reconstruction algorithm for ultrasonic images in nondestructive testing.
Guarneri, Giovanni Alfredo; Pipa, Daniel Rodrigues; Neves Junior, Flávio; de Arruda, Lúcia Valéria Ramos; Zibetti, Marcelo Victor Wüst
2015-01-01
Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan-about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR). PMID:25905700
A Sparse Reconstruction Algorithm for Ultrasonic Images in Nondestructive Testing
Guarneri, Giovanni Alfredo; Pipa, Daniel Rodrigues; Junior, Flávio Neves; de Arruda, Lúcia Valéria Ramos; Zibetti, Marcelo Victor Wüst
2015-01-01
Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan—about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR). PMID:25905700
Averaging Internal Consistency Reliability Coefficients
ERIC Educational Resources Information Center
Feldt, Leonard S.; Charter, Richard A.
2006-01-01
Seven approaches to averaging reliability coefficients are presented. Each approach starts with a unique definition of the concept of "average," and no approach is more correct than the others. Six of the approaches are applicable to internal consistency coefficients. The seventh approach is specific to alternate-forms coefficients. Although the…
Attitude Consistency Among American Youth.
ERIC Educational Resources Information Center
Mott, Frank L.; Mott, Susan H.
Attitudes of youth (ages 14-21) toward fertility expectations and women's roles are examined for consistency (e.g., whether high career expectations are correlated with a desire for fewer children). Approximately 12,000 White, Black, and Hispanic youth rated their attitudes toward statements that a woman's place is in the home, employment of wives…
Consistent Adjoint Driven Importance Sampling using Space, Energy and Angle
Peplow, Douglas E.; Mosher, Scott W; Evans, Thomas M
2012-08-01
For challenging radiation transport problems, hybrid methods combine the accuracy of Monte Carlo methods with the global information present in deterministic methods. One of the most successful hybrid methods is CADIS Consistent Adjoint Driven Importance Sampling. This method uses a deterministic adjoint solution to construct a biased source distribution and consistent weight windows to optimize a specific tally in a Monte Carlo calculation. The method has been implemented into transport codes using just the spatial and energy information from the deterministic adjoint and has been used in many applications to compute tallies with much higher figures-of-merit than analog calculations. CADIS also outperforms user-supplied importance values, which usually take long periods of user time to develop. This work extends CADIS to develop weight windows that are a function of the position, energy, and direction of the Monte Carlo particle. Two types of consistent source biasing are presented: one method that biases the source in space and energy while preserving the original directional distribution and one method that biases the source in space, energy, and direction. Seven simple example problems are presented which compare the use of the standard space/energy CADIS with the new space/energy/angle treatments.
Consistency-based ellipse detection method for complicated images
NASA Astrophysics Data System (ADS)
Zhang, Lijun; Huang, Xuexiang; Feng, Weichun; Liang, Shuli; Hu, Tianjian
2016-05-01
Accurate ellipse detection in complicated images is a challenging problem due to corruptions from image clutter, noise, or occlusion of other objects. To cope with this problem, an edge-following-based ellipse detection method is proposed which promotes the performances of the subprocesses based on consistency. The ellipse detector models edge connectivity by line segments and exploits inconsistent endpoints of the line segments to split the edge contours into smooth arcs. The smooth arcs are further refined with a novel arc refinement method which iteratively improves the consistency degree of the smooth arc. A two-phase arc integration method is developed to group disconnected elliptical arcs belonging to the same ellipse, and two constraints based on consistency are defined to increase the effectiveness and speed of the merging process. Finally, an efficient ellipse validation method is proposed to evaluate the saliency of the elliptic hypotheses. Detailed evaluation on synthetic images shows that our method outperforms other state-of-the-art ellipse detection methods in terms of effectiveness and speed. Additionally, we test our detector on three challenging real-world datasets. The F-measure score and execution time of results demonstrate that our method is effective and fast in complicated images. Therefore, the proposed method is suitable for practical applications.
Feature Selection via Modified Gravitational Optimization Algorithm
NASA Astrophysics Data System (ADS)
Nabizadeh, Nooshin; John, Nigel
2015-03-01
Feature selection is the process of selecting a subset of relevant and most informative features, which efficiently represents the input data. We proposed a feature selection algorithm based on n-dimensional gravitational optimization algorithm (NGOA), which is based on the principle of gravitational fields. The objective function of optimization algorithm is a non-linear function of variables, which are called masses and defined based on extracted features. The forces between the masses as well as their new locations are calculated using the value of the objective function and the values of masses. We extracted variety of features applying different wavelet transforms and statistical methods on FLAIR and T1-weighted MR brain images. There are two classes of normal and abnormal tissues. Extracted features are divided into groups of five features. The best feature is selected in each group using N-dimensional gravitational optimization algorithm and support vector machine classifier. Then the selected features from each group make several groups of five features again and so on till desired number of features is selected. The advantage of NGOA algorithm is that the possibility of being drawn into a local optimal solution is very low. The experimental results show that our method outperforms some standard feature selection algorithms on both real-data and simulated brain tumor data.
Project resource reallocation algorithm
NASA Technical Reports Server (NTRS)
Myers, J. E.
1981-01-01
A methodology for adjusting baseline cost estimates according to project schedule changes is described. An algorithm which performs a linear expansion or contraction of the baseline project resource distribution in proportion to the project schedule expansion or contraction is presented. Input to the algorithm consists of the deck of cards (PACE input data) prepared for the baseline project schedule as well as a specification of the nature of the baseline schedule change. Output of the algorithm is a new deck of cards with all work breakdown structure block and element of cost estimates redistributed for the new project schedule. This new deck can be processed through PACE to produce a detailed cost estimate for the new schedule.
A hybrid artificial bee colony algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Alqattan, Zakaria N.; Abdullah, Rosni
2015-02-01
Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).
Consistent interpretations of quantum mechanics
NASA Astrophysics Data System (ADS)
Omnès, Roland
1992-04-01
Within the last decade, significant progress has been made towards a consistent and complete reformulation of the Copenhagen interpretation (an interpretation consisting in a formulation of the experimental aspects of physics in terms of the basic formalism; it is consistent if free from internal contradiction and complete if it provides precise predictions for all experiments). The main steps involved decoherence (the transition from linear superpositions of macroscopic states to a mixing), Griffiths histories describing the evolution of quantum properties, a convenient logical structure for dealing with histories, and also some progress in semiclassical physics, which was made possible by new methods. The main outcome is a theory of phenomena, viz., the classically meaningful properties of a macroscopic system. It shows in particular how and when determinism is valid. This theory can be used to give a deductive form to measurement theory, which now covers some cases that were initially devised as counterexamples against the Copenhagen interpretation. These theories are described, together with their applications to some key experiments and some of their consequences concerning epistemology.
Kernel simplex growing algorithm for hyperspectral endmember extraction
NASA Astrophysics Data System (ADS)
Zhao, Liaoying; Zheng, Junpeng; Li, Xiaorun; Wang, Lijiao
2014-01-01
In order to effectively extract endmembers for hyperspectral imagery where linear mixing model may not be appropriate due to multiple scattering effects, this paper extends the simplex growing algorithm (SGA) to its kernel version. A new simplex volume formula without dimension reduction is used in SGA to form a new simplex growing algorithm (NSGA). The original data are nonlinearly mapped into a high-dimensional space where the scatters can be ignored. To avoid determining complex nonlinear mapping, a kernel function is used to extend the NSGA to kernel NSGA (KNSGA). Experimental results of simulated and real data prove that the proposed KNSGA approach outperforms SGA and NSGA.
NASA Astrophysics Data System (ADS)
Hild, Kenneth E.; Alleva, Giovanna; Nagarajan, Srikantan; Comani, Silvia
2007-01-01
In this study we compare the performance of six independent components analysis (ICA) algorithms on 16 real fetal magnetocardiographic (fMCG) datasets for the application of extracting the fetal cardiac signal. We also compare the extraction results for real data with the results previously obtained for synthetic data. The six ICA algorithms are FastICA, CubICA, JADE, Infomax, MRMI-SIG and TDSEP. The results obtained using real fMCG data indicate that the FastICA method consistently outperforms the others in regard to separation quality and that the performance of an ICA method that uses temporal information suffers in the presence of noise. These two results confirm the previous results obtained using synthetic fMCG data. There were also two notable differences between the studies based on real and synthetic data. The differences are that all six ICA algorithms are independent of gestational age and sensor dimensionality for synthetic data, but depend on gestational age and sensor dimensionality for real data. It is possible to explain these differences by assuming that the number of point sources needed to completely explain the data is larger than the dimensionality used in the ICA extraction.
A novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies
2012-01-01
Background Identification of causal SNPs in most genome wide association studies relies on approaches that consider each SNP individually. However, there is a strong correlation structure among SNPs that needs to be taken into account. Hence, increasingly modern computationally expensive regression methods are employed for SNP selection that consider all markers simultaneously and thus incorporate dependencies among SNPs. Results We develop a novel multivariate algorithm for large scale SNP selection using CAR score regression, a promising new approach for prioritizing biomarkers. Specifically, we propose a computationally efficient procedure for shrinkage estimation of CAR scores from high-dimensional data. Subsequently, we conduct a comprehensive comparison study including five advanced regression approaches (boosting, lasso, NEG, MCP, and CAR score) and a univariate approach (marginal correlation) to determine the effectiveness in finding true causal SNPs. Conclusions Simultaneous SNP selection is a challenging task. We demonstrate that our CAR score-based algorithm consistently outperforms all competing approaches, both uni- and multivariate, in terms of correctly recovered causal SNPs and SNP ranking. An R package implementing the approach as well as R code to reproduce the complete study presented here is available from http://strimmerlab.org/software/care/. PMID:23113980
A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles.
Soto, Ricardo; Crawford, Broderick; Galleguillos, Cristian; Paredes, Fernando; Norero, Enrique
2015-01-01
The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n(2) × n(2) grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n(2). Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods. PMID:26078751
Martins, F V C; Carrano, E G; Wanner, E F; Takahashi, R H C; Mateus, G R; Nakamura, F G
2014-01-01
Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors' knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances. PMID:24102647
NASA Astrophysics Data System (ADS)
Hu, Yan-Yan; Li, Dong-Sheng
2016-01-01
The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.
The successively temporal error concealment algorithm using error-adaptive block matching principle
NASA Astrophysics Data System (ADS)
Lee, Yu-Hsuan; Wu, Tsai-Hsing; Chen, Chao-Chyun
2014-09-01
Generally, the temporal error concealment (TEC) adopts the blocks around the corrupted block (CB) as the search pattern to find the best-match block in previous frame. Once the CB is recovered, it is referred to as the recovered block (RB). Although RB can be the search pattern to find the best-match block of another CB, RB is not the same as its original block (OB). The error between the RB and its OB limits the performance of TEC. The successively temporal error concealment (STEC) algorithm is proposed to alleviate this error. The STEC procedure consists of tier-1 and tier-2. The tier-1 divides a corrupted macroblock into four corrupted 8 × 8 blocks and generates a recovering order for them. The corrupted 8 × 8 block with the first place of recovering order is recovered in tier-1, and remaining 8 × 8 CBs are recovered in tier-2 along the recovering order. In tier-2, the error-adaptive block matching principle (EA-BMP) is proposed for the RB as the search pattern to recover remaining corrupted 8 × 8 blocks. The proposed STEC outperforms sophisticated TEC algorithms on average PSNR by 0.3 dB on the packet error rate of 20% at least.
A Hybrid alldifferent-Tabu Search Algorithm for Solving Sudoku Puzzles
Crawford, Broderick; Paredes, Fernando; Norero, Enrique
2015-01-01
The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n2 × n2 grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n2. Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods. PMID:26078751
NASA Technical Reports Server (NTRS)
Barth, Timothy J.; Lomax, Harvard
1987-01-01
The past decade has seen considerable activity in algorithm development for the Navier-Stokes equations. This has resulted in a wide variety of useful new techniques. Some examples for the numerical solution of the Navier-Stokes equations are presented, divided into two parts. One is devoted to the incompressible Navier-Stokes equations, and the other to the compressible form.
A scalable and practical one-pass clustering algorithm for recommender system
NASA Astrophysics Data System (ADS)
Khalid, Asra; Ghazanfar, Mustansar Ali; Azam, Awais; Alahmari, Saad Ali
2015-12-01
KMeans clustering-based recommendation algorithms have been proposed claiming to increase the scalability of recommender systems. One potential drawback of these algorithms is that they perform training offline and hence cannot accommodate the incremental updates with the arrival of new data, making them unsuitable for the dynamic environments. From this line of research, a new clustering algorithm called One-Pass is proposed, which is a simple, fast, and accurate. We show empirically that the proposed algorithm outperforms K-Means in terms of recommendation and training time while maintaining a good level of accuracy.
NASA Astrophysics Data System (ADS)
Souto-Iglesias, Antonio; Macià, Fabricio; González, Leo M.; Cercos-Pita, Jose L.
2013-03-01
The consistency of the Moving Particle Semi-implicit (MPS) method in reproducing the gradient, divergence and Laplacian differential operators is discussed in the present paper. Its relation to the Smoothed Particle Hydrodynamics (SPH) method is rigorously established. The application of the MPS method to solve the Navier-Stokes equations using a fractional step approach is treated, unveiling inconsistency problems when solving the Poisson equation for the pressure. A new corrected MPS method incorporating boundary terms is proposed. Applications to one dimensional boundary value Dirichlet and mixed Neumann-Dirichlet problems and to two-dimensional free-surface flows are presented.
Memory for Hand-Use Depends on Consistency of Handedness
Edlin, James M.; Carris, Emily K.; Lyle, Keith B.
2013-01-01
Individuals who do not consistently use the same hand to perform unimanual tasks (inconsistent-handed) outperform consistent right- and left-handed individuals on tests of episodic memory. We explored whether the inconsistent-hander (ICH) memory advantage extends to memory for unimanual hand use itself. Are ICHs better able to remember which hand they used to perform actions? Opposing predictions are possible, stemming from the finding that some regions of the corpus callosum are larger in ICHs, especially those that connect motor areas. One hypothesis is that greater callosally mediated interhemispheric interaction produces ICHs’ superior retrieval of episodic memories, and this may extend to episodic memories for hand use. Alternatively, we also hypothesized that greater interhemispheric interaction could produce more bilateral activation in motor areas during the performance and retrieval of unimanual actions. This could interfere with ICHs’ ability to remember which hand they used. To test these competing predictions in the current study, consistent- and inconsistent-handers performed unimanual actions, half of which required manipulating objects and half of which did not. Each action was performed four times in one of five conditions that differed in the ratio of left to right hand use: always left (4:0), usually left (3:1), equal (2:2), usually right (1:3), or always right (0:4). We compared consistent- and inconsistent-handers on recall of the left:right ratio for each action. ICHs remembered how they performed actions better than consistent-handers, regardless of ratio. These findings provide another example of superior episodic retrieval in ICHs. We discuss how greater interaction might benefit memory for hand use. PMID:24027522
MARGA: multispectral adaptive region growing algorithm for brain extraction on axial MRI.
Roura, Eloy; Oliver, Arnau; Cabezas, Mariano; Vilanova, Joan C; Rovira, Alex; Ramió-Torrentà, Lluís; Lladó, Xavier
2014-02-01
Brain extraction, also known as skull stripping, is one of the most important preprocessing steps for many automatic brain image analysis. In this paper we present a new approach called Multispectral Adaptive Region Growing Algorithm (MARGA) to perform the skull stripping process. MARGA is based on a region growing (RG) algorithm which uses the complementary information provided by conventional magnetic resonance images (MRI) such as T1-weighted and T2-weighted to perform the brain segmentation. MARGA can be seen as an extension of the skull stripping method proposed by Park and Lee (2009) [1], enabling their use in both axial views and low quality images. Following the same idea, we first obtain seed regions that are then spread using a 2D RG algorithm which behaves differently in specific zones of the brain. This adaptation allows to deal with the fact that middle MRI slices have better image contrast between the brain and non-brain regions than superior and inferior brain slices where the contrast is smaller. MARGA is validated using three different databases: 10 simulated brains from the BrainWeb database; 2 data sets from the National Alliance for Medical Image Computing (NAMIC) database, the first one consisting in 10 normal brains and 10 brains of schizophrenic patients acquired with a 3T GE scanner, and the second one consisting in 5 brains from lupus patients acquired with a 3T Siemens scanner; and 10 brains of multiple sclerosis patients acquired with a 1.5T scanner. We have qualitatively and quantitatively compared MARGA with the well-known Brain Extraction Tool (BET), Brain Surface Extractor (BSE) and Statistical Parametric Mapping (SPM) approaches. The obtained results demonstrate the validity of MARGA, outperforming the results of those standard techniques. PMID:24380649
Efficient Record Linkage Algorithms Using Complete Linkage Clustering
Mamun, Abdullah-Al; Aseltine, Robert; Rajasekaran, Sanguthevar
2016-01-01
Data from different agencies share data of the same individuals. Linking these datasets to identify all the records belonging to the same individuals is a crucial and challenging problem, especially given the large volumes of data. A large number of available algorithms for record linkage are prone to either time inefficiency or low-accuracy in finding matches and non-matches among the records. In this paper we propose efficient as well as reliable sequential and parallel algorithms for the record linkage problem employing hierarchical clustering methods. We employ complete linkage hierarchical clustering algorithms to address this problem. In addition to hierarchical clustering, we also use two other techniques: elimination of duplicate records and blocking. Our algorithms use sorting as a sub-routine to identify identical copies of records. We have tested our algorithms on datasets with millions of synthetic records. Experimental results show that our algorithms achieve nearly 100% accuracy. Parallel implementations achieve almost linear speedups. Time complexities of these algorithms do not exceed those of previous best-known algorithms. Our proposed algorithms outperform previous best-known algorithms in terms of accuracy consuming reasonable run times. PMID:27124604
Maintaining consistency in distributed systems
NASA Technical Reports Server (NTRS)
Birman, Kenneth P.
1991-01-01
In systems designed as assemblies of independently developed components, concurrent access to data or data structures normally arises within individual programs, and is controlled using mutual exclusion constructs, such as semaphores and monitors. Where data is persistent and/or sets of operation are related to one another, transactions or linearizability may be more appropriate. Systems that incorporate cooperative styles of distributed execution often replicate or distribute data within groups of components. In these cases, group oriented consistency properties must be maintained, and tools based on the virtual synchrony execution model greatly simplify the task confronting an application developer. All three styles of distributed computing are likely to be seen in future systems - often, within the same application. This leads us to propose an integrated approach that permits applications that use virtual synchrony with concurrent objects that respect a linearizability constraint, and vice versa. Transactional subsystems are treated as a special case of linearizability.
Self-consistent klystron simulations
Carlsten, B.E.; Tallerico, P.J.
1985-01-01
A numerical analysis of large-signal klystron behavior based on general wave-particle interaction theory is presented. The computer code presented is tailored for the minimum amount of complexity needed in klystron simulation. The code includes self-consistent electron motion, space-charge fields, and intermediate and output fields. It also includes use of time periodicity to simplify the problem, accurate representation of the space-charge fields, accurate representation of the cavity standing-wave fields, and a sophisticated particle-pushing routine. In the paper, examples are given that show the effects of cavity detunings, of varying the magnetic field profile, of electron beam asymmetries from the gun, and of variations in external load impedance. 4 refs., 7 figs.
Thermodynamically consistent continuum dislocation dynamics
NASA Astrophysics Data System (ADS)
Hochrainer, Thomas
2016-03-01
Dislocation based modeling of plasticity is one of the central challenges at the crossover of materials science and continuum mechanics. Developing a continuum theory of dislocations requires the solution of two long standing problems: (i) to represent dislocation kinematics in terms of a reasonable number of variables and (ii) to derive averaged descriptions of the dislocation dynamics (i.e. material laws) in terms of these variables. The kinematic problem (i) was recently solved through the introduction of continuum dislocation dynamics (CDD), which provides kinematically consistent evolution equations of dislocation alignment tensors, presuming a given average dislocation velocity (Hochrainer, T., 2015, Multipole expansion of continuum dislocations dynamics in terms of alignment tensors. Philos. Mag. 95 (12), 1321-1367). In the current paper we demonstrate how a free energy formulation may be used to solve the dynamic closure problem (ii) in CDD. We do so exemplarily for the lowest order CDD variant for curved dislocations in a single slip situation. In this case, a thermodynamically consistent average dislocation velocity is found to comprise five mesoscopic shear stress contributions. For a postulated free energy expression we identify among these stress contributions a back-stress term and a line-tension term, both of which have already been postulated for CDD. A new stress contribution occurs which is missing in earlier CDD models including the statistical continuum theory of straight parallel edge dislocations (Groma, I., Csikor, F.F., Zaiser, M., 2003. Spatial correlations and higher-order gradient terms in a continuum description of dislocation dynamics. Acta Mater. 51, 1271-1281). Furthermore, two entirely new stress contributions arise from the curvature of dislocations.
ERIC Educational Resources Information Center
Pawl, Andrew; Teodorescu, Raluca E.; Peterson, Joseph D.
2013-01-01
We have developed simple data-mining algorithms to assess the consistency and the randomness of student responses to problems consisting of multiple true or false statements. In this paper we describe the algorithms and use them to analyze data from introductory physics courses. We investigate statements that emerge as outliers because the class…
Depth consistency evaluation for error-pose detection
NASA Astrophysics Data System (ADS)
Jin, Sou-Young; Choi, Ho-Jin; Iraqi, Youssef
2013-12-01
With the development of depth sensors, i.e. Kinect, it is now possible to predict human body poses from a depthmap without any manual labeling. The predicted poses can be used as meaningful features for many applications such as human action recognition. However, existing pose estimation algorithms are not perfect, which can seriously affect the performance of its following applications. In this paper, we propose a novel method to detect erroneous poses. Human poses are captured by Kinect SDK which predicts body joints and connects them with straight lines to represent a pose. We observe depth gradient of pixels located on a body part is consistent when the body part is predicted correctly. With this observation, our algorithm examines depth gradients of pixels on each body part. During the depth gradient processing, our algorithm also considers occlusions. Once a sudden change is detected in depth values on a body part, we check whether the gradient is still consistent excluding the sudden change region. We tested our algorithm on many human activities and our experimental results show that our algorithm acceptably detects erroneous poses in real time.
Ferragina, A; de los Campos, G; Vazquez, A I; Cecchinato, A; Bittante, G
2015-11-01
The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict "difficult-to-predict" dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm(-1) were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from
Receiver Diversity Combining Using Evolutionary Algorithms in Rayleigh Fading Channel
Akbari, Mohsen; Manesh, Mohsen Riahi
2014-01-01
In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods. PMID:25045725
Alternating minimization algorithm for speckle reduction with a shifting technique.
Woo, Hyenkyun; Yun, Sangwoon
2012-04-01
Speckles (multiplicative noise) in synthetic aperture radar (SAR) make it difficult to interpret the observed image. Due to the edge-preserving feature of total variation (TV), variational models with TV regularization have attracted much interest in reducing speckles. Algorithms based on the augmented Lagrangian function have been proposed to efficiently solve speckle-reduction variational models with TV regularization. However, these algorithms require inner iterations or inverses involving the Laplacian operator at each iteration. In this paper, we adapt Tseng's alternating minimization algorithm with a shifting technique to efficiently remove the speckle without any inner iterations or inverses involving the Laplacian operator. The proposed method is very simple and highly parallelizable; therefore, it is very efficient to despeckle huge-size SAR images. Numerical results show that our proposed method outperforms the state-of-the-art algorithms for speckle-reduction variational models with a TV regularizer in terms of central-processing-unit time. PMID:22106149
Linear antenna array optimization using flower pollination algorithm.
Saxena, Prerna; Kothari, Ashwin
2016-01-01
Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance. PMID:27066339
GRAVITATIONALLY CONSISTENT HALO CATALOGS AND MERGER TREES FOR PRECISION COSMOLOGY
Behroozi, Peter S.; Wechsler, Risa H.; Wu, Hao-Yi; Busha, Michael T.; Klypin, Anatoly A.; Primack, Joel R. E-mail: rwechsler@stanford.edu
2013-01-20
We present a new algorithm for generating merger trees and halo catalogs which explicitly ensures consistency of halo properties (mass, position, and velocity) across time steps. Our algorithm has demonstrated the ability to improve both the completeness (through detecting and inserting otherwise missing halos) and purity (through detecting and removing spurious objects) of both merger trees and halo catalogs. In addition, our method is able to robustly measure the self-consistency of halo finders; it is the first to directly measure the uncertainties in halo positions, halo velocities, and the halo mass function for a given halo finder based on consistency between snapshots in cosmological simulations. We use this algorithm to generate merger trees for two large simulations (Bolshoi and Consuelo) and evaluate two halo finders (ROCKSTAR and BDM). We find that both the ROCKSTAR and BDM halo finders track halos extremely well; in both, the number of halos which do not have physically consistent progenitors is at the 1%-2% level across all halo masses. Our code is publicly available at http://code.google.com/p/consistent-trees. Our trees and catalogs are publicly available at http://hipacc.ucsc.edu/Bolshoi/.
Kwarciak, Kamil; Radom, Marcin; Formanowicz, Piotr
2016-04-01
The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called "one and many" assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called "one, two and many", one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analyzed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on: http://bioserver.cs.put.poznan.pl/download/ACO4mSBH.zip. PMID:26878124
Self-consistent flattened isochrones
NASA Astrophysics Data System (ADS)
Binney, James
2014-05-01
We present a family of self-consistent axisymmetric stellar systems that have analytic distribution functions (DFs) of the form f(J), so they depend on three integrals of motion and have triaxial velocity ellipsoids. The models, which are generalizations of Hénon's isochrone sphere, have four dimensionless parameters, two determining the part of the DF that is even in Lz and two determining the odd part of the DF (which determines the azimuthal velocity distribution). Outside their cores, the velocity ellipsoids of all models tend to point to the model's centre, and we argue that this behaviour is generic, so near the symmetry axis of a flattened model, the long axis of the velocity ellipsoid is naturally aligned with the symmetry axis and not perpendicular to it as in many published dynamical models of well-studied galaxies. By varying one of the DF parameters, the intensity of rotation can be increased from zero up to a maximum value set by the requirement that the DF be non-negative. Since angle-action coordinates are easily computed for these models, they are ideally suited for perturbative treatments and stability analysis. They can also be used to choose initial conditions for an N-body model that starts in perfect equilibrium, and to model observations of early-type galaxies. The modelling technique introduced here is readily extended to different radial density profiles, more complex kinematics and multicomponent systems. A number of important technical issues surrounding the determination of the models' observable properties are explained in two appendices.
Preconditioned Alternating Projection Algorithms for Maximum a Posteriori ECT Reconstruction.
Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng
2012-11-01
We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constrain involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the preconditioned alternating projection algorithm. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality. PMID:23271835
Preconditioned Alternating Projection Algorithms for Maximum a Posteriori ECT Reconstruction
Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng
2012-01-01
We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constrain involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the preconditioned alternating projection algorithm. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality. PMID:23271835
Adaptive image contrast enhancement algorithm for point-based rendering
NASA Astrophysics Data System (ADS)
Xu, Shaoping; Liu, Xiaoping P.
2015-03-01
Surgical simulation is a major application in computer graphics and virtual reality, and most of the existing work indicates that interactive real-time cutting simulation of soft tissue is a fundamental but challenging research problem in virtual surgery simulation systems. More specifically, it is difficult to achieve a fast enough graphic update rate (at least 30 Hz) on commodity PC hardware by utilizing traditional triangle-based rendering algorithms. In recent years, point-based rendering (PBR) has been shown to offer the potential to outperform the traditional triangle-based rendering in speed when it is applied to highly complex soft tissue cutting models. Nevertheless, the PBR algorithms are still limited in visual quality due to inherent contrast distortion. We propose an adaptive image contrast enhancement algorithm as a postprocessing module for PBR, providing high visual rendering quality as well as acceptable rendering efficiency. Our approach is based on a perceptible image quality technique with automatic parameter selection, resulting in a visual quality comparable to existing conventional PBR algorithms. Experimental results show that our adaptive image contrast enhancement algorithm produces encouraging results both visually and numerically compared to representative algorithms, and experiments conducted on the latest hardware demonstrate that the proposed PBR framework with the postprocessing module is superior to the conventional PBR algorithm and that the proposed contrast enhancement algorithm can be utilized in (or compatible with) various variants of the conventional PBR algorithm.
A new improved artificial bee colony algorithm for ship hull form optimization
NASA Astrophysics Data System (ADS)
Huang, Fuxin; Wang, Lijue; Yang, Chi
2016-04-01
The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.
Macpherson, Peter; Lalloo, David G; Thindwa, Deus; Webb, Emily L; Squire, S Bertel; Chipungu, Geoffrey A; Desmond, Nicola; Makombe, Simon D; Taegtmeyer, Miriam; Choko, Augustine T; Corbett, Elizabeth L
2014-02-01
The accuracy of a novel community health worker antiretroviral therapy eligibility assessment tool was examined in community members in Blantyre, Malawi. Nurses independently performed World Health Organization (WHO) staging and CD4 counts. One hundred ten (55.6%) of 198 HIV-positive participants had a CD4 count of <350 cells per cubic millimeter. The community health worker tool significantly outperformed WHO clinical staging in identifying CD4 count of <350 cells per cubic millimeter in terms of sensitivity (41% vs. 19%), positive predictive value (75% vs. 68%), negative predictive values (53% vs. 47%), and area under the receiver-operator curve (0.62 vs. 0.54; P = 0.017). Reliance on WHO staging is likely to result in missed and delayed antiretroviral therapy initiation. PMID:23846567
A graph spectrum based geometric biclustering algorithm.
Wang, Doris Z; Yan, Hong
2013-01-21
Biclustering is capable of performing simultaneous clustering on two dimensions of a data matrix and has many applications in pattern classification. For example, in microarray experiments, a subset of genes is co-expressed in a subset of conditions, and biclustering algorithms can be used to detect the coherent patterns in the data for further analysis of function. In this paper, we present a graph spectrum based geometric biclustering (GSGBC) algorithm. In the geometrical view, biclusters can be seen as different linear geometrical patterns in high dimensional spaces. Based on this, the modified Hough transform is used to find the Hough vector (HV) corresponding to sub-bicluster patterns in 2D spaces. A graph can be built regarding each HV as a node. The graph spectrum is utilized to identify the eigengroups in which the sub-biclusters are grouped naturally to produce larger biclusters. Through a comparative study, we find that the GSGBC achieves as good a result as GBC and outperforms other kinds of biclustering algorithms. Also, compared with the original geometrical biclustering algorithm, it reduces the computing time complexity significantly. We also show that biologically meaningful biclusters can be identified by our method from real microarray gene expression data. PMID:23079285
Improving the algorithm of temporal relation propagation
NASA Astrophysics Data System (ADS)
Shen, Jifeng; Xu, Dan; Liu, Tongming
2005-03-01
In the military Multi Agent System, every agent needs to analyze the temporal relationships among the tasks or combat behaviors, and it"s very important to reflect the battlefield situation in time. The temporal relation among agents is usually very complex, and we model it with interval algebra (IA) network. Therefore an efficient temporal reasoning algorithm is vital in battle MAS model. The core of temporal reasoning is path consistency algorithm, an efficient path consistency algorithm is necessary. In this paper we used the Interval Matrix Calculus (IMC) method to represent the temporal relation, and optimized the path consistency algorithm by improving the efficiency of propagation of temporal relation based on the Allen's path consistency algorithm.
A multi-scale non-local means algorithm for image de-noising
NASA Astrophysics Data System (ADS)
Nercessian, Shahan; Panetta, Karen A.; Agaian, Sos S.
2012-06-01
A highly studied problem in image processing and the field of electrical engineering in general is the recovery of a true signal from its noisy version. Images can be corrupted by noise during their acquisition or transmission stages. As noisy images are visually very poor in quality, and complicate further processing stages of computer vision systems, it is imperative to develop algorithms which effectively remove noise in images. In practice, it is a difficult task to effectively remove the noise while simultaneously retaining the edge structures within the image. Accordingly, many de-noising algorithms have been considered attempt to intelligent smooth the image while still preserving its details. Recently, a non-local means (NLM) de-noising algorithm was introduced, which exploited the redundant nature of images to achieve image de-noising. The algorithm was shown to outperform current de-noising standards, including Gaussian filtering, anisotropic diffusion, total variation minimization, and multi-scale transform coefficient thresholding. However, the NLM algorithm was developed in the spatial domain, and therefore, does not leverage the benefit that multi-scale transforms provide a framework in which signals can be better distinguished by noise. Accordingly, in this paper, a multi-scale NLM (MS-NLM) algorithm is proposed, which combines the advantage of the NLM algorithm and multi-scale image processing techniques. Experimental results via computer simulations illustrate that the MS-NLM algorithm outperforms the NLM, both visually and quantitatively.
Spaceborne SAR Imaging Algorithm for Coherence Optimized
Qiu, Zhiwei; Yue, Jianping; Wang, Xueqin; Yue, Shun
2016-01-01
This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application. PMID:26871446
Lin, Frank Yeong-Sung; Hsiao, Chiu-Han; Yen, Hong-Hsu; Hsieh, Yu-Jen
2013-01-01
One of the important applications in Wireless Sensor Networks (WSNs) is video surveillance that includes the tasks of video data processing and transmission. Processing and transmission of image and video data in WSNs has attracted a lot of attention in recent years. This is known as Wireless Visual Sensor Networks (WVSNs). WVSNs are distributed intelligent systems for collecting image or video data with unique performance, complexity, and quality of service challenges. WVSNs consist of a large number of battery-powered and resource constrained camera nodes. End-to-end delay is a very important Quality of Service (QoS) metric for video surveillance application in WVSNs. How to meet the stringent delay QoS in resource constrained WVSNs is a challenging issue that requires novel distributed and collaborative routing strategies. This paper proposes a Near-Optimal Distributed QoS Constrained (NODQC) routing algorithm to achieve an end-to-end route with lower delay and higher throughput. A Lagrangian Relaxation (LR)-based routing metric that considers the “system perspective” and “user perspective” is proposed to determine the near-optimal routing paths that satisfy end-to-end delay constraints with high system throughput. The empirical results show that the NODQC routing algorithm outperforms others in terms of higher system throughput with lower average end-to-end delay and delay jitter. In this paper, for the first time, the algorithm shows how to meet the delay QoS and at the same time how to achieve higher system throughput in stringently resource constrained WVSNs.
Enhanced ATR algorithm for high resolution multi-band sonar imagery
NASA Astrophysics Data System (ADS)
Aridgides, Tom; Fernández, Manuel
2008-04-01
An improved automatic target recognition (ATR) processing string has been developed. The overall processing string consists of pre-processing, subimage adaptive clutter filtering (SACF), normalization, detection, data regularization, feature extraction, optimal subset feature selection, feature orthogonalization and classification processing blocks. A new improvement was made to the processing string, data regularization, which entails computing the input data mean, clipping the data to a multiple of its mean and scaling it, prior to feature extraction. The classified objects of 3 distinct strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new high-resolution three-frequency band sonar imagery. The ATR processing strings were individually tuned to the corresponding three-frequency band data, making use of the new processing improvement, data regularization, which resulted in a 3:1 reduction in false alarms. Two significant fusion algorithm improvements were made. First, a nonlinear 2nd order (Volterra) feature LLRT fusion algorithm was developed. Second, a repeated application of a subset Volterra feature selection / feature orthogonalization / LLRT fusion block was utilized. It was shown that cascaded Volterra feature LLRT fusion of the ATR processing strings outperforms baseline summing and single-stage Volterra feature LLRT algorithms, yielding significant improvements over the best single ATR processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate.
NASA Astrophysics Data System (ADS)
Zhu, Li; He, Yongxiang; Xue, Haidong; Chen, Leichen
Traditional genetic algorithms (GA) displays a disadvantage of early-constringency in dealing with scheduling problem. To improve the crossover operators and mutation operators self-adaptively, this paper proposes a self-adaptive GA at the target of multitask scheduling optimization under limited resources. The experiment results show that the proposed algorithm outperforms the traditional GA in evolutive ability to deal with complex task scheduling optimization.
Bowen, J.; Dozier, G.
1996-12-31
This paper introduces a hybrid evolutionary hill-climbing algorithm that quickly solves (Constraint Satisfaction Problems (CSPs)). This hybrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent constraint network. This hybrid outperforms a well known hill-climbing algorithm, the Iterative Descent Method, on a test suite of 750 randomly generated CSPs.
Doshi, Jimit; Erus, Guray; Ou, Yangming; Resnick, Susan M; Gur, Ruben C; Gur, Raquel E; Satterthwaite, Theodore D; Furth, Susan; Davatzikos, Christos
2016-02-15
Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images. PMID:26679328
A novel bee swarm optimization algorithm for numerical function optimization
NASA Astrophysics Data System (ADS)
Akbari, Reza; Mohammadi, Alireza; Ziarati, Koorush
2010-10-01
The optimization algorithms which are inspired from intelligent behavior of honey bees are among the most recently introduced population based techniques. In this paper, a novel algorithm called bee swarm optimization, or BSO, and its two extensions for improving its performance are presented. The BSO is a population based optimization technique which is inspired from foraging behavior of honey bees. The proposed approach provides different patterns which are used by the bees to adjust their flying trajectories. As the first extension, the BSO algorithm introduces different approaches such as repulsion factor and penalizing fitness (RP) to mitigate the stagnation problem. Second, to maintain efficiently the balance between exploration and exploitation, time-varying weights (TVW) are introduced into the BSO algorithm. The proposed algorithm (BSO) and its two extensions (BSO-RP and BSO-RPTVW) are compared with existing algorithms which are based on intelligent behavior of honey bees, on a set of well known numerical test functions. The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.
Three hypothesis algorithm with occlusion reasoning for multiple people tracking
NASA Astrophysics Data System (ADS)
Reta, Carolina; Altamirano, Leopoldo; Gonzalez, Jesus A.; Medina-Carnicer, Rafael
2015-01-01
This work proposes a detection-based tracking algorithm able to locate and keep the identity of multiple people, who may be occluded, in uncontrolled stationary environments. Our algorithm builds a tracking graph that models spatio-temporal relationships among attributes of interacting people to predict and resolve partial and total occlusions. When a total occlusion occurs, the algorithm generates various hypotheses about the location of the occluded person considering three cases: (a) the person keeps the same direction and speed, (b) the person follows the direction and speed of the occluder, and (c) the person remains motionless during occlusion. By analyzing the graph, our algorithm can detect trajectories produced by false alarms and estimate the location of missing or occluded people. Our algorithm performs acceptably under complex conditions, such as partial visibility of individuals getting inside or outside the scene, continuous interactions and occlusions among people, wrong or missing information on the detection of persons, as well as variation of the person's appearance due to illumination changes and background-clutter distracters. Our algorithm was evaluated on test sequences in the field of intelligent surveillance achieving an overall precision of 93%. Results show that our tracking algorithm outperforms even trajectory-based state-of-the-art algorithms.
An Improved Physarum polycephalum Algorithm for the Shortest Path Problem
Wang, Qing; Adamatzky, Andrew; Chan, Felix T. S.; Mahadevan, Sankaran
2014-01-01
Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm. PMID:24982960
Generalized Pattern Search Algorithm for Peptide Structure Prediction
Nicosia, Giuseppe; Stracquadanio, Giovanni
2008-01-01
Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD Cα. PMID:18487293
A Hybrid Evolutionary Algorithm for Wheat Blending Problem
Bonyadi, Mohammad Reza; Michalewicz, Zbigniew; Barone, Luigi
2014-01-01
This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts with a filtering process that follows predefined rules to reduce the search space. Then the linear-relaxed version of the problem is solved using a standard linear programming algorithm. The result is used in conjunction with a solution generated by a heuristic method to generate an initial solution. After that, a hybrid of an evolutionary algorithm, a heuristic method, and a linear programming solver is used to improve the quality of the solution. A local search based posttuning method is also incorporated into the algorithm. The proposed algorithm has been tested on artificial test cases and also real data from past years. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in terms of both quality and speed. PMID:24707222
An improved Physarum polycephalum algorithm for the shortest path problem.
Zhang, Xiaoge; Wang, Qing; Adamatzky, Andrew; Chan, Felix T S; Mahadevan, Sankaran; Deng, Yong
2014-01-01
Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm. PMID:24982960
Landerl, Karin; Reitsma, Pieter
2005-12-01
In Dutch, vowel duration spelling is phonologically consistent but morphologically inconsistent (e.g., paar-paren). In German, it is phonologically inconsistent but morphologically consistent (e.g., Paar-Paare). Contrasting the two orthographies allowed us to examine the role of phonological and morphological consistency in the acquisition of the same orthographic feature. Dutch and German children in Grades 2 to 4 spelled singular and plural word forms and in a second task identified the correct spelling of singular and plural forms of the same nonword. Dutch children were better in word spelling, but German children outperformed the Dutch children in nonword selection. Also, whereas German children performed on a similar level for singular and plural items, Dutch children showed a large discrepancy. The results indicate that children use phonological and morphological rules from an early age but that the developmental balance between the two sources of information is constrained by the specific orthography. PMID:15975590
Surface-consistent matching filters for time-lapse processing
NASA Astrophysics Data System (ADS)
Al Mutlaq, Mahdi H.
The problem of mismatch between repeated time-lapse seismic surveys remains a challenge, particularly for land acquisition. In this dissertation, we present a new algorithm, which is an extension of the surface-consistent model, and which minimizes the mismatch between surveys, hence improving repeatability. We introduce the concept of surface-consistent matching filters (SCMF) for processing time-lapse seismic data, where matching filters are convolutional filters that minimize the sum-squared error between two signals. Since in the Fourier domain, a matching filter is the spectral ratio of the two signals, we extend the well known surface-consistent hypothesis such that the data term is a trace-by-trace spectral ratio of two datasets instead of only one (i.e. surface-consistent deconvolution). To avoid unstable division of spectra, we compute the spectral ratios in the time domain by first designing trace-sequential, least-squares matching filters, then Fourier transforming them. A subsequent least-squares solution then factors the trace-sequential matching filters into four operators: two surface-consistent (source and receiver), and two subsurface-consistent (offset and midpoint). We apply the algorithm to two datasets: a synthetic time-lapse model and field data from a CO2 monitoring site in Northern Alberta. In addition, two common time-lapse processing schemes (independent processing and simultaneous processing) are compared. We present a modification of the simultaneous processing scheme as a direct result of applying the new SCMF algorithm. The results of applying the SCMF together with the new modified simultaneous processing flow reveal the potential benefit of the method, however some challenges remain, specifically in the presence of random noise.
Baudoin, T; Grgić, M V; Zadravec, D; Geber, G; Tomljenović, D; Kalogjera, L
2013-12-01
ENT navigation has given new opportunities in performing Endoscopic Sinus Surgery (ESS) and improving surgical outcome of the patients` treatment. ESS assisted by a navigation system could be called Navigated Endoscopic Sinus Surgery (NESS). As it is generally accepted that the NESS should be performed only in cases of complex anatomy and pathology, it has not yet been established as a state-of-the-art procedure and thus not used on a daily basis. This paper presents an algorithm for use of a navigation system for basic ESS in the treatment of chronic rhinosinusitis (CRS). The algorithm includes five units that should be highlighted using a navigation system. They are as follows: 1) nasal vestibule unit, 2) OMC unit, 3) anterior ethmoid unit, 4) posterior ethmoid unit, and 5) sphenoid unit. Each unit has a shape of a triangular pyramid and consists of at least four reference points or landmarks. As many landmarks as possible should be marked when determining one of the five units. Navigated orientation in each unit should always precede any surgical intervention. The algorithm should improve the learning curve of trainees and enable surgeons to use the navigation system routinely and systematically. PMID:24260766
Service Discovery Framework Supported by EM Algorithm and Bayesian Classifier
NASA Astrophysics Data System (ADS)
Peng, Yanbin
Service oriented computing has become the main stream research field nowadays. Meanwhile, machine learning is a promising AI technology which can enhance the performance of traditional algorithm. Therefore, aiming at solving service discovery problem, this paper imports Bayesian classifier to web service discovery framework, which can improve service querying speed. In this framework, services in service library become training set of Bayesian classifier, service query becomes a testing sample. Service matchmaking process can be executed in related service class, which has fewer services, thus can save time. Due to don't know the class of service in training set, EM algorithm is used to estimate prior probability and likelihood functions. Experiment results show that the EM algorithm and Bayesian classifier supported method outperforms other methods in time complexity.
New validation algorithm for data association in SLAM.
Guerra, Edmundo; Munguia, Rodrigo; Bolea, Yolanda; Grau, Antoni
2013-09-01
In this work, a novel data validation algorithm for a single-camera SLAM system is introduced. A 6-degree-of-freedom monocular SLAM method based on the delayed inverse-depth (DI-D) feature initialization is used as a benchmark. This SLAM methodology has been improved with the introduction of the proposed data association batch validation technique, the highest order hypothesis compatibility test, HOHCT. This new algorithm is based on the evaluation of statistically compatible hypotheses, and a search algorithm designed to exploit the characteristics of delayed inverse-depth technique. In order to show the capabilities of the proposed technique, experimental tests have been compared with classical methods. The results of the proposed technique outperformed the results of the classical approaches. PMID:23701896
LAHS: A novel harmony search algorithm based on learning automata
NASA Astrophysics Data System (ADS)
Enayatifar, Rasul; Yousefi, Moslem; Abdullah, Abdul Hanan; Darus, Amer Nordin
2013-12-01
This study presents a learning automata-based harmony search (LAHS) for unconstrained optimization of continuous problems. The harmony search (HS) algorithm performance strongly depends on the fine tuning of its parameters, including the harmony consideration rate (HMCR), pitch adjustment rate (PAR) and bandwidth (bw). Inspired by the spur-in-time responses in the musical improvisation process, learning capabilities are employed in the HS to select these parameters based on spontaneous reactions. An extensive numerical investigation is conducted on several well-known test functions, and the results are compared with the HS algorithm and its prominent variants, including the improved harmony search (IHS), global-best harmony search (GHS) and self-adaptive global-best harmony search (SGHS). The numerical results indicate that the LAHS is more efficient in finding optimum solutions and outperforms the existing HS algorithm variants.
Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
Demeyer, Sofie; Michoel, Tom; Fostier, Jan; Audenaert, Pieter; Pickavet, Mario; Demeester, Piet
2013-01-01
Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma/. PMID:23620730
Demeyer, Sofie; Michoel, Tom; Fostier, Jan; Audenaert, Pieter; Pickavet, Mario; Demeester, Piet
2013-01-01
Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma/. PMID:23620730
Adaptive optics image deconvolution based on a modified Richardson-Lucy algorithm
NASA Astrophysics Data System (ADS)
Chen, Bo; Geng, Ze-xun; Yan, Xiao-dong; Yang, Yang; Sui, Xue-lian; Zhao, Zhen-lei
2007-12-01
Adaptive optical (AO) system provides a real-time compensation for atmospheric turbulence. However, the correction is often only partial, and a deconvolution is required for reaching the diffraction limit. The Richardson-Lucy (R-L) Algorithm is the technique most widely used for AO image deconvolution, but Standard R-L Algorithm (SRLA) is often puzzled by speckling phenomenon, wraparound artifact and noise problem. A Modified R-L Algorithm (MRLA) for AO image deconvolution is presented. This novel algorithm applies Magain's correct sampling approach and incorporating noise statistics to Standard R-L Algorithm. The alternant iterative method is applied to estimate PSF and object in the novel algorithm. Comparing experiments for indoor data and AO image are done with SRLA and the MRLA in this paper. Experimental results show that this novel MRLA outperforms the SRLA.
Voronoi particle merging algorithm for PIC codes
NASA Astrophysics Data System (ADS)
Luu, Phuc T.; Tückmantel, T.; Pukhov, A.
2016-05-01
We present a new particle-merging algorithm for the particle-in-cell method. Based on the concept of the Voronoi diagram, the algorithm partitions the phase space into smaller subsets, which consist of only particles that are in close proximity in the phase space to each other. We show the performance of our algorithm in the case of the two-stream instability and the magnetic shower.
Applications of genetic algorithms and neural networks to interatomic potentials
NASA Astrophysics Data System (ADS)
Hobday, Steven; Smith, Roger; BelBruno, Joe
1999-06-01
Applications of two modern artificial intelligence (AI) techniques, genetic algorithms (GA) and neural networks (NN) to computer simulations are reported. It is shown that the GA are very useful tools for determining the minimum energy structures of clusters of atoms described by interatomic potential functions and generally outperform other optimisation methods for this task. A number of applications are given including covalent, and close packed structures of single or multi-component atomic species. It is also shown that (many body) interatomic potential functions for multi-component systems can be derived by training a specially constructed NN on a variety of structural data.
Proctor, Darby; Essler, Jennifer; Pinto, Ana I.; Wismer, Sharon; Stoinski, Tara; Brosnan, Sarah F.; Bshary, Redouan
2012-01-01
The insight that animals' cognitive abilities are linked to their evolutionary history, and hence their ecology, provides the framework for the comparative approach. Despite primates renowned dietary complexity and social cognition, including cooperative abilities, we here demonstrate that cleaner wrasse outperform three primate species, capuchin monkeys, chimpanzees and orang-utans, in a foraging task involving a choice between two actions, both of which yield identical immediate rewards, but only one of which yields an additional delayed reward. The foraging task decisions involve partner choice in cleaners: they must service visiting client reef fish before resident clients to access both; otherwise the former switch to a different cleaner. Wild caught adult, but not juvenile, cleaners learned to solve the task quickly and relearned the task when it was reversed. The majority of primates failed to perform above chance after 100 trials, which is in sharp contrast to previous studies showing that primates easily learn to choose an action that yields immediate double rewards compared to an alternative action. In conclusion, the adult cleaners' ability to choose a superior action with initially neutral consequences is likely due to repeated exposure in nature, which leads to specific learned optimal foraging decision rules. PMID:23185293
PCA-LBG-based algorithms for VQ codebook generation
NASA Astrophysics Data System (ADS)
Tsai, Jinn-Tsong; Yang, Po-Yuan
2015-04-01
Vector quantisation (VQ) codebooks are generated by combining principal component analysis (PCA) algorithms with Linde-Buzo-Gray (LBG) algorithms. All training vectors are grouped according to the projected values of the principal components. The PCA-LBG-based algorithms include (1) PCA-LBG-Median, which selects the median vector of each group, (2) PCA-LBG-Centroid, which adopts the centroid vector of each group, and (3) PCA-LBG-Random, which randomly selects a vector of each group. The LBG algorithm finds a codebook based on the better vectors sent to an initial codebook by the PCA. The PCA performs an orthogonal transformation to convert a set of potentially correlated variables into a set of variables that are not linearly correlated. Because the orthogonal transformation efficiently distinguishes test image vectors, the proposed PCA-LBG-based algorithm is expected to outperform conventional algorithms in designing VQ codebooks. The experimental results confirm that the proposed PCA-LBG-based algorithms indeed obtain better results compared to existing methods reported in the literature.
Another hybrid conjugate gradient algorithm for unconstrained optimization
NASA Astrophysics Data System (ADS)
Andrei, Neculai
2008-02-01
Another hybrid conjugate gradient algorithm is subject to analysis. The parameter ? k is computed as a convex combination of beta ^{{HS}}_{k} (Hestenes-Stiefel) and beta ^{{DY}}_{k} (Dai-Yuan) algorithms, i.eE beta ^{C}_{k} = {left( {1 - theta _{k} } right)}beta ^{{HS}}_{k} + theta _{k} beta ^{{DY}}_{k} . The parameter ? k in the convex combination is computed in such a way so that the direction corresponding to the conjugate gradient algorithm to be the Newton direction and the pair (s k , y k ) to satisfy the quasi-Newton equation nabla ^{2} f{left( {x_{{k + 1}} } right)}s_{k} = y_{k} , where s_{k} = x_{{k + 1}} - x_{k} and y_{k} = g_{{k + 1}} - g_{k} . The algorithm uses the standard Wolfe line search conditions. Numerical comparisons with conjugate gradient algorithms show that this hybrid computational scheme outperforms the Hestenes-Stiefel and the Dai-Yuan conjugate gradient algorithms as well as the hybrid conjugate gradient algorithms of Dai and Yuan. A set of 750 unconstrained optimization problems are used, some of them from the CUTE library.
Santra, Tapesh; Delatola, Eleni Ioanna
2016-01-01
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values. PMID:27444576
NASA Astrophysics Data System (ADS)
Santra, Tapesh; Delatola, Eleni Ioanna
2016-07-01
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.
Santra, Tapesh; Delatola, Eleni Ioanna
2016-01-01
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values. PMID:27444576
Stability of Bareiss algorithm
NASA Astrophysics Data System (ADS)
Bojanczyk, Adam W.; Brent, Richard P.; de Hoog, F. R.
1991-12-01
In this paper, we present a numerical stability analysis of Bareiss algorithm for solving a symmetric positive definite Toeplitz system of linear equations. We also compare Bareiss algorithm with Levinson algorithm and conclude that the former has superior numerical properties.
High-performance speech recognition using consistency modeling
NASA Astrophysics Data System (ADS)
Digilakis, Vassilios; Monaco, Peter; Murveit, Hy; Weintraub, Mitchel
1994-03-01
The goal of this project conducted by SRI International (SRI) is to develop consistency modeling technology. Consistency modeling aims to reduce the number of improper independence assumptions used in traditional speech-recognition algorithms so that the resulting speech-recognition hypotheses are more self-consistent and, therefore, more accurate. Consistency is achieved by conditioning HMM output distributions on state and observations histories, P(x/s,H). The technical objective of the project is to find the proper form of the probability distribution, P; the proper history vector, H; the proper feature vector, x; and to develop the infrastructure (e.g. efficient estimation and search techniques) so that consistency modeling can be effectively used. During the first year of this effort, SRI focused on developing the appropriate base technologies for consistency modeling. We developed genonic hidden Markov model (HMM) technology, our choice for P above, and Progressive Search technology for HMM systems which allows us to develop and use complex HMM formulations in an efficient manner. Papers describing these two techniques are included in the appendix of this report and are briefly summarized below. This report also describes other accomplishments of Year 1 including the initial exploitation of discrete and continuous consistency modeling and the development of a scheme for efficiently computing Gaussian probabilities.
[Psychometric properties of a scale: internal consistency].
Campo-Arias, Adalberto; Oviedo, Heidi C
2008-01-01
Internal consistency reliability is the degree of correlation between a scale's items. Internal consistency is calculated by Kuder-Richardson's formula 20 for dichotomous choices and Cronbach's alpha for polytomous items. 0.70 to 0.90 internal consistency is acceptable. 5-25 participants are needed for each item when computing the internal consistency of a twenty-item scale. Internal consistency varies according to population and then it is necessary to report it always that scale is used. PMID:19360231
Quality and Consistency of the NASA Ocean Color Data Record
NASA Technical Reports Server (NTRS)
Franz, Bryan A.
2012-01-01
The NASA Ocean Biology Processing Group (OBPG) recently reprocessed the multimission ocean color time-series from SeaWiFS, MODIS-Aqua, and MODIS-Terra using common algorithms and improved instrument calibration knowledge. Here we present an analysis of the quality and consistency of the resulting ocean color retrievals, including spectral water-leaving reflectance, chlorophyll a concentration, and diffuse attenuation. Statistical analysis of satellite retrievals relative to in situ measurements will be presented for each sensor, as well as an assessment of consistency in the global time-series for the overlapping periods of the missions. Results will show that the satellite retrievals are in good agreement with in situ measurements, and that the sensor ocean color data records are highly consistent over the common mission lifespan for the global deep oceans, but with degraded agreement in higher productivity, higher complexity coastal regions.
Linear Multigrid Techniques in Self-consistent Electronic Structure Calculations
Fattebert, J-L
2000-05-23
Ab initio DFT electronic structure calculations involve an iterative process to solve the Kohn-Sham equations for an Hamiltonian depending on the electronic density. We discretize these equations on a grid by finite differences. Trial eigenfunctions are improved at each step of the algorithm using multigrid techniques to efficiently reduce the error at all length scale, until self-consistency is achieved. In this paper we focus on an iterative eigensolver based on the idea of inexact inverse iteration, using multigrid as a preconditioner. We also discuss how this technique can be used for electrons described by general non-orthogonal wave functions, and how that leads to a linear scaling with the system size for the computational cost of the most expensive parts of the algorithm.
Zhang, Ling; Zhang, Yaojun; Wang, Hong; Zou, Jianwen; Siemann, Evan
2013-01-01
Two mechanisms that have been proposed to explain success of invasive plants are unusual biotic interactions, such as enemy release or enhanced mutualisms, and increased resource availability. However, while these mechanisms are usually considered separately, both may be involved in successful invasions. Biotic interactions may be positive or negative and may interact with nutritional resources in determining invasion success. In addition, the effects of different nutrients on invasions may vary. Finally, genetic variation in traits between populations located in introduced versus native ranges may be important for biotic interactions and/or resource use. Here, we investigated the roles of soil biota, resource availability, and plant genetic variation using seedlings of Triadica sebifera in an experiment in the native range (China). We manipulated nitrogen (control or 4 g/m2), phosphorus (control or 0.5 g/m2), soil biota (untreated or sterilized field soil), and plant origin (4 populations from the invasive range, 4 populations from the native range) in a full factorial experiment. Phosphorus addition increased root, stem, and leaf masses. Leaf mass and height growth depended on population origin and soil sterilization. Invasive populations had higher leaf mass and growth rates than native populations did in fresh soil but they had lower, comparable leaf mass and growth rates in sterilized soil. Invasive populations had higher growth rates with phosphorus addition but native ones did not. Soil sterilization decreased specific leaf area in both native and exotic populations. Negative effects of soil sterilization suggest that soil pathogens may not be as important as soil mutualists for T. sebifera performance. Moreover, interactive effects of sterilization and origin suggest that invasive T. sebifera may have evolved more beneficial relationships with the soil biota. Overall, seedlings from the invasive range outperformed those from the native range, however, an
Benhamou, Simon; Collet, Julien
2015-12-21
The "Lévy Foraging Hypothesis" promotes Lévy walk (LW) as the best strategy to forage for patchily but unpredictably located prey. This strategy mixes extensive and intensive searching phases in a mostly cue-free way through strange, scale-free kinetics. It is however less efficient than a cue-driven two-scale Composite Brownian walk (CBW) when the resources encountered are systematically detected. Nevertheless, it could be assumed that the intrinsic capacity of LW to trigger cue-free intensive searching at random locations might be advantageous when resources are not only scarcely encountered but also so cryptic that the probability to detect those encountered during movement is low. Surprisingly, this situation, which should be quite common in natural environments, has almost never been studied. Only a few studies have considered "saltatory" foragers, which are fully "blind" while moving and thus detect prey only during scanning pauses, but none of them compared the efficiency of LW vs. CBW in this context or in less extreme contexts where the detection probability during movement is not null but very low. In a study based on computer simulations, we filled the bridge between the concepts of "pure continuous" and "pure saltatory" foraging by considering that the probability to detect resources encountered while moving may range from 0 to 1. We showed that regularly stopping to scan the environment can indeed improve efficiency, but only at very low detection probabilities. Furthermore, the LW is then systematically outperformed by a mixed cue-driven/internally-driven CBW. It is thus more likely that evolution tends to favour strategies that rely on environmental feedbacks rather than on strange kinetics. PMID:26463680
Zhang, Ling; Zhang, Yaojun; Wang, Hong; Zou, Jianwen; Siemann, Evan
2013-01-01
Two mechanisms that have been proposed to explain success of invasive plants are unusual biotic interactions, such as enemy release or enhanced mutualisms, and increased resource availability. However, while these mechanisms are usually considered separately, both may be involved in successful invasions. Biotic interactions may be positive or negative and may interact with nutritional resources in determining invasion success. In addition, the effects of different nutrients on invasions may vary. Finally, genetic variation in traits between populations located in introduced versus native ranges may be important for biotic interactions and/or resource use. Here, we investigated the roles of soil biota, resource availability, and plant genetic variation using seedlings of Triadica sebifera in an experiment in the native range (China). We manipulated nitrogen (control or 4 g/m(2)), phosphorus (control or 0.5 g/m(2)), soil biota (untreated or sterilized field soil), and plant origin (4 populations from the invasive range, 4 populations from the native range) in a full factorial experiment. Phosphorus addition increased root, stem, and leaf masses. Leaf mass and height growth depended on population origin and soil sterilization. Invasive populations had higher leaf mass and growth rates than native populations did in fresh soil but they had lower, comparable leaf mass and growth rates in sterilized soil. Invasive populations had higher growth rates with phosphorus addition but native ones did not. Soil sterilization decreased specific leaf area in both native and exotic populations. Negative effects of soil sterilization suggest that soil pathogens may not be as important as soil mutualists for T. sebifera performance. Moreover, interactive effects of sterilization and origin suggest that invasive T. sebifera may have evolved more beneficial relationships with the soil biota. Overall, seedlings from the invasive range outperformed those from the native range, however
Doi, Hideyuki; Takahara, Teruhiko; Minamoto, Toshifumi; Matsuhashi, Saeko; Uchii, Kimiko; Yamanaka, Hiroki
2015-05-01
Environmental DNA (eDNA) has been used to investigate species distributions in aquatic ecosystems. Most of these studies use real-time polymerase chain reaction (PCR) to detect eDNA in water; however, PCR amplification is often inhibited by the presence of organic and inorganic matter. In droplet digital PCR (ddPCR), the sample is partitioned into thousands of nanoliter droplets, and PCR inhibition may be reduced by the detection of the end-point of PCR amplification in each droplet, independent of the amplification efficiency. In addition, real-time PCR reagents can affect PCR amplification and consequently alter detection rates. We compared the effectiveness of ddPCR and real-time PCR using two different PCR reagents for the detection of the eDNA from invasive bluegill sunfish, Lepomis macrochirus, in ponds. We found that ddPCR had higher detection rates of bluegill eDNA in pond water than real-time PCR with either of the PCR reagents, especially at low DNA concentrations. Limits of DNA detection, which were tested by spiking the bluegill DNA to DNA extracts from the ponds containing natural inhibitors, found that ddPCR had higher detection rate than real-time PCR. Our results suggest that ddPCR is more resistant to the presence of PCR inhibitors in field samples than real-time PCR. Thus, ddPCR outperforms real-time PCR methods for detecting eDNA to document species distributions in natural habitats, especially in habitats with high concentrations of PCR inhibitors. PMID:25850372
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Cao, Leilei; Xu, Lihong; Goodman, Erik D.
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
2015-01-01
The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement. PMID:25879054
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
NASA Astrophysics Data System (ADS)
Verrelst, Jochem; Rivera, J. P.; Alonso, L.; Guanter, L.; Moreno, J.
2012-04-01
ESA’s upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT- 5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms could be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from the ESA-led field campaign SPARC (Barrax, Spain), it was recently found [1] that Gaussian processes regression (GPR) outperformed competitive machine learning algorithms such as neural networks, support vector regression) and kernel ridge regression both in terms of accuracy and computational speed. For various Sentinel configurations (S2-10m, S2- 20m, S2-60m and S3-300m) three important biophysical parameters were estimated: leaf chlorophyll content (Chl), leaf area index (LAI) and fractional vegetation cover (FVC). GPR was the only method that reached the 10% precision required by end users in the estimation of Chl. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models to other images was evaluated. The associated confidence maps proved to be a good indicator for evaluating the robustness of the trained models. Consistent retrievals were obtained across the different images, particularly over agricultural sites. To make the method suitable for operational use, however, the poorer confidences over bare soil areas suggest that the training dataset should be expanded with inputs from various land cover types.