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
NASA Astrophysics Data System (ADS)
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
2016-09-01
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.
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
Levin, Roger
2005-09-01
Consistency is a reflection of having the right model, the right systems and the right implementation. As Vince Lombardi, the legendary coach of the Green Bay Packers, once said, "You don't do things right once in a while. You do them right all the time." To provide the ultimate level of patient care, reduce stress for the dentist and staff members and ensure high practice profitability, consistency is key.
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.
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.
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. PMID:19206962
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
Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm
NASA Astrophysics Data System (ADS)
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.
2015-07-01
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 January 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. 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 SCIAMACHY and 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° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD
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.
Algorithms for Maintaining a Consistent Knowledge Base in Distributed Multiagent Environments
NASA Astrophysics Data System (ADS)
Ustymenko, Stanislav; Schwartz, Daniel G.
In this paper, we design algorithms for a system that allows Semantic Web agents to reason within what has come to be known as the Web of Trust. We integrate reasoning about belief and trust, so agents can reason about information from different sources and deal with contradictions. Software agents interact to support users who publish, share and search for documents in a distributed repository. Each agent maintains an individualized topic taxonomy for the user it represents, updating it with information obtained from other agents. Additionally, an agent maintains and updates trust relationships with other agents.
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
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.
NASA Astrophysics Data System (ADS)
Jerg, M.; Stengel, M.; Hollmann, R.; Poulsen, C.
2012-04-01
The ultimate objective of the ESA Climate Change Initiative (CCI) Cloud project is to provide long-term coherent cloud property data sets exploiting and improving on the synergetic capabilities of past, existing, and upcoming European and American satellite missions. The synergetic approach allows not only for improved accuracy and extended temporal and spatial sampling of retrieved cloud properties better than those provided by single instruments alone but potentially also for improved (inter-)calibration and enhanced homogeneity and stability of the derived time series. Such advances are required by the scientific community to facilitate further progress in satellite-based climate monitoring, which leads to a better understanding of climate. Some of the primary objectives of ESA Cloud CCI Cloud are (1) the development of inter-calibrated radiance data sets, so called Fundamental Climate Data Records - for ESA and non ESA instruments through an international collaboration, (2) the development of an optimal estimation based retrieval framework for cloud related essential climate variables like cloud cover, cloud top height and temperature, liquid and ice water path, and (3) the development of two multi-annual global data sets for the mentioned cloud properties including uncertainty estimates. These two data sets are characterized by different combinations of satellite systems: the AVHRR heritage product comprising (A)ATSR, AVHRR and MODIS and the novel (A)ATSR - MERIS product which is based on a synergetic retrieval using both instruments. Both datasets cover the years 2007-2009 in the first project phase. ESA Cloud CCI will also carry out a comprehensive validation of the cloud property products and provide a common data base as in the framework of the Global Energy and Water Cycle Experiment (GEWEX). The presentation will give an overview of the ESA Cloud CCI project and its goals and approaches and then continue with results from the Round Robin algorithm
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…
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.
Moussa, Jonathan E.
2014-01-07
The random-phase approximation with second-order screened exchange (RPA+SOSEX) is a model of electron correlation energy with two caveats: its accuracy depends on an arbitrary choice of mean field, and it scales as O(n{sup 5}) operations and O(n{sup 3}) memory for n electrons. We derive a new algorithm that reduces its scaling to O(n{sup 3}) operations and O(n{sup 2}) memory using controlled approximations and a new self-consistent field that approximates Brueckner coupled-cluster doubles theory with RPA+SOSEX, referred to as Brueckner RPA theory. The algorithm comparably reduces the scaling of second-order Møller-Plesset perturbation theory with smaller cost prefactors than RPA+SOSEX. Within a semiempirical model, we study H{sub 2} dissociation to test accuracy and H{sub n} rings to verify scaling.
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
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
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
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.
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
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
Luo, Ju-Hua; Zhang, Jing-Cheng; Huang, Wen-Jiang; Yang, Gui-Jun; Gu, Xiao-He; Yang, Hao
2010-12-01
To ascertain whether the thermal infrared image of HJ-1B which has the similar sensor parameter and setting to Landsat 5 TM6 image is applicable for retrieving the land surface temperature (LST), a comparison of retrieved LST between two types of sensors was conducted. Two scenes of thermal infrared images that came from different sensors were acquired in 5th, Apr 2009, which covered the same region in Beijing. To retrieve LST, a generalized single-channel algorithm developed by Jiménez-Muñoz and Sobrino was applied. The LST of study area for both images was thus generated. Based on the LST mapping results and corresponding statistics, an apparent trend could be observed which indicated the consistency in both LST value and its spatial distribution. Consequently, the performance of HJ-IB IRS serving as the data source for LST retrieval was assessed and illustrated in this study. Besides, a high temporal resolution as well as wide swath of the HJ-IRS data suggested its potential in application.
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
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
Adult vultures outperform juveniles in challenging thermal soaring conditions.
Harel, Roi; Horvitz, Nir; Nathan, Ran
2016-06-13
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.
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.
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
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.
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.
Revisiting PLUMBER: Why Do Simple Data-driven Models Outperform Modern Land Surface Models?
NASA Astrophysics Data System (ADS)
Nijssen, B.; Clark, M. P.; Haughton, N.; Abramowitz, G.
2015-12-01
PLUMBER, a recent benchmarking study for the performance of land surface models (LSMs), demonstrated that simple data-driven models outperform modern LSMs at FLUXNET stations. Specifically, data-driven models out-performed LSMs in partitioning net radiation into turbulent heat fluxes over a wide range of performance criteria. The question is why. After all, LSMs combine process understanding with site information and might be expected to outperform simple data-driven models that are trained out-of-sample and that do not include an explicit representation of past states such as soil moisture or heat storage. In other words, the data-driven models have no explicit representation of memory, which we know to be important for land surface energy and moisture states. Here, we revisit the PLUMBER results with the aim to understand why simple data-driven models outperform LSMs. First, we analyze the PLUMBER results to determine the conditions under which data-driven models outperform LSMs. We then use the Structure for Unifying Multiple Modeling Alternatives (SUMMA) to construct LSMs of varying complexity to relate model performance to process representation. SUMMA is a hydrologic modeling approach that enables a controlled and systematic analysis of alternative modeling options. Results are intended to identify development priorities for LSMs.
A pegging algorithm for separable continuous nonlinear knapsack problems with box constraints
NASA Astrophysics Data System (ADS)
Kim, Gitae; Wu, Chih-Hang
2012-10-01
This article proposes an efficient pegging algorithm for solving separable continuous nonlinear knapsack problems with box constraints. A well-known pegging algorithm for solving this problem is the Bitran-Hax algorithm, a preferred choice for large-scale problems. However, at each iteration, it must calculate an optimal dual variable and update all free primal variables, which is time consuming. The proposed algorithm checks the box constraints implicitly using the bounds on the Lagrange multiplier without explicitly calculating primal variables at each iteration as well as updating the dual solution in a more efficient manner. Results of computational experiments have shown that the proposed algorithm consistently outperforms the Bitran-Hax in all baseline testing and two real-time application models. The proposed algorithm shows significant potential for many other mathematical models in real-world applications with straightforward extensions.
Using Outperformance Pay to Motivate Academics: Insiders' Accounts of Promises and Problems
ERIC Educational Resources Information Center
Field, Laurie
2015-01-01
Many researchers have investigated the appropriateness of pay for outperformance, (also called "merit-based pay" and "performance-based pay") for academics, but a review of this body of work shows that the voice of academics themselves is largely absent. This article is a contribution to addressing this gap, summarising the…
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.
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
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
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.
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.
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
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.
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay
The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.
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.
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
Karayiannis, Nicolaos B; Randolph-Gips, Mary M
2005-03-01
This paper presents the development of soft clustering and learning vector quantization (LVQ) algorithms that rely on a weighted norm to measure the distance between the feature vectors and their prototypes. The development of LVQ and clustering algorithms is based on the minimization of a reformulation function under the constraint that the generalized mean of the norm weights be constant. According to the proposed formulation, the norm weights can be computed from the data in an iterative fashion together with the prototypes. An error analysis provides some guidelines for selecting the parameter involved in the definition of the generalized mean in terms of the feature variances. The algorithms produced from this formulation are easy to implement and they are almost as fast as clustering algorithms relying on the Euclidean norm. An experimental evaluation on four data sets indicates that the proposed algorithms outperform consistently clustering algorithms relying on the Euclidean norm and they are strong competitors to non-Euclidean algorithms which are computationally more demanding.
Sequential Feedback Scheme Outperforms the Parallel Scheme for Hamiltonian Parameter Estimation
NASA Astrophysics Data System (ADS)
Yuan, Haidong
2016-10-01
Measurement and estimation of parameters are essential for science and engineering, where the main quest is to find the highest achievable precision with the given resources and design schemes to attain it. Two schemes, the sequential feedback scheme and the parallel scheme, are usually studied in the quantum parameter estimation. While the sequential feedback scheme represents the most general scheme, it remains unknown whether it can outperform the parallel scheme for any quantum estimation tasks. In this Letter, we show that the sequential feedback scheme has a threefold improvement over the parallel scheme for Hamiltonian parameter estimations on two-dimensional systems, and an order of O (d +1 ) improvement for Hamiltonian parameter estimation on d -dimensional systems. We also show that, contrary to the conventional belief, it is possible to simultaneously achieve the highest precision for estimating all three components of a magnetic field, which sets a benchmark on the local precision limit for the estimation of a magnetic field.
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.
Do Evidence-Based Youth Psychotherapies Outperform Usual Clinical Care? A Multilevel Meta-Analysis
Weisz, John R.; Kuppens, Sofie; Eckshtain, Dikla; Ugueto, Ana M.; Hawley, Kristin M.; Jensen-Doss, Amanda
2013-01-01
Context Research across four decades has produced numerous empirically-tested evidence-based psychotherapies (EBPs) for youth psychopathology, developed to improve upon usual clinical interventions. Advocates argue that these should replace usual care; but do the EBPs produce better outcomes than usual care? Objective This question was addressed in a meta-analysis of 52 randomized trials directly comparing EBPs to usual care. Analyses assessed the overall effect of EBPs vs. usual care, and candidate moderators; multilevel analysis was used to address the dependency among effect sizes that is common but typically unaddressed in psychotherapy syntheses. Data Sources The PubMed, PsychINFO, and Dissertation Abstracts International databases were searched for studies from January 1, 1960 – December 31, 2010. Study Selection 507 randomized youth psychotherapy trials were identified. Of these, the 52 studies that compared EBPs to usual care were included in the meta-analysis. Data Extraction Sixteen variables (participant, treatment, and study characteristics) were extracted from each study, and effect sizes were calculated for all EBP versus usual care comparisons. Data Synthesis EBPs outperformed usual care. Mean effect size was 0.29; the probability was 58% that a randomly selected youth receiving an EBP would be better off after treatment than a randomly selected youth receiving usual care. Three variables moderated treatment benefit: Effect sizes decreased for studies conducted outside North America, for studies in which all participants were impaired enough to qualify for diagnoses, and for outcomes reported by people other than the youths and parents in therapy. For certain key groups (e.g., studies using clinically referred samples and diagnosed samples), significant EBP effects were not demonstrated. Conclusions EBPs outperformed usual care, but the EBP advantage was modest and moderated by youth, location, and assessment characteristics. There is room for
Algorithm aversion: people erroneously avoid algorithms after seeing them err.
Dietvorst, Berkeley J; Simmons, Joseph P; Massey, Cade
2015-02-01
Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.
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.
Efficient algorithms for the laboratory discovery of optimal quantum controls
NASA Astrophysics Data System (ADS)
Turinici, Gabriel; Le Bris, Claude; Rabitz, Herschel
2004-07-01
The laboratory closed-loop optimal control of quantum phenomena, expressed as minimizing a suitable cost functional, is currently implemented through an optimization algorithm coupled to the experimental apparatus. In practice, the most commonly used search algorithms are variants of genetic algorithms. As an alternative choice, a direct search deterministic algorithm is proposed in this paper. For the simple simulations studied here, it outperforms the existing approaches. An additional algorithm is introduced in order to reveal some properties of the cost functional landscape.
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…
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
Kew, William; Mitchell, John B O
2015-09-01
The application of Machine Learning to cheminformatics is a large and active field of research, but there exist few papers which discuss whether ensembles of different Machine Learning methods can improve upon the performance of their component methodologies. Here we investigated a variety of methods, including kernel-based, tree, linear, neural networks, and both greedy and linear ensemble methods. These were all tested against a standardised methodology for regression with data relevant to the pharmaceutical development process. This investigation focused on QSPR problems within drug-like chemical space. We aimed to investigate which methods perform best, and how the 'wisdom of crowds' principle can be applied to ensemble predictors. It was found that no single method performs best for all problems, but that a dynamic, well-structured ensemble predictor would perform very well across the board, usually providing an improvement in performance over the best single method. Its use of weighting factors allows the greedy ensemble to acquire a bigger contribution from the better performing models, and this helps the greedy ensemble generally to outperform the simpler linear ensemble. Choice of data preprocessing methodology was found to be crucial to performance of each method too.
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.
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
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
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.
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
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
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
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.
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
Guo, Wensheng; Yang, Guowu; Wu, Wei; He, Lei; Sun, Mingyu
2014-01-01
In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.
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.
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…
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…
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.
Indexing Consistency and Quality.
ERIC Educational Resources Information Center
Zunde, Pranas; Dexter, Margaret E.
Proposed is a measure of indexing consistency based on the concept of "fuzzy sets." By this procedure a higher consistency value is assigned 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 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…
Consistency relation in cosmology
Chiba, Takeshi; Takahashi, Ryuichi
2007-05-15
We provide a consistency relation between cosmological observables in general relativity without relying on the equation of state of dark energy. The consistency relation should be satisfied if general relativity is the correct theory of gravity and dark energy clustering is negligible. As an extension, we also provide the DGP counterpart of the relation.
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
Interior search algorithm (ISA): a novel approach for global optimization.
Gandomi, Amir H
2014-07-01
This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune.
NASA Astrophysics Data System (ADS)
Galvan-Sosa, M.; Portilla, J.; Hernandez-Rueda, J.; Siegel, J.; Moreno, L.; Solis, J.
2014-09-01
In this work, we have developed and implemented a powerful search strategy for optimization of nonlinear optical effects by means of femtosecond pulse shaping, based on topological concepts derived from quantum control theory. Our algorithm [Multiple One-Dimensional Search (MODS)] is based on deterministic optimization of a single solution rather than pseudo-random optimization of entire populations as done by commonly used evolutionary algorithms. We have tested MODS against a genetic algorithm in a nontrivial problem consisting in optimizing the Kerr gating signal (self-interaction) of a shaped laser pulse in a detuned Michelson interferometer configuration. The obtained results show that our search method (MODS) strongly outperforms the genetic algorithm in terms of both convergence speed and quality of the solution. These findings demonstrate the applicability of concepts of quantum control theory to nonlinear laser-matter interaction problems, even in the presence of significant experimental noise.
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.
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.
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
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.
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%.
Infanticide and moral consistency.
McMahan, Jeff
2013-05-01
The aim of this essay is to show that there are no easy options for those who are disturbed by the suggestion that infanticide may on occasion be morally permissible. The belief that infanticide is always wrong is doubtfully compatible with a range of widely shared moral beliefs that underlie various commonly accepted practices. Any set of beliefs about the morality of abortion, infanticide and the killing of animals that is internally consistent and even minimally credible will therefore unavoidably contain some beliefs that are counterintuitive.
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
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.
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
Measured GFR Does Not Outperform Estimated GFR in Predicting CKD-related Complications
Propert, Kathleen; Xie, Dawei; Hamm, Lee; He, Jiang; Miller, Edgar; Ojo, Akinlolu; Shlipak, Michael; Teal, Valerie; Townsend, Raymond; Weir, Matthew; Wilson, Jillian; Feldman, Harold
2011-01-01
Although many assume that measurement of glomerular filtration rate (GFR) using a marker such as iothalamate (iGFR) is superior to equation-estimated GFR (eGFR), each of these methods has distinct disadvantages. Because physicians often use renal function to guide the screening for various CKD-associated complications, one method to compare the clinical utility of iGFR and eGFR is to determine the strength of their association with CKD-associated comorbidities. Using a subset of 1214 participants in the Chronic Renal Insufficiency Cohort (CRIC) Study, we determined the cross-sectional associations between known complications of CKD and iGFR, eGFR estimated from serum creatinine (eGFR_Cr), and eGFR estimated from cystatin C (eGFR_cysC). We found that none of the measures of renal function strongly associated with CKD complications and that the relative strengths of associations varied according to the outcome of interest. For example, iGFR demonstrated better discrimination than eGFR_Cr and eGFR_cysC for outcomes of anemia and hemoglobin concentration; however, both eGFR_Cr and eGFR_cysC demonstrated better discrimination than iGFR for outcomes of hyperphosphatemia and phosphorus level. iGFR and eGFR had similar strengths of association with hyperkalemia/potassium level and with metabolic acidosis/bicarbonate level. In conclusion, iothalamate measurement of GFR is not consistently superior to equation-based estimations of GFR in explaining CKD-related comorbidities. These results raise questions regarding the conventional view that iGFR is the “gold standard” measure of kidney function. PMID:21921144
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.
Designing neuroclassifier fusion system by immune genetic algorithm
NASA Astrophysics Data System (ADS)
Liang, Jimin; Zhao, Heng; Yang, Wanhai
2001-09-01
A multiple neural network classifier fusion system design method using immune genetic algorithm (IGA) is proposed. The IGA is modeled after the mechanics of human immunity. By using vaccination and immune selection in the evolution procedures, the IGA outperforms the traditional genetic algorithms in restraining the degenerate phenomenon and increasing the converging speed. The fusion system consists of N neural network classifiers that work independently and in parallel to classify a given input pattern. The classifiers' outputs are aggregated by a fusion scheme to decide the collective classification results. The goal of the system design is to obtain a fusion system with both good generalization and efficiency in space and time. Two kinds of measures, the accuracy of classification and the size of the neural networks, are used by IGA to evaluate the fusion system. The vaccines are abstracted by a self-adaptive scheme during the evolutionary process. A numerical experiment on the 'alternate labels' problem is implemented and the comparisons of IGA with traditional genetic algorithm are presented.
Sasaki, Takao; Granovskiy, Boris; Mann, Richard P; Sumpter, David J T; Pratt, Stephen C
2013-08-20
"Collective intelligence" and "wisdom of crowds" refer to situations in which groups achieve more accurate perception and better decisions than solitary agents. Whether groups outperform individuals should depend on the kind of task and its difficulty, but the nature of this relationship remains unknown. Here we show that colonies of Temnothorax ants outperform individuals for a difficult perception task but that individuals do better than groups when the task is easy. Subjects were required to choose the better of two nest sites as the quality difference was varied. For small differences, colonies were more likely than isolated ants to choose the better site, but this relationship was reversed for large differences. We explain these results using a mathematical model, which shows that positive feedback between group members effectively integrates information and sharpens the discrimination of fine differences. When the task is easier the same positive feedback can lock the colony into a suboptimal choice. These results suggest the conditions under which crowds do or do not become wise. PMID:23898161
Guigueno, Mélanie F.; MacDougall-Shackleton, Scott A.; Sherry, David F.
2015-01-01
Spatial cognition in females and males can differ in species in which there are sex-specific patterns in the use of space. Brown-headed cowbirds are brood parasites that show a reversal of sex-typical space use often seen in mammals. Female cowbirds, search for, revisit and parasitize hosts nests, have a larger hippocampus than males and have better memory than males for a rewarded location in an open spatial environment. In the current study, we tested female and male cowbirds in breeding and non-breeding conditions on a touchscreen delayed-match-to-sample task using both spatial and colour stimuli. Our goal was to determine whether sex differences in spatial memory in cowbirds generalizes to all spatial tasks or is task-dependant. Both sexes performed better on the spatial than on the colour touchscreen task. On the spatial task, breeding males outperformed breeding females. On the colour task, females and males did not differ, but females performed better in breeding condition than in non-breeding condition. Although female cowbirds were observed to outperform males on a previous larger-scale spatial task, males performed better than females on a task testing spatial memory in the cowbirds’ immediate visual field. Spatial abilities in cowbirds can favour males or females depending on the type of spatial task, as has been observed in mammals, including humans. PMID:26083573
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
Realization of a scalable Shor algorithm.
Monz, Thomas; Nigg, Daniel; Martinez, Esteban A; Brandl, Matthias F; Schindler, Philipp; Rines, Richard; Wang, Shannon X; Chuang, Isaac L; Blatt, Rainer
2016-03-01
Certain algorithms for quantum computers are able to outperform their classical counterparts. In 1994, Peter Shor came up with a quantum algorithm that calculates the prime factors of a large number vastly more efficiently than a classical computer. For general scalability of such algorithms, hardware, quantum error correction, and the algorithmic realization itself need to be extensible. Here we present the realization of a scalable Shor algorithm, as proposed by Kitaev. We factor the number 15 by effectively employing and controlling seven qubits and four "cache qubits" and by implementing generalized arithmetic operations, known as modular multipliers. This algorithm has been realized scalably within an ion-trap quantum computer and returns the correct factors with a confidence level exceeding 99%. PMID:26941315
Efficient algorithms for the laboratory discovery of optimal quantum controls.
Turinici, Gabriel; Le Bris, Claude; Rabitz, Herschel
2004-01-01
The laboratory closed-loop optimal control of quantum phenomena, expressed as minimizing a suitable cost functional, is currently implemented through an optimization algorithm coupled to the experimental apparatus. In practice, the most commonly used search algorithms are variants of genetic algorithms. As an alternative choice, a direct search deterministic algorithm is proposed in this paper. For the simple simulations studied here, it outperforms the existing approaches. An additional algorithm is introduced in order to reveal some properties of the cost functional landscape. PMID:15324201
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
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.
Efficiency of Evolutionary Algorithms for Calibration of Watershed Models
NASA Astrophysics Data System (ADS)
Ahmadi, M.; Arabi, M.
2009-12-01
of SWAT at multiple locations presents a challenge. Also, it became evident that the multi objective algorithm consistently outperforms the single objective methods.
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.
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
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.
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
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 constraint consensus memetic algorithm for solving constrained optimization problems
NASA Astrophysics Data System (ADS)
Hamza, Noha M.; Sarker, Ruhul A.; Essam, Daryl L.; Deb, Kalyanmoy; Elsayed, Saber M.
2014-11-01
Constraint handling is an important aspect of evolutionary constrained optimization. Currently, the mechanism used for constraint handling with evolutionary algorithms mainly assists the selection process, but not the actual search process. In this article, first a genetic algorithm is combined with a class of search methods, known as constraint consensus methods, that assist infeasible individuals to move towards the feasible region. This approach is also integrated with a memetic algorithm. The proposed algorithm is tested and analysed by solving two sets of standard benchmark problems, and the results are compared with other state-of-the-art algorithms. The comparisons show that the proposed algorithm outperforms other similar algorithms. The algorithm has also been applied to solve a practical economic load dispatch problem, where it also shows superior performance over other algorithms.
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.
Dynamic Programming Algorithm vs. Genetic Algorithm: Which is Faster?
NASA Astrophysics Data System (ADS)
Petković, Dušan
The article compares two different approaches for the optimization problem of large join queries (LJQs). Almost all commercial database systems use a form of the dynamic programming algorithm to solve the ordering of join operations for large join queries, i.e. joins with more than dozen join operations. The property of the dynamic programming algorithm is that the execution time increases significantly in the case, where the number of join operations in a query is large. Genetic algorithms (GAs), as a data mining technique, have been shown as a promising technique in solving the ordering of join operations in LJQs. Using the existing implementation of GA, we compare the dynamic programming algorithm implemented in commercial database systems with the corresponding GA module. Our results show that the use of a genetic algorithm is a better solution for optimization of large join queries, i.e., that such a technique outperforms the implementations of the dynamic programming algorithm in conventional query optimization components for very large join queries.
Algorithm Engineering - An Attempt at a Definition
NASA Astrophysics Data System (ADS)
Sanders, Peter
This paper defines algorithm engineering as a general methodology for algorithmic research. The main process in this methodology is a cycle consisting of algorithm design, analysis, implementation and experimental evaluation that resembles Popper’s scientific method. Important additional issues are realistic models, algorithm libraries, benchmarks with real-world problem instances, and a strong coupling to applications. Algorithm theory with its process of subsequent modelling, design, and analysis is not a competing approach to algorithmics but an important ingredient of algorithm engineering.
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.
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.
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
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
Volume Haptics with Topology-Consistent Isosurfaces.
Corenthy, Loc; Otaduy, Miguel A; Pastor, Luis; Garcia, Marcos
2015-01-01
Haptic interfaces offer an intuitive way to interact with and manipulate 3D datasets, and may simplify the interpretation of visual information. This work proposes an algorithm to provide haptic feedback directly from volumetric datasets, as an aid to regular visualization. The haptic rendering algorithm lets users perceive isosurfaces in volumetric datasets, and it relies on several design features that ensure a robust and efficient rendering. A marching tetrahedra approach enables the dynamic extraction of a piecewise linear continuous isosurface. Robustness is achieved using a continuous collision detection step coupled with state-of-the-art proxy-based rendering methods over the extracted isosurface. The introduced marching tetrahedra approach guarantees that the extracted isosurface will match the topology of an equivalent isosurface computed using trilinear interpolation. The proposed haptic rendering algorithm improves the consistency between haptic and visual cues computing a second proxy on the isosurface displayed on screen. Our experiments demonstrate the improvements on the isosurface extraction stage as well as the robustness and the efficiency of the complete algorithm.
Experimental Investigation of Three Machine Learning Algorithms for ITS Dataset
NASA Astrophysics Data System (ADS)
Yearwood, J. L.; Kang, B. H.; Kelarev, A. V.
The present article is devoted to experimental investigation of the performance of three machine learning algorithms for ITS dataset in their ability to achieve agreement with classes published in the biologi cal literature before. The ITS dataset consists of nuclear ribosomal DNA sequences, where rather sophisticated alignment scores have to be used as a measure of distance. These scores do not form a Minkowski metric and the sequences cannot be regarded as points in a finite dimensional space. This is why it is necessary to develop novel machine learning ap proaches to the analysis of datasets of this sort. This paper introduces a k-committees classifier and compares it with the discrete k-means and Nearest Neighbour classifiers. It turns out that all three machine learning algorithms are efficient and can be used to automate future biologically significant classifications for datasets of this kind. A simplified version of a synthetic dataset, where the k-committees classifier outperforms k-means and Nearest Neighbour classifiers, is also presented.
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.
A low computational complexity algorithm for ECG signal compression.
Blanco-Velasco, Manuel; Cruz-Roldán, Fernando; López-Ferreras, Francisco; Bravo-Santos, Angel; Martínez-Muñoz, Damián
2004-09-01
In this work, a filter bank-based algorithm for electrocardiogram (ECG) signals compression is proposed. The new coder consists of three different stages. In the first one--the subband decomposition stage--we compare the performance of a nearly perfect reconstruction (N-PR) cosine-modulated filter bank with the wavelet packet (WP) technique. Both schemes use the same coding algorithm, thus permitting an effective comparison. The target of the comparison is the quality of the reconstructed signal, which must remain within predetermined accuracy limits. We employ the most widely used quality criterion for the compressed ECG: the percentage root-mean-square difference (PRD). It is complemented by means of the maximum amplitude error (MAX). The tests have been done for the 12 principal cardiac leads, and the amount of compression is evaluated by means of the mean number of bits per sample (MBPS) and the compression ratio (CR). The implementation cost for both the filter bank and the WP technique has also been studied. The results show that the N-PR cosine-modulated filter bank method outperforms the WP technique in both quality and efficiency. PMID:15271283
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
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
A hybrid skull-stripping algorithm based on adaptive balloon snake models
NASA Astrophysics Data System (ADS)
Liu, Hung-Ting; Sheu, Tony W. H.; Chang, Herng-Hua
2013-02-01
Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in a wide variety of skull-stripping applications.
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.
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.
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.
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
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
Azzopardi, George; Petkov, Nicolai
2012-03-01
Simple cells in primary visual cortex are believed to extract local contour information from a visual scene. The 2D Gabor function (GF) model has gained particular popularity as a computational model of a simple cell. However, it short-cuts the LGN, it cannot reproduce a number of properties of real simple cells, and its effectiveness in contour detection tasks has never been compared with the effectiveness of alternative models. We propose a computational model that uses as afferent inputs the responses of model LGN cells with center-surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances of the CORF and GF models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths. The RF map of the proposed CORF model, determined with simulated reverse correlation, can be divided in elongated excitatory and inhibitory regions typical of simple cells. The modulated response to shifted gratings that this model shows is also characteristic of a simple cell. Furthermore, the CORF model exhibits cross orientation suppression, contrast invariant orientation tuning and response saturation. These properties are observed in real simple cells, but are not possessed by the GF model. The proposed CORF model outperforms the GF model in contour detection with high statistical confidence (RuG data set: p<10(-4), and Berkeley data set: p<10(-4)). The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells. PMID:22526357
Zhang, Hong; Huang, Jiaxing; 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.
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.
An efficient algorithm for calculating the exact Hausdorff distance.
Taha, Abdel Aziz; Hanbury, Allan
2015-11-01
The Hausdorff distance (HD) between two point sets is a commonly used dissimilarity measure for comparing point sets and image segmentations. Especially when very large point sets are compared using the HD, for example when evaluating magnetic resonance volume segmentations, or when the underlying applications are based on time critical tasks, like motion detection, then the computational complexity of HD algorithms becomes an important issue. In this paper we propose a novel efficient algorithm for computing the exact Hausdorff distance. In a runtime analysis, the proposed algorithm is demonstrated to have nearly-linear complexity. Furthermore, it has efficient performance for large point set sizes as well as for large grid size; performs equally for sparse and dense point sets; and finally it is general without restrictions on the characteristics of the point set. The proposed algorithm is tested against the HD algorithm of the widely used national library of medicine insight segmentation and registration toolkit (ITK) using magnetic resonance volumes with extremely large size. The proposed algorithm outperforms the ITK HD algorithm both in speed and memory required. In an experiment using trajectories from a road network, the proposed algorithm significantly outperforms an HD algorithm based on R-Trees. PMID:26440258
Quantum Algorithms for Problems in Number Theory, Algebraic Geometry, and Group Theory
NASA Astrophysics Data System (ADS)
van Dam, Wim; Sasaki, Yoshitaka
2013-09-01
Quantum computers can execute algorithms that sometimes dramatically outperform classical computation. Undoubtedly the best-known example of this is Shor's discovery of an efficient quantum algorithm for factoring integers, whereas the same problem appears to be intractable on classical computers. Understanding what other computational problems can be solved significantly faster using quantum algorithms is one of the major challenges in the theory of quantum computation, and such algorithms motivate the formidable task of building a large-scale quantum computer. This article will review the current state of quantum algorithms, focusing on algorithms for problems with an algebraic flavor that achieve an apparent superpolynomial speedup over classical computation.
Optimizing the Learning Order of Chinese Characters Using a Novel Topological Sort Algorithm
Wang, Jinzhao
2016-01-01
We present a novel algorithm for optimizing the order in which Chinese characters are learned, one that incorporates the benefits of learning them in order of usage frequency and in order of their hierarchal structural relationships. We show that our work outperforms previously published orders and algorithms. Our algorithm is applicable to any scheduling task where nodes have intrinsic differences in importance and must be visited in topological order. PMID:27706234
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.
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
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.
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.
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.
iPoint: an integer programming based algorithm for inferring protein subnetworks.
Atias, Nir; Sharan, Roded
2013-07-01
Large scale screening experiments have become the workhorse of molecular biology, producing data at an ever increasing scale. The interpretation of such data, particularly in the context of a protein interaction network, has the potential to shed light on the molecular pathways underlying the phenotype or the process in question. A host of approaches have been developed in recent years to tackle this reconstruction challenge. These approaches aim to infer a compact subnetwork that connects the genes revealed by the screen while optimizing local (individual path lengths) or global (likelihood) aspects of the subnetwork. Yosef et al. [Mol. Syst. Biol., 2009, 5, 248] were the first to provide a joint optimization of both criteria, albeit approximate in nature. Here we devise an integer linear programming formulation for the joint optimization problem, allowing us to solve it to optimality in minutes on current networks. We apply our algorithm, iPoint, to various data sets in yeast and human and evaluate its performance against state-of-the-art algorithms. We show that iPoint attains very compact and accurate solutions that outperform previous network inference algorithms with respect to their local and global attributes, their consistency across multiple experiments targeting the same pathway, and their agreement with current biological knowledge.
Fast proximity algorithm for MAP ECT reconstruction
NASA Astrophysics Data System (ADS)
Li, Si; Krol, Andrzej; Shen, Lixin; Xu, Yuesheng
2012-03-01
We arrived at the fixed-point formulation of the total variation maximum a posteriori (MAP) regularized emission computed tomography (ECT) reconstruction problem and we proposed an iterative alternating scheme to numerically calculate the fixed point. We theoretically proved that our algorithm converges to unique solutions. Because the obtained algorithm exhibits slow convergence speed, we further developed the proximity algorithm in the transformed image space, i.e. the preconditioned proximity algorithm. We used the bias-noise curve method to select optimal regularization hyperparameters for both our algorithm and expectation maximization with total variation regularization (EM-TV). We showed in the numerical experiments that our proposed algorithms, with an appropriately selected preconditioner, outperformed conventional EM-TV algorithm in many critical aspects, such as comparatively very low noise and bias for Shepp-Logan phantom. This has major ramification for nuclear medicine because clinical implementation of our preconditioned fixed-point algorithms might result in very significant radiation dose reduction in the medical applications of emission tomography.
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
Al-Ameen, Zohair; Sulong, Ghazali
2015-01-01
Contrast is a distinctive visual attribute that indicates the quality of an image. Computed Tomography (CT) images are often characterized as poor quality due to their low-contrast nature. Although many innovative ideas have been proposed to overcome this problem, the outcomes, especially in terms of accuracy, visual quality and speed, are falling short and there remains considerable room for improvement. Therefore, an improved version of the single-scale Retinex algorithm is proposed to enhance the contrast while preserving the standard brightness and natural appearance, with low implementation time and without accentuating the noise for CT images. The novelties of the proposed algorithm consist of tuning the standard single-scale Retinex, adding a normalized-ameliorated Sigmoid function and adapting some parameters to improve its enhancement ability. The proposed algorithm is tested with synthetically and naturally degraded low-contrast CT images, and its performance is also verified with contemporary enhancement techniques using two prevalent quality evaluation metrics-SSIM and UIQI. The results obtained from intensive experiments exhibited significant improvement not only in enhancing the contrast but also in increasing the visual quality of the processed images. Finally, the proposed low-complexity algorithm provided satisfactory results with no apparent errors and outperformed all the comparative methods.
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.
Fast algorithm for relaxation processes in big-data systems
NASA Astrophysics Data System (ADS)
Hwang, S.; Lee, D.-S.; Kahng, B.
2014-10-01
Relaxation processes driven by a Laplacian matrix can be found in many real-world big-data systems, for example, in search engines on the World Wide Web and the dynamic load-balancing protocols in mesh networks. To numerically implement such processes, a fast-running algorithm for the calculation of the pseudoinverse of the Laplacian matrix is essential. Here we propose an algorithm which computes quickly and efficiently the pseudoinverse of Markov chain generator matrices satisfying the detailed-balance condition, a general class of matrices including the Laplacian. The algorithm utilizes the renormalization of the Gaussian integral. In addition to its applicability to a wide range of problems, the algorithm outperforms other algorithms in its ability to compute within a manageable computing time arbitrary elements of the pseudoinverse of a matrix of size millions by millions. Therefore our algorithm can be used very widely in analyzing the relaxation processes occurring on large-scale networked systems.
Electricity load forecasting using support vector regression with memetic algorithms.
Hu, Zhongyi; Bao, Yukun; 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.
Quantum gate decomposition algorithms.
Slepoy, Alexander
2006-07-01
Quantum computing algorithms can be conveniently expressed in a format of a quantum logical circuits. Such circuits consist of sequential coupled operations, termed ''quantum gates'', or quantum analogs of bits called qubits. We review a recently proposed method [1] for constructing general ''quantum gates'' operating on an qubits, as composed of a sequence of generic elementary ''gates''.
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
The Principle of Energetic Consistency
NASA Technical Reports Server (NTRS)
Cohn, Stephen E.
2009-01-01
A basic result in estimation theory is that the minimum variance estimate of the dynamical state, given the observations, is the conditional mean estimate. This result holds independently of the specifics of any dynamical or observation nonlinearity or stochasticity, requiring only that the probability density function of the state, conditioned on the observations, has two moments. For nonlinear dynamics that conserve a total energy, this general result implies the principle of energetic consistency: if the dynamical variables are taken to be the natural energy variables, then the sum of the total energy of the conditional mean and the trace of the conditional covariance matrix (the total variance) is constant between observations. Ensemble Kalman filtering methods are designed to approximate the evolution of the conditional mean and covariance matrix. For them the principle of energetic consistency holds independently of ensemble size, even with covariance localization. However, full Kalman filter experiments with advection dynamics have shown that a small amount of numerical dissipation can cause a large, state-dependent loss of total variance, to the detriment of filter performance. The principle of energetic consistency offers a simple way to test whether this spurious loss of variance limits ensemble filter performance in full-blown applications. The classical second-moment closure (third-moment discard) equations also satisfy the principle of energetic consistency, independently of the rank of the conditional covariance matrix. Low-rank approximation of these equations offers an energetically consistent, computationally viable alternative to ensemble filtering. Current formulations of long-window, weak-constraint, four-dimensional variational methods are designed to approximate the conditional mode rather than the conditional mean. Thus they neglect the nonlinear bias term in the second-moment closure equation for the conditional mean. The principle of
NASA Astrophysics Data System (ADS)
Matsui, Shouichi; Watanabe, Isamu; Tokoro, Ken-Ichi
A new genetic algorithm is proposed for solving job-shop scheduling problems where the total number of search points is limited. The objective of the problem is to minimize the makespan. The solution is represented by an operation sequence, i.e., a permutation of operations. The proposed algorithm is based on the framework of the parameter-free genetic algorithm. It encodes a permutation using random keys into a chromosome. A schedule is derived from a permutation using a hybrid scheduling (HS), and the parameter of HS is also encoded in a chromosome. Experiments using benchmark problems show that the proposed algorithm outperforms the previously proposed algorithms, genetic algorithm by Shi et al. and the improved local search by Nakano et al., for large-scale problems under the constraint of limited number of search points.
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.
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…
NASA Astrophysics Data System (ADS)
Pu, Yirong; Kelsall, Sarah; Ziph-Schatzberg, Leah; Hubbard, Allyn
2009-05-01
Increasing battlefield awareness can improve both the effectiveness and timeliness of response in hostile military situations. A system that processes acoustic data is proposed to handle a variety of possible applications. The front-end of the existing biomimetic acoustic direction finding system, a mammalian peripheral auditory system model, provides the back-end system with what amounts to spike trains. The back-end system consists of individual algorithms tailored to extract specific information. The back-end algorithms are transportable to FPGA platforms and other general-purpose computers. The algorithms can be modified for use with both fixed and mobile, existing sensor platforms. Currently, gunfire classification and localization algorithms based on both neural networks and pitch are being developed and tested. The neural network model is trained under supervised learning to differentiate and trace various gunfire acoustic signatures and reduce the effect of different frequency responses of microphones on different hardware platforms. The model is being tested against impact and launch acoustic signals of various mortars, supersonic and muzzle-blast of rifle shots, and other weapons. It outperforms the cross-correlation algorithm with regard to computational efficiency, memory requirements, and noise robustness. The spike-based pitch model uses the times between successive spike events to calculate the periodicity of the signal. Differences in the periodicity signatures and comparisons of the overall spike activity are used to classify mortar size and event type. The localization of the gunfire acoustic signals is further computed based on the classification result and the location of microphones and other parameters of the existing hardware platform implementation.
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.
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.
Evans, D; Eccles, D; Rahman, N; Young, K; Bulman, M; Amir, E; Shenton, A; Howell, A; Lalloo, F
2004-01-01
Methods: DNA samples from affected subjects from 422 non-Jewish families with a history of breast and/or ovarian cancer were screened for BRCA1 mutations and a subset of 318 was screened for BRCA2 by whole gene screening techniques. Using a combination of results from screening and the family history of mutation negative and positive kindreds, a simple scoring system (Manchester scoring system) was devised to predict pathogenic mutations and particularly to discriminate at the 10% likelihood level. A second separate dataset of 192 samples was subsequently used to test the model's predictive value. This was further validated on a third set of 258 samples and compared against existing models. Results: The scoring system includes a cut-off at 10 points for each gene. This equates to >10% probability of a pathogenic mutation in BRCA1 and BRCA2 individually. The Manchester scoring system had the best trade-off between sensitivity and specificity at 10% prediction for the presence of mutations as shown by its highest C-statistic and was far superior to BRCAPRO. Conclusion: The scoring system is useful in identifying mutations particularly in BRCA2. The algorithm may need modifying to include pathological data when calculating whether to screen for BRCA1 mutations. It is considerably less time-consuming for clinicians than using computer models and if implemented routinely in clinical practice will aid in selecting families most suitable for DNA sampling for diagnostic testing. PMID:15173236
Consistent transport coefficients in astrophysics
NASA Technical Reports Server (NTRS)
Fontenla, Juan M.; Rovira, M.; Ferrofontan, C.
1986-01-01
A consistent theory for dealing with transport phenomena in stellar atmospheres starting with the kinetic equations and introducing three cases (LTE, partial LTE, and non-LTE) was developed. The consistent hydrodynamical equations were presented for partial-LTE, the transport coefficients defined, and a method shown to calculate them. The method is based on the numerical solution of kinetic equations considering Landau, Boltzmann, and Focker-Planck collision terms. Finally a set of results for the transport coefficients derived for a partially ionized hydrogen gas with radiation was shown, considering ionization and recombination as well as elastic collisions. The results obtained imply major changes is some types of theoretical model calculations and can resolve some important current problems concerning energy and mass balance in the solar atmosphere. It is shown that energy balance in the lower solar transition region can be fully explained by means of radiation losses and conductive flux.
Consistent interpretations of quantum mechanics
Omnes, R. )
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.
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.
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
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.
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).
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…
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.
Stochastic optimization of a cold atom experiment using a genetic algorithm
Rohringer, W.; Buecker, R.; Manz, S.; Betz, T.; Koller, Ch.; Goebel, M.; Perrin, A.; Schmiedmayer, J.; Schumm, T.
2008-12-29
We employ an evolutionary algorithm to automatically optimize different stages of a cold atom experiment without human intervention. This approach closes the loop between computer based experimental control systems and automatic real time analysis and can be applied to a wide range of experimental situations. The genetic algorithm quickly and reliably converges to the most performing parameter set independent of the starting population. Especially in many-dimensional or connected parameter spaces, the automatic optimization outperforms a manual search.
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.
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.
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
Sahraeian, Sayed Mohammad Ebrahim; Yoon, Byung-Jun
2013-01-01
In this paper we introduce an efficient algorithm for alignment of multiple large-scale biological networks. In this scheme, we first compute a probabilistic similarity measure between nodes that belong to different networks using a semi-Markov random walk model. The estimated probabilities are further enhanced by incorporating the local and the cross-species network similarity information through the use of two different types of probabilistic consistency transformations. The transformed alignment probabilities are used to predict the alignment of multiple networks based on a greedy approach. We demonstrate that the proposed algorithm, called SMETANA, outperforms many state-of-the-art network alignment techniques, in terms of computational efficiency, alignment accuracy, and scalability. Our experiments show that SMETANA can easily align tens of genome-scale networks with thousands of nodes on a personal computer without any difficulty. The source code of SMETANA is available upon request. The source code of SMETANA can be downloaded from http://www.ece.tamu.edu/~bjyoon/SMETANA/. PMID:23874484
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.
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 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.
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
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/.
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.
Syndromic Algorithms for Detection of Gambiense Human African Trypanosomiasis in South Sudan
Palmer, Jennifer J.; Surur, Elizeous I.; Goch, Garang W.; Mayen, Mangar A.; Lindner, Andreas K.; Pittet, Anne; Kasparian, Serena; Checchi, Francesco; Whitty, Christopher J. M.
2013-01-01
Background Active screening by mobile teams is considered the best method for detecting human African trypanosomiasis (HAT) caused by Trypanosoma brucei gambiense but the current funding context in many post-conflict countries limits this approach. As an alternative, non-specialist health care workers (HCWs) in peripheral health facilities could be trained to identify potential cases who need testing based on their symptoms. We explored the predictive value of syndromic referral algorithms to identify symptomatic cases of HAT among a treatment-seeking population in Nimule, South Sudan. Methodology/Principal Findings Symptom data from 462 patients (27 cases) presenting for a HAT test via passive screening over a 7 month period were collected to construct and evaluate over 14,000 four item syndromic algorithms considered simple enough to be used by peripheral HCWs. For comparison, algorithms developed in other settings were also tested on our data, and a panel of expert HAT clinicians were asked to make referral decisions based on the symptom dataset. The best performing algorithms consisted of three core symptoms (sleep problems, neurological problems and weight loss), with or without a history of oedema, cervical adenopathy or proximity to livestock. They had a sensitivity of 88.9–92.6%, a negative predictive value of up to 98.8% and a positive predictive value in this context of 8.4–8.7%. In terms of sensitivity, these out-performed more complex algorithms identified in other studies, as well as the expert panel. The best-performing algorithm is predicted to identify about 9/10 treatment-seeking HAT cases, though only 1/10 patients referred would test positive. Conclusions/Significance In the absence of regular active screening, improving referrals of HAT patients through other means is essential. Systematic use of syndromic algorithms by peripheral HCWs has the potential to increase case detection and would increase their participation in HAT programmes. The
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.
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.
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
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
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
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
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.
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
Receiver diversity combining using evolutionary algorithms in Rayleigh fading channel.
Akbari, Mohsen; Manesh, Mohsen Riahi; El-Saleh, Ayman A; Reza, Ahmed Wasif
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.
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
Naive Bayes-Guided Bat Algorithm for Feature Selection
Taha, Ahmed Majid; Mustapha, Aida; Chen, Soong-Der
2013-01-01
When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets. PMID:24396295
Kilic, Veli Tayfun; Erturk, Vakur B; Demir, Hilmi Volkan
2012-01-15
Optical antennas are of fundamental importance for the strongly localizing field beyond the diffraction limit. We report that planar optical antennas made of split-ring architecture are numerically found in three-dimensional simulations to outperform dipole antennas for the enhancement of localized field intensity inside their gap regions. The computational results (finite-difference time-domain) indicate that the resulting field localization, which is of the order of many thousandfold, in the case of the split-ring resonators is at least 2 times stronger than the one in the dipole antennas resonant at the same operating wavelength, while the two antenna types feature the same gap size and tip sharpness.
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
NASA Technical Reports Server (NTRS)
Nobbs, Steven G.
1995-01-01
An overview of the performance seeking control (PSC) algorithm and details of the important components of the algorithm are given. The onboard propulsion system models, the linear programming optimization, and engine control interface are described. The PSC algorithm receives input from various computers on the aircraft including the digital flight computer, digital engine control, and electronic inlet control. The PSC algorithm contains compact models of the propulsion system including the inlet, engine, and nozzle. The models compute propulsion system parameters, such as inlet drag and fan stall margin, which are not directly measurable in flight. The compact models also compute sensitivities of the propulsion system parameters to change in control variables. The engine model consists of a linear steady state variable model (SSVM) and a nonlinear model. The SSVM is updated with efficiency factors calculated in the engine model update logic, or Kalman filter. The efficiency factors are used to adjust the SSVM to match the actual engine. The propulsion system models are mathematically integrated to form an overall propulsion system model. The propulsion system model is then optimized using a linear programming optimization scheme. The goal of the optimization is determined from the selected PSC mode of operation. The resulting trims are used to compute a new operating point about which the optimization process is repeated. This process is continued until an overall (global) optimum is reached before applying the trims to the controllers.
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.
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
Are Informant Reports of Personality More Internally Consistent Than Self Reports of Personality?
Balsis, Steve; Cooper, Luke D; Oltmanns, Thomas F
2015-08-01
The present study examined whether informant-reported personality was more or less internally consistent than self-reported personality in an epidemiological community sample (n = 1,449). Results indicated that across the 5 NEO (Neuroticism-Extraversion-Openness) personality factors and the 10 personality disorder trait dimensions, informant reports tended to be more internally consistent than self reports, as indicated by equal or higher Cronbach's alpha scores and higher average interitem correlations. In addition, the informant reports collectively outperformed the self reports for predicting responses on a global measure of health, indicating that the informant reports are not only more reliable than self reports, but they can also be useful in predicting an external criterion. Collectively these findings indicate that informant reports tend to have greater internal consistency than self reports.
Consistency test for simple specifications of automation systems
Chebotarev, A.N.
1995-01-01
This article continues the topic of functional synthesis of automaton systems for discrete-information processing. A language of functional specification of automaton systems based on the logic of one-place predicates of an integer argument has been described. A specification in this language defines a nondeterministic superword X-Y-function, i.e., a function that maps superwords in the alphabet X into sets of superwords in the alphabet Y (the alphabets X and Y are specification-dependent), which corresponds to an initialized nondeterministic X-Y-automaton. The specification G is consistent if the function defined by the specification corresponds to an automaton A{sub G} with a nonempty state set. Consistency tests for the initial specification and for various intermediate specifications obtained in the process of functional synthesis of the automaton system are of fundamental importance for the verificational method of automaton system design developed in the framework of the proposed topic. We need sufficiently efficient algorithms to test consistency of specifications. An algorithm proposal constructs the coresponding automaton A{sub G} for any simple specifications G. The consistency of a specification is thus decided constructively. However, this solution is not always convenient, because it usually involves a highly time-consuming procedure to construct a nondeterministic automaton with a very large number of states. In this paper, we propose a convenient approach that combines automaton and logic methods and established consistency or inconsistency of a specification without constructing the corresponding autmaton.
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.
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
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.
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
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.
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.
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.
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
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.
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.
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.
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
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
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
Consistent realignment of 3D diffusion tensor MRI eigenvectors
NASA Astrophysics Data System (ADS)
Beg, Mirza Faisal; Dickie, Ryan; Golds, Gregory; Younes, Laurent
2007-03-01
Diffusion tensor MR image data gives at each voxel in the image a symmetric, positive definite matrix that is denoted as the diffusion tensor at that voxel location. The eigenvectors of the tensor represent the principal directions of anisotopy in water diffusion. The eigenvector with the largest eigenvalue indicates the local orientation of tissue fibers in 3D as water is expected to diffuse preferentially up and down along the fiber tracts. Although there is no anatomically valid positive or negative direction to these fiber tracts, for many applications, it is of interest to assign an artificial direction to the fiber tract by choosing one of the two signs of the principal eigenvector in such a way that in local neighborhoods the assigned directions are consistent and vary smoothly in space. We demonstrate here an algorithm for realigning the principal eigenvectors by flipping their sign such that it assigns a locally consistent and spatially smooth fiber direction to the eigenvector field based on a Monte-Carlo algorithm adapted from updating clusters of spin systems. We present results that show the success of this algorithm on 11 available unsegmented canine cardiac volumes of both healthy and failing hearts.
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
Li, Jiazhong; Bai, Fang; Liu, Huanxiang; Gramatica, Paola
2015-12-01
The concept of ligand efficiency is defined as biological activity in each molecular size and is widely accepted throughout the drug design community. Among different LE indices, surface efficiency index (SEI) was reported to be the best one in support vector machine modeling, much better than the generally and traditionally used end-point pIC50. In this study, 2D multiple linear regression and 3D comparative molecular field analysis methods are employed to investigate the structure-activity relationships of a series of androgen receptor antagonists, using pIC50 and SEI as dependent variables to verify the influence of using different kinds of end-points. The obtained results suggest that SEI outperforms pIC50 on both MLR and CoMFA models with higher stability and predictive ability. After analyzing the characteristics of the two dependent variables SEI and pIC50, we deduce that the superiority of SEI maybe lie in that SEI could reflect the relationship between molecular structures and corresponding bioactivities, in nature, better than pIC50. This study indicates that SEI could be a more rational parameter to be optimized in the drug discovery process than pIC50.
Relative-Error-Covariance Algorithms
NASA Technical Reports Server (NTRS)
Bierman, Gerald J.; Wolff, Peter J.
1991-01-01
Two algorithms compute error covariance of difference between optimal estimates, based on data acquired during overlapping or disjoint intervals, of state of discrete linear system. Provides quantitative measure of mutual consistency or inconsistency of estimates of states. Relative-error-covariance concept applied, to determine degree of correlation between trajectories calculated from two overlapping sets of measurements and construct real-time test of consistency of state estimates based upon recently acquired data.
[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
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.
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.
Improved Exact Enumerative Algorithms for the Planted (l, d)-Motif Search Problem.
Tanaka, Shunji
2014-01-01
In this paper efficient exact algorithms are proposed for the planted ( l, d)-motif search problem. This problem is to find all motifs of length l that are planted in each input string with at most d mismatches. The "quorum" version of this problem is also treated in this paper to find motifs planted not in all input strings but in at least q input strings. The proposed algorithms are based on the previous algorithms called qPMSPruneI and qPMS7 that traverse a search tree starting from a l-length substring of an input string. To improve these previous algorithms, several techniques are introduced, which contribute to reducing the computation time for the traversal. In computational experiments, it will be shown that the proposed algorithms outperform the previous algorithms.
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.
MRCK_3D contact detonation algorithm
Rougier, Esteban; Munjiza, Antonio
2010-01-01
Large-scale Combined Finite-Discrete Element Methods (FEM-DEM) and Discrete Element Methods (DEM) simulations involving contact of a large number of separate bod ies need an efficient, robust and flexible contact detection algorithm. In this work the MRCK-3D search algorithm is outlined and its main CPU perfonnances are evaluated. One of the most important aspects of this newly developed search algorithm is that it is applicable to systems consisting of many bodies of different shapes and sizes.
Assessment of self-consistent field convergence in spin-dependent relativistic calculations
NASA Astrophysics Data System (ADS)
Nakano, Masahiko; Seino, Junji; Nakai, Hiromi
2016-07-01
This Letter assesses the self-consistent field (SCF) convergence behavior in the generalized Hartree-Fock (GHF) method. Four acceleration algorithms were implemented for efficient SCF convergence in the GHF method: the damping algorithm, the conventional direct inversion in the iterative subspace (DIIS), the energy-DIIS (EDIIS), and a combination of DIIS and EDIIS. Four different systems with varying complexity were used to investigate the SCF convergence using these algorithms, ranging from atomic systems to metal complexes. The numerical assessments demonstrated the effectiveness of a combination of DIIS and EDIIS for GHF calculations in comparison with the other discussed algorithms.
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.
Comparison and improvement of algorithms for computing minimal cut sets
2013-01-01
Background Constrained minimal cut sets (cMCSs) have recently been introduced as a framework to enumerate minimal genetic intervention strategies for targeted optimization of metabolic networks. Two different algorithmic schemes (adapted Berge algorithm and binary integer programming) have been proposed to compute cMCSs from elementary modes. However, in their original formulation both algorithms are not fully comparable. Results Here we show that by a small extension to the integer program both methods become equivalent. Furthermore, based on well-known preprocessing procedures for integer programming we present efficient preprocessing steps which can be used for both algorithms. We then benchmark the numerical performance of the algorithms in several realistic medium-scale metabolic models. The benchmark calculations reveal (i) that these preprocessing steps can lead to an enormous speed-up under both algorithms, and (ii) that the adapted Berge algorithm outperforms the binary integer approach. Conclusions Generally, both of our new implementations are by at least one order of magnitude faster than other currently available implementations. PMID:24191903
Coevolving memetic algorithms: a review and progress report.
Smith, Jim E
2007-02-01
Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems. This paper presents a rationale for such systems and places them in the context of other recent work on adaptive memetic algorithms. It then proposes a general structure within which a population of LS algorithms can be evolved in tandem with the solutions to which they are applied. Previous research started with a simple self-adaptive system before moving on to more complex models. Results showed that the algorithm was able to discover and exploit certain forms of structure and regularities within the problems. This "metalearning" of problem features provided a means of creating highly scalable algorithms. This work is briefly reviewed to highlight some of the important findings and behaviors exhibited. Based on this analysis, new results are then presented from systems with more flexible representations, which, again, show significant improvements. Finally, the current state of, and future directions for, research in this area is discussed.
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.
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
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.
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.
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
Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm
Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang
2012-01-01
Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms. PMID:23226565
Inferring gene regulatory networks by singular value decomposition and gravitation field algorithm.
Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang
2012-01-01
Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.
Huang, Xiaohui; Ye, Yunming; Zhang, Haijun
2014-08-01
Kmeans-type clustering aims at partitioning a data set into clusters such that the objects in a cluster are compact and the objects in different clusters are well separated. However, most kmeans-type clustering algorithms rely on only intracluster compactness while overlooking intercluster separation. In this paper, a series of new clustering algorithms by extending the existing kmeans-type algorithms is proposed by integrating both intracluster compactness and intercluster separation. First, a set of new objective functions for clustering is developed. Based on these objective functions, the corresponding updating rules for the algorithms are then derived analytically. The properties and performances of these algorithms are investigated on several synthetic and real-life data sets. Experimental studies demonstrate that our proposed algorithms outperform the state-of-the-art kmeans-type clustering algorithms with respect to four metrics: accuracy, RandIndex, Fscore, and normal mutual information.
Consistent tangent matrices for density-dependent finite plasticity models
NASA Astrophysics Data System (ADS)
Pérez-Foguet, Agustí; Rodríguez-Ferran, Antonio; Huerta, Antonio
2001-09-01
The consistent tangent matrix for density-dependent plastic models within the theory of isotropic multiplicative hyperelastoplasticity is presented here. Plastic equations expressed as general functions of the Kirchhoff stresses and density are considered. They include the Cauchy-based plastic models as a particular case. The standard exponential return-mapping algorithm is applied, with the density playing the role of a fixed parameter during the nonlinear plastic corrector problem. The consistent tangent matrix has the same structure as in the usual density-independent plastic models. A simple additional term takes into account the influence of the density on the plastic corrector problem. Quadratic convergence results are shown for several representative examples involving geomaterial and powder constitutive models.
A highly accurate heuristic algorithm for the haplotype assembly problem
2013-01-01
Background Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation in human DNA. The sequence of SNPs in each of the two copies of a given chromosome in a diploid organism is referred to as a haplotype. Haplotype information has many applications such as gene disease diagnoses, drug design, etc. The haplotype assembly problem is defined as follows: Given a set of fragments sequenced from the two copies of a chromosome of a single individual, and their locations in the chromosome, which can be pre-determined by aligning the fragments to a reference DNA sequence, the goal here is to reconstruct two haplotypes (h1, h2) from the input fragments. Existing algorithms do not work well when the error rate of fragments is high. Here we design an algorithm that can give accurate solutions, even if the error rate of fragments is high. Results We first give a dynamic programming algorithm that can give exact solutions to the haplotype assembly problem. The time complexity of the algorithm is O(n × 2t × t), where n is the number of SNPs, and t is the maximum coverage of a SNP site. The algorithm is slow when t is large. To solve the problem when t is large, we further propose a heuristic algorithm on the basis of the dynamic programming algorithm. Experiments show that our heuristic algorithm can give very accurate solutions. Conclusions We have tested our algorithm on a set of benchmark datasets. Experiments show that our algorithm can give very accurate solutions. It outperforms most of the existing programs when the error rate of the input fragments is high. PMID:23445458
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
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
Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani
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.
A Community Detection Algorithm Based on Topology Potential and Spectral Clustering
Wang, Zhixiao; Chen, Zhaotong; Zhao, Ya; Chen, Shaoda
2014-01-01
Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes' topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods. PMID:25147846
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.
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
Rüst, Christoph Alexander; Lepers, Romuald; Rosemann, Thomas; Knechtle, Beat
2014-01-01
This study investigated the change in sex differences across years in ultra-distance swimming performances at the 36-km 'Maratona del Golfo Capri-Napoli' race held from 1954 to 2013. Changes in swimming performance of 662 men and 228 women over the 59-year period were investigated using linear, non-linear and hierarchical regression analyses. Race times of the annual fastest swimmers decreased linearly for women from 731 min to 391 min (r (2) = 0.60, p < 0.0001) and for men from 600 min to 373 min (r (2) = 0.30, p < 0.0001). Race times of the annual top three swimmers decreased linearly between 1963 and 2013 for women from 736.8 ± 78.4 min to 396.6 ± 4.5 min (r (2) = 0.58, p < 0.0001) and for men from 627.1 ± 34.5 min to 374.1 ± 0.3 min (r (2) = 0.42, p < 0.0001). The sex difference in performance for the annual fastest decreased linearly from 39.2% (1955) to 4.7% (2013) (r (2) = 0.33, p < 0.0001). For the annual three fastest competitors, the sex difference in performance decreased linearly from 38.2 ± 14.0% (1963) to 6.0 ± 1.0% (2013) (r (2) = 0.43, p < 0.0001). In conclusion, ultra-distance swimmers improved their performance at the 'Maratona del Golfo Capri-Napoli' over the last ~60 years and the fastest women reduced the gap with the fastest men linearly from ~40% to ~5-6%. The linear change in both race times and sex differences may suggest that women will be able to achieve men's performance or even to outperform men in the near future in an open-water ultra-distance swimming event such as the 'Maratona del Golfo Capri-Napoli'.
Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization
Shim, Jung Eun; Hwang, Sohyun; Lee, Insuk
2015-01-01
A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms – direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network – in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity. PMID:26091506
Lim, Néhémy; Şenbabaoğlu, Yasin; Michailidis, George; d’Alché-Buc, Florence
2013-01-01
Motivation: Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. A number of approaches using either perturbation (knock-out) or wild-type time-series data have appeared in the literature addressing this problem, with the latter using linear temporal models. Nonlinear dynamical models are particularly appropriate for this inference task, given the generation mechanism of the time-series data. In this study, we introduce a novel nonlinear autoregressive model based on operator-valued kernels that simultaneously learns the model parameters, as well as the network structure. Results: A flexible boosting algorithm (OKVAR-Boost) that shares features from L2-boosting and randomization-based algorithms is developed to perform the tasks of parameter learning and network inference for the proposed model. Specifically, at each boosting iteration, a regularized Operator-valued Kernel-based Vector AutoRegressive model (OKVAR) is trained on a random subnetwork. The final model consists of an ensemble of such models. The empirical estimation of the ensemble model’s Jacobian matrix provides an estimation of the network structure. The performance of the proposed algorithm is first evaluated on a number of benchmark datasets from the DREAM3 challenge and then on real datasets related to the In vivo Reverse-Engineering and Modeling Assessment (IRMA) and T-cell networks. The high-quality results obtained strongly indicate that it outperforms existing approaches. Availability: The OKVAR-Boost Matlab code is available as the archive: http://amis-group.fr/sourcecode-okvar-boost/OKVARBoost-v1.0.zip. Contact: florence.dalche@ibisc.univ-evry.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23574736
Student Effort, Consistency, and Online Performance
ERIC Educational Resources Information Center
Patron, Hilde; Lopez, Salvador
2011-01-01
This paper examines how student effort, consistency, motivation, and marginal learning, influence student grades in an online course. We use data from eleven Microeconomics courses taught online for a total of 212 students. Our findings show that consistency, or less time variation, is a statistically significant explanatory variable, whereas…
Does Acquiescence Affect Individual Items Consistently?
ERIC Educational Resources Information Center
Kam, Chester Chun Seng; Zhou, Mingming
2015-01-01
Previous research has found the effects of acquiescence to be generally consistent across item "aggregates" within a single survey (i.e., essential tau-equivalence), but it is unknown whether this phenomenon is consistent at the" individual item" level. This article evaluated the often assumed but inadequately tested…
40 CFR 55.12 - Consistency updates.
Code of Federal Regulations, 2011 CFR
2011-07-01
... 40 Protection of Environment 5 2011-07-01 2011-07-01 false Consistency updates. 55.12 Section 55.12 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) OUTER CONTINENTAL SHELF AIR REGULATIONS § 55.12 Consistency updates. (a) The Administrator will...
Consistent-handed individuals are more authoritarian.
Lyle, Keith B; Grillo, Michael C
2014-01-01
Individuals differ in the consistency with which they use one hand over the other to perform everyday activities. Some individuals are very consistent, habitually using a single hand to perform most tasks. Others are relatively inconsistent, and hence make greater use of both hands. More- versus less-consistent individuals have been shown to differ in numerous aspects of personality and cognition. In several respects consistent-handed individuals resemble authoritarian individuals. For example, both consistent-handedness and authoritarianism have been linked to cognitive inflexibility. Therefore we hypothesised that consistent-handedness is an external marker for authoritarianism. Confirming our hypothesis, we found that consistent-handers scored higher than inconsistent-handers on a measure of submission to authority, were more likely to identify with a conservative political party (Republican), and expressed less-positive attitudes towards out-groups. We propose that authoritarianism may be influenced by the degree of interaction between the left and right brain hemispheres, which has been found to differ between consistent- and inconsistent-handed individuals. PMID:23586369
Consistency and Enhancement Processes in Understanding Emotions
ERIC Educational Resources Information Center
Stets, Jan E.; Asencio, Emily K.
2008-01-01
Many theories in the sociology of emotions assume that emotions emerge from the cognitive consistency principle. Congruence among cognitions produces good feelings whereas incongruence produces bad feelings. A work situation is simulated in which managers give feedback to workers that is consistent or inconsistent with what the workers expect to…
24 CFR 91.510 - Consistency determinations.
Code of Federal Regulations, 2014 CFR
2014-04-01
..., the proposed activities are consistent with the jurisdiction's strategic plan, and the location of the... of consistency of the application with the approved consolidated plan for the jurisdiction may be... unit of general local government that: is required to have a consolidated plan, is authorized to use...
24 CFR 91.510 - Consistency determinations.
Code of Federal Regulations, 2013 CFR
2013-04-01
..., the proposed activities are consistent with the jurisdiction's strategic plan, and the location of the... of consistency of the application with the approved consolidated plan for the jurisdiction may be... unit of general local government that: is required to have a consolidated plan, is authorized to use...
[Hand Preference: Cognitive Development, Asymmetry, and Consistency.
ERIC Educational Resources Information Center
Bathurst, Kay; And Others
Reported are results of three studies: (1) Hand Preference Consistency during Infancy and Preschool Years (K. Bathurst and A. W. Gottfried), (2) Asymmetry of Verbal Processing: Influence of Family Handedness (K. Bathurst and D. W. Kee), (3) Consistency of Hand Preference and Cognitive Development in Young Children (K. Bathurst and A. W.…
44 CFR 206.349 - Consistency determinations.
Code of Federal Regulations, 2013 CFR
2013-10-01
... § 206.349 Consistency determinations. Section 6(a)(6) of CBRA requires that certain actions be consistent with the purposes of that statute if the actions are to be carried out on a unit of the CBRA. The... associated with the coastal barriers along with Atlantic and Gulf coasts. For those actions where...
Steps toward Promoting Consistency in Educational Decisions
ERIC Educational Resources Information Center
Klein, Joseph
2010-01-01
Purpose: The literature indicates the advantages of decisions formulated through intuition, as well as the limitations, such as lack of consistency in similar situations. The principle of consistency (invariance), requiring that two equivalent versions of choice-problems will produce the same preference, is violated in intuitive judgment. This…
Library of Continuation Algorithms
2005-03-01
LOCA (Library of Continuation Algorithms) is scientific software written in C++ that provides advanced analysis tools for nonlinear systems. In particular, it provides parameter continuation algorithms. bifurcation tracking algorithms, and drivers for linear stability analysis. The algorithms are aimed at large-scale applications that use Newtons method for their nonlinear solve.
Assessing Predictors of Changes in Protein Stability upon Mutation Using Self-Consistency
Thiltgen, Grant; Goldstein, Richard A.
2012-01-01
The ability to predict the effect of mutations on protein stability is important for a wide range of tasks, from protein engineering to assessing the impact of SNPs to understanding basic protein biophysics. A number of methods have been developed that make these predictions, but assessing the accuracy of these tools is difficult given the limitations and inconsistencies of the experimental data. We evaluate four different methods based on the ability of these methods to generate consistent results for forward and back mutations, and examine how this ability varies with the nature and location of the mutation. We find that, while one method seems to outperform the others, the ability of these methods to make accurate predictions is limited. PMID:23144695
Algorithmic Animation in Education--Review of Academic Experience
ERIC Educational Resources Information Center
Esponda-Arguero, Margarita
2008-01-01
This article is a review of the pedagogical experience obtained with systems for algorithmic animation. Algorithms consist of a sequence of operations whose effect on data structures can be visualized using a computer. Students learn algorithms by stepping the animation through the different individual operations, possibly reversing their effect.…
An Experimental Method for the Active Learning of Greedy Algorithms
ERIC Educational Resources Information Center
Velazquez-Iturbide, J. Angel
2013-01-01
Greedy algorithms constitute an apparently simple algorithm design technique, but its learning goals are not simple to achieve.We present a didacticmethod aimed at promoting active learning of greedy algorithms. The method is focused on the concept of selection function, and is based on explicit learning goals. It mainly consists of an…
3-D consistency dynamic constitutive model of concrete
NASA Astrophysics Data System (ADS)
Xiao, Shiyun; Li, Hongnan; Lin, Gao
2010-06-01
Based on the consistency-viscoplastic constitutive model, the static William-Warnke model with threeparameters is modified and a consistency-viscoplastic William-Warnke model with three-parameters is developed that considers the effect of strain rates. Then, the tangent modulus of the consistency viscoplastic model is introduced and an implicit backward Elure iterative algorithm is developed. Comparisons between the numerical simulations and experimental data show that the consistency model properly provides the uniaxial and biaxial dynamic behaviors of concrete. To study the effect of strain rates on the dynamic response of concrete structures, the proposed model is used in the analysis of the dynamic response of a simply-supported beam and the results show that the strain rate has a significant effect on the displacement and stress magnitudes and distributions. Finally, the seismic responses of a 278 m high arch dam are obtained and compared by using the linear elastic model, as well as rate-independent and rate-dependent William-Warnke three-parameter models. The results indicate that the strain rate affects the first principal stresses, and the maximal equivalent viscoplastic strain rate of the arch dam. Numerical calculations and analyses reveal that considering the strain rate is important in the safety assessments of arch dams located in seismically active areas.
A Breeder Algorithm for Stellarator Optimization
NASA Astrophysics Data System (ADS)
Wang, S.; Ware, A. S.; Hirshman, S. P.; Spong, D. A.
2003-10-01
An optimization algorithm that combines the global parameter space search properties of a genetic algorithm (GA) with the local parameter search properties of a Levenberg-Marquardt (LM) algorithm is described. Optimization algorithms used in the design of stellarator configurations are often classified as either global (such as GA and differential evolution algorithm) or local (such as LM). While nonlinear least-squares methods such as LM are effective at minimizing a cost-function based on desirable plasma properties such as quasi-symmetry and ballooning stability, whether or not this is a local or global minimum is unknown. The advantage of evolutionary algorithms such as GA is that they search a wider range of parameter space and are not susceptible to getting stuck in a local minimum of the cost function. Their disadvantage is that in some cases the evolutionary algorithms are ineffective at finding a minimum state. Here, we describe the initial development of the Breeder Algorithm (BA). BA consists of a genetic algorithm outer loop with an inner loop in which each generation is refined using a LM step. Initial results for a quasi-poloidal stellarator optimization will be presented, along with a comparison to existing optimization algorithms.
Comparison of cone beam artifacts reduction: two pass algorithm vs TV-based CS algorithm
NASA Astrophysics Data System (ADS)
Choi, Shinkook; Baek, Jongduk
2015-03-01
In a cone beam computed tomography (CBCT), the severity of the cone beam artifacts is increased as the cone angle increases. To reduce the cone beam artifacts, several modified FDK algorithms and compressed sensing based iterative algorithms have been proposed. In this paper, we used two pass algorithm and Gradient-Projection-Barzilai-Borwein (GPBB) algorithm to reduce the cone beam artifacts, and compared their performance using structural similarity (SSIM) index. In two pass algorithm, it is assumed that the cone beam artifacts are mainly caused by extreme-density(ED) objects, and therefore the algorithm reproduces the cone beam artifacts(i.e., error image) produced by ED objects, and then subtract it from the original image. GPBB algorithm is a compressed sensing based iterative algorithm which minimizes an energy function for calculating the gradient projection with the step size determined by the Barzilai- Borwein formulation, therefore it can estimate missing data caused by the cone beam artifacts. To evaluate the performance of two algorithms, we used testing objects consisting of 7 ellipsoids separated along the z direction and cone beam artifacts were generated using 30 degree cone angle. Even though the FDK algorithm produced severe cone beam artifacts with a large cone angle, two pass algorithm reduced the cone beam artifacts with small residual errors caused by inaccuracy of ED objects. In contrast, GPBB algorithm completely removed the cone beam artifacts and restored the original shape of the objects.
A high-accuracy algorithm for designing arbitrary holographic atom traps.
Pasienski, Matthew; Demarco, Brian
2008-02-01
We report the realization of a new iterative Fourier-transform algorithm for creating holograms that can diffract light into an arbitrary two-dimensional intensity profile. We show that the predicted intensity distributions are smooth with a fractional error from the target distribution at the percent level. We demonstrate that this new algorithm outperforms the most frequently used alternatives typically by one and two orders of magnitude in accuracy and roughness, respectively. The techniques described in this paper outline a path to creating arbitrary holographic atom traps in which the only remaining hurdle is physical implementation.
Growth algorithms for lattice heteropolymers at low temperatures
NASA Astrophysics Data System (ADS)
Hsu, Hsiao-Ping; Mehra, Vishal; Nadler, Walter; Grassberger, Peter
2003-01-01
Two improved versions of the pruned-enriched-Rosenbluth method (PERM) are proposed and tested on simple models of lattice heteropolymers. Both are found to outperform not only the previous version of PERM, but also all other stochastic algorithms which have been employed on this problem, except for the core directed chain growth method (CG) of Beutler and Dill. In nearly all test cases they are faster in finding low-energy states, and in many cases they found new lowest energy states missed in previous papers. The CG method is superior to our method in some cases, but less efficient in others. On the other hand, the CG method uses heavily heuristics based on presumptions about the hydrophobic core and does not give thermodynamic properties, while the present method is a fully blind general purpose algorithm giving correct Boltzmann-Gibbs weights, and can be applied in principle to any stochastic sampling problem.
Memetic algorithms for ligand expulsion from protein cavities
NASA Astrophysics Data System (ADS)
Rydzewski, J.; Nowak, W.
2015-09-01
Ligand diffusion through a protein interior is a fundamental process governing biological signaling and enzymatic catalysis. A complex topology of channels in proteins leads often to difficulties in modeling ligand escape pathways by classical molecular dynamics simulations. In this paper, two novel memetic methods for searching the exit paths and cavity space exploration are proposed: Memory Enhanced Random Acceleration (MERA) Molecular Dynamics (MD) and Immune Algorithm (IA). In MERA, a pheromone concept is introduced to optimize an expulsion force. In IA, hybrid learning protocols are exploited to predict ligand exit paths. They are tested on three protein channels with increasing complexity: M2 muscarinic G-protein-coupled receptor, enzyme nitrile hydratase, and heme-protein cytochrome P450cam. In these cases, the memetic methods outperform simulated annealing and random acceleration molecular dynamics. The proposed algorithms are general and appropriate in all problems where an accelerated transport of an object through a network of channels is studied.
A new distributed systems scheduling algorithm: a swarm intelligence approach
NASA Astrophysics Data System (ADS)
Haghi Kashani, Mostafa; Sarvizadeh, Raheleh; Jameii, Mahdi
2011-12-01
The scheduling problem in distributed systems is known as an NP-complete problem, and methods based on heuristic or metaheuristic search have been proposed to obtain optimal and suboptimal solutions. The task scheduling is a key factor for distributed systems to gain better performance. In this paper, an efficient method based on memetic algorithm is developed to solve the problem of distributed systems scheduling. With regard to load balancing efficiently, Artificial Bee Colony (ABC) has been applied as local search in the proposed memetic algorithm. The proposed method has been compared to existing memetic-Based approach in which Learning Automata method has been used as local search. The results demonstrated that the proposed method outperform the above mentioned method in terms of communication cost.
Voronoi-based localisation algorithm for mobile sensor networks
NASA Astrophysics Data System (ADS)
Guan, Zixiao; Zhang, Yongtao; Zhang, Baihai; Dong, Lijing
2016-11-01
Localisation is an essential and important part in wireless sensor networks (WSNs). Many applications require location information. So far, there are less researchers studying on mobile sensor networks (MSNs) than static sensor networks (SSNs). However, MSNs are required in more and more areas such that the number of anchor nodes can be reduced and the location accuracy can be improved. In this paper, we firstly propose a range-free Voronoi-based Monte Carlo localisation algorithm (VMCL) for MSNs. We improve the localisation accuracy by making better use of the information that a sensor node gathers. Then, we propose an optimal region selection strategy of Voronoi diagram based on VMCL, called ORSS-VMCL, to increase the efficiency and accuracy for VMCL by adapting the size of Voronoi area during the filtering process. Simulation results show that the accuracy of these two algorithms, especially ORSS-VMCL, outperforms traditional MCL.
A hierarchical algorithm for molecular similarity (H-FORMS).
Ramirez-Manzanares, Alonso; Peña, Joaquin; Azpiroz, Jon M; Merino, Gabriel
2015-07-15
A new hierarchical method to determine molecular similarity is introduced. The goal of this method is to detect if a pair of molecules has the same structure by estimating a rigid transformation that aligns the molecules and a correspondence function that matches their atoms. The algorithm firstly detect similarity based on the global spatial structure. If this analysis is not sufficient, the algorithm computes novel local structural rotation-invariant descriptors for the atom neighborhood and uses this information to match atoms. Two strategies (deterministic and stochastic) on the matching based alignment computation are tested. As a result, the atom-matching based on local similarity indexes decreases the number of testing trials and significantly reduces the dimensionality of the Hungarian assignation problem. The experiments on well-known datasets show that our proposal outperforms state-of-the-art methods in terms of the required computational time and accuracy.
Memetic algorithms for ligand expulsion from protein cavities.
Rydzewski, J; Nowak, W
2015-09-28
Ligand diffusion through a protein interior is a fundamental process governing biological signaling and enzymatic catalysis. A complex topology of channels in proteins leads often to difficulties in modeling ligand escape pathways by classical molecular dynamics simulations. In this paper, two novel memetic methods for searching the exit paths and cavity space exploration are proposed: Memory Enhanced Random Acceleration (MERA) Molecular Dynamics (MD) and Immune Algorithm (IA). In MERA, a pheromone concept is introduced to optimize an expulsion force. In IA, hybrid learning protocols are exploited to predict ligand exit paths. They are tested on three protein channels with increasing complexity: M2 muscarinic G-protein-coupled receptor, enzyme nitrile hydratase, and heme-protein cytochrome P450cam. In these cases, the memetic methods outperform simulated annealing and random acceleration molecular dynamics. The proposed algorithms are general and appropriate in all problems where an accelerated transport of an object through a network of channels is studied. PMID:26428990
On the initial state and consistency relations
Berezhiani, Lasha; Khoury, Justin E-mail: jkhoury@sas.upenn.edu
2014-09-01
We study the effect of the initial state on the consistency conditions for adiabatic perturbations. In order to be consistent with the constraints of General Relativity, the initial state must be diffeomorphism invariant. As a result, we show that initial wavefunctional/density matrix has to satisfy a Slavnov-Taylor identity similar to that of the action. We then investigate the precise ways in which modified initial states can lead to violations of the consistency relations. We find two independent sources of violations: i) the state can include initial non-Gaussianities; ii) even if the initial state is Gaussian, such as a Bogoliubov state, the modified 2-point function can modify the q-vector → 0 analyticity properties of the vertex functional and result in violations of the consistency relations.
Consistency of homogenization schemes in linear poroelasticity
NASA Astrophysics Data System (ADS)
Pichler, Bernhard; Dormieux, Luc
2008-08-01
In view of extending classical micromechanics of poroelasticity to the non-saturated regime, one has to deal with different pore stresses which may be affected by the size and the shape of the pores. Introducing the macrostrain and these pore stresses as loading parameters, the macrostress of a representative volume element of a porous material can be derived by means of Levin's theorem or by means of the direct formulation of the stress average rule, respectively. A consistency requirement for a given homogenization scheme is obtained from the condition that the two approaches should yield identical results. Classical approaches (Mori-Tanaka scheme, self-consistent scheme) are shown to be only conditionally consistent. In contrast, the Ponte Castañeda-Willis scheme proves to provide consistent descriptions both of porous matrix-inclusion composites and of porous polycrystals. To cite this article: B. Pichler, L. Dormieux, C. R. Mecanique 336 (2008).
Safety performance functions incorporating design consistency variables.
Montella, Alfonso; Imbriani, Lella Liana
2015-01-01
Highway design which ensures that successive elements are coordinated in such a way as to produce harmonious and homogeneous driver performances along the road is considered consistent and safe. On the other hand, an alignment which requires drivers to handle high speed gradients and does not meet drivers' expectancy is considered inconsistent and produces higher crash frequency. To increase the usefulness and the reliability of existing safety performance functions and contribute to solve inconsistencies of existing highways as well as inconsistencies arising in the design phase, we developed safety performance functions for rural motorways that incorporate design consistency measures. Since the design consistency variables were used only for curves, two different sets of models were fitted for tangents and curves. Models for the following crash characteristics were fitted: total, single-vehicle run-off-the-road, other single vehicle, multi vehicle, daytime, nighttime, non-rainy weather, rainy weather, dry pavement, wet pavement, property damage only, slight injury, and severe injury (including fatal). The design consistency parameters in this study are based on operating speed models developed through an instrumented vehicle equipped with a GPS continuous speed tracking from a field experiment conducted on the same motorway where the safety performance functions were fitted (motorway A16 in Italy). Study results show that geometric design consistency has a significant effect on safety of rural motorways. Previous studies on the relationship between geometric design consistency and crash frequency focused on two-lane rural highways since these highways have the higher crash rates and are generally characterized by considerable inconsistencies. Our study clearly highlights that the achievement of proper geometric design consistency is a key design element also on motorways because of the safety consequences of design inconsistencies. The design consistency measures
Consistency relations for non-Gaussianity
NASA Astrophysics Data System (ADS)
Li, Miao; Wang, Yi
2008-09-01
We investigate consistency relations for non-Gaussianity. We provide a model-independent dynamical proof for the consistency relation for three-point correlation functions from the Hamiltonian and field redefinition. This relation can be applied to single-field inflation, multi-field inflation and the curvaton scenario. This relation can also be generalized to n-point correlation functions up to arbitrary order in perturbation theory and with arbitrary number of loops.
Quantum Algorithm for Linear Programming Problems
NASA Astrophysics Data System (ADS)
Joag, Pramod; Mehendale, Dhananjay
The quantum algorithm (PRL 103, 150502, 2009) solves a system of linear equations with exponential speedup over existing classical algorithms. We show that the above algorithm can be readily adopted in the iterative algorithms for solving linear programming (LP) problems. The first iterative algorithm that we suggest for LP problem follows from duality theory. It consists of finding nonnegative solution of the equation forduality condition; forconstraints imposed by the given primal problem and for constraints imposed by its corresponding dual problem. This problem is called the problem of nonnegative least squares, or simply the NNLS problem. We use a well known method for solving the problem of NNLS due to Lawson and Hanson. This algorithm essentially consists of solving in each iterative step a new system of linear equations . The other iterative algorithms that can be used are those based on interior point methods. The same technique can be adopted for solving network flow problems as these problems can be readily formulated as LP problems. The suggested quantum algorithm cansolveLP problems and Network Flow problems of very large size involving millions of variables.
NASA Astrophysics Data System (ADS)
Biazzo, Indaco; Braunstein, Alfredo; Zecchina, Riccardo
2012-08-01
We study the behavior of an algorithm derived from the cavity method for the prize-collecting steiner tree (PCST) problem on graphs. The algorithm is based on the zero temperature limit of the cavity equations and as such is formally simple (a fixed point equation resolved by iteration) and distributed (parallelizable). We provide a detailed comparison with state-of-the-art algorithms on a wide range of existing benchmarks, networks, and random graphs. Specifically, we consider an enhanced derivative of the Goemans-Williamson heuristics and the dhea solver, a branch and cut integer linear programming based approach. The comparison shows that the cavity algorithm outperforms the two algorithms in most large instances both in running time and quality of the solution. Finally we prove a few optimality properties of the solutions provided by our algorithm, including optimality under the two postprocessing procedures defined in the Goemans-Williamson derivative and global optimality in some limit cases.
Biazzo, Indaco; Braunstein, Alfredo; Zecchina, Riccardo
2012-08-01
We study the behavior of an algorithm derived from the cavity method for the prize-collecting steiner tree (PCST) problem on graphs. The algorithm is based on the zero temperature limit of the cavity equations and as such is formally simple (a fixed point equation resolved by iteration) and distributed (parallelizable). We provide a detailed comparison with state-of-the-art algorithms on a wide range of existing benchmarks, networks, and random graphs. Specifically, we consider an enhanced derivative of the Goemans-Williamson heuristics and the dhea solver, a branch and cut integer linear programming based approach. The comparison shows that the cavity algorithm outperforms the two algorithms in most large instances both in running time and quality of the solution. Finally we prove a few optimality properties of the solutions provided by our algorithm, including optimality under the two postprocessing procedures defined in the Goemans-Williamson derivative and global optimality in some limit cases.
Self-consistent asset pricing models
NASA Astrophysics Data System (ADS)
Malevergne, Y.; Sornette, D.
2007-08-01
We discuss the foundations of factor or regression models in the light of the self-consistency condition that the market portfolio (and more generally the risk factors) is (are) constituted of the assets whose returns it is (they are) supposed to explain. As already reported in several articles, self-consistency implies correlations between the return disturbances. As a consequence, the alphas and betas of the factor model are unobservable. Self-consistency leads to renormalized betas with zero effective alphas, which are observable with standard OLS regressions. When the conditions derived from internal consistency are not met, the model is necessarily incomplete, which means that some sources of risk cannot be replicated (or hedged) by a portfolio of stocks traded on the market, even for infinite economies. Analytical derivations and numerical simulations show that, for arbitrary choices of the proxy which are different from the true market portfolio, a modified linear regression holds with a non-zero value αi at the origin between an asset i's return and the proxy's return. Self-consistency also introduces “orthogonality” and “normality” conditions linking the betas, alphas (as well as the residuals) and the weights of the proxy portfolio. Two diagnostics based on these orthogonality and normality conditions are implemented on a basket of 323 assets which have been components of the S&P500 in the period from January 1990 to February 2005. These two diagnostics show interesting departures from dynamical self-consistency starting about 2 years before the end of the Internet bubble. Assuming that the CAPM holds with the self-consistency condition, the OLS method automatically obeys the resulting orthogonality and normality conditions and therefore provides a simple way to self-consistently assess the parameters of the model by using proxy portfolios made only of the assets which are used in the CAPM regressions. Finally, the factor decomposition with the
Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.
Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo
2015-08-01
Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.
Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories
NASA Technical Reports Server (NTRS)
Burchett, Bradley T.
2003-01-01
The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.
Sort-Mid tasks scheduling algorithm in grid computing.
Reda, Naglaa M; Tawfik, A; Marzok, Mohamed A; Khamis, Soheir M
2015-11-01
Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan. PMID:26644937
Sort-Mid tasks scheduling algorithm in grid computing.
Reda, Naglaa M; Tawfik, A; Marzok, Mohamed A; Khamis, Soheir M
2015-11-01
Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan.
A Novel Tracking Algorithm via Feature Points Matching
Luo, Nan; Sun, Quansen; Chen, Qiang; Ji, Zexuan; Xia, Deshen
2015-01-01
Visual target tracking is a primary task in many computer vision applications and has been widely studied in recent years. Among all the tracking methods, the mean shift algorithm has attracted extraordinary interest and been well developed in the past decade due to its excellent performance. However, it is still challenging for the color histogram based algorithms to deal with the complex target tracking. Therefore, the algorithms based on other distinguishing features are highly required. In this paper, we propose a novel target tracking algorithm based on mean shift theory, in which a new type of image feature is introduced and utilized to find the corresponding region between the neighbor frames. The target histogram is created by clustering the features obtained in the extraction strategy. Then, the mean shift process is adopted to calculate the target location iteratively. Experimental results demonstrate that the proposed algorithm can deal with the challenging tracking situations such as: partial occlusion, illumination change, scale variations, object rotation and complex background clutter. Meanwhile, it outperforms several state-of-the-art methods. PMID:25617769
Sort-Mid tasks scheduling algorithm in grid computing
Reda, Naglaa M.; Tawfik, A.; Marzok, Mohamed A.; Khamis, Soheir M.
2014-01-01
Scheduling tasks on heterogeneous resources distributed over a grid computing system is an NP-complete problem. The main aim for several researchers is to develop variant scheduling algorithms for achieving optimality, and they have shown a good performance for tasks scheduling regarding resources selection. However, using of the full power of resources is still a challenge. In this paper, a new heuristic algorithm called Sort-Mid is proposed. It aims to maximizing the utilization and minimizing the makespan. The new strategy of Sort-Mid algorithm is to find appropriate resources. The base step is to get the average value via sorting list of completion time of each task. Then, the maximum average is obtained. Finally, the task has the maximum average is allocated to the machine that has the minimum completion time. The allocated task is deleted and then, these steps are repeated until all tasks are allocated. Experimental tests show that the proposed algorithm outperforms almost other algorithms in terms of resources utilization and makespan. PMID:26644937
A beam hardening correction method based on HL consistency
NASA Astrophysics Data System (ADS)
Mou, Xuanqin; Tang, Shaojie; Yu, Hengyong
2006-08-01
XCT with polychromatic tube spectrum causes artifact called beam hardening effect. The current correction in CT device is carried by apriori polynomial from water phantom experiment. This paper proposes a new beam hardening correction algorithm that the correction polynomial depends on the relativity of projection data in angles, which obeys Helgasson-Ludwig Consistency (HL Consistency). Firstly, a bi-polynomial is constructed to characterize the beam hardening effect based on the physical model of medical x-ray imaging. In this bi-polynomial, a factor r(γ,β) represents the ratio of the attenuation contributions caused by high density mass (bone, etc.) to low density mass (muscle, vessel, blood, soft tissue, fat, etc.) respectively in the projection angle β and fan angle γ. Secondly, let r(γ,β)=0, the bi-polynomial is degraded as a sole-polynomial. The coefficient of this polynomial can be calculated based on HL Consistency. Then, the primary correction is reached, which is also more efficient in theoretical than the correction method in current CT devices. Thirdly, based on the result of a normal CT reconstruction from the corrected projection data, r(γ,β) can be estimated. Fourthly, the coefficient of bi-polynomial can also be calculated based HL Consistency and the final correction are achieved. Experiments of circular cone beam CT indicate this method an excellent property. Correcting beam hardening effect based on HL Consistency, not only achieving a self-adaptive and more precise correction, but also getting rid of regular inconvenient water phantom experiments, will renovate the correction technique of current CT devices.
A simple way to improve path consistency processing in interval algebra networks
Bessiere, C.
1996-12-31
Reasoning about qualitative temporal information is essential in many artificial intelligence problems. In particular, many tasks can be solved using the interval-based temporal algebra introduced by Allen (A1183). In this framework, one of the main tasks is to compute the transitive closure of a network of relations between intervals (also called path consistency in a CSP-like terminology). Almost all previous path consistency algorithms proposed in the temporal reasoning literature were based on the constraint reasoning algorithms PC-1 and PC-2 (Mac77). In this paper, we first show that the most efficient of these algorithms is the one which stays the closest to PC-2. Afterwards, we propose a new algorithm, using the idea {open_quotes}one support is sufficient{close_quotes} (as AC-3 (Mac77) does for arc consistency in constraint networks). Actually, to apply this idea, we simply changed the way composition-intersection of relations was achieved during the path consistency process in previous algorithms.
Quantifying the Consistency of Scientific Databases
Šubelj, Lovro; Bajec, Marko; Mileva Boshkoska, Biljana; Kastrin, Andrej; Levnajić, Zoran
2015-01-01
Science is a social process with far-reaching impact on our modern society. In recent years, for the first time we are able to scientifically study the science itself. This is enabled by massive amounts of data on scientific publications that is increasingly becoming available. The data is contained in several databases such as Web of Science or PubMed, maintained by various public and private entities. Unfortunately, these databases are not always consistent, which considerably hinders this study. Relying on the powerful framework of complex networks, we conduct a systematic analysis of the consistency among six major scientific databases. We found that identifying a single "best" database is far from easy. Nevertheless, our results indicate appreciable differences in mutual consistency of different databases, which we interpret as recipes for future bibliometric studies. PMID:25984946
Consistency and derangements in brane tilings
NASA Astrophysics Data System (ADS)
Hanany, Amihay; Jejjala, Vishnu; Ramgoolam, Sanjaye; Seong, Rak-Kyeong
2016-09-01
Brane tilings describe Lagrangians (vector multiplets, chiral multiplets, and the superpotential) of four-dimensional { N }=1 supersymmetric gauge theories. These theories, written in terms of a bipartite graph on a torus, correspond to worldvolume theories on N D3-branes probing a toric Calabi–Yau threefold singularity. A pair of permutations compactly encapsulates the data necessary to specify a brane tiling. We show that geometric consistency for brane tilings, which ensures that the corresponding quantum field theories are well behaved, imposes constraints on the pair of permutations, restricting certain products constructed from the pair to have no one-cycles. Permutations without one-cycles are known as derangements. We illustrate this formulation of consistency with known brane tilings. Counting formulas for consistent brane tilings with an arbitrary number of chiral bifundamental fields are written down in terms of delta functions over symmetric groups.
Consistency and derangements in brane tilings
NASA Astrophysics Data System (ADS)
Hanany, Amihay; Jejjala, Vishnu; Ramgoolam, Sanjaye; Seong, Rak-Kyeong
2016-09-01
Brane tilings describe Lagrangians (vector multiplets, chiral multiplets, and the superpotential) of four-dimensional { N }=1 supersymmetric gauge theories. These theories, written in terms of a bipartite graph on a torus, correspond to worldvolume theories on N D3-branes probing a toric Calabi-Yau threefold singularity. A pair of permutations compactly encapsulates the data necessary to specify a brane tiling. We show that geometric consistency for brane tilings, which ensures that the corresponding quantum field theories are well behaved, imposes constraints on the pair of permutations, restricting certain products constructed from the pair to have no one-cycles. Permutations without one-cycles are known as derangements. We illustrate this formulation of consistency with known brane tilings. Counting formulas for consistent brane tilings with an arbitrary number of chiral bifundamental fields are written down in terms of delta functions over symmetric groups.
Temporal and kinematic consistency predict sequence awareness.
Jaynes, Molly J; Schieber, Marc H; Mink, Jonathan W
2016-10-01
Many human motor skills can be represented as a hierarchical series of movement patterns. Awareness of underlying patterns can improve performance and decrease cognitive load. Subjects (n = 30) tapped a finger sequence with changing stimulus-to-response mapping and a common movement sequence. Thirteen subjects (43 %) became aware that they were tapping a familiar movement sequence during the experiment. Subjects who became aware of the underlying motor pattern tapped with greater kinematic and temporal consistency from task onset, but consistency was not sufficient for awareness. We found no effect of age, musical experience, tapping evenness, or inter-key-interval on awareness of the pattern in the motor response. We propose that temporal or kinematic consistency reinforces a pattern representation, but cognitive engagement with the contents of the sequence is necessary to bring the pattern to conscious awareness. These findings predict benefit for movement strategies that limit temporal and kinematic variability during motor learning. PMID:27324192
Wide baseline stereo matching based on double topological relationship consistency
NASA Astrophysics Data System (ADS)
Zou, Xiaohong; Liu, Bin; Song, Xiaoxue; Liu, Yang
2009-07-01
Stereo matching is one of the most important branches in computer vision. In this paper, an algorithm is proposed for wide-baseline stereo vision matching. Here, a novel scheme is presented called double topological relationship consistency (DCTR). The combination of double topological configuration includes the consistency of first topological relationship (CFTR) and the consistency of second topological relationship (CSTR). It not only sets up a more advanced model on matching, but discards mismatches by iteratively computing the fitness of the feature matches and overcomes many problems of traditional methods depending on the powerful invariance to changes in the scale, rotation or illumination across large view changes and even occlusions. Experimental examples are shown where the two cameras have been located in very different orientations. Also, epipolar geometry can be recovered using RANSAC by far the most widely method adopted possibly. By the method, we can obtain correspondences with high precision on wide baseline matching problems. Finally, the effectiveness and reliability of this method are demonstrated in wide-baseline experiments on the image pairs.
Internal Consistency of the NVAP Water Vapor Dataset
NASA Technical Reports Server (NTRS)
Suggs, Ronnie J.; Jedlovec, Gary J.; Arnold, James E. (Technical Monitor)
2001-01-01
The NVAP (NASA Water Vapor Project) dataset is a global dataset at 1 x 1 degree spatial resolution consisting of daily, pentad, and monthly atmospheric precipitable water (PW) products. The analysis blends measurements from the Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), the Special Sensor Microwave/Imager (SSM/I), and radiosonde observations into a daily collage of PW. The original dataset consisted of five years of data from 1988 to 1992. Recent updates have added three additional years (1993-1995) and incorporated procedural and algorithm changes from the original methodology. Since each of the PW sources (TOVS, SSM/I, and radiosonde) do not provide global coverage, each of these sources compliment one another by providing spatial coverage over regions and during times where the other is not available. For this type of spatial and temporal blending to be successful, each of the source components should have similar or compatible accuracies. If this is not the case, regional and time varying biases may be manifested in the NVAP dataset. This study examines the consistency of the NVAP source data by comparing daily collocated TOVS and SSM/I PW retrievals with collocated radiosonde PW observations. The daily PW intercomparisons are performed over the time period of the dataset and for various regions.
Consistent matter couplings for Plebanski gravity
NASA Astrophysics Data System (ADS)
Tennie, Felix; Wohlfarth, Mattias N. R.
2010-11-01
We develop a scheme for the minimal coupling of all standard types of tensor and spinor field matter to Plebanski gravity. This theory is a geometric reformulation of vacuum general relativity in terms of two-form frames and connection one-forms, and provides a covariant basis for various quantization approaches. Using the spinor formalism we prove the consistency of the newly proposed matter coupling by demonstrating the full equivalence of Plebanski gravity plus matter to Einstein-Cartan gravity. As a by-product we also show the consistency of some previous suggestions for matter actions.
Dynamically consistent Jacobian inverse for mobile manipulators
NASA Astrophysics Data System (ADS)
Ratajczak, Joanna; Tchoń, Krzysztof
2016-06-01
By analogy to the definition of the dynamically consistent Jacobian inverse for robotic manipulators, we have designed a dynamically consistent Jacobian inverse for mobile manipulators built of a non-holonomic mobile platform and a holonomic on-board manipulator. The endogenous configuration space approach has been exploited as a source of conceptual guidelines. The new inverse guarantees a decoupling of the motion in the operational space from the forces exerted in the endogenous configuration space and annihilated by the dual Jacobian inverse. A performance study of the new Jacobian inverse as a tool for motion planning is presented.
Accuracy and consistency of modern elastomeric pumps.
Weisman, Robyn S; Missair, Andres; Pham, Phung; Gutierrez, Juan F; Gebhard, Ralf E
2014-01-01
Continuous peripheral nerve blockade has become a popular method of achieving postoperative analgesia for many surgical procedures. The safety and reliability of infusion pumps are dependent on their flow rate accuracy and consistency. Knowledge of pump rate profiles can help physicians determine which infusion pump is best suited for their clinical applications and specific patient population. Several studies have investigated the accuracy of portable infusion pumps. Using methodology similar to that used by Ilfeld et al, we investigated the accuracy and consistency of several current elastomeric pumps. PMID:25140510
Anticholinergic substances: A single consistent conformation
Pauling, Peter; Datta, Narayandas
1980-01-01
An interactive computer-graphics analysis of 24 antagonists of acetylcholine at peripheral autonomic post-ganglionic (muscarinic) nervous junctions and at similar junctions in the central nervous system, the crystal structures of which are known, has led to the determination of a single, consistent, energetically favorable conformation for all 24 substances, although their observed crystal structure conformations vary widely. The absolute configuration and the single, consistent (ideal) conformation of the chemical groups required for maximum anticholinergic activity are described quantitatively. Images PMID:16592775
NASA Astrophysics Data System (ADS)
Xu, Shuo; Ji, Ze; Truong Pham, Duc; Yu, Fan
2011-11-01
The simultaneous mission assignment and home allocation for hospital service robots studied is a Multidimensional Assignment Problem (MAP) with multiobjectives and multiconstraints. A population-based metaheuristic, the Binary Bees Algorithm (BBA), is proposed to optimize this NP-hard problem. Inspired by the foraging mechanism of honeybees, the BBA's most important feature is an explicit functional partitioning between global search and local search for exploration and exploitation, respectively. Its key parts consist of adaptive global search, three-step elitism selection (constraint handling, non-dominated solutions selection, and diversity preservation), and elites-centred local search within a Hamming neighbourhood. Two comparative experiments were conducted to investigate its single objective optimization, optimization effectiveness (indexed by the S-metric and C-metric) and optimization efficiency (indexed by computational burden and CPU time) in detail. The BBA outperformed its competitors in almost all the quantitative indices. Hence, the above overall scheme, and particularly the searching history-adapted global search strategy was validated.
NASA Astrophysics Data System (ADS)
Selouani, Sid-Ahmed; O'Shaughnessy, Douglas
2003-12-01
Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to[InlineEquation not available: see fulltext.] dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.
Mental Tectonics - Rendering Consistent μMaps
NASA Astrophysics Data System (ADS)
Schmid, Falko
The visualization of spatial information for wayfinding assistance requires a substantial amount of display area. Depending on the particular route, even large screens can be insufficient to visualize all information at once and in a scale such that users can understand the specific course of the route and its spatial context. Personalized wayfinding maps, such as μMaps are a possible solution for small displays: they explicitly consider the prior knowledge of a user with the environment and tailor maps toward it. The resulting schematic maps require substantially less space due to the knowledge based visual information reduction. In this paper we extend and improve the underlying algorithms of μMaps to enable efficient handling of fragmented user profiles as well as the mapping of fragmented maps. Furthermore we introduce the concept of mental tectonics, a process that harmonizes mental conceptual spatial representations with entities of a geographic frame of reference.
Evaluation of Electroencephalography Source Localization Algorithms with Multiple Cortical Sources
Bradley, Allison; Yao, Jun; Dewald, Jules; Richter, Claus-Peter
2016-01-01
Background Source localization algorithms often show multiple active cortical areas as the source of electroencephalography (EEG). Yet, there is little data quantifying the accuracy of these results. In this paper, the performance of current source density source localization algorithms for the detection of multiple cortical sources of EEG data has been characterized. Methods EEG data were generated by simulating multiple cortical sources (2–4) with the same strength or two sources with relative strength ratios of 1:1 to 4:1, and adding noise. These data were used to reconstruct the cortical sources using current source density (CSD) algorithms: sLORETA, MNLS, and LORETA using a p-norm with p equal to 1, 1.5 and 2. Precision (percentage of the reconstructed activity corresponding to simulated activity) and Recall (percentage of the simulated sources reconstructed) of each of the CSD algorithms were calculated. Results While sLORETA has the best performance when only one source is present, when two or more sources are present LORETA with p equal to 1.5 performs better. When the relative strength of one of the sources is decreased, all algorithms have more difficulty reconstructing that source. However, LORETA 1.5 continues to outperform other algorithms. If only the strongest source is of interest sLORETA is recommended, while LORETA with p equal to 1.5 is recommended if two or more of the cortical sources are of interest. These results provide guidance for choosing a CSD algorithm to locate multiple cortical sources of EEG and for interpreting the results of these algorithms. PMID:26809000
Local, smooth, and consistent Jacobi set simplification
Bhatia, Harsh; Wang, Bei; Norgard, Gregory; Pascucci, Valerio; Bremer, Peer -Timo
2014-10-31
The relation between two Morse functions defined on a smooth, compact, and orientable 2-manifold can be studied in terms of their Jacobi set. The Jacobi set contains points in the domain where the gradients of the two functions are aligned. Both the Jacobi set itself as well as the segmentation of the domain it induces, have shown to be useful in various applications. In practice, unfortunately, functions often contain noise and discretization artifacts, causing their Jacobi set to become unmanageably large and complex. Although there exist techniques to simplify Jacobi sets, they are unsuitable for most applications as they lack fine-grained control over the process, and heavily restrict the type of simplifications possible. In this paper, we introduce a new framework that generalizes critical point cancellations in scalar functions to Jacobi set in two dimensions. We present a new interpretation of Jacobi set simplification based on the perspective of domain segmentation. Generalizing the cancellation of critical points from scalar functions to Jacobi sets, we focus on simplifications that can be realized by smooth approximations of the corresponding functions, and show how these cancellations imply simultaneous simplification of contiguous subsets of the Jacobi set. Using these extended cancellations as atomic operations, we introduce an algorithm to successively cancel subsets of the Jacobi set with minimal modifications to some user-defined metric. We show that for simply connected domains, our algorithm reduces a given Jacobi set to its minimal configuration, that is, one with no birth–death points (a birth–death point is a specific type of singularity within the Jacobi set where the level sets of the two functions and the Jacobi set have a common normal direction).
Local, smooth, and consistent Jacobi set simplification
Bhatia, Harsh; Wang, Bei; Norgard, Gregory; Pascucci, Valerio; Bremer, Peer -Timo
2014-10-31
The relation between two Morse functions defined on a smooth, compact, and orientable 2-manifold can be studied in terms of their Jacobi set. The Jacobi set contains points in the domain where the gradients of the two functions are aligned. Both the Jacobi set itself as well as the segmentation of the domain it induces, have shown to be useful in various applications. In practice, unfortunately, functions often contain noise and discretization artifacts, causing their Jacobi set to become unmanageably large and complex. Although there exist techniques to simplify Jacobi sets, they are unsuitable for most applications as they lackmore » fine-grained control over the process, and heavily restrict the type of simplifications possible. In this paper, we introduce a new framework that generalizes critical point cancellations in scalar functions to Jacobi set in two dimensions. We present a new interpretation of Jacobi set simplification based on the perspective of domain segmentation. Generalizing the cancellation of critical points from scalar functions to Jacobi sets, we focus on simplifications that can be realized by smooth approximations of the corresponding functions, and show how these cancellations imply simultaneous simplification of contiguous subsets of the Jacobi set. Using these extended cancellations as atomic operations, we introduce an algorithm to successively cancel subsets of the Jacobi set with minimal modifications to some user-defined metric. We show that for simply connected domains, our algorithm reduces a given Jacobi set to its minimal configuration, that is, one with no birth–death points (a birth–death point is a specific type of singularity within the Jacobi set where the level sets of the two functions and the Jacobi set have a common normal direction).« less
Reasoning about systolic algorithms
Purushothaman, S.
1986-01-01
Systolic algorithms are a class of parallel algorithms, with small grain concurrency, well suited for implementation in VLSI. They are intended to be implemented as high-performance, computation-bound back-end processors and are characterized by a tesselating interconnection of identical processing elements. This dissertation investigates the problem of providing correctness of systolic algorithms. The following are reported in this dissertation: (1) a methodology for verifying correctness of systolic algorithms based on solving the representation of an algorithm as recurrence equations. The methodology is demonstrated by proving the correctness of a systolic architecture for optimal parenthesization. (2) The implementation of mechanical proofs of correctness of two systolic algorithms, a convolution algorithm and an optimal parenthesization algorithm, using the Boyer-Moore theorem prover. (3) An induction principle for proving correctness of systolic arrays which are modular. Two attendant inference rules, weak equivalence and shift transformation, which capture equivalent behavior of systolic arrays, are also presented.
Algorithm-development activities
NASA Technical Reports Server (NTRS)
Carder, Kendall L.
1994-01-01
The task of algorithm-development activities at USF continues. The algorithm for determining chlorophyll alpha concentration, (Chl alpha) and gelbstoff absorption coefficient for SeaWiFS and MODIS-N radiance data is our current priority.
Wang, Jiaxi; Lin, Boliang; Jin, Junchen
2016-01-01
The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998
Genetic algorithm approach for adaptive power and subcarrier allocation in multi-user OFDM systems
NASA Astrophysics Data System (ADS)
Reddy, Y. B.; Naraghi-Pour, Mort
2007-04-01
In this paper, a novel genetic algorithm application is proposed for adaptive power and subcarrier allocation in multi-user Orthogonal Frequency Division Multiplexing (OFDM) systems. To test the application, a simple genetic algorithm was implemented in MATLAB language. With the goal of minimizing the overall transmit power while ensuring the fulfillment of each user's rate and bit error rate (BER) requirements, the proposed algorithm acquires the needed allocation through genetic search. The simulations were tested for BER 0.1 to 0.00001, data rate of 256 bit per OFDM block and chromosome length of 128. The results show that genetic algorithm outperforms the results in [3] in subcarrier allocation. The convergence of GA model with 8 users and 128 subcarriers performs better in power requirement compared to that in [4] but converges more slowly.
Advanced time integration algorithms for dislocation dynamics simulations of work hardening
Sills, Ryan B.; Aghaei, Amin; Cai, Wei
2016-04-25
Efficient time integration is a necessity for dislocation dynamics simulations of work hardening to achieve experimentally relevant strains. In this work, an efficient time integration scheme using a high order explicit method with time step subcycling and a newly-developed collision detection algorithm are evaluated. First, time integrator performance is examined for an annihilating Frank–Read source, showing the effects of dislocation line collision. The integrator with subcycling is found to significantly out-perform other integration schemes. The performance of the time integration and collision detection algorithms is then tested in a work hardening simulation. The new algorithms show a 100-fold speed-up relativemore » to traditional schemes. As a result, subcycling is shown to improve efficiency significantly while maintaining an accurate solution, and the new collision algorithm allows an arbitrarily large time step size without missing collisions.« less
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy
Tian, Yuling; Zhang, Hongxian
2016-01-01
For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic–there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.
Tian, Yuling; Zhang, Hongxian
2016-01-01
For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions. PMID:27487242
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.
Tian, Yuling; Zhang, Hongxian
2016-01-01
For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.
Design and Implementation of Broadcast Algorithms for Extreme-Scale Systems
Shamis, Pavel; Graham, Richard L; Gorentla Venkata, Manjunath; Ladd, Joshua
2011-01-01
The scalability and performance of collective communication operations limit the scalability and performance of many scientific applications. This paper presents two new blocking and nonblocking Broadcast algorithms for communicators with arbitrary communication topology, and studies their performance. These algorithms benefit from increased concurrency and a reduced memory footprint, making them suitable for use on large-scale systems. Measuring small, medium, and large data Broadcasts on a Cray-XT5, using 24,576 MPI processes, the Cheetah algorithms outperform the native MPI on that system by 51%, 69%, and 9%, respectively, at the same process count. These results demonstrate an algorithmic approach to the implementation of the important class of collective communications, which is high performing, scalable, and also uses resources in a scalable manner.
Combining algorithms in automatic detection of QRS complexes in ECG signals.
Meyer, Carsten; Fernández Gavela, José; Harris, Matthew
2006-07-01
QRS complex and specifically R-Peak detection is the crucial first step in every automatic electrocardiogram analysis. Much work has been carried out in this field, using various methods ranging from filtering and threshold methods, through wavelet methods, to neural networks and others. Performance is generally good, but each method has situations where it fails. In this paper, we suggest an approach to automatically combine different QRS complex detection algorithms, here the Pan-Tompkins and wavelet algorithms, to benefit from the strengths of both methods. In particular, we introduce parameters allowing to balance the contribution of the individual algorithms; these parameters are estimated in a data-driven way. Experimental results and analysis are provided on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database. We show that our combination approach outperforms both individual algorithms. PMID:16871713
Advanced time integration algorithms for dislocation dynamics simulations of work hardening
NASA Astrophysics Data System (ADS)
Sills, Ryan B.; Aghaei, Amin; Cai, Wei
2016-05-01
Efficient time integration is a necessity for dislocation dynamics simulations of work hardening to achieve experimentally relevant strains. In this work, an efficient time integration scheme using a high order explicit method with time step subcycling and a newly-developed collision detection algorithm are evaluated. First, time integrator performance is examined for an annihilating Frank-Read source, showing the effects of dislocation line collision. The integrator with subcycling is found to significantly out-perform other integration schemes. The performance of the time integration and collision detection algorithms is then tested in a work hardening simulation. The new algorithms show a 100-fold speed-up relative to traditional schemes. Subcycling is shown to improve efficiency significantly while maintaining an accurate solution, and the new collision algorithm allows an arbitrarily large time step size without missing collisions.
Jin, Junchen
2016-01-01
The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998
A swarm intelligence based memetic algorithm for task allocation in distributed systems
NASA Astrophysics Data System (ADS)
Sarvizadeh, Raheleh; Haghi Kashani, Mostafa
2011-12-01
This paper proposes a Swarm Intelligence based Memetic algorithm for Task Allocation and scheduling in distributed systems. The tasks scheduling in distributed systems is known as an NP-complete problem. Hence, many genetic algorithms have been proposed for searching optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without considering the techniques that can reduce the complexity of the optimization. Spending too much time for doing scheduling is considered the main shortcoming of these approaches. Therefore, in this paper memetic algorithm has been used to cope with this shortcoming. With regard to load balancing efficiently, Bee Colony Optimization (BCO) has been applied as local search in the proposed memetic algorithm. Extended experimental results demonstrated that the proposed method outperformed the existing GA-based method in terms of CPU utilization.
A swarm intelligence based memetic algorithm for task allocation in distributed systems
NASA Astrophysics Data System (ADS)
Sarvizadeh, Raheleh; Haghi Kashani, Mostafa
2012-01-01
This paper proposes a Swarm Intelligence based Memetic algorithm for Task Allocation and scheduling in distributed systems. The tasks scheduling in distributed systems is known as an NP-complete problem. Hence, many genetic algorithms have been proposed for searching optimal solutions from entire solution space. However, these existing approaches are going to scan the entire solution space without considering the techniques that can reduce the complexity of the optimization. Spending too much time for doing scheduling is considered the main shortcoming of these approaches. Therefore, in this paper memetic algorithm has been used to cope with this shortcoming. With regard to load balancing efficiently, Bee Colony Optimization (BCO) has been applied as local search in the proposed memetic algorithm. Extended experimental results demonstrated that the proposed method outperformed the existing GA-based method in terms of CPU utilization.
Consistency of Students' Pace in Online Learning
ERIC Educational Resources Information Center
Hershkovitz, Arnon; Nachmias, Rafi
2009-01-01
The purpose of this study is to investigate the consistency of students' behavior regarding their pace of actions over sessions within an online course. Pace in a session is defined as the number of logged actions divided by session length (in minutes). Log files of 6,112 students were collected, and datasets were constructed for examining pace…
Developing consistent time series landsat data products
Technology Transfer Automated Retrieval System (TEKTRAN)
The Landsat series satellite has provided earth observation data record continuously since early 1970s. There are increasing demands on having a consistent time series of Landsat data products. In this presentation, I will summarize the work supported by the USGS Landsat Science Team project from 20...
Image recognition and consistency of response
NASA Astrophysics Data System (ADS)
Haygood, Tamara M.; Ryan, John; Liu, Qing Mary A.; Bassett, Roland; Brennan, Patrick C.
2012-02-01
Purpose: To investigate the connection between conscious recognition of an image previously encountered in an experimental setting and consistency of response to the experimental question.
Materials and Methods: Twenty-four radiologists viewed 40 frontal chest radiographs and gave their opinion as to the position of a central venous catheter. One-to-three days later they again viewed 40 frontal chest radiographs and again gave their opinion as to the position of the central venous catheter. Half of the radiographs in the second set were repeated images from the first set and half were new. The radiologists were asked of each image whether it had been included in the first set. For this study, we are evaluating only the 20 repeated images. We used the Kruskal-Wallis test and Fisher's exact test to determine the relationship between conscious recognition of a previously interpreted image and consistency in interpretation of the image.
Results. There was no significant correlation between recognition of the image and consistency in response regarding the position of the central venous catheter. In fact, there was a trend in the opposite direction, with radiologists being slightly more likely to give a consistent response with respect to images they did not recognize than with respect to those they did recognize.
Conclusion: Radiologists' recognition of previously-encountered images in an observer-performance study does not noticeably color their interpretation on the second encounter.
Consistent Visual Analyses of Intrasubject Data
ERIC Educational Resources Information Center
Kahng, SungWoo; Chung, Kyong-Mee; Gutshall, Katharine; Pitts, Steven C.; Kao, Joyce; Girolami, Kelli
2010-01-01
Visual inspection of single-case data is the primary method of interpretation of the effects of an independent variable on a dependent variable in applied behavior analysis. The purpose of the current study was to replicate and extend the results of DeProspero and Cohen (1979) by reexamining the consistency of visual analysis across raters. We…
Environmental Decision Support with Consistent Metrics
One of the most effective ways to pursue environmental progress is through the use of consistent metrics within a decision making framework. The US Environmental Protection Agency’s Sustainable Technology Division has developed TRACI, the Tool for the Reduction and Assessment of...
36 CFR 241.22 - Consistency determinations.
Code of Federal Regulations, 2014 CFR
2014-07-01
... FISH AND WILDLIFE Conservation of Fish, Wildlife, and Their Habitat, Chugach National Forest, Alaska... conservation of fish, wildlife, and their habitat. A use or activity may be determined to be consistent if it will not materially interfere with or detract from the conservation of fish, wildlife and their...
36 CFR 241.22 - Consistency determinations.
Code of Federal Regulations, 2013 CFR
2013-07-01
... FISH AND WILDLIFE Conservation of Fish, Wildlife, and Their Habitat, Chugach National Forest, Alaska... conservation of fish, wildlife, and their habitat. A use or activity may be determined to be consistent if it will not materially interfere with or detract from the conservation of fish, wildlife and their...
36 CFR 241.22 - Consistency determinations.
Code of Federal Regulations, 2012 CFR
2012-07-01
... FISH AND WILDLIFE Conservation of Fish, Wildlife, and Their Habitat, Chugach National Forest, Alaska... conservation of fish, wildlife, and their habitat. A use or activity may be determined to be consistent if it will not materially interfere with or detract from the conservation of fish, wildlife and their...
24 CFR 91.510 - Consistency determinations.
Code of Federal Regulations, 2010 CFR
2010-04-01
... 24 Housing and Urban Development 1 2010-04-01 2010-04-01 false Consistency determinations. 91.510 Section 91.510 Housing and Urban Development Office of the Secretary, Department of Housing and Urban... HOPWA grant is a city that is the most populous unit of general local government in an EMSA, it...
Effecting Consistency across Curriculum: A Case Study
ERIC Educational Resources Information Center
Devasagayam, P. Raj; Mahaffey, Thomas R.
2008-01-01
Continuous quality improvement is the clarion call across all business schools which is driving the emphasis on assessing the attainment of learning outcomes. An issue that deems special attention in assurance of learning outcomes is related to consistency across courses and, more specifically, across multiple sections of the same course taught by…
Consistency and stability of recombinant fermentations.
Wiebe, M E; Builder, S E
1994-01-01
Production of proteins of consistent quality in heterologous, genetically-engineered expression systems is dependent upon identifying the manufacturing process parameters which have an impact on product structure, function, or purity, validating acceptable ranges for these variables, and performing the manufacturing process as specified. One of the factors which may affect product consistency is genetic instability of the primary product sequence, as well as instability of genes which code for proteins responsible for post-translational modification of the product. Approaches have been developed for mammalian expression systems to assure that product quality is not changing through mechanisms of genetic instability. Sensitive protein analytical methods, particularly peptide mapping, are used to evaluate product structure directly, and are more sensitive in detecting genetic instability than is direct genetic analysis by nucleotide sequencing of the recombinant gene or mRNA. These methods are being employed to demonstrate that the manufacturing process consistently yields a product of defined structure from cells cultured through the range of cell ages used in the manufacturing process and well beyond the maximum cell age defined for the process. The combination of well designed validation studies which demonstrate consistent product quality as a function of cell age, and rigorous quality control of every product lot by sensitive protein analytical methods provide the necessary assurance that product structure is not being altered through mechanisms of mutation and selection.
RULE GENERALITY AND CONSISTENCY IN MATHEMATICS LEARNING.
ERIC Educational Resources Information Center
SCANDURA, JOSEPH M.
PSYCHOLOGICAL PRINCIPLES INVOLVED WITH RULE GENERALITY (DEGREE OF NONSPECIFICITY) AND PERFORMANCE CONSISTENCY IN MATHEMATICAL PRESENTATIONS WERE STUDIED. SPECIFICALLY, THE PURPOSES WERE (1) TO DETERMINE IF TEST BEHAVIOR CONFORMS TO THE SCOPE OF A VERBALLY ADMINISTERED TEST RULE, (2) TO EXPLORE THE INTERPRETABILITY OF VERBAL TEST RULES, AND (3) TO…
Taking Another Look: Sensuous, Consistent Form.
ERIC Educational Resources Information Center
Townley, Mary Ross
1983-01-01
There is a natural progression from making single objects to creating sculpture. By modeling the forms of objects like funnels and light bulbs, students become aware of the quality of curves and the edges of angles. Sculptural form in architecture can be understood as consistency in the forms. (CS)
Consistency of Toddler Engagement across Two Settings
ERIC Educational Resources Information Center
Aguiar, Cecilia; McWilliam, R. A.
2013-01-01
This study documented the consistency of child engagement across two settings, toddler child care classrooms and mother-child dyadic play. One hundred twelve children, aged 14-36 months (M = 25.17, SD = 6.06), randomly selected from 30 toddler child care classrooms from the district of Porto, Portugal, participated. Levels of engagement were…
Testing block subdivision algorithms on block designs
NASA Astrophysics Data System (ADS)
Wiseman, Natalie; Patterson, Zachary
2016-01-01
Integrated land use-transportation models predict future transportation demand taking into account how households and firms arrange themselves partly as a function of the transportation system. Recent integrated models require parcels as inputs and produce household and employment predictions at the parcel scale. Block subdivision algorithms automatically generate parcel patterns within blocks. Evaluating block subdivision algorithms is done by way of generating parcels and comparing them to those in a parcel database. Three block subdivision algorithms are evaluated on how closely they reproduce parcels of different block types found in a parcel database from Montreal, Canada. While the authors who developed each of the algorithms have evaluated them, they have used their own metrics and block types to evaluate their own algorithms. This makes it difficult to compare their strengths and weaknesses. The contribution of this paper is in resolving this difficulty with the aim of finding a better algorithm suited to subdividing each block type. The proposed hypothesis is that given the different approaches that block subdivision algorithms take, it's likely that different algorithms are better adapted to subdividing different block types. To test this, a standardized block type classification is used that consists of mutually exclusive and comprehensive categories. A statistical method is used for finding a better algorithm and the probability it will perform well for a given block type. Results suggest the oriented bounding box algorithm performs better for warped non-uniform sites, as well as gridiron and fragmented uniform sites. It also produces more similar parcel areas and widths. The Generalized Parcel Divider 1 algorithm performs better for gridiron non-uniform sites. The Straight Skeleton algorithm performs better for loop and lollipop networks as well as fragmented non-uniform and warped uniform sites. It also produces more similar parcel shapes and patterns.
INSENS classification algorithm report
Hernandez, J.E.; Frerking, C.J.; Myers, D.W.
1993-07-28
This report describes a new algorithm developed for the Imigration and Naturalization Service (INS) in support of the INSENS project for classifying vehicles and pedestrians using seismic data. This algorithm is less sensitive to nuisance alarms due to environmental events than the previous algorithm. Furthermore, the algorithm is simple enough that it can be implemented in the 8-bit microprocessor used in the INSENS system.
Accurate Finite Difference Algorithms
NASA Technical Reports Server (NTRS)
Goodrich, John W.
1996-01-01
Two families of finite difference algorithms for computational aeroacoustics are presented and compared. All of the algorithms are single step explicit methods, they have the same order of accuracy in both space and time, with examples up to eleventh order, and they have multidimensional extensions. One of the algorithm families has spectral like high resolution. Propagation with high order and high resolution algorithms can produce accurate results after O(10(exp 6)) periods of propagation with eight grid points per wavelength.
Consistency and variability in functional localisers
Duncan, Keith J.; Pattamadilok, Chotiga; Knierim, Iris; Devlin, Joseph T.
2009-01-01
A critical assumption underlying the use of functional localiser scans is that the voxels identified as the functional region-of-interest (fROI) are essentially the same as those activated by the main experimental manipulation. Intra-subject variability in the location of the fROI violates this assumption, reducing the sensitivity of the analysis and biasing the results. Here we investigated consistency and variability in fROIs in a set of 45 volunteers. They performed two functional localiser scans to identify word- and object-sensitive regions of ventral and lateral occipito-temporal cortex, respectively. In the main analyses, fROIs were defined as the category-selective voxels in each region and consistency was measured as the spatial overlap between scans. Consistency was greatest when minimally selective thresholds were used to define “active” voxels (p < 0.05 uncorrected), revealing that approximately 65% of the voxels were commonly activated by both scans. In contrast, highly selective thresholds (p < 10− 4 to 10− 6) yielded the lowest consistency values with less than 25% overlap of the voxels active in both scans. In other words, intra-subject variability was surprisingly high, with between one third and three quarters of the voxels in a given fROI not corresponding to those activated in the main task. This level of variability stands in striking contrast to the consistency seen in retinotopically-defined areas and has important implications for designing robust but efficient functional localiser scans. PMID:19289173
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
Precession-nutation procedures consistent with IAU 2006 resolutions
NASA Astrophysics Data System (ADS)
Wallace, P. T.; Capitaine, N.
2006-12-01
Context: .The 2006 IAU General Assembly has adopted the P03 model of Capitaine et al. (2003a) recommended by the WG on precession and the ecliptic (Hilton et al. 2006) to replace the IAU 2000 model, which comprised the Lieske et al. (1977) model with adjusted rates. Practical implementations of this new "IAU 2006" model are therefore required, involving choices of procedures and algorithms. Aims: .The purpose of this paper is to recommend IAU 2006 based precession-nutation computing procedures, suitable for different classes of application and achieving high standards of consistency. Methods: .We discuss IAU 2006 based procedures and algorithms for generating the rotation matrices that transform celestial to terrestrial coordinates, taking into account frame bias (B), P03 precession (P), P03-adjusted IAU 2000A nutation (N) and Earth rotation. The NPB portion can refer either to the equinox or to the celestial intermediate origin (CIO), requiring either the Greenwich sidereal time (GST) or the Earth rotation angle (ERA) as the measure of Earth rotation. Where GST is used, it is derived from ERA and the equation of the origins (EO) rather than through an explicit formula as in the past, and the EO itself is derived from the CIO locator. Results: .We provide precession-nutation procedures for two different classes of full-accuracy application, namely (i) the construction of algorithm collections such as the Standards Of Fundamental Astronomy (SOFA) library and (ii) IERS Conventions, and in addition some concise procedures for applications where the highest accuracy is not a requirement. The appendix contains a fully worked numerical example, to aid implementors and to illustrate the consistency of the two full-accuracy procedures which, for the test date, agree to better than 1 μas. Conclusions: .The paper recommends, for case (i), procedures based on angles to represent the PB and N components and, for case (ii), procedures based on series for the CIP X,Y. The two
NASA Astrophysics Data System (ADS)
Ghani Abro, Abdul; Mohamad-Saleh, Junita
2014-10-01
The prime motive of economic load dispatch (ELD) is to optimize the production cost of electrical power generation through appropriate division of load demand among online generating units. Bio-inspired optimization algorithms have outperformed classical techniques for optimizing the production cost. Probability-selection artificial bee colony (PS-ABC) algorithm is a recently proposed variant of ABC optimization algorithm. PS-ABC generates optimal solutions using three different mutation equations simultaneously. The results show improved performance of PS-ABC over the ABC algorithm. Nevertheless, all the mutation equations of PS-ABC are excessively self-reinforced and, hence, PS-ABC is prone to premature convergence. Therefore, this research work has replaced the mutation equations and has improved the scout-bee stage of PS-ABC for enhancing the algorithm's performance. The proposed algorithm has been compared with many ABC variants and numerous other optimization algorithms on benchmark functions and ELD test cases. The adapted ELD test cases comprise of transmission losses, multiple-fuel effect, valve-point effect and toxic gases emission constraints. The results reveal that the proposed algorithm has the best capability to yield the optimal solution for the problem among the compared algorithms.
Luo, Liyan; Xu, Luping; Zhang, Hua
2015-07-07
In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms.
An Autonomous Star Identification Algorithm Based on One-Dimensional Vector Pattern for Star Sensors
Luo, Liyan; Xu, Luping; Zhang, Hua
2015-01-01
In order to enhance the robustness and accelerate the recognition speed of star identification, an autonomous star identification algorithm for star sensors is proposed based on the one-dimensional vector pattern (one_DVP). In the proposed algorithm, the space geometry information of the observed stars is used to form the one-dimensional vector pattern of the observed star. The one-dimensional vector pattern of the same observed star remains unchanged when the stellar image rotates, so the problem of star identification is simplified as the comparison of the two feature vectors. The one-dimensional vector pattern is adopted to build the feature vector of the star pattern, which makes it possible to identify the observed stars robustly. The characteristics of the feature vector and the proposed search strategy for the matching pattern make it possible to achieve the recognition result as quickly as possible. The simulation results demonstrate that the proposed algorithm can effectively accelerate the star identification. Moreover, the recognition accuracy and robustness by the proposed algorithm are better than those by the pyramid algorithm, the modified grid algorithm, and the LPT algorithm. The theoretical analysis and experimental results show that the proposed algorithm outperforms the other three star identification algorithms. PMID:26198233
Consistent Pauli reduction on group manifolds
NASA Astrophysics Data System (ADS)
Baguet, A.; Pope, C. N.; Samtleben, H.
2016-01-01
We prove an old conjecture by Duff, Nilsson, Pope and Warner asserting that the NSsbnd NS sector of supergravity (and more general the bosonic string) allows for a consistent Pauli reduction on any d-dimensional group manifold G, keeping the full set of gauge bosons of the G × G isometry group of the bi-invariant metric on G. The main tool of the construction is a particular generalised Scherk-Schwarz reduction ansatz in double field theory which we explicitly construct in terms of the group's Killing vectors. Examples include the consistent reduction from ten dimensions on S3 ×S3 and on similar product spaces. The construction is another example of globally geometric non-toroidal compactifications inducing non-geometric fluxes.
Consistency relation for cosmic magnetic fields
NASA Astrophysics Data System (ADS)
Jain, Rajeev Kumar; Sloth, Martin S.
2012-12-01
If cosmic magnetic fields are indeed produced during inflation, they are likely to be correlated with the scalar metric perturbations that are responsible for the cosmic microwave background anisotropies and large scale structure. Within an archetypical model of inflationary magnetogenesis, we show that there exists a new simple consistency relation for the non-Gaussian cross correlation function of the scalar metric perturbation with two powers of the magnetic field in the squeezed limit where the momentum of the metric perturbation vanishes. We emphasize that such a consistency relation turns out to be extremely useful to test some recent calculations in the literature. Apart from primordial non-Gaussianity induced by the curvature perturbations, such a cross correlation might provide a new observational probe of inflation and can in principle reveal the primordial nature of cosmic magnetic fields.
Self-Consistent Scattering and Transport Calculations
NASA Astrophysics Data System (ADS)
Hansen, S. B.; Grabowski, P. E.
2015-11-01
An average-atom model with ion correlations provides a compact and complete description of atomic-scale physics in dense, finite-temperature plasmas. The self-consistent ionic and electronic distributions from the model enable calculation of x-ray scattering signals and conductivities for material across a wide range of temperatures and densities. We propose a definition for the bound electronic states that ensures smooth behavior of these measurable properties under pressure ionization and compare the predictions of this model with those of less consistent models for Be, C, Al, and Fe. SNL is a multi-program laboratory managed and operated by Sandia Corp., a wholly owned subsidiary of Lockheed Martin Corp, for the U.S. DoE NNSA under contract DE-AC04-94AL85000. This work was supported by DoE OFES Early Career grant FWP-14-017426.
Self-consistency in Capital Markets
NASA Astrophysics Data System (ADS)
Benbrahim, Hamid
2013-03-01
Capital Markets are considered, at least in theory, information engines whereby traders contribute to price formation with their diverse perspectives. Regardless whether one believes in efficient market theory on not, actions by individual traders influence prices of securities, which in turn influence actions by other traders. This influence is exerted through a number of mechanisms including portfolio balancing, margin maintenance, trend following, and sentiment. As a result market behaviors emerge from a number of mechanisms ranging from self-consistency due to wisdom of the crowds and self-fulfilling prophecies, to more chaotic behavior resulting from dynamics similar to the three body system, namely the interplay between equities, options, and futures. This talk will address questions and findings regarding the search for self-consistency in capital markets.
Observers are consistent when rating image conspicuity.
Cerf, Moran; Cleary, Daniel R; Peters, Robert J; Einhäuser, Wolfgang; Koch, Christof
2007-11-01
Human perception of an image's conspicuity depends on the stimulus itself and the observer's semantic interpretation. We investigated the relative contribution of the former, sensory-driven, component. Participants viewed sequences of images from five different classes-fractals, overhead satellite imagery, grayscale and colored natural scenes, and magazine covers-and graded each numerically according to its perceived conspicuity. We found significant consistency in this rating within and between observers for all image categories. In a subsequent recognition memory test, performance was significantly above chance for all categories, with the weakest memory for satellite imagery, and reaching near ceiling for magazine covers. When repeating the experiment after one year, ratings remained consistent within each observer and category, despite the absence of explicit scene memory. Our findings suggest that the rating of image conspicuity is driven by image-immanent, sensory factors common to all observers.
Consistency Test and Constraint of Quintessence
Chen, Chien-Wen; Gu, Je-AN; Chen, Pisin; /SLAC /Taiwan, Natl. Taiwan U.
2012-04-30
In this paper we highlight our recent work in arXiv:0803.4504. In that work, we proposed a new consistency test of quintessence models for dark energy. Our test gave a simple and direct signature if certain category of quintessence models was not consistent with the observational data. For a category that passed the test, we further constrained its characteristic parameter. Specifically, we found that the exponential potential was ruled out at the 95% confidence level and the power-law potential was ruled out at the 68% confidence level based on the current observational data. We also found that the confidence interval of the index of the power-law potential was between -2 and 0 at the 95% confidence level.
Consistency of color representation in smart phones.
Dain, Stephen J; Kwan, Benjamin; Wong, Leslie
2016-03-01
One of the barriers to the construction of consistent computer-based color vision tests has been the variety of monitors and computers. Consistency of color on a variety of screens has necessitated calibration of each setup individually. Color vision examination with a carefully controlled display has, as a consequence, been a laboratory rather than a clinical activity. Inevitably, smart phones have become a vehicle for color vision tests. They have the advantage that the processor and screen are associated and there are fewer models of smart phones than permutations of computers and monitors. Colorimetric consistency of display within a model may be a given. It may extend across models from the same manufacturer but is unlikely to extend between manufacturers especially where technologies vary. In this study, we measured the same set of colors in a JPEG file displayed on 11 samples of each of four models of smart phone (iPhone 4s, iPhone5, Samsung Galaxy S3, and Samsung Galaxy S4) using a Photo Research PR-730. The iPhones are white LED backlit LCD and the Samsung are OLEDs. The color gamut varies between models and comparison with sRGB space shows 61%, 85%, 117%, and 110%, respectively. The iPhones differ markedly from the Samsungs and from one another. This indicates that model-specific color lookup tables will be needed. Within each model, the primaries were quite consistent (despite the age of phone varying within each sample). The worst case in each model was the blue primary; the 95th percentile limits in the v' coordinate were ±0.008 for the iPhone 4 and ±0.004 for the other three models. The u'v' variation in white points was ±0.004 for the iPhone4 and ±0.002 for the others, although the spread of white points between models was u'v'±0.007. The differences are essentially the same for primaries at low luminance. The variation of colors intermediate between the primaries (e.g., red-purple, orange) mirror the variation in the primaries. The variation in
Clutter discrimination algorithm simulation in pulse laser radar imaging
NASA Astrophysics Data System (ADS)
Zhang, Yan-mei; Li, Huan; Guo, Hai-chao; Su, Xuan; Zhu, Fule
2015-10-01
Pulse laser radar imaging performance is greatly influenced by different kinds of clutter. Various algorithms are developed to mitigate clutter. However, estimating performance of a new algorithm is difficult. Here, a simulation model for estimating clutter discrimination algorithms is presented. This model consists of laser pulse emission, clutter jamming, laser pulse reception and target image producing. Additionally, a hardware platform is set up gathering clutter data reflected by ground and trees. The data logging is as clutter jamming input in the simulation model. The hardware platform includes a laser diode, a laser detector and a high sample rate data logging circuit. The laser diode transmits short laser pulses (40ns FWHM) at 12.5 kilohertz pulse rate and at 905nm wavelength. An analog-to-digital converter chip integrated in the sample circuit works at 250 mega samples per second. The simulation model and the hardware platform contribute to a clutter discrimination algorithm simulation system. Using this system, after analyzing clutter data logging, a new compound pulse detection algorithm is developed. This new algorithm combines matched filter algorithm and constant fraction discrimination (CFD) algorithm. Firstly, laser echo pulse signal is processed by matched filter algorithm. After the first step, CFD algorithm comes next. Finally, clutter jamming from ground and trees is discriminated and target image is produced. Laser radar images are simulated using CFD algorithm, matched filter algorithm and the new algorithm respectively. Simulation result demonstrates that the new algorithm achieves the best target imaging effect of mitigating clutter reflected by ground and trees.
Perturbation resilience and superiorization of iterative algorithms
NASA Astrophysics Data System (ADS)
Censor, Y.; Davidi, R.; Herman, G. T.
2010-06-01
Iterative algorithms aimed at solving some problems are discussed. For certain problems, such as finding a common point in the intersection of a finite number of convex sets, there often exist iterative algorithms that impose very little demand on computer resources. For other problems, such as finding that point in the intersection at which the value of a given function is optimal, algorithms tend to need more computer memory and longer execution time. A methodology is presented whose aim is to produce automatically for an iterative algorithm of the first kind a 'superiorized version' of it that retains its computational efficiency but nevertheless goes a long way toward solving an optimization problem. This is possible to do if the original algorithm is 'perturbation resilient', which is shown to be the case for various projection algorithms for solving the consistent convex feasibility problem. The superiorized versions of such algorithms use perturbations that steer the process in the direction of a superior feasible point, which is not necessarily optimal, with respect to the given function. After presenting these intuitive ideas in a precise mathematical form, they are illustrated in image reconstruction from projections for two different projection algorithms superiorized for the function whose value is the total variation of the image.
A replica exchange Monte Carlo algorithm for protein folding in the HP model
Thachuk, Chris; Shmygelska, Alena; Hoos, Holger H
2007-01-01
Background The ab initio protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing an energy function; it is one of the most important and challenging problems in biochemistry, molecular biology and biophysics. The ab initio protein folding problem is computationally challenging and has been shown to be NP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaat0uy0HwzTfgDPnwy1egaryqtHrhAL1wy0L2yHvdaiqaacqWFneVtcqqGqbauaaa@3961@-hard even when conformations are restricted to a lattice. In this work, we implement and evaluate the replica exchange Monte Carlo (REMC) method, which has already been applied very successfully to more complex protein models and other optimization problems with complex energy landscapes, in combination with the highly effective pull move neighbourhood in two widely studied Hydrophobic Polar (HP) lattice models. Results We demonstrate that REMC is highly effective for solving instances of the square (2D) and cubic (3D) HP protein folding problem. When using the pull move neighbourhood, REMC outperforms current state-of-the-art algorithms for most benchmark instances. Additionally, we show that this new algorithm provides a larger ensemble of ground-state structures than the existing state-of-the-art methods. Furthermore, it scales well with sequence length, and it finds significantly better conformations on long biological sequences and sequences with a provably unique ground-state structure, which is believed to be a characteristic of real proteins. We also present evidence that our REMC algorithm can fold sequences which exhibit significant interaction between termini in the hydrophobic core relatively easily. Conclusion We demonstrate that REMC utilizing the pull move neighbourhood
An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.
Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen
2016-01-01
The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001
An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.
Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen
2016-01-01
The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.
An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks
Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen
2016-01-01
The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001
MUSIC algorithms for rebar detection
NASA Astrophysics Data System (ADS)
Solimene, Raffaele; Leone, Giovanni; Dell'Aversano, Angela
2013-12-01
The MUSIC (MUltiple SIgnal Classification) algorithm is employed to detect and localize an unknown number of scattering objects which are small in size as compared to the wavelength. The ensemble of objects to be detected consists of both strong and weak scatterers. This represents a scattering environment challenging for detection purposes as strong scatterers tend to mask the weak ones. Consequently, the detection of more weakly scattering objects is not always guaranteed and can be completely impaired when the noise corrupting data is of a relatively high level. To overcome this drawback, here a new technique is proposed, starting from the idea of applying a two-stage MUSIC algorithm. In the first stage strong scatterers are detected. Then, information concerning their number and location is employed in the second stage focusing only on the weak scatterers. The role of an adequate scattering model is emphasized to improve drastically detection performance in realistic scenarios.
Temporal consistent depth map upscaling for 3DTV
NASA Astrophysics Data System (ADS)
Schwarz, Sebastian; Sjöström, Mârten; Olsson, Roger
2014-03-01
The ongoing success of three-dimensional (3D) cinema fuels increasing efforts to spread the commercial success of 3D to new markets. The possibilities of a convincing 3D experience at home, such as three-dimensional television (3DTV), has generated a great deal of interest within the research and standardization community. A central issue for 3DTV is the creation and representation of 3D content. Acquiring scene depth information is a fundamental task in computer vision, yet complex and error-prone. Dedicated range sensors, such as the Time of-Flight camera (ToF), can simplify the scene depth capture process and overcome shortcomings of traditional solutions, such as active or passive stereo analysis. Admittedly, currently available ToF sensors deliver only a limited spatial resolution. However, sophisticated depth upscaling approaches use texture information to match depth and video resolution. At Electronic Imaging 2012 we proposed an upscaling routine based on error energy minimization, weighted with edge information from an accompanying video source. In this article we develop our algorithm further. By adding temporal consistency constraints to the upscaling process, we reduce disturbing depth jumps and flickering artifacts in the final 3DTV content. Temporal consistency in depth maps enhances the 3D experience, leading to a wider acceptance of 3D media content. More content in better quality can boost the commercial success of 3DTV.
Evaluating Temporal Consistency in Marine Biodiversity Hotspots
Barner, Allison K.; Benkwitt, Cassandra E.; Boersma, Kate S.; Cerny-Chipman, Elizabeth B.; Ingeman, Kurt E.; Kindinger, Tye L.; Lindsley, Amy J.; Nelson, Jake; Reimer, Jessica N.; Rowe, Jennifer C.; Shen, Chenchen; Thompson, Kevin A.; Heppell, Selina S.
2015-01-01
With the ongoing crisis of biodiversity loss and limited resources for conservation, the concept of biodiversity hotspots has been useful in determining conservation priority areas. However, there has been limited research into how temporal variability in biodiversity may influence conservation area prioritization. To address this information gap, we present an approach to evaluate the temporal consistency of biodiversity hotspots in large marine ecosystems. Using a large scale, public monitoring dataset collected over an eight year period off the US Pacific Coast, we developed a methodological approach for avoiding biases associated with hotspot delineation. We aggregated benthic fish species data from research trawls and calculated mean hotspot thresholds for fish species richness and Shannon’s diversity indices over the eight year dataset. We used a spatial frequency distribution method to assign hotspot designations to the grid cells annually. We found no areas containing consistently high biodiversity through the entire study period based on the mean thresholds, and no grid cell was designated as a hotspot for greater than 50% of the time-series. To test if our approach was sensitive to sampling effort and the geographic extent of the survey, we followed a similar routine for the northern region of the survey area. Our finding of low consistency in benthic fish biodiversity hotspots over time was upheld, regardless of biodiversity metric used, whether thresholds were calculated per year or across all years, or the spatial extent for which we calculated thresholds and identified hotspots. Our results suggest that static measures of benthic fish biodiversity off the US West Coast are insufficient for identification of hotspots and that long-term data are required to appropriately identify patterns of high temporal variability in biodiversity for these highly mobile taxa. Given that ecological communities are responding to a changing climate and other
Evaluating Temporal Consistency in Marine Biodiversity Hotspots.
Piacenza, Susan E; Thurman, Lindsey L; Barner, Allison K; Benkwitt, Cassandra E; Boersma, Kate S; Cerny-Chipman, Elizabeth B; Ingeman, Kurt E; Kindinger, Tye L; Lindsley, Amy J; Nelson, Jake; Reimer, Jessica N; Rowe, Jennifer C; Shen, Chenchen; Thompson, Kevin A; Heppell, Selina S
2015-01-01
With the ongoing crisis of biodiversity loss and limited resources for conservation, the concept of biodiversity hotspots has been useful in determining conservation priority areas. However, there has been limited research into how temporal variability in biodiversity may influence conservation area prioritization. To address this information gap, we present an approach to evaluate the temporal consistency of biodiversity hotspots in large marine ecosystems. Using a large scale, public monitoring dataset collected over an eight year period off the US Pacific Coast, we developed a methodological approach for avoiding biases associated with hotspot delineation. We aggregated benthic fish species data from research trawls and calculated mean hotspot thresholds for fish species richness and Shannon's diversity indices over the eight year dataset. We used a spatial frequency distribution method to assign hotspot designations to the grid cells annually. We found no areas containing consistently high biodiversity through the entire study period based on the mean thresholds, and no grid cell was designated as a hotspot for greater than 50% of the time-series. To test if our approach was sensitive to sampling effort and the geographic extent of the survey, we followed a similar routine for the northern region of the survey area. Our finding of low consistency in benthic fish biodiversity hotspots over time was upheld, regardless of biodiversity metric used, whether thresholds were calculated per year or across all years, or the spatial extent for which we calculated thresholds and identified hotspots. Our results suggest that static measures of benthic fish biodiversity off the US West Coast are insufficient for identification of hotspots and that long-term data are required to appropriately identify patterns of high temporal variability in biodiversity for these highly mobile taxa. Given that ecological communities are responding to a changing climate and other
Evaluating Temporal Consistency in Marine Biodiversity Hotspots.
Piacenza, Susan E; Thurman, Lindsey L; Barner, Allison K; Benkwitt, Cassandra E; Boersma, Kate S; Cerny-Chipman, Elizabeth B; Ingeman, Kurt E; Kindinger, Tye L; Lindsley, Amy J; Nelson, Jake; Reimer, Jessica N; Rowe, Jennifer C; Shen, Chenchen; Thompson, Kevin A; Heppell, Selina S
2015-01-01
With the ongoing crisis of biodiversity loss and limited resources for conservation, the concept of biodiversity hotspots has been useful in determining conservation priority areas. However, there has been limited research into how temporal variability in biodiversity may influence conservation area prioritization. To address this information gap, we present an approach to evaluate the temporal consistency of biodiversity hotspots in large marine ecosystems. Using a large scale, public monitoring dataset collected over an eight year period off the US Pacific Coast, we developed a methodological approach for avoiding biases associated with hotspot delineation. We aggregated benthic fish species data from research trawls and calculated mean hotspot thresholds for fish species richness and Shannon's diversity indices over the eight year dataset. We used a spatial frequency distribution method to assign hotspot designations to the grid cells annually. We found no areas containing consistently high biodiversity through the entire study period based on the mean thresholds, and no grid cell was designated as a hotspot for greater than 50% of the time-series. To test if our approach was sensitive to sampling effort and the geographic extent of the survey, we followed a similar routine for the northern region of the survey area. Our finding of low consistency in benthic fish biodiversity hotspots over time was upheld, regardless of biodiversity metric used, whether thresholds were calculated per year or across all years, or the spatial extent for which we calculated thresholds and identified hotspots. Our results suggest that static measures of benthic fish biodiversity off the US West Coast are insufficient for identification of hotspots and that long-term data are required to appropriately identify patterns of high temporal variability in biodiversity for these highly mobile taxa. Given that ecological communities are responding to a changing climate and other
Self-consistent gravitational self-force
NASA Astrophysics Data System (ADS)
Pound, Adam
2010-01-01
I review the problem of motion for small bodies in general relativity, with an emphasis on developing a self-consistent treatment of the gravitational self-force. An analysis of the various derivations extant in the literature leads me to formulate an asymptotic expansion in which the metric is expanded while a representative worldline is held fixed. I discuss the utility of this expansion for both exact point particles and asymptotically small bodies, contrasting it with a regular expansion in which both the metric and the worldline are expanded. Based on these preliminary analyses, I present a general method of deriving self-consistent equations of motion for arbitrarily structured (sufficiently compact) small bodies. My method utilizes two expansions: an inner expansion that keeps the size of the body fixed, and an outer expansion that lets the body shrink while holding its worldline fixed. By imposing the Lorenz gauge, I express the global solution to the Einstein equation in the outer expansion in terms of an integral over a worldtube of small radius surrounding the body. Appropriate boundary data on the tube are determined from a local-in-space expansion in a buffer region where both the inner and outer expansions are valid. This buffer-region expansion also results in an expression for the self-force in terms of irreducible pieces of the metric perturbation on the worldline. Based on the global solution, these pieces of the perturbation can be written in terms of a tail integral over the body’s past history. This approach can be applied at any order to obtain a self-consistent approximation that is valid on long time scales, both near and far from the small body. I conclude by discussing possible extensions of my method and comparing it to alternative approaches.
Chen, Tinggui; Xiao, Renbin
2014-01-01
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023
Chen, Tinggui; Xiao, Renbin
2014-01-01
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023
Consistent energy treatment for radiation transport methods
NASA Astrophysics Data System (ADS)
Douglass, Steven James
The approximations used in the standard multigroup method and cross section condensation procedure introduce several known errors, such those caused by spectral core environment effects and the neglect of the energy and angular coupling of the flux when condensing the total cross section. In this dissertation, a multigroup formulation is developed which maintains direct consistency with the continuous energy or fine-group structure, exhibiting the accuracy of the detailed energy spectrum within the coarse-group calculation. Two methods are then developed which seek to invert the condensation process -- turning the standard one-way condensation (from fine-group to coarse-group) into the first step of a two-way iterative process. The first method is based on the previously published Generalized Energy Condensation, which established a framework for obtaining the finegroup flux by preserving the flux energy spectrum in orthogonal energy expansion functions, but did not maintain a consistent coarse-group formulation. It is demonstrated that with a consistent extension of the GEC, a cross section recondensation scheme can be used to correct for the spectral core environment error. This is then verified numerically in a 1D VHTR core. In addition, a more practical and efficient new method, termed the "Subgroup Decomposition (SGD) Method," is developed which eliminates the need for expansion functions altogether, and allows the fine-group flux to be decomposed from a consistent coarse-group flux with minimal additional computation or memory requirements. This method, as a special case of a more general spline-approximation for radiation transport, is shown to be highly effective in a cross section recondensation scheme, providing fine-group results in a fraction of the time generally necessary to obtain a fine-group solution. In addition, a whole-core BWR benchmark problem is generated based on operating reactor parameters, in 2D and 3D. This contributes to the furthering
Consistency of the triplet seesaw model revisited
NASA Astrophysics Data System (ADS)
Bonilla, Cesar; Fonseca, Renato M.; Valle, J. W. F.
2015-10-01
Adding a scalar triplet to the Standard Model is one of the simplest ways of giving mass to neutrinos, providing at the same time a mechanism to stabilize the theory's vacuum. In this paper, we revisit these aspects of the type-II seesaw model pointing out that the bounded-from-below conditions for the scalar potential in use in the literature are not correct. We discuss some scenarios where the correction can be significant and sketch the typical scalar boson profile expected by consistency.
Consistent Two-Dimensional Chiral Gravity
NASA Astrophysics Data System (ADS)
Smailagic, A.; Spallucci, E.
We study chiral induced gravity in the light-cone gauge and show that the theory is consistent for a particular choice of chiralities. The corresponding Kac-Moody central charge has no forbidden region of complex values. Generalized analysis of the critical exponents is given and their relation to the SL(2,R) vacuum states is elucidated. All the parameters containing information about the theory can be traced back to the characteristics of the residual symmetry group in the light-cone gauge.
Consistency relations for the conformal mechanism
Creminelli, Paolo; Joyce, Austin; Khoury, Justin; Simonović, Marko E-mail: joyceau@sas.upenn.edu E-mail: marko.simonovic@sissa.it
2013-04-01
We systematically derive the consistency relations associated to the non-linearly realized symmetries of theories with spontaneously broken conformal symmetry but with a linearly-realized de Sitter subalgebra. These identities relate (N+1)-point correlation functions with a soft external Goldstone to N-point functions. These relations have direct implications for the recently proposed conformal mechanism for generating density perturbations in the early universe. We study the observational consequences, in particular a novel one-loop contribution to the four-point function, relevant for the stochastic scale-dependent bias and CMB μ-distortion.
Improved robust point matching with label consistency
NASA Astrophysics Data System (ADS)
Bhagalia, Roshni; Miller, James V.; Roy, Arunabha
2010-03-01
Robust point matching (RPM) jointly estimates correspondences and non-rigid warps between unstructured point-clouds. RPM does not, however, utilize information of the topological structure or group memberships of the data it is matching. In numerous medical imaging applications, each extracted point can be assigned group membership attributes or labels based on segmentation, partitioning, or clustering operations. For example, points on the cortical surface of the brain can be grouped according to the four lobes. Estimated warps should enforce the topological structure of such point-sets, e.g. points belonging to the temporal lobe in the two point-sets should be mapped onto each other. We extend the RPM objective function to incorporate group membership labels by including a Label Entropy (LE) term. LE discourages mappings that transform points within a single group in one point-set onto points from multiple distinct groups in the other point-set. The resulting Labeled Point Matching (LPM) algorithm requires a very simple modification to the standard RPM update rules. We demonstrate the performance of LPM on coronary trees extracted from cardiac CT images. We partitioned the point sets into coronary sections without a priori anatomical context, yielding potentially disparate labelings (e.g. [1,2,3] --> [a,b,c,d]). LPM simultaneously estimated label correspondences, point correspondences, and a non-linear warp. Non-matching branches were treated wholly through the standard RPM outlier process akin to non-matching points. Results show LPM produces warps that are more physically meaningful than RPM alone. In particular, LPM mitigates unrealistic branch crossings and results in more robust non-rigid warp estimates.
Multi-objective Job Shop Rescheduling with Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Hao, Xinchang; Gen, Mitsuo
In current manufacturing systems, production processes and management are involved in many unexpected events and new requirements emerging constantly. This dynamic environment implies that operation rescheduling is usually indispensable. A wide variety of procedures and heuristics has been developed to improve the quality of rescheduling. However, most proposed approaches are derived usually with respect to simplified assumptions. As a consequence, these approaches might be inconsistent with the actual requirements in a real production environment, i.e., they are often unsuitable and inflexible to respond efficiently to the frequent changes. In this paper, a multi-objective job shop rescheduling problem (moJSRP) is formulated to improve the practical application of rescheduling. To solve the moJSRP model, an evolutionary algorithm is designed, in which a random key-based representation and interactive adaptive-weight (i-awEA) fitness assignment are embedded. To verify the effectiveness, the proposed algorithm has been compared with other apporaches and benchmarks on the robustness of moJRP optimziation. The comparison results show that iAWGA-A is better than weighted fitness method in terms of effectiveness and stability. Simlarly, iAWGA-A also outperforms other well stability approachessuch as non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm2 (SPEA2).
Optimal classification of standoff bioaerosol measurements using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Nyhavn, Ragnhild; Moen, Hans J. F.; Farsund, Øystein; Rustad, Gunnar
2011-05-01
Early warning systems based on standoff detection of biological aerosols require real-time signal processing of a large quantity of high-dimensional data, challenging the systems efficiency in terms of both computational complexity and classification accuracy. Hence, optimal feature selection is essential in forming a stable and efficient classification system. This involves finding optimal signal processing parameters, characteristic spectral frequencies and other data transformations in large magnitude variable space, stating the need for an efficient and smart search algorithm. Evolutionary algorithms are population-based optimization methods inspired by Darwinian evolutionary theory. These methods focus on application of selection, mutation and recombination on a population of competing solutions and optimize this set by evolving the population of solutions for each generation. We have employed genetic algorithms in the search for optimal feature selection and signal processing parameters for classification of biological agents. The experimental data were achieved with a spectrally resolved lidar based on ultraviolet laser induced fluorescence, and included several releases of 5 common simulants. The genetic algorithm outperform benchmark methods involving analytic, sequential and random methods like support vector machines, Fisher's linear discriminant and principal component analysis, with significantly improved classification accuracy compared to the best classical method.
A novel swarm intelligence algorithm for finding DNA motifs
Lei, Chengwei; Ruan, Jianhua
2010-01-01
Discovering DNA motifs from co-expressed or co-regulated genes is an important step towards deciphering complex gene regulatory networks and understanding gene functions. Despite significant improvement in the last decade, it still remains one of the most challenging problems in computational molecular biology. In this work, we propose a novel motif finding algorithm that finds consensus patterns using a population-based stochastic optimisation technique called Particle Swarm Optimisation (PSO), which has been shown to be effective in optimising difficult multidimensional problems in continuous domains. We propose to use a word dissimilarity graph to remap the neighborhood structure of the solution space of DNA motifs, and propose a modification of the naive PSO algorithm to accommodate discrete variables. In order to improve efficiency, we also propose several strategies for escaping from local optima and for automatically determining the termination criteria. Experimental results on simulated challenge problems show that our method is both more efficient and more accurate than several existing algorithms. Applications to several sets of real promoter sequences also show that our approach is able to detect known transcription factor binding sites, and outperforms two of the most popular existing algorithms. PMID:20090174
ASTM/NBS base stock consistency study
Frassa, K.A.
1980-11-01
This paper summarizes the scope of a cooperative ASTM/NBS program established in June 1979. The contemplated study will ascertain the batch-to-batch consistency of re-refined and virgin base stocks manufactured by various processes. For one year, approximately eight to ten different base stocks samples, will be obtained by NBS every two weeks. One set of bi-monthly samples will be forwarded to each participant, on a coded basis monthly. Seven to eight samples will be obtained from six different re-refining processes and two virgin oil samples from a similar manufacturing process. The participants will report their results on a monthly basis. The second set of samples will be retained by NBS for an interim monthly sample study, if required, based on data analysis. Each sample's properties will be evaluated using various physical tests, chemical tests, and bench tests. The total testing program should define the batch-to-batch base stock consistency short of engine testing.
Toward an internally consistent pressure scale
Fei, Yingwei; Ricolleau, Angele; Frank, Mark; Mibe, Kenji; Shen, Guoyin; Prakapenka, Vitali
2007-01-01
Our ability to interpret seismic observations including the seismic discontinuities and the density and velocity profiles in the earth's interior is critically dependent on the accuracy of pressure measurements up to 364 GPa at high temperature. Pressure scales based on the reduced shock-wave equations of state alone may predict pressure variations up to 7% in the megabar pressure range at room temperature and even higher percentage at high temperature, leading to large uncertainties in understanding the nature of the seismic discontinuities and chemical composition of the earth's interior. Here, we report compression data of gold (Au), platinum (Pt), the NaCl-B2 phase, and solid neon (Ne) at 300 K and high temperatures up to megabar pressures. Combined with existing experimental data, the compression data were used to establish internally consistent thermal equations of state of Au, Pt, NaCl-B2, and solid Ne. The internally consistent pressure scales provide a tractable, accurate baseline for comparing high pressure–temperature experimental data with theoretical calculations and the seismic observations, thereby advancing our understanding fundamental high-pressure phenomena and the chemistry and physics of the earth's interior. PMID:17483460
Consistent Kaluza-Klein sphere reductions
NASA Astrophysics Data System (ADS)
Cvetič, M.; Lü, H.; Pope, C. N.
2000-09-01
We study the circumstances under which a Kaluza-Klein reduction on an n-sphere, with a massless truncation that includes all the Yang-Mills fields of SO(n+1), can be consistent at the full non-linear level. We take as the starting point a theory comprising a p-form field strength and (possibly) a dilaton, coupled to gravity in the higher dimension D. We show that aside from the previously studied cases with (D,p)=(11,4) and (10,5) (associated with the S4 and S7 reductions of D=11 supergravity, and the S5 reduction of type IIB supergravity), the only other possibilities that allow consistent reductions are for p=2, reduced on S2, and for p=3, reduced on S3 or SD-3. We construct the fully non-linear Kaluza-Klein Ansätze in all these cases. In particular, we obtain D=3, N=8, SO(8) and D=7, N=2, SO(4) gauged supergravities from S7 and S3 reductions of N=1 supergravity in D=10.
Consistency check of {Lambda}CDM phenomenology
Lombriser, Lucas
2011-03-15
The standard model of cosmology {Lambda}CDM assumes general relativity, flat space, and the presence of a positive cosmological constant. We relax these assumptions allowing spatial curvature, a time-dependent effective dark energy equation of state, as well as modifications of the Poisson equation for the lensing potential, and modifications of the growth of linear matter density perturbations in alternate combinations. Using six parameters characterizing these relations, we check {Lambda}CDM for consistency utilizing cosmic microwave background anisotropies, cross correlations thereof with high-redshift galaxies through the integrated Sachs-Wolfe effect, the Hubble constant, supernovae, and baryon acoustic oscillation distances, as well as the relation between weak gravitational lensing and galaxy flows. In all scenarios, we find consistency of the concordance model at the 95% confidence level. However, we emphasize that constraining supplementary background parameters and parametrizations of the growth of large-scale structure separately may lead to a priori exclusion of viable departures from the concordance model.
On the consistent use of constructed observables
NASA Astrophysics Data System (ADS)
Trott, Michael
2015-02-01
We define "constructed observables" as relating experimental measurements to terms in a Lagrangian while simultaneously making assumptions about possible deviations from the Standard Model (SM), in other Lagrangian terms. Ensuring that the SM effective field theory (EFT) is constrained correctly when using constructed observables requires that their defining conditions are imposed on the EFT in a manner that is consistent with the equations of motion. Failing to do so can result in a "functionally redundant" operator basis (We define the concept of functional redundancy, which is distinct from the usual concept of an operator basis redundancy, in the introduction.) and the wrong expectation as to how experimental quantities are related in the EFT. We illustrate the issues involved considering the S parameter and the off shell triple gauge coupling (TGC) verticies. We show that the relationships between decay and the off shell TGC verticies are subject to these subtleties, and how the connections between these observables vanish in the limit of strong bounds due to LEP. The challenge of using constructed observables to consistently constrain the Standard Model EFT is only expected to grow with future LHC data, as more complex processes are studied.
Semioptimal practicable algorithmic cooling
NASA Astrophysics Data System (ADS)
Elias, Yuval; Mor, Tal; Weinstein, Yossi
2011-04-01
Algorithmic cooling (AC) of spins applies entropy manipulation algorithms in open spin systems in order to cool spins far beyond Shannon’s entropy bound. Algorithmic cooling of nuclear spins was demonstrated experimentally and may contribute to nuclear magnetic resonance spectroscopy. Several cooling algorithms were suggested in recent years, including practicable algorithmic cooling (PAC) and exhaustive AC. Practicable algorithms have simple implementations, yet their level of cooling is far from optimal; exhaustive algorithms, on the other hand, cool much better, and some even reach (asymptotically) an optimal level of cooling, but they are not practicable. We introduce here semioptimal practicable AC (SOPAC), wherein a few cycles (typically two to six) are performed at each recursive level. Two classes of SOPAC algorithms are proposed and analyzed. Both attain cooling levels significantly better than PAC and are much more efficient than the exhaustive algorithms. These algorithms are shown to bridge the gap between PAC and exhaustive AC. In addition, we calculated the number of spins required by SOPAC in order to purify qubits for quantum computation. As few as 12 and 7 spins are required (in an ideal scenario) to yield a mildly pure spin (60% polarized) from initial polarizations of 1% and 10%, respectively. In the latter case, about five more spins are sufficient to produce a highly pure spin (99.99% polarized), which could be relevant for fault-tolerant quantum computing.
NASA Astrophysics Data System (ADS)
Zhang, Zhen; Guo, Chonghui
2016-08-01
Due to the uncertainty of the decision environment and the lack of knowledge, decision-makers may use uncertain linguistic preference relations to express their preferences over alternatives and criteria. For group decision-making problems with preference relations, it is important to consider the individual consistency and the group consensus before aggregating the preference information. In this paper, consistency and consensus models for group decision-making with uncertain 2-tuple linguistic preference relations (U2TLPRs) are investigated. First of all, a formula which can construct a consistent U2TLPR from the original preference relation is presented. Based on the consistent preference relation, the individual consistency index for a U2TLPR is defined. An iterative algorithm is then developed to improve the individual consistency of a U2TLPR. To help decision-makers reach consensus in group decision-making under uncertain linguistic environment, the individual consensus and group consensus indices for group decision-making with U2TLPRs are defined. Based on the two indices, an algorithm for consensus reaching in group decision-making with U2TLPRs is also developed. Finally, two examples are provided to illustrate the effectiveness of the proposed algorithms.
NASA Astrophysics Data System (ADS)
Diaz, K. S.; Kim, E. H.; Jones, R. M.; de Leon, K. C.; Woodcroft, B. J.; Tyson, G. W.; Rich, V. I.
2014-12-01
The growing field of metaproteomics links microbial communities to their expressed functions by using mass spectrometry methods to characterize community proteins. Comparison of mass spectrometry protein search algorithms and their biases is crucial for maximizing the quality and amount of protein identifications in mass spectral data. Available algorithms employ different approaches when mapping mass spectra to peptides against a database. We compared mass spectra from four microbial proteomes derived from high-organic content soils searched with two search algorithms: 1) Sequest HT as packaged within Proteome Discoverer (v.1.4) and 2) X!Tandem as packaged in TransProteomicPipeline (v.4.7.1). Searches used matched metagenomes, and results were filtered to allow identification of high probability proteins. There was little overlap in proteins identified by both algorithms, on average just ~24% of the total. However, when adjusted for spectral abundance, the overlap improved to ~70%. Proteome Discoverer generally outperformed X!Tandem, identifying an average of 12.5% more proteins than X!Tandem, with X!Tandem identifying more proteins only in the first two proteomes. For spectrally-adjusted results, the algorithms were similar, with X!Tandem marginally outperforming Proteome Discoverer by an average of ~4%. We then assessed differences in heat shock proteins (HSP) identification by the two algorithms by BLASTing identified proteins against the Heat Shock Protein Information Resource, because HSP hits typically account for the majority signal in proteomes, due to extraction protocols. Total HSP identifications for each of the 4 proteomes were approximately ~15%, ~11%, ~17%, and ~19%, with ~14% for total HSPs with redundancies removed. Of the ~15% average of proteins from the 4 proteomes identified as HSPs, ~10% of proteins and spectra were identified by both algorithms. On average, Proteome Discoverer identified ~9% more HSPs than X!Tandem.
Feng, Yanhong; Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun
2014-01-01
An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940
Feng, Yanhong; Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun
2014-01-01
An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm.
Wang, Gai-Ge; Feng, Qingjiang; Zhao, Xiang-Jun
2014-01-01
An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. First of all, with the framework of SFLA, an improved frog-leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm. PMID:25404940
Quantum cosmological consistency condition for inflation
Calcagni, Gianluca; Kiefer, Claus; Steinwachs, Christian F. E-mail: kiefer@thp.uni-koeln.de
2014-10-01
We investigate the quantum cosmological tunneling scenario for inflationary models. Within a path-integral approach, we derive the corresponding tunneling probability distribution. A sharp peak in this distribution can be interpreted as the initial condition for inflation and therefore as a quantum cosmological prediction for its energy scale. This energy scale is also a genuine prediction of any inflationary model by itself, as the primordial gravitons generated during inflation leave their imprint in the B-polarization of the cosmic microwave background. In this way, one can derive a consistency condition for inflationary models that guarantees compatibility with a tunneling origin and can lead to a testable quantum cosmological prediction. The general method is demonstrated explicitly for the model of natural inflation.
Trisomy 21 consistently activates the interferon response.
Sullivan, Kelly D; Lewis, Hannah C; Hill, Amanda A; Pandey, Ahwan; Jackson, Leisa P; Cabral, Joseph M; Smith, Keith P; Liggett, L Alexander; Gomez, Eliana B; Galbraith, Matthew D; DeGregori, James; Espinosa, Joaquín M
2016-01-01
Although it is clear that trisomy 21 causes Down syndrome, the molecular events acting downstream of the trisomy remain ill defined. Using complementary genomics analyses, we identified the interferon pathway as the major signaling cascade consistently activated by trisomy 21 in human cells. Transcriptome analysis revealed that trisomy 21 activates the interferon transcriptional response in fibroblast and lymphoblastoid cell lines, as well as circulating monocytes and T cells. Trisomy 21 cells show increased induction of interferon-stimulated genes and decreased expression of ribosomal proteins and translation factors. An shRNA screen determined that the interferon-activated kinases JAK1 and TYK2 suppress proliferation of trisomy 21 fibroblasts, and this defect is rescued by pharmacological JAK inhibition. Therefore, we propose that interferon activation, likely via increased gene dosage of the four interferon receptors encoded on chromosome 21, contributes to many of the clinical impacts of trisomy 21, and that interferon antagonists could have therapeutic benefits. PMID:27472900
Toward a Fully Consistent Radiation Hydrodynamics
Castor, J I
2009-07-07
Dimitri Mihalas set the standard for all work in radiation hydrodynamics since 1984. The present contribution builds on 'Foundations of Radiation Hydrodynamics' to explore the relativistic effects that have prevented having a consistent non-relativistic theory. Much of what I have to say is in FRH, but the 3-D development is new. Results are presented for the relativistic radiation transport equation in the frame obtained by a Lorentz boost with the fluid velocity, and the exact momentum-integrated moment equations. The special-relativistic hydrodynamic equations are summarized, including the radiation contributions, and it is shown that exact conservation is obtained, and certain puzzles in the non-relativistic radhydro equations are explained.
Plasma Diffusion in Self-Consistent Fluctuations
NASA Technical Reports Server (NTRS)
Smets, R.; Belmont, G.; Aunai, N.
2012-01-01
The problem of particle diffusion in position space, as a consequence ofeleclromagnetic fluctuations is addressed. Numerical results obtained with a self-consistent hybrid code are presented, and a method to calculate diffusion coefficient in the direction perpendicular to the mean magnetic field is proposed. The diffusion is estimated for two different types of fluctuations. The first type (resuiting from an agyrotropic in itiai setting)is stationary, wide band white noise, and associated to Gaussian probability distribution function for the magnetic fluctuations. The second type (result ing from a Kelvin-Helmholtz instability) is non-stationary, with a power-law spectrum, and a non-Gaussian probabi lity distribution function. The results of the study allow revisiting the question of loading particles of solar wind origin in the Earth magnetosphere.
Consistent evolution in a pedestrian flow
NASA Astrophysics Data System (ADS)
Guan, Junbiao; Wang, Kaihua
2016-03-01
In this paper, pedestrian evacuation considering different human behaviors is studied by using a cellular automaton (CA) model combined with the snowdrift game theory. The evacuees are divided into two types, i.e. cooperators and defectors, and two different human behaviors, herding behavior and independent behavior, are investigated. It is found from a large amount of numerical simulations that the ratios of the corresponding evacuee clusters are evolved to consistent states despite 11 typically different initial conditions, which may largely owe to self-organization effect. Moreover, an appropriate proportion of initial defectors who are of herding behavior, coupled with an appropriate proportion of initial defectors who are of rationally independent thinking, are two necessary factors for short evacuation time.
Consistency tests for the cosmological constant.
Zunckel, Caroline; Clarkson, Chris
2008-10-31
We propose consistency tests for the cosmological constant which provide a direct observational signal if Lambda is wrong, regardless of the densities of matter and curvature. As an example of its utility, our flat case test can warn of a small transition of the equation of state w(z) from w(z)=-1 of 20% from SNAP (Supernova Acceleration Probe) quality data at 4-sigma, even when direct reconstruction techniques see virtually no evidence for deviation from Lambda. It is shown to successfully rule out a wide range of non-Lambda dark energy models with no reliance on knowledge of Omega_{m} using SNAP quality data and a large range for using 10;{5} supernovae as forecasted for the Large Synoptic Survey Telescope. PMID:18999813
Reliability and Consistency of Surface Contamination Measurements
Rouppert, F.; Rivoallan, A.; Largeron, C.
2002-02-26
Surface contamination evaluation is a tough problem since it is difficult to isolate the radiations emitted by the surface, especially in a highly irradiating atmosphere. In that case the only possibility is to evaluate smearable (removeable) contamination since ex-situ countings are possible. Unfortunately, according to our experience at CEA, these values are not consistent and thus non relevant. In this study, we show, using in-situ Fourier Transform Infra Red spectrometry on contaminated metal samples, that fixed contamination seems to be chemisorbed and removeable contamination seems to be physisorbed. The distribution between fixed and removeable contamination appears to be variable. Chemical equilibria and reversible ion exchange mechanisms are involved and are closely linked to environmental conditions such as humidity and temperature. Measurements of smearable contamination only give an indication of the state of these equilibria between fixed and removeable contamination at the time and in the environmental conditions the measurements were made.
Trisomy 21 consistently activates the interferon response.
Sullivan, Kelly D; Lewis, Hannah C; Hill, Amanda A; Pandey, Ahwan; Jackson, Leisa P; Cabral, Joseph M; Smith, Keith P; Liggett, L Alexander; Gomez, Eliana B; Galbraith, Matthew D; DeGregori, James; Espinosa, Joaquín M
2016-07-29
Although it is clear that trisomy 21 causes Down syndrome, the molecular events acting downstream of the trisomy remain ill defined. Using complementary genomics analyses, we identified the interferon pathway as the major signaling cascade consistently activated by trisomy 21 in human cells. Transcriptome analysis revealed that trisomy 21 activates the interferon transcriptional response in fibroblast and lymphoblastoid cell lines, as well as circulating monocytes and T cells. Trisomy 21 cells show increased induction of interferon-stimulated genes and decreased expression of ribosomal proteins and translation factors. An shRNA screen determined that the interferon-activated kinases JAK1 and TYK2 suppress proliferation of trisomy 21 fibroblasts, and this defect is rescued by pharmacological JAK inhibition. Therefore, we propose that interferon activation, likely via increased gene dosage of the four interferon receptors encoded on chromosome 21, contributes to many of the clinical impacts of trisomy 21, and that interferon antagonists could have therapeutic benefits.
Plasma diffusion in self-consistent fluctuations
Smets, R.; Belmont, G.; Aunai, N.; Rezeau, L.
2011-10-15
The problem of particle diffusion in position space, as a consequence of electromagnetic fluctuations is addressed. Numerical results obtained with a self-consistent hybrid code are presented, and a method to calculate diffusion coefficient in the direction perpendicular to the mean magnetic field is proposed. The diffusion is estimated for two different types of fluctuations. The first type (resulting from an agyrotropic initial setting) is stationary, wide band white noise, and associated to Gaussian probability distribution function for the magnetic fluctuations. The second type (resulting from a Kelvin-Helmholtz instability) is non-stationary, with a power-law spectrum, and a non-Gaussian probability distribution function. The results of the study allow revisiting the question of loading particles of solar wind origin in the Earth magnetosphere.
Quantifying consistent individual differences in habitat selection.
Leclerc, Martin; Vander Wal, Eric; Zedrosser, Andreas; Swenson, Jon E; Kindberg, Jonas; Pelletier, Fanie
2016-03-01
Habitat selection is a fundamental behaviour that links individuals to the resources required for survival and reproduction. Although natural selection acts on an individual's phenotype, research on habitat selection often pools inter-individual patterns to provide inferences on the population scale. Here, we expanded a traditional approach of quantifying habitat selection at the individual level to explore the potential for consistent individual differences of habitat selection. We used random coefficients in resource selection functions (RSFs) and repeatability estimates to test for variability in habitat selection. We applied our method to a detailed dataset of GPS relocations of brown bears (Ursus arctos) taken over a period of 6 years, and assessed whether they displayed repeatable individual differences in habitat selection toward two habitat types: bogs and recent timber-harvest cut blocks. In our analyses, we controlled for the availability of habitat, i.e. the functional response in habitat selection. Repeatability estimates of habitat selection toward bogs and cut blocks were 0.304 and 0.420, respectively. Therefore, 30.4 and 42.0 % of the population-scale habitat selection variability for bogs and cut blocks, respectively, was due to differences among individuals, suggesting that consistent individual variation in habitat selection exists in brown bears. Using simulations, we posit that repeatability values of habitat selection are not related to the value and significance of β estimates in RSFs. Although individual differences in habitat selection could be the results of non-exclusive factors, our results illustrate the evolutionary potential of habitat selection.
Quantifying consistent individual differences in habitat selection.
Leclerc, Martin; Vander Wal, Eric; Zedrosser, Andreas; Swenson, Jon E; Kindberg, Jonas; Pelletier, Fanie
2016-03-01
Habitat selection is a fundamental behaviour that links individuals to the resources required for survival and reproduction. Although natural selection acts on an individual's phenotype, research on habitat selection often pools inter-individual patterns to provide inferences on the population scale. Here, we expanded a traditional approach of quantifying habitat selection at the individual level to explore the potential for consistent individual differences of habitat selection. We used random coefficients in resource selection functions (RSFs) and repeatability estimates to test for variability in habitat selection. We applied our method to a detailed dataset of GPS relocations of brown bears (Ursus arctos) taken over a period of 6 years, and assessed whether they displayed repeatable individual differences in habitat selection toward two habitat types: bogs and recent timber-harvest cut blocks. In our analyses, we controlled for the availability of habitat, i.e. the functional response in habitat selection. Repeatability estimates of habitat selection toward bogs and cut blocks were 0.304 and 0.420, respectively. Therefore, 30.4 and 42.0 % of the population-scale habitat selection variability for bogs and cut blocks, respectively, was due to differences among individuals, suggesting that consistent individual variation in habitat selection exists in brown bears. Using simulations, we posit that repeatability values of habitat selection are not related to the value and significance of β estimates in RSFs. Although individual differences in habitat selection could be the results of non-exclusive factors, our results illustrate the evolutionary potential of habitat selection. PMID:26597548
Consistency of vegetation index seasonality across the Amazon rainforest
NASA Astrophysics Data System (ADS)
Maeda, Eduardo Eiji; Moura, Yhasmin Mendes; Wagner, Fabien; Hilker, Thomas; Lyapustin, Alexei I.; Wang, Yujie; Chave, Jérôme; Mõttus, Matti; Aragão, Luiz E. O. C.; Shimabukuro, Yosio
2016-10-01
Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.
Detrended cross-correlation analysis consistently extended to multifractality
NASA Astrophysics Data System (ADS)
OświeÌ§cimka, Paweł; DroŻdŻ, Stanisław; Forczek, Marcin; Jadach, Stanisław; Kwapień, Jarosław
2014-02-01
We propose an algorithm, multifractal cross-correlation analysis (MFCCA), which constitutes a consistent extension of the detrended cross-correlation analysis and is able to properly identify and quantify subtle characteristics of multifractal cross-correlations between two time series. Our motivation for introducing this algorithm is that the already existing methods, like multifractal extension, have at best serious limitations for most of the signals describing complex natural processes and often indicate multifractal cross-correlations when there are none. The principal component of the present extension is proper incorporation of the sign of fluctuations to their generalized moments. Furthermore, we present a broad analysis of the model fractal stochastic processes as well as of the real-world signals and show that MFCCA is a robust and selective tool at the same time and therefore allows for a reliable quantification of the cross-correlative structure of analyzed processes. In particular, it allows one to identify the boundaries of the multifractal scaling and to analyze a relation between the generalized Hurst exponent and the multifractal scaling parameter λq. This relation provides information about the character of potential multifractality in cross-correlations and thus enables a deeper insight into dynamics of the analyzed processes than allowed by any other related method available so far. By using examples of time series from the stock market, we show that financial fluctuations typically cross-correlate multifractally only for relatively large fluctuations, whereas small fluctuations remain mutually independent even at maximum of such cross-correlations. Finally, we indicate possible utility of MFCCA to study effects of the time-lagged cross-correlations.
Selinski, Silvia; Blaszkewicz, Meinolf; Lehmann, Marie-Louise; Ovsiannikov, Daniel; Moormann, Oliver; Guballa, Christoph; Kress, Alexander; Truss, Michael C; Gerullis, Holger; Otto, Thomas; Barski, Dimitri; Niegisch, Günter; Albers, Peter; Frees, Sebastian; Brenner, Walburgis; Thüroff, Joachim W; Angeli-Greaves, Miriam; Seidel, Thilo; Roth, Gerhard; Dietrich, Holger; Ebbinghaus, Rainer; Prager, Hans M; Bolt, Hermann M; Falkenstein, Michael; Zimmermann, Anna; Klein, Torsten; Reckwitz, Thomas; Roemer, Hermann C; Löhlein, Dietrich; Weistenhöfer, Wobbeke; Schöps, Wolfgang; Hassan Rizvi, Syed Adibul; Aslam, Muhammad; Bánfi, Gergely; Romics, Imre; Steffens, Michael; Ekici, Arif B; Winterpacht, Andreas; Ickstadt, Katja; Schwender, Holger; Hengstler, Jan G; Golka, Klaus
2011-10-01
Genotyping N-acetyltransferase 2 (NAT2) is of high relevance for individualized dosing of antituberculosis drugs and bladder cancer epidemiology. In this study we compared a recently published tagging single nucleotide polymorphism (SNP) (rs1495741) to the conventional 7-SNP genotype (G191A, C282T, T341C, C481T, G590A, A803G and G857A haplotype pairs) and systematically analysed if novel SNP combinations outperform the latter. For this purpose, we studied 3177 individuals by PCR and phenotyped 344 individuals by the caffeine test. Although the tagSNP and the 7-SNP genotype showed a high degree of correlation (R=0.933, P<0.0001) the 7-SNP genotype nevertheless outperformed the tagging SNP with respect to specificity (1.0 vs. 0.9444, P=0.0065). Considering all possible SNP combinations in a receiver operating characteristic analysis we identified a 2-SNP genotype (C282T, T341C) that outperformed the tagging SNP and was equivalent to the 7-SNP genotype. The 2-SNP genotype predicted the correct phenotype with a sensitivity of 0.8643 and a specificity of 1.0. In addition, it predicted the 7-SNP genotype with sensitivity and specificity of 0.9993 and 0.9880, respectively. The prediction of the NAT2 genotype by the 2-SNP genotype performed similar in populations of Caucasian, Venezuelan and Pakistani background. A 2-SNP genotype predicts NAT2 phenotypes with similar sensitivity and specificity as the conventional 7-SNP genotype. This procedure represents a facilitation in individualized dosing of NAT2 substrates without losing sensitivity or specificity.
A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures.
Tsang, Herbert H; Wiese, Kay C
2015-01-01
Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper discusses SARNA-Predict-pk, a RNA pseudoknotted secondary structure prediction algorithm based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and further examines the effect of the new algorithm to include prediction of RNA secondary structure with pseudoknots. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms using 20 individual known structures from seven RNA classes. We measured the sensitivity and specificity of nine prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. One is using the statistical clustering approach: Sfold and the other five are heuristic algorithms: SARNA-Predict-pk, ILM, STAR, IPknot and HotKnots algorithms. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy. This supports the use of the proposed method on pseudoknotted RNA secondary structure prediction of other known structures. PMID:26558299
Reasoning about systolic algorithms
Purushothaman, S.; Subrahmanyam, P.A.
1988-12-01
The authors present a methodology for verifying correctness of systolic algorithms. The methodology is based on solving a set of Uniform Recurrence Equations obtained from a description of systolic algorithms as a set of recursive equations. They present an approach to mechanically verify correctness of systolic algorithms, using the Boyer-Moore theorem proven. A mechanical correctness proof of an example from the literature is also presented.
Multimodal region-consistent saliency based on foreground and background priors for indoor scene
NASA Astrophysics Data System (ADS)
Zhang, J.; Wang, Q.; Zhao, Y.; Chen, S. Y.
2016-09-01
Visual saliency is a very important feature for object detection in a complex scene. However, image-based saliency is influenced by clutter background and similar objects in indoor scenes, and pixel-based saliency cannot provide consistent saliency to a whole object. Therefore, in this paper, we propose a novel method that computes visual saliency maps from multimodal data obtained from indoor scenes, whilst keeping region consistency. Multimodal data from a scene are first obtained by an RGB+D camera. This scene is then segmented into over-segments by a self-adapting approach to combine its colour image and depth map. Based on these over-segments, we develop two cues as domain knowledge to improve the final saliency map, including focus regions obtained from colour images, and planar background structures obtained from point cloud data. Thus, our saliency map is generated by compounding the information of the colour data, the depth data and the point cloud data in a scene. In the experiments, we extensively compare the proposed method with state-of-the-art methods, and we also apply the proposed method to a real robot system to detect objects of interest. The experimental results show that the proposed method outperforms other methods in terms of precisions and recall rates.
Evaluating and comparing algorithms for respiratory motion prediction.
Ernst, F; Dürichen, R; Schlaefer, A; Schweikard, A
2013-06-01
In robotic radiosurgery, it is necessary to compensate for systematic latencies arising from target tracking and mechanical constraints. This compensation is usually achieved by means of an algorithm which computes the future target position. In most scientific works on respiratory motion prediction, only one or two algorithms are evaluated on a limited amount of very short motion traces. The purpose of this work is to gain more insight into the real world capabilities of respiratory motion prediction methods by evaluating many algorithms on an unprecedented amount of data. We have evaluated six algorithms, the normalized least mean squares (nLMS), recursive least squares (RLS), multi-step linear methods (MULIN), wavelet-based multiscale autoregression (wLMS), extended Kalman filtering, and ε-support vector regression (SVRpred) methods, on an extensive database of 304 respiratory motion traces. The traces were collected during treatment with the CyberKnife (Accuray, Inc., Sunnyvale, CA, USA) and feature an average length of 71 min. Evaluation was done using a graphical prediction toolkit, which is available to the general public, as is the data we used. The experiments show that the nLMS algorithm-which is one of the algorithms currently used in the CyberKnife-is outperformed by all other methods. This is especially true in the case of the wLMS, the SVRpred, and the MULIN algorithms, which perform much better. The nLMS algorithm produces a relative root mean square (RMS) error of 75% or less (i.e., a reduction in error of 25% or more when compared to not doing prediction) in only 38% of the test cases, whereas the MULIN and SVRpred methods reach this level in more than 77%, the wLMS algorithm in more than 84% of the test cases. Our work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation. Additionally, we have seen that the structure of a patient's respiratory
Evaluating and comparing algorithms for respiratory motion prediction.
Ernst, F; Dürichen, R; Schlaefer, A; Schweikard, A
2013-06-01
In robotic radiosurgery, it is necessary to compensate for systematic latencies arising from target tracking and mechanical constraints. This compensation is usually achieved by means of an algorithm which computes the future target position. In most scientific works on respiratory motion prediction, only one or two algorithms are evaluated on a limited amount of very short motion traces. The purpose of this work is to gain more insight into the real world capabilities of respiratory motion prediction methods by evaluating many algorithms on an unprecedented amount of data. We have evaluated six algorithms, the normalized least mean squares (nLMS), recursive least squares (RLS), multi-step linear methods (MULIN), wavelet-based multiscale autoregression (wLMS), extended Kalman filtering, and ε-support vector regression (SVRpred) methods, on an extensive database of 304 respiratory motion traces. The traces were collected during treatment with the CyberKnife (Accuray, Inc., Sunnyvale, CA, USA) and feature an average length of 71 min. Evaluation was done using a graphical prediction toolkit, which is available to the general public, as is the data we used. The experiments show that the nLMS algorithm-which is one of the algorithms currently used in the CyberKnife-is outperformed by all other methods. This is especially true in the case of the wLMS, the SVRpred, and the MULIN algorithms, which perform much better. The nLMS algorithm produces a relative root mean square (RMS) error of 75% or less (i.e., a reduction in error of 25% or more when compared to not doing prediction) in only 38% of the test cases, whereas the MULIN and SVRpred methods reach this level in more than 77%, the wLMS algorithm in more than 84% of the test cases. Our work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation. Additionally, we have seen that the structure of a patient's respiratory
SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET
Lapuyade-Lahorgue, Jérôme; Visvikis, Dimitris; Hatt, Mathieu; Pradier, Olivier; Cheze Le Rest, Catherine
2015-10-15
Purpose: Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity. Methods: The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor—Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB). Results: SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004). Conclusions: SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.
An Inexact Newton–Krylov Algorithm for Constrained Diffeomorphic Image Registration*
Mang, Andreas; Biros, George
2016-01-01
We propose numerical algorithms for solving large deformation diffeomorphic image registration problems. We formulate the nonrigid image registration problem as a problem of optimal control. This leads to an infinite-dimensional partial differential equation (PDE) constrained optimization problem. The PDE constraint consists, in its simplest form, of a hyperbolic transport equation for the evolution of the image intensity. The control variable is the velocity field. Tikhonov regularization on the control ensures well-posedness. We consider standard smoothness regularization based on H1- or H2-seminorms. We augment this regularization scheme with a constraint on the divergence of the velocity field (control variable) rendering the deformation incompressible (Stokes regularization scheme) and thus ensuring that the determinant of the deformation gradient is equal to one, up to the numerical error. We use a Fourier pseudospectral discretization in space and a Chebyshev pseudospectral discretization in time. The latter allows us to reduce the number of unknowns and enables the time-adaptive inversion for nonstationary velocity fields. We use a preconditioned, globalized, matrix-free, inexact Newton–Krylov method for numerical optimization. A parameter continuation is designed to estimate an optimal regularization parameter. Regularity is ensured by controlling the geometric properties of the deformation field. Overall, we arrive at a black-box solver that exploits computational tools that are precisely tailored for solving the optimality system. We study spectral properties of the Hessian, grid convergence, numerical accuracy, computational efficiency, and deformation regularity of our scheme. We compare the designed Newton–Krylov methods with a globalized Picard method (preconditioned gradient descent). We study the influence of a varying number of unknowns in time. The reported results demonstrate excellent numerical accuracy, guaranteed local deformation
Development and large scale benchmark testing of the PROSPECTOR_3 threading algorithm.
Skolnick, Jeffrey; Kihara, Daisuke; Zhang, Yang
2004-08-15
This article describes the PROSPECTOR_3 threading algorithm, which combines various scoring functions designed to match structurally related target/template pairs. Each variant described was found to have a Z-score above which most identified templates have good structural (threading) alignments, Z(struct) (Z(good)). 'Easy' targets with accurate threading alignments are identified as single templates with Z > Z(good) or two templates, each with Z > Z(struct), having a good consensus structure in mutually aligned regions. 'Medium' targets have a pair of templates lacking a consensus structure, or a single template for which Z(struct) < Z < Z(good). PROSPECTOR_3 was applied to a comprehensive Protein Data Bank (PDB) benchmark composed of 1491 single domain proteins, 41-200 residues long and no more than 30% identical to any threading template. Of the proteins, 878 were found to be easy targets, with 761 having a root mean square deviation (RMSD) from native of less than 6.5 A. The average contact prediction accuracy was 46%, and on average 17.6 residue continuous fragments were predicted with RMSD values of 2.0 A. There were 606 medium targets identified, 87% (31%) of which had good structural (threading) alignments. On average, 9.1 residue, continuous fragments with RMSD of 2.5 A were predicted. Combining easy and medium sets, 63% (91%) of the targets had good threading (structural) alignments compared to native; the average target/template sequence identity was 22%. Only nine targets lacked matched templates. Moreover, PROSPECTOR_3 consistently outperforms PSIBLAST. Similar results were predicted for open reading frames (ORFS) < or =200 residues in the M. genitalium, E. coli and S. cerevisiae genomes. Thus, progress has been made in identification of weakly homologous/analogous proteins, with very high alignment coverage, both in a comprehensive PDB benchmark as well as in genomes.
Competing Sudakov veto algorithms
NASA Astrophysics Data System (ADS)
Kleiss, Ronald; Verheyen, Rob
2016-07-01
We present a formalism to analyze the distribution produced by a Monte Carlo algorithm. We perform these analyses on several versions of the Sudakov veto algorithm, adding a cutoff, a second variable and competition between emission channels. The formal analysis allows us to prove that multiple, seemingly different competition algorithms, including those that are currently implemented in most parton showers, lead to the same result. Finally, we test their performance in a semi-realistic setting and show that there are significantly faster alternatives to the commonly used algorithms.
Radiometric consistency in source specifications for lithography
NASA Astrophysics Data System (ADS)
Rosenbluth, Alan E.; Tirapu Azpiroz, Jaione; Lai, Kafai; Tian, Kehan; Melville, David O. S.; Totzeck, Michael; Blahnik, Vladan; Koolen, Armand; Flagello, Donis
2008-03-01
There is a surprising lack of clarity about the exact quantity that a lithographic source map should specify. Under the plausible interpretation that input source maps should tabulate radiance, one will find with standard imaging codes that simulated wafer plane source intensities appear to violate the brightness theorem. The apparent deviation (a cosine factor in the illumination pupil) represents one of many obliquity/inclination factors involved in propagation through the imaging system whose interpretation in the literature is often somewhat obscure, but which have become numerically significant in today's hyper-NA OPC applications. We show that the seeming brightness distortion in the illumination pupil arises because the customary direction-cosine gridding of this aperture yields non-uniform solid-angle subtense in the source pixels. Once the appropriate solid angle factor is included, each entry in the source map becomes proportional to the total |E|^2 that the associated pixel produces on the mask. This quantitative definition of lithographic source distributions is consistent with the plane-wave spectrum approach adopted by litho simulators, in that these simulators essentially propagate |E|^2 along the interfering diffraction orders from the mask input to the resist film. It can be shown using either the rigorous Franz formulation of vector diffraction theory, or an angular spectrum approach, that such an |E|^2 plane-wave weighting will provide the standard inclination factor if the source elements are incoherent and the mask model is accurate. This inclination factor is usually derived from a classical Rayleigh-Sommerfeld diffraction integral, and we show that the nominally discrepant inclination factors used by the various diffraction integrals of this class can all be made to yield the same result as the Franz formula when rigorous mask simulation is employed, and further that these cosine factors have a simple geometrical interpretation. On this basis
Trisomy 21 consistently activates the interferon response
Sullivan, Kelly D; Lewis, Hannah C; Hill, Amanda A; Pandey, Ahwan; Jackson, Leisa P; Cabral, Joseph M; Smith, Keith P; Liggett, L Alexander; Gomez, Eliana B; Galbraith, Matthew D; DeGregori, James; Espinosa, Joaquín M
2016-01-01
Although it is clear that trisomy 21 causes Down syndrome, the molecular events acting downstream of the trisomy remain ill defined. Using complementary genomics analyses, we identified the interferon pathway as the major signaling cascade consistently activated by trisomy 21 in human cells. Transcriptome analysis revealed that trisomy 21 activates the interferon transcriptional response in fibroblast and lymphoblastoid cell lines, as well as circulating monocytes and T cells. Trisomy 21 cells show increased induction of interferon-stimulated genes and decreased expression of ribosomal proteins and translation factors. An shRNA screen determined that the interferon-activated kinases JAK1 and TYK2 suppress proliferation of trisomy 21 fibroblasts, and this defect is rescued by pharmacological JAK inhibition. Therefore, we propose that interferon activation, likely via increased gene dosage of the four interferon receptors encoded on chromosome 21, contributes to many of the clinical impacts of trisomy 21, and that interferon antagonists could have therapeutic benefits. DOI: http://dx.doi.org/10.7554/eLife.16220.001 PMID:27472900
Self consistency grouping: a stringent clustering method
2012-01-01
Background Numerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency. Methods Our method, self consistency grouping, i.e. SCG, yields clusters whose members are closer in rank to each other than to any member outside the cluster. We do not define a distance metric; we use the best known distance metric and presume that it measures the correct distance. SCG does not impose any restriction on the size or the number of the clusters that it finds. The boundaries of clusters are determined by the inconsistencies in the ranks. In addition to the direct implementation that finds the complete structure of the (sub)clusters we implemented two faster versions. The fastest version is guaranteed to find only the clusters that are not subclusters of any other clusters and the other version yields the same output as the direct implementation but does so more efficiently. Results Our tests have demonstrated that SCG yields very few false positives. This was accomplished by introducing errors in the distance measurement. Clustering of protein domain representatives by structural similarity showed that SCG could recover homologous groups with high precision. Conclusions SCG has potential for finding biological relationships under stringent conditions. PMID:23320864
Ciliate communities consistently associated with coral diseases
NASA Astrophysics Data System (ADS)
Sweet, M. J.; Séré, M. G.
2016-07-01
Incidences of coral disease are increasing. Most studies which focus on diseases in these organisms routinely assess variations in bacterial associates. However, other microorganism groups such as viruses, fungi and protozoa are only recently starting to receive attention. This study aimed at assessing the diversity of ciliates associated with coral diseases over a wide geographical range. Here we show that a wide variety of ciliates are associated with all nine coral diseases assessed. Many of these ciliates such as Trochilia petrani and Glauconema trihymene feed on the bacteria which are likely colonizing the bare skeleton exposed by the advancing disease lesion or the necrotic tissue itself. Others such as Pseudokeronopsis and Licnophora macfarlandi are common predators of other protozoans and will be attracted by the increase in other ciliate species to the lesion interface. However, a few ciliate species (namely Varistrombidium kielum, Philaster lucinda, Philaster guamense, a Euplotes sp., a Trachelotractus sp. and a Condylostoma sp.) appear to harbor symbiotic algae, potentially from the coral themselves, a result which may indicate that they play some role in the disease pathology at the very least. Although, from this study alone we are not able to discern what roles any of these ciliates play in disease causation, the consistent presence of such communities with disease lesion interfaces warrants further investigation.
Odor recognition: familiarity, identifiability, and encoding consistency.
Rabin, M D; Cain, W S
1984-04-01
The investigation examined the association between the perceived identity of odorous stimuli and the ability to recognize the previous occurrence of them. The stimuli comprised 20 relatively familiar odorous objects such as chocolate, leather, popcorn, and soy sauce. Participants rated the familiarity of the odors and sought to identify them. At various intervals up to 7 days after initial inspection, the participants sought to recognize the odors among sets of distractor odors that included such items as soap, cloves, pipe tobacco, and so on. The recognition response entailed a confidence rating as to whether or not an item had appeared in the original set. At the time of testing, the participants also sought to identify the stimuli again. The results upheld previous findings of excellent initial recognition memory for environmentally relevant odors and slow forgetting. The results also uncovered, for the first time, a strong association between recognition memory and identifiability, rated familiarity, and the ability to use an odor label consistently at inspection and subsequent testing. Encodability seems to enhance rather than to permit recognizability. Even items identified incorrectly or inconsistently were recognized at levels above chance.
A New Pivot Algorithm for Star Identification
NASA Astrophysics Data System (ADS)
Nah, Jakyoung; Yi, Yu; Kim, Yong Ha
2014-09-01
In this study, a star identification algorithm which utilizes pivot patterns instead of apparent magnitude information was developed. The new star identification algorithm consists of two steps of recognition process. In the first step, the brightest star in a sensor image is identified using the orientation of brightness between two stars as recognition information. In the second step, cell indexes are used as new recognition information to identify dimmer stars, which are derived from the brightest star already identified. If we use the cell index information, we can search over limited portion of the star catalogue database, which enables the faster identification of dimmer stars. The new pivot algorithm does not require calibrations on the apparent magnitude of a star but it shows robust characteristics on the errors of apparent magnitude compared to conventional pivot algorithms which require the apparent magnitude information.
Genetic algorithms for the vehicle routing problem
NASA Astrophysics Data System (ADS)
Volna, Eva
2016-06-01
The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.
[Multispectral image compression algorithms for color reproduction].
Liang, Wei; Zeng, Ping; Luo, Xue-mei; Wang, Yi-feng; Xie, Kun
2015-01-01
In order to improve multispectral images compression efficiency and further facilitate their storage and transmission for the application of color reproduction and so on, in which fields high color accuracy is desired, WF serial methods is proposed, and APWS_RA algorithm is designed. Then the WF_APWS_RA algorithm, which has advantages of low complexity, good illuminant stability and supporting consistent coior reproduction across devices, is presented. The conventional MSE based wavelet embedded coding principle is first studied. And then color perception distortion criterion and visual characteristic matrix W are proposed. Meanwhile, APWS_RA algorithm is formed by optimizing the. rate allocation strategy of APWS. Finally, combined above technologies, a new coding method named WF_APWS_RA is designed. Colorimetric error criterion is used in the algorithm and APWS_RA is applied on visual weighted multispectral image. In WF_APWS_RA, affinity propagation clustering is utilized to exploit spectral correlation of weighted image. Then two-dimensional wavelet transform is used to remove the spatial redundancy. Subsequently, error compensation mechanism and rate pre-allocation are combined to accomplish the embedded wavelet coding. Experimental results show that at the same bit rate, compared with classical coding algorithms, WF serial algorithms have better performance on color retention. APWS_RA preserves least spectral error and WF APWS_RA algorithm has obvious superiority on color accuracy.
Variable depth recursion algorithm for leaf sequencing
Siochi, R. Alfredo C.
2007-02-15
The processes of extraction and sweep are basic segmentation steps that are used in leaf sequencing algorithms. A modified version of a commercial leaf sequencer changed the way that the extracts are selected and expanded the search space, but the modification maintained the basic search paradigm of evaluating multiple solutions, each one consisting of up to 12 extracts and a sweep sequence. While it generated the best solutions compared to other published algorithms, it used more computation time. A new, faster algorithm selects one extract at a time but calls itself as an evaluation function a user-specified number of times, after which it uses the bidirectional sweeping window algorithm as the final evaluation function. To achieve a performance comparable to that of the modified commercial leaf sequencer, 2-3 calls were needed, and in all test cases, there were only slight improvements beyond two calls. For the 13 clinical test maps, computation speeds improved by a factor between 12 and 43, depending on the constraints, namely the ability to interdigitate and the avoidance of the tongue-and-groove under dose. The new algorithm was compared to the original and modified versions of the commercial leaf sequencer. It was also compared to other published algorithms for 1400, random, 15x15, test maps with 3-16 intensity levels. In every single case the new algorithm provided the best solution.
A parallel unmixing algorithm for hyperspectral images
NASA Astrophysics Data System (ADS)
Robila, Stefan A.; Maciak, Lukasz G.
2006-10-01
We present a new algorithm for feature extraction in hyperspectral images based on source separation and parallel computing. In source separation, given a linear mixture of sources, the goal is to recover the components by producing an unmixing matrix. In hyperspectral imagery, the mixing transform and the separated components can be associated with endmembers and their abundances. Source separation based methods have been employed for target detection and classification of hyperspectral images. However, these methods usually involve restrictive conditions on the nature of the results such as orthogonality (in Principal Component Analysis - PCA and Orthogonal Subspace Projection - OSP) of the endmembers or statistical independence (in Independent Component Analysis - ICA) of the abundances nor do they fully satisfy all the conditions included in the Linear Mixing Model. Compared to this, our approach is based on the Nonnegative Matrix Factorization (NMF), a less constraining unmixing method. NMF has the advantage of producing positively defined data, and, with several modifications that we introduce also ensures addition to one. The endmember vectors and the abundances are obtained through a gradient based optimization approach. The algorithm is further modified to run in a parallel environment. The parallel NMF (P-NMF) significantly reduces the time complexity and is shown to also easily port to a distributed environment. Experiments with in-house and Hydice data suggest that NMF outperforms ICA, PCA and OSP for unsupervised endmember extraction. Coupled with its parallel implementation, the new method provides an efficient way for unsupervised unmixing further supporting our efforts in the development of a real time hyperspectral sensing environment with applications to industry and life sciences.
Algorithm That Synthesizes Other Algorithms for Hashing
NASA Technical Reports Server (NTRS)
James, Mark
2010-01-01
An algorithm that includes a collection of several subalgorithms has been devised as a means of synthesizing still other algorithms (which could include computer code) that utilize hashing to determine whether an element (typically, a number or other datum) is a member of a set (typically, a list of numbers). Each subalgorithm synthesizes an algorithm (e.g., a block of code) that maps a static set of key hashes to a somewhat linear monotonically increasing sequence of integers. The goal in formulating this mapping is to cause the length of the sequence thus generated to be as close as practicable to the original length of the set and thus to minimize gaps between the elements. The advantage of the approach embodied in this algorithm is that it completely avoids the traditional approach of hash-key look-ups that involve either secondary hash generation and look-up or further searching of a hash table for a desired key in the event of collisions. This algorithm guarantees that it will never be necessary to perform a search or to generate a secondary key in order to determine whether an element is a member of a set. This algorithm further guarantees that any algorithm that it synthesizes can be executed in constant time. To enforce these guarantees, the subalgorithms are formulated to employ a set of techniques, each of which works very effectively covering a certain class of hash-key values. These subalgorithms are of two types, summarized as follows: Given a list of numbers, try to find one or more solutions in which, if each number is shifted to the right by a constant number of bits and then masked with a rotating mask that isolates a set of bits, a unique number is thereby generated. In a variant of the foregoing procedure, omit the masking. Try various combinations of shifting, masking, and/or offsets until the solutions are found. From the set of solutions, select the one that provides the greatest compression for the representation and is executable in the
Ju, Chunhua
2013-01-01
Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods. PMID:24381525
NASA Astrophysics Data System (ADS)
Chatfield, David C.; Reeves, Melissa S.; Truhlar, Donald G.; Duneczky, Csilla; Schwenke, David W.
1992-12-01
Complex dense matrices corresponding to the D + H2 and O + HD reactions were solved using a complex generalized minimal residual (GMRes) algorithm described by Saad and Schultz (1986) and Saad (1990). To provide a test case with a different structure, the H + H2 system was also considered. It is shown that the computational effort for solutions with the GMRes algorithm depends on the dimension of the linear system, the total energy of the scattering problem, and the accuracy criterion. In several cases with dimensions in the range 1110-5632, the GMRes algorithm outperformed the LAPACK direct solver, with speedups for the linear equation solution as large as a factor of 23.
NASA Technical Reports Server (NTRS)
Chatfield, David C.; Reeves, Melissa S.; Truhlar, Donald G.; Duneczky, Csilla; Schwenke, David W.
1992-01-01
Complex dense matrices corresponding to the D + H2 and O + HD reactions were solved using a complex generalized minimal residual (GMRes) algorithm described by Saad and Schultz (1986) and Saad (1990). To provide a test case with a different structure, the H + H2 system was also considered. It is shown that the computational effort for solutions with the GMRes algorithm depends on the dimension of the linear system, the total energy of the scattering problem, and the accuracy criterion. In several cases with dimensions in the range 1110-5632, the GMRes algorithm outperformed the LAPACK direct solver, with speedups for the linear equation solution as large as a factor of 23.
A consensus algorithm for approximate string matching and its application to QRS complex detection
NASA Astrophysics Data System (ADS)
Alba, Alfonso; Mendez, Martin O.; Rubio-Rincon, Miguel E.; Arce-Santana, Edgar R.
2016-08-01
In this paper, a novel algorithm for approximate string matching (ASM) is proposed. The novelty resides in the fact that, unlike most other methods, the proposed algorithm is not based on the Hamming or Levenshtein distances, but instead computes a score for each symbol in the search text based on a consensus measure. Those symbols with sufficiently high scores will likely correspond to approximate instances of the pattern string. To demonstrate the usefulness of the proposed method, it has been applied to the detection of QRS complexes in electrocardiographic signals with competitive results when compared against the classic Pan-Tompkins (PT) algorithm. The proposed method outperformed PT in 72% of the test cases, with no extra computational cost.
A new machine learning algorithm for removal of salt and pepper noise
NASA Astrophysics Data System (ADS)
Wang, Yi; Adhami, Reza; Fu, Jian
2015-07-01
Supervised machine learning algorithm has been extensively studied and applied to different fields of image processing in past decades. This paper proposes a new machine learning algorithm, called margin setting (MS), for restoring images that are corrupted by salt and pepper impulse noise. Margin setting generates decision surface to classify the noise pixels and non-noise pixels. After the noise pixels are detected, a modified ranked order mean (ROM) filter is used to replace the corrupted pixels for images reconstruction. Margin setting algorithm is tested with grayscale and color images for different noise densities. The experimental results are compared with those of the support vector machine (SVM) and standard median filter (SMF). The results show that margin setting outperforms these methods with higher Peak Signal-to-Noise Ratio (PSNR), lower mean square error (MSE), higher image enhancement factor (IEF) and higher Structural Similarity Index (SSIM).
NASA Astrophysics Data System (ADS)
Liu, Jianming; Grant, Steven L.; Benesty, Jacob
2015-12-01
A new reweighted proportionate affine projection algorithm (RPAPA) with memory and row action projection (MRAP) is proposed in this paper. The reweighted PAPA is derived from a family of sparseness measures, which demonstrate performance similar to mu-law and the l 0 norm PAPA but with lower computational complexity. The sparseness of the channel is taken into account to improve the performance for dispersive system identification. Meanwhile, the memory of the filter's coefficients is combined with row action projections (RAP) to significantly reduce computational complexity. Simulation results demonstrate that the proposed RPAPA MRAP algorithm outperforms both the affine projection algorithm (APA) and PAPA, and has performance similar to l 0 PAPA and mu-law PAPA, in terms of convergence speed and tracking ability. Meanwhile, the proposed RPAPA MRAP has much lower computational complexity than PAPA, mu-law PAPA, and l 0 PAPA, etc., which makes it very appealing for real-time implementation.
Pattern-set generation algorithm for the one-dimensional multiple stock sizes cutting stock problem
NASA Astrophysics Data System (ADS)
Cui, Yaodong; Cui, Yi-Ping; Zhao, Zhigang
2015-09-01
A pattern-set generation algorithm (PSG) for the one-dimensional multiple stock sizes cutting stock problem (1DMSSCSP) is presented. The solution process contains two stages. In the first stage, the PSG solves the residual problems repeatedly to generate the patterns in the pattern set, where each residual problem is solved by the column-generation approach, and each pattern is generated by solving a single large object placement problem. In the second stage, the integer linear programming model of the 1DMSSCSP is solved using a commercial solver, where only the patterns in the pattern set are considered. The computational results of benchmark instances indicate that the PSG outperforms existing heuristic algorithms and rivals the exact algorithm in solution quality.
A Variable Splitting based Algorithm for Fast Multi-Coil Blind Compressed Sensing MRI reconstruction
Bhave, Sampada; Lingala, Sajan Goud; Jacob, Mathews
2015-01-01
Recent work on blind compressed sensing (BCS) has shown that exploiting sparsity in dictionaries that are learnt directly from the data at hand can outperform compressed sensing (CS) that uses fixed dictionaries. A challenge with BCS however is the large computational complexity during its optimization, which limits its practical use in several MRI applications. In this paper, we propose a novel optimization algorithm that utilize variable splitting strategies to significantly improve the convergence speed of the BCS optimization. The splitting allows us to efficiently decouple the sparse coefficient, and dictionary update steps from the data fidelity term, resulting in subproblems that take closed form analytical solutions, which otherwise require slower iterative conjugate gradient algorithms. Through experiments on multi coil parametric MRI data, we demonstrate the superior performance of BCS, while achieving convergence speed up factors of over 15 fold over the previously proposed implementation of the BCS algorithm. PMID:25570473
Rain detection and removal algorithm using motion-compensated non-local mean filter
NASA Astrophysics Data System (ADS)
Song, B. C.; Seo, S. J.
2015-03-01
This paper proposed a novel rain detection and removal algorithm robust against camera motions. It is very difficult to detect and remove rain in video with camera motion. So, most previous works assume that camera is fixed. However, these methods are not useful for application. The proposed algorithm initially detects possible rain streaks by using spatial properties such as luminance and structure of rain streaks. Then, the rain streak candidates are selected based on Gaussian distribution model. Next, a non-rain block matching algorithm is performed between adjacent frames to find similar blocks to each including rain pixels. If the similar blocks to the block are obtained, the rain region of the block is reconstructed by non-local mean (NLM) filtering using the similar neighbors. Experimental results show that the proposed method outperforms previous works in terms of objective and subjective visual quality.
Sound practices for consistent human visual inspection.
Melchore, James A
2011-03-01
Numerous presentations and articles on manual inspection of pharmaceutical drug products have been released, since the pioneering articles on inspection by Knapp and associates Knapp and Kushner (J Parenter Drug Assoc 34:14, 1980); Knapp and Kushner (Bull Parenter Drug Assoc 34:369, 1980); Knapp and Kushner (J Parenter Sci Technol 35:176, 1981); Knapp and Kushner (J Parenter Sci Technol 37:170, 1983). This original work by Knapp and associates provided the industry with a statistical means of evaluating inspection performance. This methodology enabled measurement of individual inspector performance, performance of the entire inspector pool and provided basic suggestions for the conduct of manual inspection. Since that time, numerous subject matter experts (SMEs) have presented additional valuable information for the conduct of manual inspection Borchert et al. (J Parenter Sci Technol 40:212, 1986); Knapp and Abramson (J Parenter Sci Technol 44:74, 1990); Shabushnig et al. (1994); Knapp (1999); Knapp (2005); Cherris (2005); Budd (2005); Barber and Thomas (2005); Knapp (2005); Melchore (2007); Leversee and Ronald (2007); Melchore (2009); Budd (2007); Borchert et al. (1986); Berdovich (2005); Berdovich (2007); Knapp (2007); Leversee and Shabushing (2009); Budd (2009). Despite this abundance of knowledge, neither government regulations nor the multiple compendia provide more than minimal guidance or agreement for the conduct of manual inspection. One has to search the literature for useful information that has been published by SMEs in the field of Inspection. The purpose of this article is to restate the sound principles proclaimed by SMEs with the hope that they serve as a useful guideline to bring greater consistency to the conduct of manual inspection.
Consistent scaling of persistence time in metapopulations.
Yaari, Gur; Ben-Zion, Yossi; Shnerb, Nadav M; Vasseur, David A
2012-05-01
Recent theory and experimental work in metapopulations and metacommunities demonstrates that long-term persistence is maximized when the rate at which individuals disperse among patches within the system is intermediate; if too low, local extinctions are more frequent than recolonizations, increasing the chance of regional-scale extinctions, and if too high, dynamics exhibit region-wide synchrony, and local extinctions occur in near unison across the region. Although common, little is known about how the size and topology of the metapopulation (metacommunity) affect this bell-shaped relationship between dispersal rate and regional persistence time. Using a suite of mathematical models, we examined the effects of dispersal, patch number, and topology on the regional persistence time when local populations are subject to demographic stochasticity. We found that the form of the relationship between regional persistence time and the number of patches is consistent across all models studied; however, the form of the relationship is distinctly different among low, intermediate, and high dispersal rates. Under low and intermediate dispersal rates, regional persistence times increase logarithmically and exponentially (respectively) with increasing numbers of patches, whereas under high dispersal, the form of the relationship depends on local dynamics. Furthermore, we demonstrate that the forms of these relationships, which give rise to the bell-shaped relationship between dispersal rate and persistence time, are a product of recolonization and the region-wide synchronization (or lack thereof) of population dynamics. Identifying such metapopulation attributes that impact extinction risk is of utmost importance for managing and conserving the earth's evermore fragmented populations.
Comparative exoplanetology with consistent retrieval methods
NASA Astrophysics Data System (ADS)
Barstow, Joanna Katy; Aigrain, Suzanne; Irwin, Patrick Gerard Joseph; Sing, David
2016-10-01
The number of hot Jupiters with broad wavelength spectroscopic data has finally become large enough to make comparative planetology a reasonable proposition. New results presented by Sing et al. (2016) showcase ten hot Jupiters with spectra from the Hubble Space Telescope and photometry from Spitzer, providing insights into the presence of clouds and hazes.Spectral retrieval methods allow interpretation of exoplanet spectra using simple models, with minimal prior assumptions. This is particularly useful for exotic exoplanets, for which we may not yet fully understand the physical processes responsible for their atmospheric characteristics. Consistent spectral retrieval of a range of exoplanets can allow robust comparisons of their derived atmospheric properties.I will present a retrieval analysis using the NEMESIS code (Irwin et al. 2008) of the ten hot Jupiter spectra presented by Sing et al. (2016). The only distinctive aspects of the model for each planet are the mass and radius, and the temperature range explored. All other a priori model parameters are common to all ten objects. We test a range of cloud and haze scenarios, which include: Rayleigh-dominated and grey clouds; different cloud top pressures; and both vertically extended and vertically confined clouds.All ten planets, with the exception of WASP-39b, can be well represented by models with at least some haze or cloud. Our analysis of cloud properties has uncovered trends in cloud top pressure, vertical extent and particle size with planet equilibrium temperature. Taken together, we suggest that these trends indicate condensation and sedimentation of at least two different cloud species across planets of different temperatures, with condensates forming higher up in hotter atmospheres and moving progressively further down in cooler planets.Sing, D. et al. (2016), Nature, 529, 59Irwin, P. G. J. et al. (2008), JQSRT, 109, 1136
View from Europe: stability, consistency or pragmatism
Dunster, H.J.
1988-08-01
The last few years of this decade look like a period of reappraisal of radiation protection standards. The revised risk estimates from Japan will be available, and the United Nations Scientific Committee on the Effects of Atomic Radiation will be publishing new reports on biological topics. The International Commission on Radiological Protection (ICRP) has started a review of its basic recommendations, and the new specification for dose equivalent in radiation fields of the International Commission on Radiation Units and Measurements (ICRU) will be coming into use. All this is occurring at a time when some countries are still trying to catch up with committed dose equivalent and the recently recommended change in the value of the quality factor for neutrons. In Europe, the problems of adapting to new ICRP recommendations are considerable. The European Community, including 12 states and nine languages, takes ICRP recommendations as a basis and develops council directives that are binding on member states, which have then to arrange for their own regulatory changes. Any substantial adjustments could take 5 y or more to work through the system. Clearly, the regulatory preference is for stability. Equally clearly, trade unions and public interest groups favor a rapid response to scientific developments (provided that the change is downward). Organizations such as the ICRP have to balance their desire for internal consistency and intellectual purity against the practical problems of their clients in adjusting to change. This paper indicates some of the changes that might be necessary over the next few years and how, given a pragmatic approach, they might be accommodated in Europe without too much regulatory confusion.
Improving electrofishing catch consistency by standardizing power
Burkhardt, Randy W.; Gutreuter, Steve
1995-01-01
The electrical output of electrofishing equipment is commonly standardized by using either constant voltage or constant amperage, However, simplified circuit and wave theories of electricity suggest that standardization of power (wattage) available for transfer from water to fish may be critical for effective standardization of electrofishing. Electrofishing with standardized power ensures that constant power is transferable to fish regardless of water conditions. The in situ performance of standardized power output is poorly known. We used data collected by the interagency Long Term Resource Monitoring Program (LTRMP) in the upper Mississippi River system to assess the effectiveness of standardizing power output. The data consisted of 278 electrofishing collections, comprising 9,282 fishes in eight species groups, obtained during 1990 from main channel border, backwater, and tailwater aquatic areas in four reaches of the upper Mississippi River and one reach of the Illinois River. Variation in power output explained an average of 14.9% of catch variance for night electrofishing and 12.1 % for day electrofishing. Three patterns in catch per unit effort were observed for different species: increasing catch with increasing power, decreasing catch with increasing power, and no power-related pattern. Therefore, in addition to reducing catch variation, controlling power output may provide some capability to select particular species. The LTRMP adopted standardized power output beginning in 1991; standardized power output is adjusted for variation in water conductivity and water temperature by reference to a simple chart. Our data suggest that by standardizing electrofishing power output, the LTRMP has eliminated substantial amounts of catch variation at virtually no additional cost.
Optimized Laplacian image sharpening algorithm based on graphic processing unit
NASA Astrophysics Data System (ADS)
Ma, Tinghuai; Li, Lu; Ji, Sai; Wang, Xin; Tian, Yuan; Al-Dhelaan, Abdullah; Al-Rodhaan, Mznah
2014-12-01
In classical Laplacian image sharpening, all pixels are processed one by one, which leads to large amount of computation. Traditional Laplacian sharpening processed on CPU is considerably time-consuming especially for those large pictures. In this paper, we propose a parallel implementation of Laplacian sharpening based on Compute Unified Device Architecture (CUDA), which is a computing platform of Graphic Processing Units (GPU), and analyze the impact of picture size on performance and the relationship between the processing time of between data transfer time and parallel computing time. Further, according to different features of different memory, an improved scheme of our method is developed, which exploits shared memory in GPU instead of global memory and further increases the efficiency. Experimental results prove that two novel algorithms outperform traditional consequentially method based on OpenCV in the aspect of computing speed.
An Intelligent Model for Pairs Trading Using Genetic Algorithms
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236
Visual tracking method based on cuckoo search algorithm
NASA Astrophysics Data System (ADS)
Gao, Ming-Liang; Yin, Li-Ju; Zou, Guo-Feng; Li, Hai-Tao; Liu, Wei
2015-07-01
Cuckoo search (CS) is a new meta-heuristic optimization algorithm that is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. It has been found to be efficient in solving global optimization problems. An application of CS is presented to solve the visual tracking problem. The relationship between optimization and visual tracking is comparatively studied and the parameters' sensitivity and adjustment of CS in the tracking system are experimentally studied. To demonstrate the tracking ability of a CS-based tracker, a comparative study of tracking accuracy and speed of the CS-based tracker with six "state-of-art" trackers, namely, particle filter, meanshift, PSO, ensemble tracker, fragments tracker, and compressive tracker are presented. Comparative results show that the CS-based tracker outperforms the other trackers.
An Intelligent Model for Pairs Trading Using Genetic Algorithms.
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236
Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm
NASA Technical Reports Server (NTRS)
Oyama, Akira; Liou, Meng-Sing
2001-01-01
A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.
The Dropout Learning Algorithm
Baldi, Pierre; Sadowski, Peter
2014-01-01
Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analysis of the ensemble averaging properties of dropout in linear networks, which is useful to understand the non-linear case. The ensemble averaging properties of dropout in non-linear logistic networks result from three fundamental equations: (1) the approximation of the expectations of logistic functions by normalized geometric means, for which bounds and estimates are derived; (2) the algebraic equality between normalized geometric means of logistic functions with the logistic of the means, which mathematically characterizes logistic functions; and (3) the linearity of the means with respect to sums, as well as products of independent variables. The results are also extended to other classes of transfer functions, including rectified linear functions. Approximation errors tend to cancel each other and do not accumulate. Dropout can also be connected to stochastic neurons and used to predict firing rates, and to backpropagation by viewing the backward propagation as ensemble averaging in a dropout linear network. Moreover, the convergence properties of dropout can be understood in terms of stochastic gradient descent. Finally, for the regularization properties of dropout, the expectation of the dropout gradient is the gradient of the corresponding approximation ensemble, regularized by an adaptive weight decay term with a propensity for self-consistent variance minimization and sparse representations. PMID:24771879
Totally parallel multilevel algorithms
NASA Technical Reports Server (NTRS)
Frederickson, Paul O.
1988-01-01
Four totally parallel algorithms for the solution of a sparse linear system have common characteristics which become quite apparent when they are implemented on a highly parallel hypercube such as the CM2. These four algorithms are Parallel Superconvergent Multigrid (PSMG) of Frederickson and McBryan, Robust Multigrid (RMG) of Hackbusch, the FFT based Spectral Algorithm, and Parallel Cyclic Reduction. In fact, all four can be formulated as particular cases of the same totally parallel multilevel algorithm, which are referred to as TPMA. In certain cases the spectral radius of TPMA is zero, and it is recognized to be a direct algorithm. In many other cases the spectral radius, although not zero, is small enough that a single iteration per timestep keeps the local error within the required tolerance.
Evaluating and comparing algorithms for respiratory motion prediction
NASA Astrophysics Data System (ADS)
Ernst, F.; Dürichen, R.; Schlaefer, A.; Schweikard, A.
2013-06-01
In robotic radiosurgery, it is necessary to compensate for systematic latencies arising from target tracking and mechanical constraints. This compensation is usually achieved by means of an algorithm which computes the future target position. In most scientific works on respiratory motion prediction, only one or two algorithms are evaluated on a limited amount of very short motion traces. The purpose of this work is to gain more insight into the real world capabilities of respiratory motion prediction methods by evaluating many algorithms on an unprecedented amount of data. We have evaluated six algorithms, the normalized least mean squares (nLMS), recursive least squares (RLS), multi-step linear methods (MULIN), wavelet-based multiscale autoregression (wLMS), extended Kalman filtering, and ε-support vector regression (SVRpred) methods, on an extensive database of 304 respiratory motion traces. The traces were collected during treatment with the CyberKnife (Accuray, Inc., Sunnyvale, CA, USA) and feature an average length of 71 min. Evaluation was done using a graphical prediction toolkit, which is available to the general public, as is the data we used. The experiments show that the nLMS algorithm—which is one of the algorithms currently used in the CyberKnife—is outperformed by all other methods. This is especially true in the case of the wLMS, the SVRpred, and the MULIN algorithms, which perform much better. The nLMS algorithm produces a relative root mean square (RMS) error of 75% or less (i.e., a reduction in error of 25% or more when compared to not doing prediction) in only 38% of the test cases, whereas the MULIN and SVRpred methods reach this level in more than 77%, the wLMS algorithm in more than 84% of the test cases. Our work shows that the wLMS algorithm is the most accurate algorithm and does not require parameter tuning, making it an ideal candidate for clinical implementation. Additionally, we have seen that the structure of a patient
Consistent probabilistic outputs for protein function prediction
Obozinski, Guillaume; Lanckriet, Gert; Grant, Charles; Jordan, Michael I; Noble, William Stafford
2008-01-01
In predicting hierarchical protein function annotations, such as terms in the Gene Ontology (GO), the simplest approach makes predictions for each term independently. However, this approach has the unfortunate consequence that the predictor may assign to a single protein a set of terms that are inconsistent with one another; for example, the predictor may assign a specific GO term to a given protein ('purine nucleotide binding') but not assign the parent term ('nucleotide binding'). Such predictions are difficult to interpret. In this work, we focus on methods for calibrating and combining independent predictions to obtain a set of probabilistic predictions that are consistent with the topology of the ontology. We call this procedure 'reconciliation'. We begin with a baseline method for predicting GO terms from a collection of data types using an ensemble of discriminative classifiers. We apply the method to a previously described benchmark data set, and we demonstrate that the resulting predictions are frequently inconsistent with the topology of the GO. We then consider 11 distinct reconciliation methods: three heuristic methods; four variants of a Bayesian network; an extension of logistic regression to the structured case; and three novel projection methods - isotonic regression and two variants of a Kullback-Leibler projection method. We evaluate each method in three different modes - per term, per protein and joint - corresponding to three types of prediction tasks. Although the principal goal of reconciliation is interpretability, it is important to assess whether interpretability comes at a cost in terms of precision and recall. Indeed, we find that many apparently reasonable reconciliation methods yield reconciled probabilities with significantly lower precision than the original, unreconciled estimates. On the other hand, we find that isotonic regression usually performs better than the underlying, unreconciled method, and almost never performs worse
NASA Astrophysics Data System (ADS)
Long, Kim Chenming
application of the proposed algorithm, TSEA, with several state-of-the-art multiobjective optimization algorithms reveals that TSEA outperforms these algorithms by providing retrofit solutions with greater reliability for the same costs (i.e., closer to the Pareto-optimal front) after the algorithms are executed for the same number of generations. This research also demonstrates that TSEA competes with and, in some situations, outperforms state-of-the-art multiobjective optimization algorithms such as NSGA II and SPEA 2 when applied to classic bicriteria test problems in the technical literature and other complex, sizable real-world applications. The successful implementation of TSEA contributes to the safety of aeronautical structures by providing a systematic way to guide aircraft structural retrofitting efforts, as well as a potentially useful algorithm for a wide range of multiobjective optimization problems in engineering and other fields.
Study of mass consistency LES/FDF techniques for chemically reacting flows
NASA Astrophysics Data System (ADS)
Celis, Cesar; Figueira da Silva, Luís Fernando
2015-07-01
A hybrid large eddy simulation/filtered density function (LES/FDF) approach is used for studying chemically reacting flows with detailed chemistry. In particular, techniques utilised for ensuring a mass consistent coupling between LES and FDF are discussed. The purpose of these techniques is to maintain a correct spatial distribution of the computational particles representing specified amounts of fluid. A particular mass consistency technique due to Y.Z. Zhang and D.C. Haworth (A general mass consistency algorithm for hybrid particle/finite-volume PDF methods, J. Comput. Phys. 194 (2004), pp. 156-193) and their associated algorithms are implemented in a pressure-based computational fluid dynamics code suitable for the simulation of variable density flows, representative of those encountered in actual combustion applications. To assess the effectiveness of the referenced technique for enforcing LES/FDF mass consistency, two- and three-dimensional simulations of a temporal mixing layer using detailed and reduced chemistry mechanisms are carried out. The parametric analysis performed focuses on determining the influence on the level of mass consistency errors of parameters such as the initial number of particles per cell and the initial density ratio of the mixing layers. Particular emphasis is put on the computational burden that represents the use of such a mass consistency technique. The results show the suitability of this type of technique for ensuring the mass consistency required when utilising hybrid LES/FDF approaches. The level of agreement of the computed results with experimental data is also illustrated.
Multi-Modal Robust Inverse-Consistent Linear Registration
Wachinger, Christian; Golland, Polina; Magnain, Caroline; Fischl, Bruce; Reuter, Martin
2016-01-01
Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for crossmodal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss–Newton update and show that it is equivalent to the efficient secondorder minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images. PMID:25470798
A new mixed self-consistent field procedure
NASA Astrophysics Data System (ADS)
Alvarez-Ibarra, A.; Köster, A. M.
2015-10-01
A new approach for the calculation of three-centre electronic repulsion integrals (ERIs) is developed, implemented and benchmarked in the framework of auxiliary density functional theory (ADFT). The so-called mixed self-consistent field (mixed SCF) divides the computationally costly ERIs in two sets: far-field and near-field. Far-field ERIs are calculated using the newly developed double asymptotic expansion as in the direct SCF scheme. Near-field ERIs are calculated only once prior to the SCF procedure and stored in memory, as in the conventional SCF scheme. Hence the name, mixed SCF. The implementation is particularly powerful when used in parallel architectures, since all RAM available are used for near-field ERI storage. In addition, the efficient distribution algorithm performs minimal intercommunication operations between processors, avoiding a potential bottleneck. One-, two- and three-dimensional systems are used for benchmarking, showing substantial time reduction in the ERI calculation for all of them. A Born-Oppenheimer molecular dynamics calculation for the Na+55 cluster is also shown in order to demonstrate the speed-up for small systems achievable with the mixed SCF. Dedicated to Sourav Pal on the occasion of his 60th birthday.
Local multiplicative Schwarz algorithms for convection-diffusion equations
NASA Technical Reports Server (NTRS)
Cai, Xiao-Chuan; Sarkis, Marcus
1995-01-01
We develop a new class of overlapping Schwarz type algorithms for solving scalar convection-diffusion equations discretized by finite element or finite difference methods. The preconditioners consist of two components, namely, the usual two-level additive Schwarz preconditioner and the sum of some quadratic terms constructed by using products of ordered neighboring subdomain preconditioners. The ordering of the subdomain preconditioners is determined by considering the direction of the flow. We prove that the algorithms are optimal in the sense that the convergence rates are independent of the mesh size, as well as the number of subdomains. We show by numerical examples that the new algorithms are less sensitive to the direction of the flow than either the classical multiplicative Schwarz algorithms, and converge faster than the additive Schwarz algorithms. Thus, the new algorithms are more suitable for fluid flow applications than the classical additive or multiplicative Schwarz algorithms.
CARVE--a constructive algorithm for real-valued examples.
Young, S; Downs, T
1998-01-01
A constructive neural-network algorithm is presented. For any consistent classification task on real-valued training vectors, the algorithm constructs a feedforward network with a single hidden layer of threshold units which implements the task. The algorithm, which we call CARVE, extends the "sequential learning" algorithm of Marchand et al. from Boolean inputs to the real-valued input case, and uses convex hull methods for the determination of the network weights. The algorithm is an efficient training scheme for producing near-minimal network solutions for arbitrary classification tasks. The algorithm is applied to a number of benchmark problems including Gorman and Sejnowski's sonar data, the Monks problems and Fisher's iris data. A significant application of the constructive algorithm is in providing an initial network topology and initial weights for other neural-network training schemes and this is demonstrated by application to backpropagation.
Image watermarking using a dynamically weighted fuzzy c-means algorithm
NASA Astrophysics Data System (ADS)
Kang, Myeongsu; Ho, Linh Tran; Kim, Yongmin; Kim, Cheol Hong; Kim, Jong-Myon
2011-10-01
Digital watermarking has received extensive attention as a new method of protecting multimedia content from unauthorized copying. In this paper, we present a nonblind watermarking system using a proposed dynamically weighted fuzzy c-means (DWFCM) technique combined with discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD) techniques for copyright protection. The proposed scheme efficiently selects blocks in which the watermark is embedded using new membership values of DWFCM as the embedding strength. We evaluated the proposed algorithm in terms of robustness against various watermarking attacks and imperceptibility compared to other algorithms [DWT-DCT-based and DCT- fuzzy c-means (FCM)-based algorithms]. Experimental results indicate that the proposed algorithm outperforms other algorithms in terms of robustness against several types of attacks, such as noise addition (Gaussian noise, salt and pepper noise), rotation, Gaussian low-pass filtering, mean filtering, median filtering, Gaussian blur, image sharpening, histogram equalization, and JPEG compression. In addition, the proposed algorithm achieves higher values of peak signal-to-noise ratio (approximately 49 dB) and lower values of measure-singular value decomposition (5.8 to 6.6) than other algorithms.
Logeswaran, Rajasvaran; Chen, Li-Choo
2008-12-01
Service architectures are necessary for providing value-added services in telecommunications networks, including those in medical institutions. Separation of service logic and control from the actual call switching is the main idea of these service architectures, examples include Intelligent Network (IN), Telecommunications Information Network Architectures (TINA), and Open Service Access (OSA). In the Distributed Service Architectures (DSA), instances of the same object type can be placed on different physical nodes. Hence, the network performance can be enhanced by introducing load balancing algorithms to efficiently distribute the traffic between object instances, such that the overall throughput and network performance can be optimised. In this paper, we propose a new load balancing algorithm called "Node Status Algorithm" for DSA infrastructure applicable to electronic-based medical institutions. The simulation results illustrate that this proposed algorithm is able to outperform the benchmark load balancing algorithms-Random Algorithm and Shortest Queue Algorithm, especially under medium and heavily loaded network conditions, which are typical of the increasing bandwidth utilization and processing requirements at paperless hospitals and in the telemedicine environment.
A heuristic approach based on Clarke-Wright algorithm for open vehicle routing problem.
Pichpibul, Tantikorn; Kawtummachai, Ruengsak
2013-01-01
We propose a heuristic approach based on the Clarke-Wright algorithm (CW) to solve the open version of the well-known capacitated vehicle routing problem in which vehicles are not required to return to the depot after completing service. The proposed CW has been presented in four procedures composed of Clarke-Wright formula modification, open-route construction, two-phase selection, and route postimprovement. Computational results show that the proposed CW is competitive and outperforms classical CW in all directions. Moreover, the best known solution is also obtained in 97% of tested instances (60 out of 62).
A Practical Stemming Algorithm for Online Search Assistance.
ERIC Educational Resources Information Center
Ulmschneider, John E.; Doszkocs, Tamas
1983-01-01
Describes a two-phase stemming algorithm which consists of word root identification and automatic selection of word variants starting with same word root from inverted file. Use of algorithm in book catalog file is discussed. Ten references and example of subject search are appended. (EJS)
Gravitation field algorithm and its application in gene cluster
2010-01-01
Background Searching optima is one of the most challenging tasks in clustering genes from available experimental data or given functions. SA, GA, PSO and other similar efficient global optimization methods are used by biotechnologists. All these algorithms are based on the imitation of natural phenomena. Results This paper proposes a novel searching optimization algorithm called Gravitation Field Algorithm (GFA) which is derived from the famous astronomy theory Solar Nebular Disk Model (SNDM) of planetary formation. GFA simulates the Gravitation field and outperforms GA and SA in some multimodal functions optimization problem. And GFA also can be used in the forms of unimodal functions. GFA clusters the dataset well from the Gene Expression Omnibus. Conclusions The mathematical proof demonstrates that GFA could be convergent in the global optimum by probability 1 in three conditions for one independent variable mass functions. In addition to these results, the fundamental optimization concept in this paper is used to analyze how SA and GA affect the global search and the inherent defects in SA and GA. Some results and source code (in Matlab) are publicly available at http://ccst.jlu.edu.cn/CSBG/GFA. PMID:20854683
An Evolved Wavelet Library Based on Genetic Algorithm
Vaithiyanathan, D.; Seshasayanan, R.; Kunaraj, K.; Keerthiga, J.
2014-01-01
As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31 dB improvement in the average PSNR and a 0.39 dB improvement in the maximum PSNR. PMID:25405225
NASA Astrophysics Data System (ADS)
Zheng, Jun-Xi; Zhang, Ping; Li, Fang; Du, Guang-Long
2016-09-01
Although the sequence-dependent setup times flowshop problem with the total weighted tardiness minimization objective exists widely in industry, work on the problem has been scant in the existing literature. To the authors' best knowledge, the NEH?EWDD heuristic and the Iterated Greedy (IG) algorithm with descent local search have been regarded as the high performing heuristic and the state-of-the-art algorithm for the problem, which are both based on insertion search. In this article firstly, an efficient backtracking algorithm and a novel heuristic (HPIS) are presented for insertion search. Accordingly, two heuristics are introduced, one is NEH?EWDD with HPIS for insertion search, and the other is the combination of NEH?EWDD and both the two methods. Furthermore, the authors improve the IG algorithm with the proposed methods. Finally, experimental results show that both the proposed heuristics and the improved IG (IG*) significantly outperform the original ones.
Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; Ukkusuri, Satish V.
2015-01-31
Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plans in terms of average delay, number of stops, and vehicular emissions at the network level.
NASA Astrophysics Data System (ADS)
de Lamare, Rodrigo C.; Diniz, Paulo S. R.
2013-03-01
This work presents blind constrained constant modulus (CCM) adaptive algorithms based on the set-membership filtering (SMF) concept and incorporates dynamic bounds {for interference suppression} applications. We develop stochastic gradient and recursive least squares type algorithms based on the CCM design criterion in accordance with the specifications of the SMF concept. We also propose a blind framework that includes channel and amplitude estimators that take into account parameter estimation dependency, multiple access interference (MAI) and inter-symbol interference (ISI) to address the important issue of bound specification in multiuser communications. A convergence and tracking analysis of the proposed algorithms is carried out along with the development of analytical expressions to predict their performance. Simulations for a number of scenarios of interest with a DS-CDMA system show that the proposed algorithms outperform previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or underbounding.
Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; Ukkusuri, Satish V.
2015-01-31
Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less
Rempp, Florian; Mahler, Guenter; Michel, Mathias
2007-09-15
We introduce a scheme to perform the cooling algorithm, first presented by Boykin et al. in 2002, for an arbitrary number of times on the same set of qbits. We achieve this goal by adding an additional SWAP gate and a bath contact to the algorithm. This way one qbit may repeatedly be cooled without adding additional qbits to the system. By using a product Liouville space to model the bath contact we calculate the density matrix of the system after a given number of applications of the algorithm.
NASA Technical Reports Server (NTRS)
Chan, Hak-Wai; Yan, Tsun-Yee
1989-01-01
Algorithm developed for optimal routing of packets of data along links of multilink, multinode digital communication network. Algorithm iterative and converges to cost-optimal assignment independent of initial assignment. Each node connected to other nodes through links, each containing number of two-way channels. Algorithm assigns channels according to message traffic leaving and arriving at each node. Modified to take account of different priorities among packets belonging to different users by using different delay constraints or imposing additional penalties via cost function.
NASA Astrophysics Data System (ADS)
Ahmed, Yasser A.; Afifi, Hossam; Rubino, Gerardo
1999-05-01
This paper present a new algorithm for stereo matching. The main idea is to decompose the original problem into independent hierarchical and more elementary problems that can be solved faster without any complicated mathematics using BBD. To achieve that, we use a new image feature called 'continuity feature' instead of classical noise. This feature can be extracted from any kind of images by a simple process and without using a searching technique. A new matching technique is proposed to match the continuity feature. The new algorithm resolves the main disadvantages of feature based stereo matching algorithms.
On consistent mapping in distributed environments using mobile sensors
NASA Astrophysics Data System (ADS)
Saha, Roshmik
The problem of robotic mapping, also known as simultaneous localization and mapping (SLAM), by a mobile agent for large distributed environments is addressed in this dissertation. This has sometimes been referred to as the holy grail in the robotics community, and is the stepping stone towards making a robot completely autonomous. A hybrid solution to the SLAM problem is proposed based on "first localize then map" principle. It is provably consistent and has great potential for real time application. It provides significant improvements over state-of-the-art Bayesian approaches by reducing the computational complexity of the SLAM problem without sacrificing consistency. The localization is achieved using a feature based extended Kalman filter (EKF) which utilizes a sparse set of reliable features. The common issues of data association, loop closure and computational cost of EKF based methods are kept tractable owing to the sparsity of the feature set. A novel frequentist mapping technique is proposed for estimating the dense part of the environment using the sensor observations. Given the pose estimate of the robot, this technique can consistently map the surrounding environment. The technique has linear time complexity in map components and for the case of bounded sensor noise, it is shown that the frequentist mapping technique has constant time complexity which makes it capable of estimating large distributed environments in real time. The frequentist mapping technique is a stochastic approximation algorithm and is shown to converge to the true map probabilities almost surely. The Hybrid SLAM software is developed in the C-language and is capable of handling real experimental data as well as simulations. The Hybrid SLAM technique is shown to perform well in simulations, experiments with an iRobot Create, and on standard datasets from the Robotics Data Set Repository, known as Radish. It is demonstrated that the Hy··id SLAM technique can successfully map large
Age consistency between exoplanet hosts and field stars
NASA Astrophysics Data System (ADS)
Bonfanti, A.; Ortolani, S.; Nascimbeni, V.
2016-01-01
Context. Transiting planets around stars are discovered mostly through photometric surveys. Unlike radial velocity surveys, photometric surveys do not tend to target slow rotators, inactive or metal-rich stars. Nevertheless, we suspect that observational biases could also impact transiting-planet hosts. Aims: This paper aims to evaluate how selection effects reflect on the evolutionary stage of both a limited sample of transiting-planet host stars (TPH) and a wider sample of planet-hosting stars detected through radial velocity analysis. Then, thanks to uniform derivation of stellar ages, a homogeneous comparison between exoplanet hosts and field star age distributions is developed. Methods: Stellar parameters have been computed through our custom-developed isochrone placement algorithm, according to Padova evolutionary models. The notable aspects of our algorithm include the treatment of element diffusion, activity checks in terms of log{R'HK} and vsini, and the evaluation of the stellar evolutionary speed in the Hertzsprung-Russel diagram in order to better constrain age. Working with TPH, the observational stellar mean density ρ⋆ allows us to compute stellar luminosity even if the distance is not available, by combining ρ⋆ with the spectroscopic log g. Results: The median value of the TPH ages is 5 Gyr. Even if this sample is not very large, however the result is very similar to what we found for the sample of spectroscopic hosts, whose modal and median values are [3, 3.5) Gyr and 4.8 Gyr, respectively. Thus, these stellar samples suffer almost the same selection effects. An analysis of MS stars of the solar neighbourhood belonging to the same spectral types bring to an age distribution similar to the previous ones and centered around solar age value. Therefore, the age of our Sun is consistent with the age distribution of solar neighbourhood stars with spectral types from late F to early K, regardless of whether they harbour planets or not. We considered
Contrast-Based 3D/2D Registration of the Left Atrium: Fast versus Consistent
Kowalewski, Christopher; Kurzidim, Klaus; Strobel, Norbert; Hornegger, Joachim
2016-01-01
For augmented fluoroscopy during cardiac ablation, a preoperatively acquired 3D model of a patient's left atrium (LA) can be registered to X-ray images recorded during a contrast agent (CA) injection. An automatic registration method that works also for small amounts of CA is desired. We propose two similarity measures: The first focuses on edges of the patient anatomy. The second computes a contrast agent distribution estimate (CADE) inside the 3D model and rates its consistency with the CA as seen in biplane fluoroscopic images. Moreover, temporal filtering on the obtained registration results of a sequence is applied using a Markov chain framework. Evaluation was performed on 11 well-contrasted clinical angiographic sequences and 10 additional sequences with less CA. For well-contrasted sequences, the error for all 73 frames was 7.9 ± 6.3 mm and it dropped to 4.6 ± 4.0 mm when registering to an automatically selected, well enhanced frame in each sequence. Temporal filtering reduced the error for all frames from 7.9 ± 6.3 mm to 5.7 ± 4.6 mm. The error was typically higher if less CA was used. A combination of both similarity measures outperforms a previously proposed similarity measure. The mean accuracy for well contrasted sequences is in the range of other proposed manual registration methods. PMID:27051412
Parallel asynchronous systems and image processing algorithms
NASA Technical Reports Server (NTRS)
Coon, D. D.; Perera, A. G. U.
1989-01-01
A new hardware approach to implementation of image processing algorithms is described. The approach is based on silicon devices which would permit an independent analog processing channel to be dedicated to evey pixel. A laminar architecture consisting of a stack of planar arrays of the device would form a two-dimensional array processor with a 2-D array of inputs located directly behind a focal plane detector array. A 2-D image data stream would propagate in neuronlike asynchronous pulse coded form through the laminar processor. Such systems would integrate image acquisition and image processing. Acquisition and processing would be performed concurrently as in natural vision systems. The research is aimed at implementation of algorithms, such as the intensity dependent summation algorithm and pyramid processing structures, which are motivated by the operation of natural vision systems. Implementation of natural vision algorithms would benefit from the use of neuronlike information coding and the laminar, 2-D parallel, vision system type architecture. Besides providing a neural network framework for implementation of natural vision algorithms, a 2-D parallel approach could eliminate the serial bottleneck of conventional processing systems. Conversion to serial format would occur only after raw intensity data has been substantially processed. An interesting challenge arises from the fact that the mathematical formulation of natural vision algorithms does not specify the means of implementation, so that hardware implementation poses intriguing questions involving vision science.
Zhou, Yongquan; Xie, Jian; Li, Liangliang; Ma, Mingzhi
2014-01-01
Bat algorithm (BA) is a novel stochastic global optimization algorithm. Cloud model is an effective tool in transforming between qualitative concepts and their quantitative representation. Based on the bat echolocation mechanism and excellent characteristics of cloud model on uncertainty knowledge representation, a new cloud model bat algorithm (CBA) is proposed. This paper focuses on remodeling echolocation model based on living and preying characteristics of bats, utilizing the transformation theory of cloud model to depict the qualitative concept: "bats approach their prey." Furthermore, Lévy flight mode and population information communication mechanism of bats are introduced to balance the advantage between exploration and exploitation. The simulation results show that the cloud model bat algorithm has good performance on functions optimization. PMID:24967425
System engineering approach to GPM retrieval algorithms
Rose, C. R.; Chandrasekar, V.
2004-01-01
System engineering principles and methods are very useful in large-scale complex systems for developing the engineering requirements from end-user needs. Integrating research into system engineering is a challenging task. The proposed Global Precipitation Mission (GPM) satellite will use a dual-wavelength precipitation radar to measure and map global precipitation with unprecedented accuracy, resolution and areal coverage. The satellite vehicle, precipitation radars, retrieval algorithms, and ground validation (GV) functions are all critical subsystems of the overall GPM system and each contributes to the success of the mission. Errors in the radar measurements and models can adversely affect the retrieved output values. Ground validation (GV) systems are intended to provide timely feedback to the satellite and retrieval algorithms based on measured data. These GV sites will consist of radars and DSD measurement systems and also have intrinsic constraints. One of the retrieval algorithms being studied for use with GPM is the dual-wavelength DSD algorithm that does not use the surface reference technique (SRT). The underlying microphysics of precipitation structures and drop-size distributions (DSDs) dictate the types of models and retrieval algorithms that can be used to estimate precipitation. Many types of dual-wavelength algorithms have been studied. Meneghini (2002) analyzed the performance of single-pass dual-wavelength surface-reference-technique (SRT) based algorithms. Mardiana (2003) demonstrated that a dual-wavelength retrieval algorithm could be successfully used without the use of the SRT. It uses an iterative approach based on measured reflectivities at both wavelengths and complex microphysical models to estimate both No and Do at each range bin. More recently, Liao (2004) proposed a solution to the Do ambiguity problem in rain within the dual-wavelength algorithm and showed a possible melting layer model based on stratified spheres. With the No and Do
An Exact Algorithm to Compute the Double-Cut-and-Join Distance for Genomes with Duplicate Genes.
Shao, Mingfu; Lin, Yu; Moret, Bernard M E
2015-05-01
Computing the edit distance between two genomes is a basic problem in the study of genome evolution. The double-cut-and-join (DCJ) model has formed the basis for most algorithmic research on rearrangements over the last few years. The edit distance under the DCJ model can be computed in linear time for genomes without duplicate genes, while the problem becomes NP-hard in the presence of duplicate genes. In this article, we propose an integer linear programming (ILP) formulation to compute the DCJ distance between two genomes with duplicate genes. We also provide an efficient preprocessing approach to simplify the ILP formulation while preserving optimality. Comparison on simulated genomes demonstrates that our method outperforms MSOAR in computing the edit distance, especially when the genomes contain long duplicated segments. We also apply our method to assign orthologous gene pairs among human, mouse, and rat genomes, where once again our method outperforms MSOAR.
2013-07-29
The OpenEIS Algorithm package seeks to provide a low-risk path for building owners, service providers and managers to explore analytical methods for improving building control and operational efficiency. Users of this software can analyze building data, and learn how commercial implementations would provide long-term value. The code also serves as a reference implementation for developers who wish to adapt the algorithms for use in commercial tools or service offerings.
The Superior Lambert Algorithm
NASA Astrophysics Data System (ADS)
der, G.
2011-09-01
Lambert algorithms are used extensively for initial orbit determination, mission planning, space debris correlation, and missile targeting, just to name a few applications. Due to the significance of the Lambert problem in Astrodynamics, Gauss, Battin, Godal, Lancaster, Gooding, Sun and many others (References 1 to 15) have provided numerous formulations leading to various analytic solutions and iterative methods. Most Lambert algorithms and their computer programs can only work within one revolution, break down or converge slowly when the transfer angle is near zero or 180 degrees, and their multi-revolution limitations are either ignored or barely addressed. Despite claims of robustness, many Lambert algorithms fail without notice, and the users seldom have a clue why. The DerAstrodynamics lambert2 algorithm, which is based on the analytic solution formulated by Sun, works for any number of revolutions and converges rapidly at any transfer angle. It provides significant capability enhancements over every other Lambert algorithm in use today. These include improved speed, accuracy, robustness, and multirevolution capabilities as well as implementation simplicity. Additionally, the lambert2 algorithm provides a powerful tool for solving the angles-only problem without artificial singularities (pointed out by Gooding in Reference 16), which involves 3 lines of sight captured by optical sensors, or systems such as the Air Force Space Surveillance System (AFSSS). The analytic solution is derived from the extended Godal’s time equation by Sun, while the iterative method of solution is that of Laguerre, modified for robustness. The Keplerian solution of a Lambert algorithm can be extended to include the non-Keplerian terms of the Vinti algorithm via a simple targeting technique (References 17 to 19). Accurate analytic non-Keplerian trajectories can be predicted for satellites and ballistic missiles, while performing at least 100 times faster in speed than most
Sampling Within k-Means Algorithm to Cluster Large Datasets
Bejarano, Jeremy; Bose, Koushiki; Brannan, Tyler; Thomas, Anita; Adragni, Kofi; Neerchal, Nagaraj; Ostrouchov, George
2011-08-01
Due to current data collection technology, our ability to gather data has surpassed our ability to analyze it. In particular, k-means, one of the simplest and fastest clustering algorithms, is ill-equipped to handle extremely large datasets on even the most powerful machines. Our new algorithm uses a sample from a dataset to decrease runtime by reducing the amount of data analyzed. We perform a simulation study to compare our sampling based k-means to the standard k-means algorithm by analyzing both the speed and accuracy of the two methods. Results show that our algorithm is significantly more efficient than the existing algorithm with comparable accuracy. Further work on this project might include a more comprehensive study both on more varied test datasets as well as on real weather datasets. This is especially important considering that this preliminary study was performed on rather tame datasets. Also, these datasets should analyze the performance of the algorithm on varied values of k. Lastly, this paper showed that the algorithm was accurate for relatively low sample sizes. We would like to analyze this further to see how accurate the algorithm is for even lower sample sizes. We could find the lowest sample sizes, by manipulating width and confidence level, for which the algorithm would be acceptably accurate. In order for our algorithm to be a success, it needs to meet two benchmarks: match the accuracy of the standard k-means algorithm and significantly reduce runtime. Both goals are accomplished for all six datasets analyzed. However, on datasets of three and four dimension, as the data becomes more difficult to cluster, both algorithms fail to obtain the correct classifications on some trials. Nevertheless, our algorithm consistently matches the performance of the standard algorithm while becoming remarkably more efficient with time. Therefore, we conclude that analysts can use our algorithm, expecting accurate results in considerably less time.
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.
Litjens, Geert; Toth, Robert; van de Ven, Wendy; Hoeks, Caroline; Kerkstra, Sjoerd; van Ginneken, Bram; Vincent, Graham; Guillard, Gwenael; Birbeck, Neil; Zhang, Jindang; Strand, Robin; Malmberg, Filip; Ou, Yangming; Davatzikos, Christos; Kirschner, Matthias; Jung, Florian; Yuan, Jing; Qiu, Wu; Gao, Qinquan; Edwards, Philip Eddie; Maan, Bianca; van der Heijden, Ferdinand; Ghose, Soumya; Mitra, Jhimli; Dowling, Jason; Barratt, Dean; Huisman, Henkjan; Madabhushi, Anant
2014-02-01
Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05) and had an efficient implementation with a run time of 8min and 3s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.
Litjens, Geert; Toth, Robert; van de Ven, Wendy; Hoeks, Caroline; Kerkstra, Sjoerd; van Ginneken, Bram; Vincent, Graham; Guillard, Gwenael; Birbeck, Neil; Zhang, Jindang; Strand, Robin; Malmberg, Filip; Ou, Yangming; Davatzikos, Christos; Kirschner, Matthias; Jung, Florian; Yuan, Jing; Qiu, Wu; Gao, Qinquan; Edwards, Philip Eddie; Maan, Bianca; van der Heijden, Ferdinand; Ghose, Soumya; Mitra, Jhimli; Dowling, Jason; Barratt, Dean; Huisman, Henkjan; Madabhushi, Anant
2014-02-01
Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p<0.05) and had an efficient implementation with a run time of 8min and 3s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi
Algorithm for Finding Similar Shapes in Large Molecular Structures Libraries
1994-10-19
The SHAPES software consists of methods and algorithms for representing and rapidly comparing molecular shapes. Molecular shapes algorithms are a class of algorithm derived and applied for recognizing when two three-dimensional shapes share common features. They proceed from the notion that the shapes to be compared are regions in three-dimensional space. The algorithms allow recognition of when localized subregions from two or more different shapes could never be superimposed by any rigid-body motion. Rigid-body motionsmore » are arbitrary combinations of translations and rotations.« less
Molecular Motors: Power Strokes Outperform Brownian Ratchets.
Wagoner, Jason A; Dill, Ken A
2016-07-01
Molecular motors convert chemical energy (typically from ATP hydrolysis) to directed motion and mechanical work. Their actions are often described in terms of "Power Stroke" (PS) and "Brownian Ratchet" (BR) mechanisms. Here, we use a transition-state model and stochastic thermodynamics to describe a range of mechanisms ranging from PS to BR. We incorporate this model into Hill's diagrammatic method to develop a comprehensive model of motor processivity that is simple but sufficiently general to capture the full range of behavior observed for molecular motors. We demonstrate that, under all conditions, PS motors are faster, more powerful, and more efficient at constant velocity than BR motors. We show that these differences are very large for simple motors but become inconsequential for complex motors with additional kinetic barrier steps. PMID:27136319
Hohenstein, Edward G.; Luehr, Nathan; Ufimtsev, Ivan S.; Martínez, Todd J.
2015-06-14
Despite its importance, state-of-the-art algorithms for performing complete active space self-consistent field (CASSCF) computations have lagged far behind those for single reference methods. We develop an algorithm for the CASSCF orbital optimization that uses sparsity in the atomic orbital (AO) basis set to increase the applicability of CASSCF. Our implementation of this algorithm uses graphical processing units (GPUs) and has allowed us to perform CASSCF computations on molecular systems containing more than one thousand atoms. Additionally, we have implemented analytic gradients of the CASSCF energy; the gradients also benefit from GPU acceleration as well as sparsity in the AO basis.
Accuracy and Consistency of Respiratory Gating in Abdominal Cancer Patients
Ge, Jiajia; Santanam, Lakshmi; Yang, Deshan; Parikh, Parag J.
2013-03-01
Purpose: To evaluate respiratory gating accuracy and intrafractional consistency for abdominal cancer patients treated with respiratory gated treatment on a regular linear accelerator system. Methods and Materials: Twelve abdominal patients implanted with fiducials were treated with amplitude-based respiratory-gated radiation therapy. On the basis of daily orthogonal fluoroscopy, the operator readjusted the couch position and gating window such that the fiducial was within a setup margin (fiducial-planning target volume [f-PTV]) when RPM indicated “beam-ON.” Fifty-five pre- and post-treatment fluoroscopic movie pairs with synchronized respiratory gating signal were recorded. Fiducial motion traces were extracted from the fluoroscopic movies using a template matching algorithm and correlated with f-PTV by registering the digitally reconstructed radiographs with the fluoroscopic movies. Treatment was determined to be “accurate” if 50% of the fiducial area stayed within f-PTV while beam-ON. For movie pairs that lost gating accuracy, a MATLAB program was used to assess whether the gating window was optimized, the external-internal correlation (EIC) changed, or the patient moved between movies. A series of safety margins from 0.5 mm to 3 mm was added to f-PTV for reassessing gating accuracy. Results: A decrease in gating accuracy was observed in 44% of movie pairs from daily fluoroscopic movies of 12 abdominal patients. Three main causes for inaccurate gating were identified as change of global EIC over time (∼43%), suboptimal gating setup (∼37%), and imperfect EIC within movie (∼13%). Conclusions: Inconsistent respiratory gating accuracy may occur within 1 treatment session even with a daily adjusted gating window. To improve or maintain gating accuracy during treatment, we suggest using at least a 2.5-mm safety margin to account for gating and setup uncertainties.
PALLONEN, U. E.; WILLIAMS, M. L.; TIMPSON, S. C.; BOWEN, A.; ROSS, M. W.
2010-01-01
Participants’ personal condom use measures and those of their last sex partner’s were examined in five stages of change for consistent condom use among 449 urban sexually active, heterosexual, African–American crack smokers. The measures included participants’ personal and their last sex partner’s perceived responsibility, personal and perceived negative attitudes, and participants’ self-efficacy to use condoms. The relationships between the measures and the stages were examined using analyses of variance and multivariate logistic regression. Over 90% of participants did not use condoms, consistently. Two-thirds of the inconsistent users were in the precontemplation stage. The rest were equally divided between the contemplation and preparation stages. Personal responsibility outperformed other measures in initial intention to become a regular condom user; partner’s perceived responsibility dominated continued intention and actual consistent condom use. Negative attitudes and self-efficacies had strong relationships to the stages of consistent condom use in univariate analyses but these relationships became substantially weaker when the responsibility, attitude, and self-efficacy concepts were entered simultaneously into multivariate analyses. PMID:18293131
NASA Astrophysics Data System (ADS)
Pawl, Andrew; Teodorescu, Raluca E.; Peterson, Joseph D.
2013-12-01
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 has a preference for the incorrect answer and also those that emerge as outliers because the students are randomly changing their responses. These outliers are found to include several statements that are known in the literature to expose student misconceptions. Combining this fact with comments made by students and results of complementary assessments provides evidence that the tendency of a group of students to change their answer to a true or false statement or to remain consistent can serve as indicators of whether the class has understood the relevant concept. Our algorithms enable teachers to employ problems of the type described as a tool to identify specific aspects of a course that require improvement. They also enable researchers to employ such problems in experiments designed to probe aspects of students’ thought processes and behavior. Additionally, our results demonstrate that at least one category of research-inspired problems (ranking tasks) can be adapted to the linked true or false format and productively used as an assessment tool in an online setting.
A MPR optimization algorithm for FSO communication system with star topology
NASA Astrophysics Data System (ADS)
Zhao, Linlin; Chi, Xuefen; Li, Peng; Guan, Lin
2015-12-01
In this paper, we introduce the multi-packet reception (MPR) technology to the outdoor free space optical (FSO) communication system to provide excellent throughput gain. Hence, we address two challenges: how to realize the MPR technology in the varying atmospheric turbulence channel and how to adjust the MPR capability to support as many devices transmitting simultaneously as possible in the system with bit error rate (BER) constraints. Firstly, we explore the reliability ordering with minimum mean square error successive interference cancellation (RO-MMSE-SIC) algorithm to realize the MPR technology in the FSO communication system and derive the closed-form BER expression of the RO-MMSE-SIC algorithm. Then, based on the derived BER expression, we propose the adaptive MPR capability optimization algorithm so that the MPR capability is adapted to different turbulence channel states. Consequently, the excellent throughput gain is obtained in the varying atmospheric channel. The simulation results show that our RO-MMSE-SIC algorithm outperforms the conventional MMSE-SIC algorithm. And the derived exact BER expression is verified by Monte Carlo simulations. The validity and the indispensability of the proposed adaptive MPR capability optimization algorithm are verified as well.
A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers.
Turrado, Concepción Crespo; Sánchez Lasheras, Fernando; Calvo-Rollé, José Luis; Piñón-Pazos, Andrés-José; Melero, Manuel G; de Cos Juez, Francisco Javier
2016-01-01
The storage of data is a key process in the study of electrical power networks related to the search for harmonics and the finding of a lack of balance among phases. The presence of missing data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, current in each phase and power factor) affects any time series study in a negative way that has to be addressed. When this occurs, missing data imputation algorithms are required. These algorithms are able to substitute the data that are missing for estimated values. This research presents a new algorithm for the missing data imputation method based on Self-Organized Maps Neural Networks and Mahalanobis distances and compares it not only with a well-known technique called Multivariate Imputation by Chained Equations (MICE) but also with an algorithm previously proposed by the authors called Adaptive Assignation Algorithm (AAA). The results obtained demonstrate how the proposed method outperforms both algorithms. PMID:27626419
NASA Astrophysics Data System (ADS)
Peña, M.
2016-10-01
Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.
A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers.
Turrado, Concepción Crespo; Sánchez Lasheras, Fernando; Calvo-Rollé, José Luis; Piñón-Pazos, Andrés-José; Melero, Manuel G; de Cos Juez, Francisco Javier
2016-01-01
The storage of data is a key process in the study of electrical power networks related to the search for harmonics and the finding of a lack of balance among phases. The presence of missing data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, current in each phase and power factor) affects any time series study in a negative way that has to be addressed. When this occurs, missing data imputation algorithms are required. These algorithms are able to substitute the data that are missing for estimated values. This research presents a new algorithm for the missing data imputation method based on Self-Organized Maps Neural Networks and Mahalanobis distances and compares it not only with a well-known technique called Multivariate Imputation by Chained Equations (MICE) but also with an algorithm previously proposed by the authors called Adaptive Assignation Algorithm (AAA). The results obtained demonstrate how the proposed method outperforms both algorithms.
A Two-Pass Exact Algorithm for Selection on Parallel Disk Systems
Mi, Tian; Rajasekaran, Sanguthevar
2014-01-01
Numerous OLAP queries process selection operations of “top N”, median, “top 5%”, in data warehousing applications. Selection is a well-studied problem that has numerous applications in the management of data and databases since, typically, any complex data query can be reduced to a series of basic operations such as sorting and selection. The parallel selection has also become an important fundamental operation, especially after parallel databases were introduced. In this paper, we present a deterministic algorithm Recursive Sampling Selection (RSS) to solve the exact out-of-core selection problem, which we show needs no more than (2 + ε) passes (ε being a very small fraction). We have compared our RSS algorithm with two other algorithms in the literature, namely, the Deterministic Sampling Selection and QuickSelect on the Parallel Disks Systems. Our analysis shows that DSS is a (2 + ε)-pass algorithm when the total number of input elements N is a polynomial in the memory size M (i.e., N = Mc for some constant c). While, our proposed algorithm RSS runs in (2 + ε) passes without any assumptions. Experimental results indicate that both RSS and DSS outperform QuickSelect on the Parallel Disks Systems. Especially, the proposed algorithm RSS is more scalable and robust to handle big data when the input size is far greater than the core memory size, including the case of N ≫ Mc. PMID:25374478
An efficient central DOA tracking algorithm for multiple incoherently distributed sources
NASA Astrophysics Data System (ADS)
Hassen, Sonia Ben; Samet, Abdelaziz
2015-12-01
In this paper, we develop a new tracking method for the direction of arrival (DOA) parameters assuming multiple incoherently distributed (ID) sources. The new approach is based on a simple covariance fitting optimization technique exploiting the central and noncentral moments of the source angular power densities to estimate the central DOAs. The current estimates are treated as measurements provided to the Kalman filter that model the dynamic property of directional changes for the moving sources. Then, the covariance-fitting-based algorithm and the Kalman filtering theory are combined to formulate an adaptive tracking algorithm. Our algorithm is compared to the fast approximated power iteration-total least square-estimation of signal parameters via rotational invariance technique (FAPI-TLS-ESPRIT) algorithm using the TLS-ESPRIT method and the subspace updating via FAPI-algorithm. It will be shown that the proposed algorithm offers an excellent DOA tracking performance and outperforms the FAPI-TLS-ESPRIT method especially at low signal-to-noise ratio (SNR) values. Moreover, the performances of the two methods increase as the SNR values increase. This increase is more prominent with the FAPI-TLS-ESPRIT method. However, their performances degrade when the number of sources increases. It will be also proved that our method depends on the form of the angular distribution function when tracking the central DOAs. Finally, it will be shown that the more the sources are spaced, the more the proposed method can exactly track the DOAs.
Yurtkuran, Alkın; Emel, Erdal
2016-01-01
The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature. PMID:26819591
Iyer, Swathi P; Shafran, Izhak; Grayson, David; Gates, Kathleen; Nigg, Joel T; Fair, Damien A
2013-07-15
Resting state functional connectivity MRI (rs-fcMRI) is a popular technique used to gauge the functional relatedness between regions in the brain for typical and special populations. Most of the work to date determines this relationship by using Pearson's correlation on BOLD fMRI timeseries. However, it has been recognized that there are at least two key limitations to this method. First, it is not possible to resolve the direct and indirect connections/influences. Second, the direction of information flow between the regions cannot be differentiated. In the current paper, we follow-up on recent work by Smith et al. (2011), and apply PC algorithm to both simulated data and empirical data to determine whether these two factors can be discerned with group average, as opposed to single subject, functional connectivity data. When applied on simulated individual subjects, the algorithm performs well determining indirect and direct connection but fails in determining directionality. However, when applied at group level, PC algorithm gives strong results for both indirect and direct connections and the direction of information flow. Applying the algorithm on empirical data, using a diffusion-weighted imaging (DWI) structural connectivity matrix as the baseline, the PC algorithm outperformed the direct correlations. We conclude that, under certain conditions, the PC algorithm leads to an improved estimate of brain network structure compared to the traditional connectivity analysis based on correlations.
A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers
Turrado, Concepción Crespo; Sánchez Lasheras, Fernando; Calvo-Rollé, José Luis; Piñón-Pazos, Andrés-José; Melero, Manuel G.; de Cos Juez, Francisco Javier
2016-01-01
The storage of data is a key process in the study of electrical power networks related to the search for harmonics and the finding of a lack of balance among phases. The presence of missing data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, current in each phase and power factor) affects any time series study in a negative way that has to be addressed. When this occurs, missing data imputation algorithms are required. These algorithms are able to substitute the data that are missing for estimated values. This research presents a new algorithm for the missing data imputation method based on Self-Organized Maps Neural Networks and Mahalanobis distances and compares it not only with a well-known technique called Multivariate Imputation by Chained Equations (MICE) but also with an algorithm previously proposed by the authors called Adaptive Assignation Algorithm (AAA). The results obtained demonstrate how the proposed method outperforms both algorithms. PMID:27626419
Yurtkuran, Alkın
2016-01-01
The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature. PMID:26819591
Yurtkuran, Alkın; Emel, Erdal
2016-01-01
The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.
Classifying scaled and rotated textures using a region-matched algorithm
NASA Astrophysics Data System (ADS)
Yao, Chih-Chia; Chen, Yu-Tin
2012-07-01
A novel method to correct texture variations resulting from scale magnification, narrowing caused by cropping into the original size, or spatial rotation is discussed. The variations usually occur in images captured by a camera using different focal lengths. A representative region-matched algorithm is developed to improve texture classification after magnification, narrowing, and spatial rotation. By using a minimum ellipse, a representative region-matched algorithm encloses a specific region extracted by the J-image segmentation algorithm. After translating the coordinates, the equation of an ellipse in the rotated texture can be formulated as that of an ellipse in the original texture. The rotated invariant property of ellipse provides an efficient method to identify the rotated texture. Additionally, the scale-variant representative region can be classified by adopting scale-invariant parameters. Moreover, a hybrid texture filter is developed. In the hybrid texture filter, the scheme of texture feature extraction includes the Gabor wavelet and the representative region-matched algorithm. Support vector machines are introduced as the classifier. The proposed hybrid texture filter performs excellently with respect to classifying both the stochastic and structural textures. Furthermore, experimental results demonstrate that the proposed algorithm outperforms conventional design algorithms.
A Computationally Efficient Mel-Filter Bank VAD Algorithm for Distributed Speech Recognition Systems
NASA Astrophysics Data System (ADS)
Vlaj, Damjan; Kotnik, Bojan; Horvat, Bogomir; Kačič, Zdravko
2005-12-01
This paper presents a novel computationally efficient voice activity detection (VAD) algorithm and emphasizes the importance of such algorithms in distributed speech recognition (DSR) systems. When using VAD algorithms in telecommunication systems, the required capacity of the speech transmission channel can be reduced if only the speech parts of the signal are transmitted. A similar objective can be adopted in DSR systems, where the nonspeech parameters are not sent over the transmission channel. A novel approach is proposed for VAD decisions based on mel-filter bank (MFB) outputs with the so-called Hangover criterion. Comparative tests are presented between the presented MFB VAD algorithm and three VAD algorithms used in the G.729, G.723.1, and DSR (advanced front-end) Standards. These tests were made on the Aurora 2 database, with different signal-to-noise (SNRs) ratios. In the speech recognition tests, the proposed MFB VAD outperformed all the three VAD algorithms used in the standards by [InlineEquation not available: see fulltext.] relative (G.723.1 VAD), by [InlineEquation not available: see fulltext.] relative (G.729 VAD), and by [InlineEquation not available: see fulltext.] relative (DSR VAD) in all SNRs.