Straeter, T. A.
1972-01-01
The Davidon-Broyden class of rank one, quasi-Newton minimization methods is extended from Euclidean spaces to infinite-dimensional, real Hilbert spaces. For several techniques of choosing the step size, conditions are found which assure convergence of the associated iterates to the location of the minimum of a positive definite quadratic functional. For those techniques, convergence is achieved without the problem of the computation of a one-dimensional minimum at each iteration. The application of this class of minimization methods for the direct computation of the solution of an optimal control problem is outlined. The performance of various members of the class are compared by solving a sample optimal control problem. Finally, the sample problem is solved by other known gradient methods, and the results are compared with those obtained with the rank one quasi-Newton methods.
On a common generalization of Shelah's 2-rank, dp-rank, and o-minimal dimension
Guingona, Vincent; Hill, Cameron Donnay
2013-01-01
In this paper, we build a dimension theory related to Shelah's 2-rank, dp-rank, and o-minimal dimension. We call this dimension op-dimension. We exhibit the notion of the n-multi-order property, generalizing the order property, and use this to create op-rank, which generalizes 2-rank. From this we build op-dimension. We show that op-dimension bounds dp-rank, that op-dimension is sub-additive, and op-dimension generalizes o-minimal dimension in o-minimal theories.
LogDet Rank Minimization with Application to Subspace Clustering.
Kang, Zhao; Peng, Chong; Cheng, Jie; Cheng, Qiang
2015-01-01
Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet) function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.
LogDet Rank Minimization with Application to Subspace Clustering
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Zhao Kang
2015-01-01
Full Text Available Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.
Robust Alternating Low-Rank Representation by joint Lp- and L2,p-norm minimization.
Zhang, Zhao; Zhao, Mingbo; Li, Fanzhang; Zhang, Li; Yan, Shuicheng
2017-12-01
We propose a robust Alternating Low-Rank Representation (ALRR) model formed by an alternating forward-backward representation process. For forward representation, ALRR first recovers the low-rank PCs and random corruptions by an adaptive local Robust PCA (RPCA). Then, ALRR performs a joint L p -norm and L 2,p -norm minimization (0representation, while the L 2,p -norm on the reconstruction error can handle outlier pursuit. After that, ALRR returns the coefficients as adaptive weights to local RPCA for updating PCs and dictionary in the backward representation process. Thus, ALRR is regarded as an integration of local RPCA with adaptive weights plus sparse LRR with a self-expressive low-rank dictionary. To enable ALRR to handle outside data efficiently, a projective ALRR that can extract features from data directly by embedding is also derived. To solve the L 2,p -norm based minimization problem, a new iterative scheme based on the Iterative Shrinkage/Thresholding (IST) approach is presented. The relationship analysis with other related criteria show that our methods are more general. Visual and numerical results demonstrate the effectiveness of our algorithms for representation. Copyright © 2017 Elsevier Ltd. All rights reserved.
Econophysics of a ranked demand and supply resource allocation problem
Priel, Avner; Tamir, Boaz
2018-01-01
We present a two sided resource allocation problem, between demands and supplies, where both parties are ranked. For example, in Big Data problems where a set of different computational tasks is divided between a set of computers each with its own resources, or between employees and employers where both parties are ranked, the employees by their fitness and the employers by their package benefits. The allocation process can be viewed as a repeated game where in each iteration the strategy is decided by a meta-rule, based on the ranks of both parties and the results of the previous games. We show the existence of a phase transition between an absorbing state, where all demands are satisfied, and an active one where part of the demands are always left unsatisfied. The phase transition is governed by the ratio between supplies and demand. In a job allocation problem we find positive correlation between the rank of the workers and the rank of the factories; higher rank workers are usually allocated to higher ranked factories. These all suggest global emergent properties stemming from local variables. To demonstrate the global versus local relations, we introduce a local inertial force that increases the rank of employees in proportion to their persistence time in the same factory. We show that such a local force induces non trivial global effects, mostly to benefit the lower ranked employees.
Gravitino problem in minimal supergravity inflation
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Fuminori Hasegawa
2017-04-01
Full Text Available We study non-thermal gravitino production in the minimal supergravity inflation. In this minimal model utilizing orthogonal nilpotent superfields, the particle spectrum includes only graviton, gravitino, inflaton, and goldstino. We find that a substantial fraction of the cosmic energy density can be transferred to the longitudinal gravitino due to non-trivial change of its sound speed. This implies either a breakdown of the effective theory after inflation or a serious gravitino problem.
Gravitino problem in minimal supergravity inflation
Energy Technology Data Exchange (ETDEWEB)
Hasegawa, Fuminori [Institute for Cosmic Ray Research, The University of Tokyo, Kashiwa, Chiba 277-8582 (Japan); Mukaida, Kyohei [Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583 (Japan); Nakayama, Kazunori [Department of Physics, Faculty of Science, The University of Tokyo, Bunkyo-ku, Tokyo 133-0033 (Japan); Terada, Takahiro, E-mail: terada@kias.re.kr [School of Physics, Korea Institute for Advanced Study (KIAS), Seoul 02455 (Korea, Republic of); Yamada, Yusuke [Stanford Institute for Theoretical Physics and Department of Physics, Stanford University, Stanford, CA 94305 (United States)
2017-04-10
We study non-thermal gravitino production in the minimal supergravity inflation. In this minimal model utilizing orthogonal nilpotent superfields, the particle spectrum includes only graviton, gravitino, inflaton, and goldstino. We find that a substantial fraction of the cosmic energy density can be transferred to the longitudinal gravitino due to non-trivial change of its sound speed. This implies either a breakdown of the effective theory after inflation or a serious gravitino problem.
An Improved Approach to the PageRank Problems
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Yue Xie
2013-01-01
Full Text Available We introduce a partition of the web pages particularly suited to the PageRank problems in which the web link graph has a nested block structure. Based on the partition of the web pages, dangling nodes, common nodes, and general nodes, the hyperlink matrix can be reordered to be a more simple block structure. Then based on the parallel computation method, we propose an algorithm for the PageRank problems. In this algorithm, the dimension of the linear system becomes smaller, and the vector for general nodes in each block can be calculated separately in every iteration. Numerical experiments show that this approach speeds up the computation of PageRank.
Minimal mass solutions to inverse eigenvalue problems
Gladwell, G. M. L.
2006-04-01
One of the fundamental inverse problems in vibration theory is to construct an in-line system of masses and springs, fixed at one end, free at the other, so that it has a specified spectrum of natural frequencies. The solution, based on the work pioneered by Gantmacher and Krein, makes use of a second spectrum, that for the system fixed at both ends. We derive a closed form procedure to construct the system with a minimal mass for given overall stiffness from the first specified spectrum. The analogous problem of constructing a discrete model of a cantilever beam in flexural vibration having a specified spectrum uses two additional spectra corresponding to the previously free end being respectively pinned and sliding. We formulate the problem of finding a minimal mass solution for the given length and stiffness, and obtain explicit solutions in simple cases.
Losee, Robert M., Jr.
Designed to minimize information overload in telecommunications systems, the formal model developed in this paper predicts the usefulness of a message based on the available message features, and may be used to rank messages by expected importance or economic worth. The assumptions of binary and two Poisson independent probabilistic distributions…
Ranking influential spreaders is an ill-defined problem
Gu, Jain; Lee, Sungmin; Saramäki, Jari; Holme, Petter
2017-06-01
Finding influential spreaders of information and disease in networks is an important theoretical problem, and one of considerable recent interest. It has been almost exclusively formulated as a node-ranking problem —methods for identifying influential spreaders output a ranking of the nodes. In this work, we show that such a greedy heuristic does not necessarily work: the set of most influential nodes depends on the number of nodes in the set. Therefore, the set of n most important nodes to vaccinate does not need to have any node in common with the set of n + 1 most important nodes. We propose a method for quantifying the extent and impact of this phenomenon. By this method, we show that it is a common phenomenon in both empirical and model networks.
Phase Transition in the Bandwidth Minimization Problem
Rangel-Valdez, Nelson; Torres-Jimenez, Jose
It is known that some NP-Complete problems exhibit sharp phase transitions with respect to some order parameter. Moreover, a correlation between that critical behavior and the hardness of finding a solution exists in some of these problems. This paper shows experimental evidence about the existence of a critical behavior in the computational cost of solving the bandwidth minimization problem for graphs (BMPG). The experimental design involved the density of a graph as order parameter, 200000 random connected graphs of size 16 to 25 nodes, and a branch and bound algorithm taken from the literature. The results reveal a bimodal phase transition in the computational cost of solving the BMPG instances. This behavior was confirmed with the results obtained by metaheuristics that solve a known BMPG benchmark.
Hamzas, M. F. M. A.; Bareduan, S. A.; Zakaria, M. Z.; Ghazali, S.; Zairi, S.
2017-09-01
Line balancing is about arranging a production line so that there is an even flow of production from one work station to the next. There is an urge to achieve high productivity, improve the level of efficiency and reducing expenditure cost in a manufacturing process. The aim of this study to minimize the number work station for assembly line balancing. It is also to solve the assembly line balancing problem by using Ranked Positional Weight Heuristic Method. This study is focused of double sided type assembly line balancing by using Ranked Positional Weight Heuristic Method and focused to automotive industry. Based on this study, the result shows the efficiency increases from 86% to 92%. Besides that, the result shows the number of workstations also can be minimizing from 17 workstations to 16 workstations.
A new powerful nonparametric rank test for ordered alternative problem.
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Guogen Shan
Full Text Available We propose a new nonparametric test for ordered alternative problem based on the rank difference between two observations from different groups. These groups are assumed to be independent from each other. The exact mean and variance of the test statistic under the null distribution are derived, and its asymptotic distribution is proven to be normal. Furthermore, an extensive power comparison between the new test and other commonly used tests shows that the new test is generally more powerful than others under various conditions, including the same type of distribution, and mixed distributions. A real example from an anti-hypertensive drug trial is provided to illustrate the application of the tests. The new test is therefore recommended for use in practice due to easy calculation and substantial power gain.
Approximate solution of the p-median minimization problem
Il'ev, V. P.; Il'eva, S. D.; Navrotskaya, A. A.
2016-09-01
A version of the facility location problem (the well-known p-median minimization problem) and its generalization—the problem of minimizing a supermodular set function—is studied. These problems are NP-hard, and they are approximately solved by a gradient algorithm that is a discrete analog of the steepest descent algorithm. A priori bounds on the worst-case behavior of the gradient algorithm for the problems under consideration are obtained. As a consequence, a bound on the performance guarantee of the gradient algorithm for the p-median minimization problem in terms of the production and transportation cost matrix is obtained.
Truncated VSV solutions to symmetric rank-deficient problems
Fierro, Richardo D.; Hansen, Per Christian
2001-01-01
Symmetric VSV decompositions are new rank-revealing decompositions that exploit and preserve symmetry. Truncated VSV solutions are stabilized solutions computed by neglecting blocks in the VSV decomposition with small norm. We compare the truncated VSV solutions with truncated SVD solutions and give perturbation bounds for the VSV solutions. Numerical examples illustrate our results.
Truncated VSV solutions to symmetric rank-deficient problems
DEFF Research Database (Denmark)
Fierro, Richardo D.; Hansen, Per Christian
2001-01-01
Symmetric VSV decompositions are new rank-revealing decompositions that exploit and preserve symmetry. Truncated VSV solutions are stabilized solutions computed by neglecting blocks in the VSV decomposition with small norm. We compare the truncated VSV solutions with truncated SVD solutions...... and give perturbation bounds for the VSV solutions. Numerical examples illustrate our results....
Truncated VSV Solutions to Symmetric Rank-Deficient Problems
DEFF Research Database (Denmark)
Fierro, Ricardo D.; Hansen, Per Christian
2002-01-01
Symmetric VSV decompositions are new rank-revealing decompositions that exploit and preserve symmetry. Truncated VSV solutions are stabilized solutions computed by neglecting blocks in the VSV decomposition with small norm. We compare the truncated VSV solutions with truncated SVD solutions...... and give perturbation bounds for the VSV solutions. Numerical examples illustrate our results....
Minimal Solutions to the Box Problem
Chuang, Jer-Chin
2009-01-01
The "box problem" from introductory calculus seeks to maximize the volume of a tray formed by folding a strictly rectangular sheet from which identical squares have been cut from each corner. In posing such questions, one would like to choose integral side-lengths for the sheet so that the excised squares have rational or integral side-length.…
Minimal Time Problem with Impulsive Controls
Energy Technology Data Exchange (ETDEWEB)
Kunisch, Karl, E-mail: karl.kunisch@uni-graz.at [University of Graz, Institute for Mathematics and Scientific Computing (Austria); Rao, Zhiping, E-mail: zhiping.rao@ricam.oeaw.ac.at [Austrian Academy of Sciences, Radon Institute of Computational and Applied Mathematics (Austria)
2017-02-15
Time optimal control problems for systems with impulsive controls are investigated. Sufficient conditions for the existence of time optimal controls are given. A dynamical programming principle is derived and Lipschitz continuity of an appropriately defined value functional is established. The value functional satisfies a Hamilton–Jacobi–Bellman equation in the viscosity sense. A numerical example for a rider-swing system is presented and it is shown that the reachable set is enlargered by allowing for impulsive controls, when compared to nonimpulsive controls.
The Ranking of Higher Education Institutions in Russia: Some Methodological Problems.
Filinov, Nikolay B.; Ruchkina, Svetlana
2002-01-01
The ranking of higher education institutions in Russia is examined from two points of view: as a social phenomenon and as a multi-criteria decision-making problem. The first point of view introduces the idea of interested and involved parties; the second introduces certain principles on which a rational ranking methodology should be based.…
Soh, Kaycheng
2014-01-01
World university rankings (WUR) use the weight-and-sum approach to arrive at an overall measure which is then used to rank the participating universities of the world. Although the weight-and-sum procedure seems straightforward and accords with common sense, it has hidden methodological or statistical problems which render the meaning of the…
P. Boyadjieva
2012-01-01
The paper discusses the main results from the first ranking system of Bulgarian higher education institutions. It adopts two perspectives: 1) from the point of view of higher education – What are the basic achievements, problems and perspectives of Bulgarian higher education as seen through the prism of the ranking system? 2) from the point of view of the ranking system – What are the strengths and weaknesses of the ranking systems and how it can be further developed and improved? The paper a...
Better algorithms for satisfiability problems for formulas of bounded rank-width
Ganian, Robert; Obdržálek, Jan
2010-01-01
We provide a parameterized polynomial algorithm for the propositional model counting problem #SAT, the runtime of which is single-exponential in the rank-width of a formula. Previously, analogous algorithms have been known -- e.g.~[Fischer, Makowsky, and Ravve] -- with a single-exponential dependency on the clique-width of a formula. Our algorithm thus presents an exponential runtime improvement (since clique-width reaches up to exponentially higher values than rank-width), and can be of practical interest for small values of rank-width. We also provide an algorithm for the MAX-SAT problem along the same lines.
A Multivariate Solution of the Multivariate Ranking and Selection Problem
1980-02-01
theory in determining optimal test-treifnent snategies for streptococca.. sore throat and rheumatic fever . Operatiors Research, 24, 933-949. Gibbons, J. D...24, 92-103. Krischer, J. P. (1976). Utility structure cf a medical decision-making problem. Operations Research, 24, 951-971. Krishnaiah, P. R. and
Chemical screening in the United States is often conducted using scoring and ranking methodologies. Linked models accounting for chemical fate, exposure, and toxicological effects are generally preferred in Europe and in product Life Cycle Assessment. For the first time, a compar...
The maximal and minimal ranks of a quaternion matrix expression with applications
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Somayeh Rashedi
2013-10-01
Full Text Available In this paper, we establish the formulas of the extermal ranks of the quaternion matrix expression f(X1,X2 = C7 − A4X1B4 − A5X2B5 where X1, X2 are variant quaternion matrices subject to quaternion matrix equations A1X1 = C1, A2X1 = C2, A3X1 = C3, X2B1 = C4, X2B2 = C5, X2B3 = C6. As applications, we give a new necessary and sufficient condition for the existence of solutions to some systems of quaternion matrix equations. Some results can be viewed as special cases of the results of this paper.
Weighted low-rank sparse model via nuclear norm minimization for bearing fault detection
Du, Zhaohui; Chen, Xuefeng; Zhang, Han; Yang, Boyuan; Zhai, Zhi; Yan, Ruqiang
2017-07-01
It is a fundamental task in the machine fault diagnosis community to detect impulsive signatures generated by the localized faults of bearings. The main goal of this paper is to exploit the low-rank physical structure of periodic impulsive features and further establish a weighted low-rank sparse model for bearing fault detection. The proposed model mainly consists of three basic components: an adaptive partition window, a nuclear norm regularization and a weighted sequence. Firstly, due to the periodic repetition mechanism of impulsive feature, an adaptive partition window could be designed to transform the impulsive feature into a data matrix. The highlight of partition window is to accumulate all local feature information and align them. Then, all columns of the data matrix share similar waveforms and a core physical phenomenon arises, i.e., these singular values of the data matrix demonstrates a sparse distribution pattern. Therefore, a nuclear norm regularization is enforced to capture that sparse prior. However, the nuclear norm regularization treats all singular values equally and thus ignores one basic fact that larger singular values have more information volume of impulsive features and should be preserved as much as possible. Therefore, a weighted sequence with adaptively tuning weights inversely proportional to singular amplitude is adopted to guarantee the distribution consistence of large singular values. On the other hand, the proposed model is difficult to solve due to its non-convexity and thus a new algorithm is developed to search one satisfying stationary solution through alternatively implementing one proximal operator operation and least-square fitting. Moreover, the sensitivity analysis and selection principles of algorithmic parameters are comprehensively investigated through a set of numerical experiments, which shows that the proposed method is robust and only has a few adjustable parameters. Lastly, the proposed model is applied to the
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Kapil Mittal
2016-12-01
Full Text Available The manufacturing of plywood consists of simple procedural steps, but the range of problems associated with the plywood manufacturing industries, especially in the case of small-scale industries (SSI, is large. This paper describes the major problems faced by the plywood SSIs along with their cause and the ultimate effect, i.e. pruning the profits. Many cogent tools and techniques are present for the task, but an attempt has been made to apply multiple attribute decision-making (MADM approach in ranking the problems in order of their extent on the basis of various parameters. Some suggestions for the improvement purposes have also been made to overcome the top-ranked problem. The study is the first of its type in a plywood industry, although same can be applied to other similar small-scale cluster industries like steel, textile, pharmaceutical, and automobile.
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Chein-Shan Liu
2014-01-01
Full Text Available To solve an unconstrained nonlinear minimization problem, we propose an optimal algorithm (OA as well as a globally optimal algorithm (GOA, by deflecting the gradient direction to the best descent direction at each iteration step, and with an optimal parameter being derived explicitly. An invariant manifold defined for the model problem in terms of a locally quadratic function is used to derive a purely iterative algorithm and the convergence is proven. Then, the rank-two updating techniques of BFGS are employed, which result in several novel algorithms as being faster than the steepest descent method (SDM and the variable metric method (DFP. Six numerical examples are examined and compared with exact solutions, revealing that the new algorithms of OA, GOA, and the updated ones have superior computational efficiency and accuracy.
Free-energy minimization and the dark-room problem.
Friston, Karl; Thornton, Christopher; Clark, Andy
2012-01-01
Recent years have seen the emergence of an important new fundamental theory of brain function. This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation). The most comprehensive such treatment is the "free-energy minimization" formulation due to Karl Friston (see e.g., Friston and Stephan, 2007; Friston, 2010a,b - see also Fiorillo, 2010; Thornton, 2010). A recurrent puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. We do not simply seek a dark, unchanging chamber, and stay there. This is the "Dark-Room Problem." Here, we describe the problem and further unpack the issues to which it speaks. Using the same format as the prolog of Eddington's Space, Time, and Gravitation (Eddington, 1920) we present our discussion as a conversation between: an information theorist (Thornton), a physicist (Friston), and a philosopher (Clark).
Kusumawati, Rosita; Subekti, Retno
2017-04-01
Fuzzy bi-objective linear programming (FBOLP) model is bi-objective linear programming model in fuzzy number set where the coefficients of the equations are fuzzy number. This model is proposed to solve portfolio selection problem which generate an asset portfolio with the lowest risk and the highest expected return. FBOLP model with normal fuzzy numbers for risk and expected return of stocks is transformed into linear programming (LP) model using magnitude ranking function.
A heuristic for the minimization of open stacks problem
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Fernando Masanori Ashikaga
2009-08-01
Full Text Available It is suggested here a fast and easy to implement heuristic for the minimization of open stacks problem (MOSP. The problem is modeled as a traversing problem in a graph (Gmosp with a special structure (Yanasse, 1997b. It was observed in Ashikaga (2001 that, in the mean experimental case, Gmosp has large cliques and high edge density. This information was used to implement a heuristic based on the extension-rotation algorithm of Pósa (1976 for approximation of Hamiltonian Circuits. Additionally, an initial path for Pósa's algorithm is derived from the vertices of an ideally maximum clique in order to accelerate the process. Extensive computational tests show that the resulting simple approach dominates in time and mean error the fast actually know Yuen (1991 and 1995 heuristic to the problem.Sugerimos uma heurística rápida e de implementação simples para o problema de minimização de pilhas abertas (MOSP. O problema é modelado como um problema de percorrimento de arcos no grafo (Gmosp associado (Yanasse, 1997b. Foi observado em Ashikaga (2001 que o grafo Gmosp possui grandes cliques e uma alta densidade de arestas. Esta informação foi utilizada para implementar uma heurística baseada no algoritmo Extensão-Rotação de Pósa (1976 para aproximação de Circuitos Hamiltonianos. O caminho inicial para o algoritmo de Pósa é obtido a partir dos vértices de uma aproximação do maior clique do grafo para acelerar o processo. Testes computacionais extensivos mostram que a abordagem domina tanto em tempo quanto em erro médio a mais rápida heurística conhecida de Yuen (1991 e 1995.
Free-Energy Minimization and the Dark-Room Problem
Friston, Karl; Thornton, Christopher; Clark, Andy
2012-01-01
Recent years have seen the emergence of an important new fundamental theory of brain function. This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation). The most comprehensive such treatment is the “free-energy minimization” formulation due to Karl Friston (see e.g., Friston and Stephan, 2007; Friston, 2010a,b – see also Fiorillo, 2010; Thornton, 2010). A recurrent puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. We do not simply seek a dark, unchanging chamber, and stay there. This is the “Dark-Room Problem.” Here, we describe the problem and further unpack the issues to which it speaks. Using the same format as the prolog of Eddington’s Space, Time, and Gravitation (Eddington, 1920) we present our discussion as a conversation between: an information theorist (Thornton), a physicist (Friston), and a philosopher (Clark). PMID:22586414
Second order analysis of two-stage rank tests for the one-sample problem
Albers, Willem/Wim
1991-01-01
In this paper we present a rank analogue to Stein's two-stage procedure. We analyze its behavior to second order using existing asymptotic expansions for fixed sample size rank tests and recent results on combinations of independent rank statistics.
Chatzinikos, Miltiadis; Dermanis, Athanasios
2017-04-01
By considering a deformable geodetic network, deforming in a linear-in-time mode, according to a coordinate-invariant model, it becomes possible to get an insight into the rank deficiency of the stacking procedure, which is the standard method for estimating initial station coordinates and constant velocities, from coordinate time series. Comparing any two out of the infinitely many least squares estimates of stacking unknowns (initial station coordinates, velocity components and transformation parameters for the reference system in each data epoch), it is proven that the two solutions differ only by a linear-in-time trend in the transformation parameters. These pass over to the initial coordinates (the constant term) and to the velocity estimates (the time coefficient part). While the difference in initial coordinates is equivalent to a change of the reference system at the initial epoch, the differences in velocity components do not comply with those predicted by the same change of reference system for all epochs. Consequently, the different velocity component estimates, obtained by introducing different sets of minimal constraints, correspond to physically different station velocities, which are therefore non-estimable quantities. The theoretical findings are numerically verified for a global, a regional and a local network, by obtaining solutions based on four different types of minimal constraints, three usual algebraic ones (inner or partial inner) and the lately introduced kinematic constraints. Finally, by resorting to the basic ideas of Felix Tisserand, it is explained why the station velocities are non-estimable quantities in a very natural way. The problem of the optimal choice of minimal constraints and, hence, of the corresponding spatio-temporal reference system is shortly discussed.
APPLYING ROBUST RANKING METHOD IN TWO PHASE FUZZY OPTIMIZATION LINEAR PROGRAMMING PROBLEMS (FOLPP
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Monalisha Pattnaik
2014-12-01
Full Text Available Background: This paper explores the solutions to the fuzzy optimization linear program problems (FOLPP where some parameters are fuzzy numbers. In practice, there are many problems in which all decision parameters are fuzzy numbers, and such problems are usually solved by either probabilistic programming or multi-objective programming methods. Methods: In this paper, using the concept of comparison of fuzzy numbers, a very effective method is introduced for solving these problems. This paper extends linear programming based problem in fuzzy environment. With the problem assumptions, the optimal solution can still be theoretically solved using the two phase simplex based method in fuzzy environment. To handle the fuzzy decision variables can be initially generated and then solved and improved sequentially using the fuzzy decision approach by introducing robust ranking technique. Results and conclusions: The model is illustrated with an application and a post optimal analysis approach is obtained. The proposed procedure was programmed with MATLAB (R2009a version software for plotting the four dimensional slice diagram to the application. Finally, numerical example is presented to illustrate the effectiveness of the theoretical results, and to gain additional managerial insights.
Traveling salesman problems with PageRank Distance on complex networks reveal community structure
Jiang, Zhongzhou; Liu, Jing; Wang, Shuai
2016-12-01
In this paper, we propose a new algorithm for community detection problems (CDPs) based on traveling salesman problems (TSPs), labeled as TSP-CDA. Since TSPs need to find a tour with minimum cost, cities close to each other are usually clustered in the tour. This inspired us to model CDPs as TSPs by taking each vertex as a city. Then, in the final tour, the vertices in the same community tend to cluster together, and the community structure can be obtained by cutting the tour into a couple of paths. There are two challenges. The first is to define a suitable distance between each pair of vertices which can reflect the probability that they belong to the same community. The second is to design a suitable strategy to cut the final tour into paths which can form communities. In TSP-CDA, we deal with these two challenges by defining a PageRank Distance and an automatic threshold-based cutting strategy. The PageRank Distance is designed with the intrinsic properties of CDPs in mind, and can be calculated efficiently. In the experiments, benchmark networks with 1000-10,000 nodes and varying structures are used to test the performance of TSP-CDA. A comparison is also made between TSP-CDA and two well-established community detection algorithms. The results show that TSP-CDA can find accurate community structure efficiently and outperforms the two existing algorithms.
Patients' ranking of interdental "black triangles" against other common aesthetic problems.
Cunliffe, Joanne; Pretty, Iain
2009-12-01
The purpose of this study is to assess patients' aesthetic perceptions of interdental "black triangles", both in terms of the number of triangles visible and their severity; and to ascertain how patients rank the presence of "black triangles" against other aesthetic problems. It is based on a questionnaire of 80 randomly selected individuals who were asked to rate the aesthetics of digitally-manipulated images. Patients'perceptions of interdental "black triangles" were compared with their perceptions of other 'non-aesthetic' features. Interdental "black triangles" were rated as the third most disliked aesthetic problem below caries and crown margins. This study demonstrates the importance of interdental "black triangles" to patients, and therefore, as they can occur during prosthetic treatment, must be discussed with patients prior to commencing therapy.
Multiple graph regularized protein domain ranking
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Wang Jim
2012-11-01
Full Text Available Abstract Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.
Multiple graph regularized protein domain ranking
Wang, Jim Jing-Yan
2012-11-19
Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.
Obendorf, Hartmut
2009-01-01
The notion of Minimalism is proposed as a theoretical tool supporting a more differentiated understanding of reduction and thus forms a standpoint that allows definition of aspects of simplicity. This book traces the development of minimalism, defines the four types of minimalism in interaction design, and looks at how to apply it.
Shinzato, Takashi
2017-02-01
In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is intuitive that the minimal investment risk for the problem with an investment concentration constraint can be larger than that without the constraint (that is, with only the budget constraint). Moreover, a numerical experiment shows the effectiveness of our proposed analysis. In contrast, the standard operations research approach failed to identify accurately the minimal investment risk of the portfolio optimization problem.
Non-minimal quintessence: Dynamics and coincidence problem
Indian Academy of Sciences (India)
Brans–Dicke scalar–tensor theory provides a conformal coupling of the scalar ﬁeld with gravity in Einstein's frame. This model is equivalent to an interacting quintessence in which dark matter is coupled to dark energy. This provides a natural mechanism to alleviate the coincidence problem. We investigate the dynamics of ...
Non-minimal quintessence: Dynamics and coincidence problem
Indian Academy of Sciences (India)
Abstract. Brans–Dicke scalar–tensor theory provides a conformal coupling of the scalar field with gravity in Einstein's frame. This model is equivalent to an interacting quintessence in which dark matter is coupled to dark energy. This provides a natural mechanism to alleviate the coincidence problem. We investigate the ...
The Björling problem for non-minimal constant mean curvature surfaces
DEFF Research Database (Denmark)
Brander, David; Dorfmeister, Josef
2010-01-01
The classical Bjorling problem is to find the minimal surface containing a given real analytic curve with tangent planes prescribed along the curve. We consider the generalization of this problem to non-minimal constant mean curvature (CMC) surfaces, and show that it can be solved via the loop...
On the minimizers of calculus of variations problems in Hilbert spaces
Gomes, Diogo A.
2014-01-19
The objective of this paper is to discuss existence, uniqueness and regularity issues of minimizers of one dimensional calculus of variations problem in Hilbert spaces. © 2014 Springer-Verlag Berlin Heidelberg.
You Cannot Judge a Book by Its Cover: The Problems with Journal Rankings
Sangster, Alan
2015-01-01
Journal rankings lists have impacted and are impacting accounting educators and accounting education researchers around the world. Nowhere is the impact positive. It ranges from slight constraints on academic freedom to admonition, censure, reduced research allowances, non-promotion, non-short-listing for jobs, increased teaching loads, and…
On the uniqueness of minimizers for a class of variational problems with Polyconvex integrand
Awi, Romeo
2017-02-05
We prove existence and uniqueness of minimizers for a family of energy functionals that arises in Elasticity and involves polyconvex integrands over a certain subset of displacement maps. This work extends previous results by Awi and Gangbo to a larger class of integrands. First, we study these variational problems over displacements for which the determinant is positive. Second, we consider a limit case in which the functionals are degenerate. In that case, the set of admissible displacements reduces to that of incompressible displacements which are measure preserving maps. Finally, we establish that the minimizer over the set of incompressible maps may be obtained as a limit of minimizers corresponding to a sequence of minimization problems over general displacements provided we have enough regularity on the dual problems. We point out that these results defy the direct methods of the calculus of variations.
Kaycheng, Soh
2015-01-01
World university ranking systems used the weight-and-sum approach to combined indicator scores into overall scores on which the universities are then ranked. This approach assumes that the indicators all independently contribute to the overall score in the specified proportions. In reality, this assumption is doubtful as the indicators tend to…
Minimizers of a Class of Constrained Vectorial Variational Problems: Part I
Hajaiej, Hichem
2014-04-18
In this paper, we prove the existence of minimizers of a class of multiconstrained variational problems. We consider systems involving a nonlinearity that does not satisfy compactness, monotonicity, neither symmetry properties. Our approach hinges on the concentration-compactness approach. In the second part, we will treat orthogonal constrained problems for another class of integrands using density matrices method. © 2014 Springer Basel.
Minimization of Linear Functionals Defined on| Solutions of Large-Scale Discrete Ill-Posed Problems
DEFF Research Database (Denmark)
Elden, Lars; Hansen, Per Christian; Rojas, Marielba
2003-01-01
The minimization of linear functionals de ned on the solutions of discrete ill-posed problems arises, e.g., in the computation of con dence intervals for these solutions. In 1990, Elden proposed an algorithm for this minimization problem based on a parametric-programming reformulation involving...... the solution of a sequence of trust-region problems, and using matrix factorizations. In this paper, we describe MLFIP, a large-scale version of this algorithm where a limited-memory trust-region solver is used on the subproblems. We illustrate the use of our algorithm in connection with an inverse heat...
Scheduling stochastic two-machine flow shop problems to minimize expected makespan
Directory of Open Access Journals (Sweden)
Mehdi Heydari
2013-07-01
Full Text Available During the past few years, despite tremendous contribution on deterministic flow shop problem, there are only limited number of works dedicated on stochastic cases. This paper examines stochastic scheduling problems in two-machine flow shop environment for expected makespan minimization where processing times of jobs are normally distributed. Since jobs have stochastic processing times, to minimize the expected makespan, the expected sum of the second machine’s free times is minimized. In other words, by minimization waiting times for the second machine, it is possible to reach the minimum of the objective function. A mathematical method is proposed which utilizes the properties of the normal distributions. Furthermore, this method can be used as a heuristic method for other distributions, as long as the means and variances are available. The performance of the proposed method is explored using some numerical examples.
Global Low-Rank Image Restoration With Gaussian Mixture Model.
Zhang, Sibo; Jiao, Licheng; Liu, Fang; Wang, Shuang
2017-06-27
Low-rank restoration has recently attracted a lot of attention in the research of computer vision. Empirical studies show that exploring the low-rank property of the patch groups can lead to superior restoration performance, however, there is limited achievement on the global low-rank restoration because the rank minimization at image level is too strong for the natural images which seldom match the low-rank condition. In this paper, we describe a flexible global low-rank restoration model which introduces the local statistical properties into the rank minimization. The proposed model can effectively recover the latent global low-rank structure via nuclear norm, as well as the fine details via Gaussian mixture model. An alternating scheme is developed to estimate the Gaussian parameters and the restored image, and it shows excellent convergence and stability. Besides, experiments on image and video sequence datasets show the effectiveness of the proposed method in image inpainting problems.
A Look at the Generalized Heron Problem through the Lens of Majorization-Minimization.
Chi, Eric C; Lange, Kenneth
2014-02-01
In a recent issue of this journal, Mordukhovich, Nam, and Salinas pose and solve an interesting non-differentiable generalization of the Heron problem in the framework of modern convex analysis. In the generalized Heron problem, one is given k + 1 closed convex sets in ℝ d equipped with its Euclidean norm and asked to find the point in the last set such that the sum of the distances to the first k sets is minimal. In later work, the authors generalize the Heron problem even further, relax its convexity assumptions, study its theoretical properties, and pursue subgradient algorithms for solving the convex case. Here, we revisit the original problem solely from the numerical perspective. By exploiting the majorization-minimization (MM) principle of computational statistics and rudimentary techniques from differential calculus, we are able to construct a very fast algorithm for solving the Euclidean version of the generalized Heron problem.
DEFF Research Database (Denmark)
Montoya-Martinez, Jair; Artes-Rodriguez, Antonio; Pontil, Massimiliano
2014-01-01
We consider the estimation of the Brain Electrical Sources (BES) matrix from noisy electroencephalographic (EEG) measurements, commonly named as the EEG inverse problem. We propose a new method to induce neurophysiological meaningful solutions, which takes into account the smoothness, structured...... matrix and the squared Frobenius norm of the latent source matrix. We develop an alternating optimization algorithm to solve the resulting nonsmooth-nonconvex minimization problem. We analyze the convergence of the optimization procedure, and we compare, under different synthetic scenarios...
The variational minimization solutions of 3-body problems with 2 fixed centers
Zhao, Furong; Jiang, Yueyong
2017-10-01
In the Newtonian 3-body problem with two fixed centers in R3, two particles with equal masses are assumed fixed and the trajectory of the third particle is affected by the two fixed particles according to Newton's second law and the general gravitational law. We show that the minimization of the action functional on suitable classes of loops yields collision-free periodic orbits of the 3-body problem provided that some simple conditions on the period are satisfied; more precisely, geometric information is obtained for such variational minimization solutions.
Solving Minimal Covering Location Problems with Single and Multiple Node Coverage
Directory of Open Access Journals (Sweden)
Darko DRAKULIĆ
2016-12-01
Full Text Available Location science represents a very attractiveresearch field in combinatorial optimization and it is in expansion in last five decades. The main objective of location problems is determining the best position for facilities in a given set of nodes.Location science includes techniques for modelling problemsand methods for solving them. This paper presents results of solving two types of minimal covering location problems, with single and multiple node coverage, by using CPLEX optimizer and Particle Swarm Optimization method.
Carr, Edward G.; Owen-DeSchryver, Jamie S.
2007-01-01
There is growing interest in the role that physical illness and pain might play in exacerbating problem behavior in individuals with developmental disabilities. Assessment of these factors, however, is often difficult since many individuals have minimal verbal communication skills. In response to this difficulty, we developed a sequential method…
Sahli, B.; Bencheikh, L.
2010-11-01
The question of non-uniqueness in boundary integral equation formulations of exterior Neumann boundary-value problem in elasticity can be resolved by seeking the solution in the form of a single-layer potential. We present an analysis of the appropriate choice of the multipole coefficients which is optimal in the sense of minimizing the condition number of the boundary integral operator.
van Veelen, M A; Nederlof, E A L; Goossens, R H M; Schot, C J; Jakimowicz, J J
2003-07-01
The aim of this study is to gain insight into the problems encountered by the medical team related to products used for minimally invasive surgery. An inventory was made of the problems encountered during 12 endoscopic operations performed in one city hospital (Eindhoven, the Netherlands). After the observation, a questionnaire was distributed to all medical staff involved. All categories of personnel had physical, perceptional, and cognitive problems, especially surgeons, residents, and the sterile operation nurse. The main causes were the positioning of apparatus and staff, work clothing, and the limited reach of apparatus and/or instruments. Of the questionnaires, 80% were returned: 50% of the medical staff experienced perceptional problems and 63% had physical discomfort during the surgical procedure. The diversity of problems observed and/or reported by the staff during minimally invasive surgery decrease the comfort, efficiency, and safety of the operating-room work environment. Therefore, a new design approach is needed for MIS products in order to address the problems that occur with the current equipment.
Özdemir, Deniz
1998-01-01
Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 1998. Thesis (Master's) -- Bilkent University, 1998. Includes bibliographical references leaves 117-119 In this research, the problem of scheduling a set of jobs on a single machine to minimize total weighted tardiness with unequal release dates is considered. We present a new dominance rule by considering the time depending orderings between each pair of job...
Sharpe, Louise; Walker, Michael; Coughlan, Maree-Jo; Enersen, Kirsten; Blaszczynski, Alex
2005-01-01
This study aimed to evaluate the effectiveness of three proposed modifications to the structural characteristics of electronic gaming machines as harm minimisation strategies for non-problem and probable problem gamblers. Structural changes included reducing the maximum bet size, reducing reel spin and removing large note acceptors. Behavioural patterns of play were observed in 779 participants attending clubs and hotels. Observations were conducted in the gaming venue during regular gaming sessions. Eight experimental machines were designed to represent every combination of the modifications. 210 participants played at least one modified and one unmodified machine. Following play, the South Oaks Gambling Screen (SOGS) was administered. More problem than non-problem gamblers used high denomination bill acceptors and bet over one-dollar per wager. Machines modified to accept the one-dollar maximum bet were played for less time and were associated with smaller losses, fewer individual wagers and lower levels of alcohol consumption and smoking. It was concluded that the reduction of maximum bet levels was the only modification likely to be effective as a harm minimization strategy for problem gamblers.
Fymat, A. L.
1976-01-01
The paper studies the inversion of the radiative transfer equation describing the interaction of electromagnetic radiation with atmospheric aerosols. The interaction can be considered as the propagation in the aerosol medium of two light beams: the direct beam in the line-of-sight attenuated by absorption and scattering, and the diffuse beam arising from scattering into the viewing direction, which propagates more or less in random fashion. The latter beam has single scattering and multiple scattering contributions. In the former case and for single scattering, the problem is reducible to first-kind Fredholm equations, while for multiple scattering it is necessary to invert partial integrodifferential equations. A nonlinear minimization search method, applicable to the solution of both types of problems has been developed, and is applied here to the problem of monitoring aerosol pollution, namely the complex refractive index and size distribution of aerosol particles.
Cronin, Katherine A; Pieper, Bridget A; van Leeuwen, Edwin J C; Mundry, Roger; Haun, Daniel B M
2014-01-01
In the wild, chimpanzees (Pan troglodytes) are often faced with clumped food resources that they may know how to access but abstain from doing so due to social pressures. To better understand how social settings influence resource acquisition, we tested fifteen semi-wild chimpanzees from two social groups alone and in the presence of others. We investigated how resource acquisition was affected by relative social dominance, whether collaborative problem solving or (active or passive) sharing occurred amongst any of the dyads, and whether these outcomes were related to relationship quality as determined from six months of observational data. Results indicated that chimpanzees obtained fewer rewards when tested in the presence of others compared to when they were tested alone, and this loss tended to be greater when paired with a higher ranked individual. Individuals demonstrated behavioral inhibition; chimpanzees who showed proficient skill when alone often abstained from solving the task when in the presence of others. Finally, individuals with close social relationships spent more time together in the problem solving space, but collaboration and sharing were infrequent and sessions in which collaboration or sharing did occur contained more instances of aggression. Group living provides benefits and imposes costs, and these findings highlight that one cost of group living may be diminishing productive individual behaviors.
Directory of Open Access Journals (Sweden)
Katherine A Cronin
Full Text Available In the wild, chimpanzees (Pan troglodytes are often faced with clumped food resources that they may know how to access but abstain from doing so due to social pressures. To better understand how social settings influence resource acquisition, we tested fifteen semi-wild chimpanzees from two social groups alone and in the presence of others. We investigated how resource acquisition was affected by relative social dominance, whether collaborative problem solving or (active or passive sharing occurred amongst any of the dyads, and whether these outcomes were related to relationship quality as determined from six months of observational data. Results indicated that chimpanzees obtained fewer rewards when tested in the presence of others compared to when they were tested alone, and this loss tended to be greater when paired with a higher ranked individual. Individuals demonstrated behavioral inhibition; chimpanzees who showed proficient skill when alone often abstained from solving the task when in the presence of others. Finally, individuals with close social relationships spent more time together in the problem solving space, but collaboration and sharing were infrequent and sessions in which collaboration or sharing did occur contained more instances of aggression. Group living provides benefits and imposes costs, and these findings highlight that one cost of group living may be diminishing productive individual behaviors.
Cronin, Katherine A.; Pieper, Bridget A.; van Leeuwen, Edwin J. C.; Mundry, Roger; Haun, Daniel B. M.
2014-01-01
In the wild, chimpanzees (Pan troglodytes) are often faced with clumped food resources that they may know how to access but abstain from doing so due to social pressures. To better understand how social settings influence resource acquisition, we tested fifteen semi-wild chimpanzees from two social groups alone and in the presence of others. We investigated how resource acquisition was affected by relative social dominance, whether collaborative problem solving or (active or passive) sharing occurred amongst any of the dyads, and whether these outcomes were related to relationship quality as determined from six months of observational data. Results indicated that chimpanzees obtained fewer rewards when tested in the presence of others compared to when they were tested alone, and this loss tended to be greater when paired with a higher ranked individual. Individuals demonstrated behavioral inhibition; chimpanzees who showed proficient skill when alone often abstained from solving the task when in the presence of others. Finally, individuals with close social relationships spent more time together in the problem solving space, but collaboration and sharing were infrequent and sessions in which collaboration or sharing did occur contained more instances of aggression. Group living provides benefits and imposes costs, and these findings highlight that one cost of group living may be diminishing productive individual behaviors. PMID:24695486
A Two-Stage Assembly-Type Flowshop Scheduling Problem for Minimizing Total Tardiness
Directory of Open Access Journals (Sweden)
Ju-Yong Lee
2016-01-01
Full Text Available This research considers a two-stage assembly-type flowshop scheduling problem with the objective of minimizing the total tardiness. The first stage consists of two independent machines, and the second stage consists of a single machine. Two types of components are fabricated in the first stage, and then they are assembled in the second stage. Dominance properties and lower bounds are developed, and a branch and bound algorithm is presented that uses these properties and lower bounds as well as an upper bound obtained from a heuristic algorithm. The algorithm performance is evaluated using a series of computational experiments on randomly generated instances and the results are reported.
Directory of Open Access Journals (Sweden)
Z. Denton
2017-01-01
Full Text Available In this work we investigate integro-differential initial value problems with Riemann Liouville fractional derivatives where the forcing function is a sum of an increasing function and a decreasing function. We will apply the method of lower and upper solutions and develop two monotone iterative techniques by constructing two sequences that converge uniformly and monotonically to minimal and maximal solutions. In the first theorem we will construct two natural sequences and in the second theorem we will construct two intertwined sequences. Finally, we illustrate our results with an example.
Hodgins, David C; Toneatto, Tony; Makarchuk, Karyn; Skinner, Wayne; Vincent, Susan
2007-06-01
The goal of this study was to examine the efficacy of minimal treatment interventions for concerned significant others (CSOs) of problem gamblers. One hundred and eighty-six participants (82% females, 56% spouses) were randomly assigned to one of three groups: the first minimal intervention group received a self-help workbook [based on behavioral principles, modified from the Community Reinforcement and Family Therapy (CRAFT) model] and the second minimal intervention group received the workbook plus telephone support. The control condition received a treatment resource information package. Overall, all participants reported significant improvement in personal and relationship functioning and gambling behavior and consequences at the 3- and 6-month follow-up. The data demonstrated differences in favor of the interventions in three areas: days gambling, satisfaction with the program, and number who had their needs met. There was no difference in the number who had entered treatment. It may be that CSOs require more guidance and follow-up support to achieve these goals using the CRAFT procedures and strategies.
Energy Technology Data Exchange (ETDEWEB)
Gropp, W. D.; McInnes, L. C.; Smith, B. F.
1997-10-27
Developing portable and scalable software for the solution of large-scale optimization problems presents many challenges that traditional libraries do not adequately meet. Using object-oriented design in conjunction with other innovative techniques, they address these issues within the SNES (Scalable Nonlinear Equation Solvers) and SUMS (Scalable Unconstrained Minimization Solvers) packages, which are part of the multilevel PETSCs (Portable, Extensible Tools for Scientific computation) library. This paper focuses on the authors design philosophy and its benefits in providing a uniform and versatile framework for developing optimization software and solving large-scale nonlinear problems. They also consider a three-dimensional anisotropic Ginzburg-Landau model as a representative application that exploits the packages' flexible interface with user-specified data structures and customized routines for function evaluation and preconditioning.
Kim, Eunwoo; Lee, Minsik; Choi, Chong-Ho; Kwak, Nojun; Oh, Songhwai
2015-02-01
Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approximation methods are based on the l2 -norm (Frobenius norm) with principal component analysis (PCA) being the most popular among them. However, this can give a poor approximation for data contaminated by outliers (including missing data), because the l2 -norm exaggerates the negative effect of outliers. Recently, to overcome this problem, various methods based on the l1 -norm, such as robust PCA methods, have been proposed for low-rank matrix approximation. Despite the robustness of the methods, they require heavy computational effort and substantial memory for high-dimensional data, which is impractical for real-world problems. In this paper, we propose two efficient low-rank factorization methods based on the l1 -norm that find proper projection and coefficient matrices using the alternating rectified gradient method. The proposed methods are applied to a number of low-rank matrix approximation problems to demonstrate their efficiency and robustness. The experimental results show that our proposals are efficient in both execution time and reconstruction performance unlike other state-of-the-art methods.
The Minimal k-Core Problem for Modeling k-Assemblies.
Wood, Cynthia I; Hicks, Illya V
2015-12-01
The concept of cell assembly was introduced by Hebb and formalized mathematically by Palm in the framework of graph theory. In the study of associative memory, a cell assembly is a group of neurons that are strongly connected and represent a "concept" of our knowledge. This group is wired in a specific manner such that only a fraction of its neurons will excite the entire assembly. We link the concept of cell assembly to the closure of a minimal k-core and study a particular type of cell assembly called k-assembly. The goal of this paper is to find all substructures within a network that must be excited in order to activate a k-assembly. Through numerical experiments, we confirm that fractions of these important subgroups overlap. To explore the problem, we present a backtracking algorithm to find all minimal k-cores of a given undirected graph, which belongs to the class of NP-hard problems. The proposed method is a modification of the Bron and Kerbosch algorithm for finding all cliques of an undirected graph. The results in the tested graphs offer insight in analyzing graph structure and help better understand how concepts are stored.
Courses timetabling problem by minimizing the number of less preferable time slots
Oktavia, M.; Aman, A.; Bakhtiar, T.
2017-01-01
In an organization with large number of resources, timetabling is one of the most important factors of management strategy and the one that is most prone to errors or issues. Timetabling the perfect organization plan is quite a task, thus the aid of operations research or management strategy approaches is obligation. Timetabling in educational institutions can roughly be categorized into school timetabling, course timetabling, and examination timetabling, which differ from each other by their entities involved such as the type of events, the kind of institution, and the type and the relative influence of constraints. Education timetabling problem is generally a kind of complex combinatorial problem consisting of NP-complete sub-problems. It is required that the requested timetable fulfills a set of hard and soft constraints of various types. In this paper we consider a courses timetabling problem at university whose objective is to minimize the number of less preferable time slots. We mean by less preferable time slots are those devoted in early morning (07.00 - 07.50 AM) or those in the late afternoon (17.00 - 17.50 AM) that in fact beyond the working hour, those scheduled during the lunch break (12.00 - 12.50 AM), those scheduled in Wednesday 10.00 - 11.50 AM that coincides with Department Meeting, and those in Saturday which should be in fact devoted for day-off. In some cases, timetable with a number of activities scheduled in abovementioned time slots are commonly encountered. The courses timetabling for the Educational Program of General Competence (PPKU) students at odd semester at Bogor Agricultural University (IPB) has been modelled in the framework of the integer linear programming. We solved the optimization problem heuristically by categorizing all the groups into seven clusters.
Minimizing the total tardiness for the tool change scheduling problem on parallel machines
Directory of Open Access Journals (Sweden)
Antonio Costa
2016-04-01
Full Text Available This paper deals with the total tardiness minimization problem in a parallel machines manufacturing environment where tool change operations have to be scheduled along with jobs. The mentioned issue belongs to the family of scheduling problems under deterministic machine availability restrictions. A new model that considers the effects of the tool wear on the quality characteristics of the worked product is proposed. Since no mathematical programming-based approach has been developed by literature so far, two distinct mixed integer linear programming models, able to schedule jobs as well as tool change activities along the provided production horizon, have been devised. The former is an adaptation of a well-known model presented by the relevant literature for the single machine scheduling problem with tool changes. The latter has been specifically developed for the issue at hand. After a theoretical analysis aimed at revealing the differences between the proposed mathematical models in terms of computational complexity, an extensive experimental campaign has been fulfilled to assess performances of the proposed methods under the CPU time viewpoint. Obtained results have been statistically analyzed through a properly arranged ANOVA analysis.
National Research Council Canada - National Science Library
Wan-Yu Liu; Chun-Cheng Lin; Ching-Ren Chiu; You-Song Tsao; Qunwei Wang
2014-01-01
Torespondto the reduction of greenhouse gas emissions and global warming, this paper investigates the minimal-carbon-footprint time-dependent heterogeneous-fleet vehicle routing problem with alternative paths (MTHVRPP...
Sparse structure regularized ranking
Wang, Jim Jing-Yan
2014-04-17
Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.
GNU Oflox: an academic software for the minimal cost network flow problem
Directory of Open Access Journals (Sweden)
Andrés M. Sajo-Castelli
2013-07-01
Full Text Available We present an open-source software package written for GNU Octave. The software is an implementation of the Simplex algorithm for the minimal cost network flow problem oriented towards the academic environment. The implementation supports the use of Big-M and Phase I/Phase II methods and it can also start from a given feasible solution. Flexibility of the package's output configuration provides many attractive possibilities. The outputs are plain editable \\LaTeX\\ files that can be modified and orchestrated to fit most academic needs. It can be used in examination materials, homework assignments or even form part of a project. The format used to describe the network is the DIMACS min file format to which a simple extension was added in order to support the description of feasible trees in the file.
Erdtman, Elias; Jönsson, Carl
2012-01-01
This master's thesis addresses numerical methods of computing the typical ranks of tensors over the real numbers and explores some properties of tensors over finite fields. We present three numerical methods to compute typical tensor rank. Two of these have already been published and can be used to calculate the lowest typical ranks of tensors and an approximate percentage of how many tensors have the lowest typical ranks (for some tensor formats), respectively. The third method was developed...
Benner, Peter; Dolgov, Sergey; Khoromskaia, Venera; Khoromskij, Boris N.
2017-04-01
In this paper, we propose and study two approaches to approximate the solution of the Bethe-Salpeter equation (BSE) by using structured iterative eigenvalue solvers. Both approaches are based on the reduced basis method and low-rank factorizations of the generating matrices. We also propose to represent the static screen interaction part in the BSE matrix by a small active sub-block, with a size balancing the storage for rank-structured representations of other matrix blocks. We demonstrate by various numerical tests that the combination of the diagonal plus low-rank plus reduced-block approximation exhibits higher precision with low numerical cost, providing as well a distinct two-sided error estimate for the smallest eigenvalues of the Bethe-Salpeter operator. The complexity is reduced to O (Nb2) in the size of the atomic orbitals basis set, Nb, instead of the practically intractable O (Nb6) scaling for the direct diagonalization. In the second approach, we apply the quantized-TT (QTT) tensor representation to both, the long eigenvectors and the column vectors in the rank-structured BSE matrix blocks, and combine this with the ALS-type iteration in block QTT format. The QTT-rank of the matrix entities possesses almost the same magnitude as the number of occupied orbitals in the molecular systems, No chain-type molecules, while supporting sufficient accuracy.
Koohestani, Behrooz; Corne, David W.
2009-04-01
The Bandwidth Minimization Problem (BMP) is a graph layout problem which is known to be NP-complete. Since 1960, a considerable number of algorithms have been developed for addressing the BMP. At present, meta-heuristics (such as evolutionary algorithms and tabu search) are popular and successful approaches to the BMP. In such algorithms, the design of the fitness function (i.e. the metric that attempts to guide the search towards high-quality solutions) plays a key role in performance; the fitness function, along with the operators, induce the `search landscape', and careful attention to these issues may lead to landscapes that are more amenable to successful search. For example, rather than simply use the most obvious quality measure (in this case, the bandwidth itself), it is often helpful to design a more informative measure, indicating not only a solutions quality, but also encapsulating (for example) an indication of how distant this particular solution is from even better solutions. In this paper, a new fitness function and an associated new mutation operator are presented for BMP. These are incorporated within a simple Evolutionary Algorithm (EA), and evaluated on a set of 27 instances of the BMP (from the Harwell-Boeing sparse matrix collection). The results of this EA are compared with results obtained by using the standard fitness function (used in almost all previous researches on metaheuristics applied to the BMP). The results indicate clearly that the new fitness function and operator performed provide significantly superior results in the reduction of bandwidth.
Gershenson, Carlos
Studies of rank distributions have been popular for decades, especially since the work of Zipf. For example, if we rank words of a given language by use frequency (most used word in English is 'the', rank 1; second most common word is 'of', rank 2), the distribution can be approximated roughly with a power law. The same applies for cities (most populated city in a country ranks first), earthquakes, metabolism, the Internet, and dozens of other phenomena. We recently proposed ``rank diversity'' to measure how ranks change in time, using the Google Books Ngram dataset. Studying six languages between 1800 and 2009, we found that the rank diversity curves of languages are universal, adjusted with a sigmoid on log-normal scale. We are studying several other datasets (sports, economies, social systems, urban systems, earthquakes, artificial life). Rank diversity seems to be universal, independently of the shape of the rank distribution. I will present our work in progress towards a general description of the features of rank change in time, along with simple models which reproduce it
Bayón, L.; Grau, J. M.; Ruiz, M. M.; Suárez, P. M.
2012-12-01
One of the most well-known problems in the field of Microeconomics is the Firm's Cost-Minimization Problem. In this paper we establish the analytical expression for the cost function using the Cobb-Douglas model and considering maximum constraints for the inputs. Moreover we prove that it belongs to the class C1.
Vilar Jacob, Vinícius; Arroyo, José Elias C.
2016-01-01
This paper addresses a single-machine scheduling problem with sequence-dependent family setup times. In this problem the jobs are classified into families according to their similarity characteristics. Setup times are required on each occasion when the machine switches from processing jobs in one family to jobs in another family. The performance measure to be minimized is the total tardiness with respect to the given due dates of the jobs. The problem is classified as $\\mathcal{N}\\mathcal{P}$...
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Tom eFroese
2012-07-01
Full Text Available One of the major challenges faced by explanations of imitation is the ‘correspondence problem’: How is an agent able to match its bodily expression to the observed bodily expression of another agent, especially when there is no possibility of external self-observation? Current theories only consider the possibility of an innate or acquired matching mechanism belonging to an isolated individual. In this paper we evaluate an alternative that situates the explanation of imitation in the inter-individual dynamics of the interaction process itself. We implemented a minimal model of two interacting agents based on a recent psychological study of imitative behavior during minimalist perceptual crossing. The agents cannot sense the configuration of their own body, and do not have access to other’s body configuration, either. And yet surprisingly they are still capable of converging on matching bodily configurations. Analysis revealed that the agents solved this version of the correspondence problem in terms of collective properties of the interaction process. Contrary to the assumption that such properties merely serve as external input or scaffolding for individual mechanisms, it was found that the behavioral dynamics were distributed across the model as a whole.
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Akcelik, Volkan [ORNL; Flath, Pearl [University of Texas, Austin; Ghattas, Omar [University of Texas, Austin; Hill, Judith C [ORNL; Van Bloemen Waanders, Bart [Sandia National Laboratories (SNL); Wilcox, Lucas [University of Texas, Austin
2011-01-01
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inference. When the noise and prior probability densities are Gaussian, the solution to the inverse problem is also Gaussian, and is thus characterized by the mean and covariance matrix of the posterior probability density. Unfortunately, explicitly computing the posterior covariance matrix requires as many forward solutions as there are parameters, and is thus prohibitive when the forward problem is expensive and the parameter dimension is large. However, for many ill-posed inverse problems, the Hessian matrix of the data misfit term has a spectrum that collapses rapidly to zero. We present a fast method for computation of an approximation to the posterior covariance that exploits the lowrank structure of the preconditioned (by the prior covariance) Hessian of the data misfit. Analysis of an infinite-dimensional model convection-diffusion problem, and numerical experiments on large-scale 3D convection-diffusion inverse problems with up to 1.5 million parameters, demonstrate that the number of forward PDE solves required for an accurate low-rank approximation is independent of the problem dimension. This permits scalable estimation of the uncertainty in large-scale ill-posed linear inverse problems at a small multiple (independent of the problem dimension) of the cost of solving the forward problem.
Dryazhenkov, A. A.; Potapov, M. M.
2016-02-01
An algorithm for solving a quadratic minimization problem on an ellipsoidal set in a Hilbert space is proposed. The algorithm is stable to nonuniform perturbations of the operators. A key condition for its application is that we know an estimate for the norm of the exact solution. Applications to boundary control problems for the one-dimensional wave equation are considered. Numerical results are presented.
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Ahmad Zeraatkar Moghaddam
2012-01-01
Full Text Available This paper presents a mathematical model for the problem of minimizing the maximum lateness on a single machine when the deteriorated jobs are delivered to each customer in various size batches. In reality, this issue may happen within a supply chain in which delivering goods to customers entails cost. Under such situation, keeping completed jobs to deliver in batches may result in reducing delivery costs. In literature review of batch scheduling, minimizing the maximum lateness is known as NP-Hard problem; therefore the present issue aiming at minimizing the costs of delivering, in addition to the aforementioned objective function, remains an NP-Hard problem. In order to solve the proposed model, a Simulation annealing meta-heuristic is used, where the parameters are calibrated by Taguchi approach and the results are compared to the global optimal values generated by Lingo 10 software. Furthermore, in order to check the efficiency of proposed method to solve larger scales of problem, a lower bound is generated. The results are also analyzed based on the effective factors of the problem. Computational study validates the efficiency and the accuracy of the presented model.
2014-01-01
Background Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound. Results We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities. Conclusions With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery. PMID:24976868
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Hamidreza Haddad
2012-04-01
Full Text Available This paper tackles the single machine scheduling problem with dependent setup time and precedence constraints. The primary objective of this paper is minimization of total weighted tardiness. Since the complexity of the resulted problem is NP-hard we use metaheuristics method to solve the resulted model. The proposed model of this paper uses genetic algorithm to solve the problem in reasonable amount of time. Because of high sensitivity of GA to its initial values of parameters, a Taguchi approach is presented to calibrate its parameters. Computational experiments validate the effectiveness and capability of proposed method.
Combined Reduced-Rank Transform
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Anatoli Torokhti
2006-04-01
Full Text Available We propose and justify a new approach to constructing optimal nonlinear transforms of random vectors. We show that the proposed transform improves such characteristics of {rank-reduced} transforms as compression ratio, accuracy of decompression and reduces required computational work. The proposed transform ${mathcal T}_p$ is presented in the form of a sum with $p$ terms where each term is interpreted as a particular rank-reduced transform. Moreover, terms in ${mathcal T}_p$ are represented as a combination of three operations ${mathcal F}_k$, ${mathcal Q}_k$ and ${oldsymbol{varphi}}_k$ with $k=1,ldots,p$. The prime idea is to determine ${mathcal F}_k$ separately, for each $k=1,ldots,p$, from an associated rank-constrained minimization problem similar to that used in the Karhunen--Lo`{e}ve transform. The operations ${mathcal Q}_k$ and ${oldsymbol{varphi}}_k$ are auxiliary for f/inding ${mathcal F}_k$. The contribution of each term in ${mathcal T}_p$ improves the entire transform performance. A corresponding unconstrained nonlinear optimal transform is also considered. Such a transform is important in its own right because it is treated as an optimal filter without signal compression. A rigorous analysis of errors associated with the proposed transforms is given.
Vehicle Minimization for the Multimodal Pickup and Delivery Problem with Time Windows
2013-03-01
problems for parallel machines. The VRP solved is subject to time windows and capacity constraints on vehicles and offloading. The VRP is multimodal. The...addresses a special case of the VRP . The VRP considered is multimodal with time constraints. It is also subject to capacity constraints, both on...Consequently, the problem can also be considered to be a special case of the Fleet Size and Mix Problem. The primary issue in solving any VRP is
An Adaptive Reordered Method for Computing PageRank
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Yi-Ming Bu
2013-01-01
Full Text Available We propose an adaptive reordered method to deal with the PageRank problem. It has been shown that one can reorder the hyperlink matrix of PageRank problem to calculate a reduced system and get the full PageRank vector through forward substitutions. This method can provide a speedup for calculating the PageRank vector. We observe that in the existing reordered method, the cost of the recursively reordering procedure could offset the computational reduction brought by minimizing the dimension of linear system. With this observation, we introduce an adaptive reordered method to accelerate the total calculation, in which we terminate the reordering procedure appropriately instead of reordering to the end. Numerical experiments show the effectiveness of this adaptive reordered method.
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Yaping Hu
2014-11-01
Full Text Available In this paper, we extend the APG method to solve matrix l_{2,1}-norm minimization problem in multi-task feature learning. We investigate that the resulting inner subproblem has closed-form solution which can be easily determined by taking the problem's favorable structures. Under suitable conditions, we can establish a comprehensive convergence result for the proposed method. Furthermore, we present three different inexact APG algorithms by using the Lipschitz constant, the eigenvalue of Hessian matrix and the Barzilai and Borwein parameter in the inexact model, respectively. Numerical experiments on simulated data and real data set are reported to show the efficiency of proposed method.
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Lu-Chuan Ceng
2014-01-01
Full Text Available We introduce and analyze one iterative algorithm by hybrid shrinking projection method for finding a solution of the minimization problem for a convex and continuously Fréchet differentiable functional, with constraints of several problems: finitely many generalized mixed equilibrium problems, finitely many variational inequalities, the general system of variational inequalities and the fixed point problem of an asymptotically strict pseudocontractive mapping in the intermediate sense in a real Hilbert space. We prove strong convergence theorem for the iterative algorithm under suitable conditions. On the other hand, we also propose another iterative algorithm by hybrid shrinking projection method for finding a fixed point of infinitely many nonexpansive mappings with the same constraints, and derive its strong convergence under mild assumptions.
Liu, Yuanyuan; Jiao, L C; Shang, Fanhua; Yin, Fei; Liu, F
2013-12-01
In recent years, matrix rank minimization problems have aroused considerable interests from machine learning, data mining and computer vision communities. All of these problems can be solved via their convex relaxations which minimize the trace norm instead of the rank of the matrix, and have to be solved iteratively and involve singular value decomposition (SVD) at each iteration. Therefore, those algorithms for trace norm minimization problems suffer from high computation cost of multiple SVDs. In this paper, we propose an efficient Matrix Bi-Factorization (MBF) method to approximate the original trace norm minimization problem and mitigate the computation cost of performing SVDs. The proposed MBF method can be used to address a wide range of low-rank matrix recovery and completion problems such as low-rank and sparse matrix decomposition (LRSD), low-rank representation (LRR) and low-rank matrix completion (MC). We also present three small scale matrix trace norm models for LRSD, LRR and MC problems, respectively. Moreover, we develop two concrete linearized proximal alternative optimization algorithms for solving the above three problems. Experimental results on a variety of synthetic and real-world data sets validate the efficiency, robustness and effectiveness of our MBF method comparing with the state-of-the-art trace norm minimization algorithms. Copyright © 2013 Elsevier Ltd. All rights reserved.
Sajjadi, S. Maryam; Abdollahi, Hamid; Rahmanian, Reza; Bagheri, Leila
2016-03-01
A rapid, simple and inexpensive method using fluorescence spectroscopy coupled with multi-way methods for the determination of aflatoxins B1 and B2 in peanuts has been developed. In this method, aflatoxins are extracted with a mixture of water and methanol (90:10), and then monitored by fluorescence spectroscopy producing EEMs. Although the combination of EEMs and multi-way methods is commonly used to determine analytes in complex chemical systems with unknown interference(s), rank overlap problem in excitation and emission profiles may restrain the application of this strategy. If there is rank overlap in one mode, there are several three-way algorithms such as PARAFAC under some constraints that can resolve this kind of data successfully. However, the analysis of EEM data is impossible when some species have rank overlap in both modes because the information of the data matrix is equivalent to a zero-order data for that species, which is the case in our study. Aflatoxins B1 and B2 have the same shape of spectral profiles in both excitation and emission modes and we propose creating a third order data for each sample using solvent as a new additional selectivity mode. This third order data, in turn, converted to the second order data by augmentation, a fact which resurrects the second order advantage in original EEMs. The three-way data is constructed by stacking augmented data in the third way, and then analyzed by two powerful second order calibration methods (BLLS-RBL and PARAFAC) to quantify the analytes in four kinds of peanut samples. The results of both methods are in good agreement and reasonable recoveries are obtained.
Ranking Journals Using Social Choice Theory Methods: A Novel Approach in Bibliometrics
Energy Technology Data Exchange (ETDEWEB)
Aleskerov, F.T.; Pislyakov, V.; Subochev, A.N.
2016-07-01
We use data on economic, management and political science journals to produce quantitative estimates of (in)consistency of evaluations based on seven popular bibliometric indica (impact factor, 5-year impact factor, immediacy index, article influence score, h-index, SNIP and SJR). We propose a new approach to aggregating journal rankings: since rank aggregation is a multicriteria decision problem, ordinal ranking methods from social choice theory may solve it. We apply either a direct ranking method based on majority rule (the Copeland rule, the Markovian method) or a sorting procedure based on a tournament solution, such as the uncovered set and the minimal externally stable set. We demonstrate that aggregate rankings reduce the number of contradictions and represent the set of single-indicator-based rankings better than any of the seven rankings themselves. (Author)
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Lu-Chuan Ceng
2014-01-01
Full Text Available We introduce and analyze a hybrid iterative algorithm by virtue of Korpelevich's extragradient method, viscosity approximation method, hybrid steepest-descent method, and averaged mapping approach to the gradient-projection algorithm. It is proven that under appropriate assumptions, the proposed algorithm converges strongly to a common element of the fixed point set of infinitely many nonexpansive mappings, the solution set of finitely many generalized mixed equilibrium problems (GMEPs, the solution set of finitely many variational inequality problems (VIPs, the solution set of general system of variational inequalities (GSVI, and the set of minimizers of convex minimization problem (CMP, which is just a unique solution of a triple hierarchical variational inequality (THVI in a real Hilbert space. In addition, we also consider the application of the proposed algorithm to solve a hierarchical fixed point problem with constraints of finitely many GMEPs, finitely many VIPs, GSVI, and CMP. The results obtained in this paper improve and extend the corresponding results announced by many others.
Uauy, Ricardo; Corvalan, Camila; Dangour, Alan D
2009-02-01
Optimal health and well-being are now considered the true measures of human development. Integrated strategies for infant, child and adult nutrition are required that take a life-course perspective to achieve life-long health. The major nutrition challenges faced today include: (a) addressing the pending burden of undernutrition (low birth weight, severe wasting, stunting and Zn, retinol, Fe, iodine and folic acid deficits) affecting those individuals living in conditions of poverty and deprivation; (b) preventing nutrition-related chronic diseases (obesity, diabetes, CVD, some forms of cancer and osteoporosis) that, except in sub-Saharan Africa, are the main causes of death and disability globally. This challenge requires a life-course perspective as effective prevention starts before conception and continues at each stage of life. While death is unavoidable, premature death and disability can be postponed by providing the right amount and quality of food and by maintaining an active life; (c) delaying or avoiding, via appropriate nutrition and physical activity interventions, the functional declines associated with advancing age. To help tackle these challenges, it is proposed that the term 'malnutrition in all its forms', which encompasses the full spectrum of nutritional disorders, should be used to engender a broader understanding of global nutrition problems. This term may prove particularly helpful when interacting with policy makers and the public. Finally, a greater effort by the UN agencies and private and public development partners is called for to strengthen local, regional and international capacity to support the much needed change in policy and programme activities focusing on all forms of malnutrition with a unified agenda.
Neophilia Ranking of Scientific Journals
Packalen, Mikko; Bhattacharya, Jay
2017-01-01
The ranking of scientific journals is important because of the signal it sends to scientists about what is considered most vital for scientific progress. Existing ranking systems focus on measuring the influence of a scientific paper (citations)—these rankings do not reward journals for publishing innovative work that builds on new ideas. We propose an alternative ranking based on the proclivity of journals to publish papers that build on new ideas, and we implement this ranking via a text-based analysis of all published biomedical papers dating back to 1946. In addition, we compare our neophilia ranking to citation-based (impact factor) rankings; this comparison shows that the two ranking approaches are distinct. Prior theoretical work suggests an active role for our neophilia index in science policy. Absent an explicit incentive to pursue novel science, scientists underinvest in innovative work because of a coordination problem: for work on a new idea to flourish, many scientists must decide to adopt it in their work. Rankings that are based purely on influence thus do not provide sufficient incentives for publishing innovative work. By contrast, adoption of the neophilia index as part of journal-ranking procedures by funding agencies and university administrators would provide an explicit incentive for journals to publish innovative work and thus help solve the coordination problem by increasing scientists' incentives to pursue innovative work. PMID:28713181
Biswas, S.; Goswami, S. K.
2010-10-01
In the present paper an attempt has been made to place the distributed generation at an optimal location so as to improve the technical as well as economical performance. Among technical issues the sag performance and the loss have been considered. Genetic algorithm method has been used as the optimization technique in this problem. For sag analysis the impact of 3-phase symmetrical short circuit faults is considered. Total load disturbed during the faults is considered as an indicator of sag performance. The solution algorithm is demonstrated on a 34 bus radial distribution system with some lateral branches. For simplicity only one DG of predefined capacity is considered. MATLAB has been used as the programming environment.
Vickers, Douglas; Lee, Michael D; Dry, Matthew; Hughes, Peter; McMahon, Jennifer A
2006-01-01
Ormerod and Chronicle (1999) reported that optimal solutions to traveling salesperson problems were judged to be aesthetically more pleasing than poorer solutions and that solutions with more convex hull nodes were rated as better figures. To test these conclusions, solution regularity and the number of potential intersections were held constant, whereas solution optimality, the number of internal nodes, and the number of nearest neighbors in each solution were varied factorially. The results did not support the view that the convex hull is an important determinant of figural attractiveness. Also, in contrast to the findings of Ormerod and Chronicle, there were consistent individual differences. Participants appeared to be divided as to whether the most attractive figure enclosed a given area within a perimeter of minimum or maximum length. It is concluded that future research in this area cannot afford to focus exclusively on group performance measures.
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P. L. N. U. Cooray
2017-01-01
Full Text Available During the last decade, tremendous focus has been given to sustainable logistics practices to overcome environmental concerns of business practices. Since transportation is a prominent area of logistics, a new area of literature known as Green Transportation and Green Vehicle Routing has emerged. Vehicle Routing Problem (VRP has been a very active area of the literature with contribution from many researchers over the last three decades. With the computational constraints of solving VRP which is NP-hard, metaheuristics have been applied successfully to solve VRPs in the recent past. This is a threefold study. First, it critically reviews the current literature on EMVRP and the use of metaheuristics as a solution approach. Second, the study implements a genetic algorithm (GA to solve the EMVRP formulation using the benchmark instances listed on the repository of CVRPLib. Finally, the GA developed in Phase 2 was enhanced through machine learning techniques to tune its parameters. The study reveals that, by identifying the underlying characteristics of data, a particular GA can be tuned significantly to outperform any generic GA with competitive computational times. The scrutiny identifies several knowledge gaps where new methodologies can be developed to solve the EMVRPs and develops propositions for future research.
DEFF Research Database (Denmark)
Frandsen, Gudmund Skovbjerg; Frandsen, Peter Frands
2009-01-01
We consider maintaining information about the rank of a matrix under changes of the entries. For n×n matrices, we show an upper bound of O(n1.575) arithmetic operations and a lower bound of Ω(n) arithmetic operations per element change. The upper bound is valid when changing up to O(n0.575) entries...... in a single column of the matrix. We also give an algorithm that maintains the rank using O(n2) arithmetic operations per rank one update. These bounds appear to be the first nontrivial bounds for the problem. The upper bounds are valid for arbitrary fields, whereas the lower bound is valid for algebraically...... closed fields. The upper bound for element updates uses fast rectangular matrix multiplication, and the lower bound involves further development of an earlier technique for proving lower bounds for dynamic computation of rational functions....
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Vinícius Vilar Jacob
2016-01-01
Full Text Available This paper addresses a single-machine scheduling problem with sequence-dependent family setup times. In this problem the jobs are classified into families according to their similarity characteristics. Setup times are required on each occasion when the machine switches from processing jobs in one family to jobs in another family. The performance measure to be minimized is the total tardiness with respect to the given due dates of the jobs. The problem is classified as NP-hard in the ordinary sense. Since the computational complexity associated with the mathematical formulation of the problem makes it difficult for optimization solvers to deal with large-sized instances in reasonable solution time, efficient heuristic algorithms are needed to obtain near-optimal solutions. In this work we propose three heuristics based on the Iterated Local Search (ILS metaheuristic. The first heuristic is a basic ILS, the second uses a dynamic perturbation size, and the third uses a Path Relinking (PR technique as an intensification strategy. We carry out comprehensive computational and statistical experiments in order to analyze the performance of the proposed heuristics. The computational experiments show that the ILS heuristics outperform a genetic algorithm proposed in the literature. The ILS heuristic with dynamic perturbation size and PR intensification has a superior performance compared to other heuristics.
Possel, B.; Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, M.C.J.
2016-01-01
Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types
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Wan-Yu Liu
2014-07-01
Full Text Available Torespondto the reduction of greenhouse gas emissions and global warming, this paper investigates the minimal-carbon-footprint time-dependent heterogeneous-fleet vehicle routing problem with alternative paths (MTHVRPP. This finds a route with the smallestcarbon footprint, instead of the shortestroute distance, which is the conventional approach, to serve a number of customers with a heterogeneous fleet of vehicles in cases wherethere may not be only one path between each pair of customers, and the vehicle speed differs at different times of the day. Inheriting from the NP-hardness of the vehicle routing problem, the MTHVRPP is also NP-hard. This paper further proposes a genetic algorithm (GA to solve this problem. The solution representedbyour GA determines the customer serving ordering of each vehicle type. Then, the capacity check is used to classify multiple routes of each vehicle type, and the path selection determines the detailed paths of each route. Additionally, this paper improves the energy consumption model used for calculating the carbon footprint amount more precisely. Compared with the results without alternative paths, our experimental results show that the alternative path in this experimenthas a significant impact on the experimental results in terms of carbon footprint.
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de la Puente, Alejandro [Univ. of Notre Dame, IN (United States)
2012-05-01
In this work, I present a generalization of the Next-to-Minimal Supersymmetric Standard Model (NMSSM), with an explicit μ-term and a supersymmetric mass for the singlet superfield, as a route to alleviating the little hierarchy problem of the Minimal Supersymmetric Standard Model (MSSM). I analyze two limiting cases of the model, characterized by the size of the supersymmetric mass for the singlet superfield. The small and large limits of this mass parameter are studied, and I find that I can generate masses for the lightest neutral Higgs boson up to 140 GeV with top squarks below the TeV scale, all couplings perturbative up to the gauge unification scale, and with no need to fine tune parameters in the scalar potential. This model, which I call the S-MSSM is also embedded in a gauge-mediated supersymmetry breaking scheme. I find that even with a minimal embedding of the S-MSSM into a gauge mediated scheme, the mass for the lightest Higgs boson can easily be above 114 GeV, while keeping the top squarks below the TeV scale. Furthermore, I also study the forward-backward asymmetry in the t¯t system within the framework of the S-MSSM. For this purpose, non-renormalizable couplings between the first and third generation of quarks to scalars are introduced. The two limiting cases of the S-MSSM, characterized by the size of the supersymmetric mass for the singlet superfield is analyzed, and I find that in the region of small singlet supersymmetric mass a large asymmetry can be obtained while being consistent with constraints arising from flavor physics, quark masses and top quark decays.
Moments of random sums and Robbins' problem of optimal stopping
Gnedin, A.V.|info:eu-repo/dai/nl/189792809; Iksanov, A.
2011-01-01
Robbins' problem of optimal stopping is that of minimising the expected rank of an observation chosen by some nonanticipating stopping rule. We settle a conjecture regarding the value of the stopped variable under the rule that yields the minimal expected rank, by embedding the problem in a much
Vickers, Douglas; Bovet, Pierre; Lee, Michael D; Hughes, Peter
2003-01-01
The planar Euclidean version of the travelling salesperson problem (TSP) requires finding a tour of minimal length through a two-dimensional set of nodes. Despite the computational intractability of the TSP, people can produce rapid, near-optimal solutions to visually presented versions of such problems. To explain this, MacGregor et al (1999, Perception 28 1417-1428) have suggested that people use a global-to-local process, based on a perceptual tendency to organise stimuli into convex figures. We review the evidence for this idea and propose an alternative, local-to-global hypothesis, based on the detection of least distances between the nodes in an array. We present the results of an experiment in which we examined the relationships between three objective measures and performance measures of optimality and response uncertainty in tasks requiring participants to construct a closed tour or an open path. The data are not well accounted for by a process based on the convex hull. In contrast, results are generally consistent with a locally focused process based initially on the detection of nearest-neighbour clusters. Individual differences are interpreted in terms of a hierarchical process of constructing solutions, and the findings are related to a more general analysis of the role of nearest neighbours in the perception of structure and motion.
Beattle, A J; Oliver, I
1994-12-01
Biological surveys are in increasing demand while taxonomic resources continue to decline. How much formal taxonomy is required to get the job done? The answer depends on the kind of job but it is possible that taxonomic minimalism, especially (1) the use of higher taxonomic ranks, (2) the use of morphospecies rather than species (as identified by Latin binomials), and (3) the involvement of taxonomic specialists only for training and verification, may offer advantages for biodiversity assessment, environmental monitoring and ecological research. As such, formal taxonomy remains central to the process of biological inventory and survey but resources may be allocated more efficiently. For example, if formal Identification is not required, resources may be concentrated on replication and increasing sample sizes. Taxonomic minimalism may also facilitate the inclusion in these activities of important but neglected groups, especially among the invertebrates, and perhaps even microorganisms. Copyright © 1994. Published by Elsevier Ltd.
Lewandowski, Dirk
2015-01-01
Purpose: This paper discusses ranking factors suitable for library materials and shows that ranking in general is a complex process and that ranking for library materials requires a variety of techniques. Design/methodology/approach: The relevant literature is reviewed to provide a systematic overview of suitable ranking factors. The discussion is based on an overview of ranking factors used in Web search engines. Findings: While there are a wide variety of ranking factors appl...
Ranking Support Vector Machine with Kernel Approximation.
Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi
2017-01-01
Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.
PageRank tracker: from ranking to tracking.
Gong, Chen; Fu, Keren; Loza, Artur; Wu, Qiang; Liu, Jia; Yang, Jie
2014-06-01
Video object tracking is widely used in many real-world applications, and it has been extensively studied for over two decades. However, tracking robustness is still an issue in most existing methods, due to the difficulties with adaptation to environmental or target changes. In order to improve adaptability, this paper formulates the tracking process as a ranking problem, and the PageRank algorithm, which is a well-known webpage ranking algorithm used by Google, is applied. Labeled and unlabeled samples in tracking application are analogous to query webpages and the webpages to be ranked, respectively. Therefore, determining the target is equivalent to finding the unlabeled sample that is the most associated with existing labeled set. We modify the conventional PageRank algorithm in three aspects for tracking application, including graph construction, PageRank vector acquisition and target filtering. Our simulations with the use of various challenging public-domain video sequences reveal that the proposed PageRank tracker outperforms mean-shift tracker, co-tracker, semiboosting and beyond semiboosting trackers in terms of accuracy, robustness and stability.
Statistically Efficient Construction of α-Risk-Minimizing Portfolio
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Hiroyuki Taniai
2012-01-01
Full Text Available We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. (2004, an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asymptotic normality are obtained. We apply the results of Hallin et al. (2008 to the problem of constructing α-risk-minimizing portfolios using residual signs and ranks and a general reference density. Monte Carlo simulations assess the performance of the proposed method. Empirical applications are also investigated.
Ranking in evolving complex networks
Liao, Hao; Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng; Zhou, Ming-Yang
2017-05-01
Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes.
Stores, Rebecca; Stores, Gregory
2004-01-01
Background: The study concerns the unknown value of group instruction for mothers of young children with Down syndrome (DS) in preventing or minimizing sleep problems. Method: (1) Children with DS were randomly allocated to an Instruction group (given basic information about children's sleep) and a Control group for later comparison including…
Accurate low-rank matrix recovery from a small number of linear measurements
Candes, Emmanuel J
2009-01-01
We consider the problem of recovering a lowrank matrix M from a small number of random linear measurements. A popular and useful example of this problem is matrix completion, in which the measurements reveal the values of a subset of the entries, and we wish to fill in the missing entries (this is the famous Netflix problem). When M is believed to have low rank, one would ideally try to recover M by finding the minimum-rank matrix that is consistent with the data; this is, however, problematic since this is a nonconvex problem that is, generally, intractable. Nuclear-norm minimization has been proposed as a tractable approach, and past papers have delved into the theoretical properties of nuclear-norm minimization algorithms, establishing conditions under which minimizing the nuclear norm yields the minimum rank solution. We review this spring of emerging literature and extend and refine previous theoretical results. Our focus is on providing error bounds when M is well approximated by a low-rank matrix, and ...
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating e...... eigenvalues and eigenvectors. We give a number of different applications to regression and time series analysis, and show how the reduced rank regression estimator can be derived as a Gaussian maximum likelihood estimator. We briefly mention asymptotic results......The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...
On the Solution of a Nonconvex Fractional Quadratic Problem
Directory of Open Access Journals (Sweden)
S. Ketabchi
2015-06-01
Full Text Available In this paper, we give an algorithm for solving a class of nonconvex quadratic fractional problems that may arise during a correction of inconsistent set of linear inequalities. First, we show that for rank deficient matrices, an optimal solution for a nonconvex fractional minimization problem can be obtained via convex optimization approach. Then an iterative algorithm is designed to solve the problem in the full rank case. Finally, an illustrative numerical example is presented.
World University Rankings: Take with a Large Pinch of Salt
Cheng, Soh Kay
2011-01-01
Equating the unequal is misleading, and this happens consistently in comparing rankings from different university ranking systems, as the NUT saga shows. This article illustrates the problem by analyzing the 2011 rankings of the top 100 universities in the AWUR, QSWUR and THEWUR ranking results. It also discusses the reasons why the rankings…
Miehe, Christian; Mauthe, Steffen; Teichtmeister, Stephan
2015-09-01
This work develops new minimization and saddle point principles for the coupled problem of Darcy-Biot-type fluid transport in porous media at fracture. It shows that the quasi-static problem of elastically deforming, fluid-saturated porous media is related to a minimization principle for the evolution problem. This two-field principle determines the rate of deformation and the fluid mass flux vector. It provides a canonically compact model structure, where the stress equilibrium and the inverse Darcy's law appear as the Euler equations of a variational statement. A Legendre transformation of the dissipation potential relates the minimization principle to a characteristic three field saddle point principle, whose Euler equations determine the evolutions of deformation and fluid content as well as Darcy's law. A further geometric assumption results in modified variational principles for a simplified theory, where the fluid content is linked to the volumetric deformation. The existence of these variational principles underlines inherent symmetries of Darcy-Biot theories of porous media. This can be exploited in the numerical implementation by the construction of time- and space-discrete variational principles, which fully determine the update problems of typical time stepping schemes. Here, the proposed minimization principle for the coupled problem is advantageous with regard to a new unconstrained stable finite element design, while space discretizations of the saddle point principles are constrained by the LBB condition. The variational principles developed provide the most fundamental approach to the discretization of nonlinear fluid-structure interactions, showing symmetric systems in algebraic update procedures. They also provide an excellent starting point for extensions towards more complex problems. This is demonstrated by developing a minimization principle for a phase field description of fracture in fluid-saturated porous media. It is designed for an
Ranking Operations Management conferences
Steenhuis, H.J.; de Bruijn, E.J.; Gupta, Sushil; Laptaned, U
2007-01-01
Several publications have appeared in the field of Operations Management which rank Operations Management related journals. Several ranking systems exist for journals based on , for example, perceived relevance and quality, citation, and author affiliation. Many academics also publish at conferences
Ranking in Swiss system chess team tournaments
Csató, László
2015-01-01
The paper uses paired comparison-based scoring procedures for ranking the participants of a Swiss system chess team tournament. We present the main challenges of ranking in Swiss system, the features of individual and team competitions as well as the failures of official lexicographical orders. The tournament is represented as a ranking problem, our model is discussed with respect to the properties of the score, generalized row sum and least squares methods. The proposed procedure is illustra...
Rinto Yusriski; Sukoyo Sukoyo; T.M.A Ari Samadhi; Abdul Hakim Halim
2015-01-01
This research discusses an integer batch scheduling problems for a single-machine with position-dependent batch processing time due to the simultaneous effect of learning and forgetting. The decision variables are the number of batches, batch sizes, and the sequence of the resulting batches. The objective is to minimize total actual flow time, defined as total interval time between the arrival times of parts in all respective batches and their common due date. There are two proposed algorithm...
Biplots in Reduced-Rank Regression
Braak, ter C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal
Communities in Large Networks: Identification and Ranking
DEFF Research Database (Denmark)
Olsen, Martin
2008-01-01
We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....
Low-Rank Affinity Based Local-Driven Multilabel Propagation
Directory of Open Access Journals (Sweden)
Teng Li
2013-01-01
Full Text Available This paper presents a novel low-rank affinity based local-driven algorithm to robustly propagate the multilabels from training images to test images. A graph is constructed over the segmented local image regions. The labels for vertices from the training data are derived based on the context among different training images, and the derived vertex labels are propagated to the unlabeled vertices via the graph. The multitask low-rank affinity, which jointly seeks the sparsity-consistent low-rank affinities from multiple feature matrices, is applied to compute the edge weights between graph vertices. The inference process of multitask low-rank affinity is formulated as a constrained nuclear norm and ℓ2,1-norm minimization problem. The optimization is conducted efficiently with the augmented Lagrange multiplier method. Based on the learned local patch labels we can predict the multilabels for the test images. Experiments on multilabel image annotation demonstrate the encouraging results from the proposed framework.
Walwyn, Amy L.; Navarro, Daniel J.
2010-01-01
An experiment is reported comparing human performance on two kinds of visually presented traveling salesperson problems (TSPs), those reliant on Euclidean geometry and those reliant on city block geometry. Across multiple array sizes, human performance was near-optimal in both geometries, but was slightly better in the Euclidean format. Even so,…
Directory of Open Access Journals (Sweden)
Rinto Yusriski
2015-09-01
Full Text Available This research discusses an integer batch scheduling problems for a single-machine with position-dependent batch processing time due to the simultaneous effect of learning and forgetting. The decision variables are the number of batches, batch sizes, and the sequence of the resulting batches. The objective is to minimize total actual flow time, defined as total interval time between the arrival times of parts in all respective batches and their common due date. There are two proposed algorithms to solve the problems. The first is developed by using the Integer Composition method, and it produces an optimal solution. Since the problems can be solved by the first algorithm in a worst-case time complexity O(n2n-1, this research proposes the second algorithm. It is a heuristic algorithm based on the Lagrange Relaxation method. Numerical experiments show that the heuristic algorithm gives outstanding results.
Nishi, Tatsushi; Yamamoto, Shinichiro; Konishi, Masami
The storage allocation planning problem in warehouse management is to determine the allocation of products to the storage space and intermediate operations for retrieving products so as to minimize the number of operations, and maximize the collected number of products for each customer when the sequence of requests for inlet and retrieval operations are given. In this paper, we propose an efficient beam search method for generating a near optimal solution with a reasonable computation time. A heuristic procedure is also proposed in order to reduce a search space in the beam search method by using the information of subsequent inlet and retrieving requests. The validity of the proposed method is confirmed by comparing the results with the optimal solution derived by solving an MILP problem. The effectiveness of the proposed method is demonstrated by solving an actual large-sized problem consisting of more than 3000 operations.
Yano, Ken; Suyama, Takayuki
2016-01-01
This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI) systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a "bottom-up" manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives.
Directory of Open Access Journals (Sweden)
Ken Yano
2016-01-01
Full Text Available This paper proposes a novel fixed low-rank spatial filter estimation for brain computer interface (BCI systems with an application that recognizes emotions elicited by movies. The proposed approach unifies such tasks as feature extraction, feature selection, and classification, which are often independently tackled in a “bottom-up” manner, under a regularized loss minimization problem. The loss function is explicitly derived from the conventional BCI approach and solves its minimization by optimization with a nonconvex fixed low-rank constraint. For evaluation, an experiment was conducted to induce emotions by movies for dozens of young adult subjects and estimated the emotional states using the proposed method. The advantage of the proposed method is that it combines feature selection, feature extraction, and classification into a monolithic optimization problem with a fixed low-rank regularization, which implicitly estimates optimal spatial filters. The proposed method shows competitive performance against the best CSP-based alternatives.
Block models and personalized PageRank
National Research Council Canada - National Science Library
Kloumann, Isabel M; Ugander, Johan; Kleinberg, Jon
2017-01-01
...? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate in the space of "landing probabilities" of a random walk...
Directory of Open Access Journals (Sweden)
Marco Antonio Moreira de Carvalho
2011-01-01
Full Text Available Este trabalho apresenta dois métodos para a solução do Problema de Minimização de Pilhas Abertas (ou MOSP, de Minimization of Open Stacks Problem, um problema de sequenciamento de padrões oriundo do contexto de produção de peças, cuja aplicação industrial é direta. O primeiro é relativo a uma heurística baseada em teoria de grafos e critérios gulosos, enquanto o segundo é relativo a um método de programação dinâmica. Os resultados do experimento realizado comprovam a eficácia das simplificações propostas quando comparadas com os métodos da literatura.This paper presents two methods for solving the minimization of open stack problem (MOSP, a pattern sequencing problem found in production systems with direct industrial application. The first method refers to a heuristic based on graph theory and greedy criteria, while the second refers to the dynamic programming method. The results show the effectiveness of the proposed simplifications compared to the methods reported in the literature.
Maximum Waring ranks of monomials
Holmes, Erik; Plummer, Paul; Siegert, Jeremy; Teitler, Zach
2013-01-01
We show that monomials and sums of pairwise coprime monomials in four or more variables have Waring rank less than the generic rank, with a short list of exceptions. We asymptotically compare their ranks with the generic rank.
Minotti, Luca; Savaré, Giuseppe
2018-02-01
We propose the new notion of Visco-Energetic solutions to rate-independent systems {(X, E,} d) driven by a time dependent energy E and a dissipation quasi-distance d in a general metric-topological space X. As for the classic Energetic approach, solutions can be obtained by solving a modified time Incremental Minimization Scheme, where at each step the dissipation quasi-distance d is incremented by a viscous correction {δ} (for example proportional to the square of the distance d), which penalizes far distance jumps by inducing a localized version of the stability condition. We prove a general convergence result and a typical characterization by Stability and Energy Balance in a setting comparable to the standard energetic one, thus capable of covering a wide range of applications. The new refined Energy Balance condition compensates for the localized stability and provides a careful description of the jump behavior: at every jump the solution follows an optimal transition, which resembles in a suitable variational sense the discrete scheme that has been implemented for the whole construction.
Directory of Open Access Journals (Sweden)
Zhifeng Dai
2014-01-01
Full Text Available Combining the Rosen gradient projection method with the two-term Polak-Ribière-Polyak (PRP conjugate gradient method, we propose a two-term Polak-Ribière-Polyak (PRP conjugate gradient projection method for solving linear equality constraints optimization problems. The proposed method possesses some attractive properties: (1 search direction generated by the proposed method is a feasible descent direction; consequently the generated iterates are feasible points; (2 the sequences of function are decreasing. Under some mild conditions, we show that it is globally convergent with Armijio-type line search. Preliminary numerical results show that the proposed method is promising.
Information Theoretic Bounds for Low-Rank Matrix Completion
Vishwanath, Sriram
2010-01-01
This paper studies the low-rank matrix completion problem from an information theoretic perspective. The completion problem is rephrased as a communication problem of an (uncoded) low-rank matrix source over an erasure channel. The paper then uses achievability and converse arguments to present order-wise optimal bounds for the completion problem.
Bradshaw, Corey J A; Brook, Barry W
2016-01-01
There are now many methods available to assess the relative citation performance of peer-reviewed journals. Regardless of their individual faults and advantages, citation-based metrics are used by researchers to maximize the citation potential of their articles, and by employers to rank academic track records. The absolute value of any particular index is arguably meaningless unless compared to other journals, and different metrics result in divergent rankings. To provide a simple yet more objective way to rank journals within and among disciplines, we developed a κ-resampled composite journal rank incorporating five popular citation indices: Impact Factor, Immediacy Index, Source-Normalized Impact Per Paper, SCImago Journal Rank and Google 5-year h-index; this approach provides an index of relative rank uncertainty. We applied the approach to six sample sets of scientific journals from Ecology (n = 100 journals), Medicine (n = 100), Multidisciplinary (n = 50); Ecology + Multidisciplinary (n = 25), Obstetrics & Gynaecology (n = 25) and Marine Biology & Fisheries (n = 25). We then cross-compared the κ-resampled ranking for the Ecology + Multidisciplinary journal set to the results of a survey of 188 publishing ecologists who were asked to rank the same journals, and found a 0.68-0.84 Spearman's ρ correlation between the two rankings datasets. Our composite index approach therefore approximates relative journal reputation, at least for that discipline. Agglomerative and divisive clustering and multi-dimensional scaling techniques applied to the Ecology + Multidisciplinary journal set identified specific clusters of similarly ranked journals, with only Nature & Science separating out from the others. When comparing a selection of journals within or among disciplines, we recommend collecting multiple citation-based metrics for a sample of relevant and realistic journals to calculate the composite rankings and their relative uncertainty windows.
Energy Technology Data Exchange (ETDEWEB)
Davidsson, Kent; Eskilsson, David; Gyllenhammar, Marianne; Herstad Svaerd, Solvie; Kassman, Haakan; Steenari, Britt-Marie; Aamand, Lars-Erik
2006-12-15
Combustion of biofuel and waste wood is often accompanied by chlorine and alkali related operating problems such as slagging, deposit formation and corrosion on heat exchanger surfaces and bed agglomeration in fluidised bed boilers. In order to gain a greater insight into possible measures to overcome alkali related operating problems studies were carried out during 2005-2006. The results of the studies are presented in this report which includes work performed in the two following projects: 1 A5-509 Frame work - measures for simultaneous minimisation of alkali related operating problems 2 A5-505 Bed agglomeration risk related to combustion of cultivated fuels (wheat straw, red canary grass, industrial hemp) in commercial bed materials Full-scale experiments were carried out at Chalmers 12 MW{sub th} CFB boiler within the project A5-509. The purpose was to study the effect of various measures on bed agglomeration and deposit formation in connection with co-combustion of wood and straw pellets. The various measures included changing the bed material (blast furnace sand and olivine sand), adding various additives (kaolin, ammonium sulphate, elemental sulphur) and also co-combustion with sewage sludge. Furthermore results from kaolin experiments at the 26 MWth CFB boiler owned by Naessjoe Affaersverk were made available during the project and are also presented in this report. The results from the experiments at Chalmers revealed that, already at the lowest dosage of kaolin, approx. 2 kg/MWh, the bed material agglomeration temperatures increased significantly. The dosage of kaolin can presumably be reduced somewhat further while still maintaining the high agglomeration temperature. Experiments with a higher dosage of kaolin, 7 kg/MWh, proved that kaolin could also reduce the risk of deposit problems. The experiments at Naessjoe showed also that addition of kaolin increased the agglomeration temperature of the bed material. Addition of sulphur in any form resulted in a
A low-rank approach to off-the-grid sparse deconvolution
Catala, Paul; Duval, Vincent; Peyré, Gabriel
2017-10-01
We propose a new solver for the sparse spikes deconvolution problem over the space of Radon measures. A common approach to off-the-grid deconvolution considers semidefinite (SDP) relaxations of the total variation (the total mass of the measure) minimization problem. The direct resolution of this SDP is however intractable for large scale settings, since the problem size grows as f c 2d where fc is the cutoff frequency of the filter. Our first contribution introduces a penalized formulation of this semidefinite lifting, which has low-rank solutions. Our second contribution is a conditional gradient optimization scheme with non-convex updates. This algorithm leverages both the low-rank and the convolutive structure of the problem, resulting in an O(fc d log fc) complexity per iteration. Numerical simulations are promising and show that the algorithm converges in exactly k steps, k being the number of Diracs composing the solution.
When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores
Wang, Jim Jing-Yan
2017-06-28
Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.
Low-Rank Representation-Based Object Tracking Using Multitask Feature Learning with Joint Sparsity
Directory of Open Access Journals (Sweden)
Hyuncheol Kim
2014-01-01
Full Text Available We address object tracking problem as a multitask feature learning process based on low-rank representation of features with joint sparsity. We first select features with low-rank representation within a number of initial frames to obtain subspace basis. Next, the features represented by the low-rank and sparse property are learned using a modified joint sparsity-based multitask feature learning framework. Both the features and sparse errors are then optimally updated using a novel incremental alternating direction method. The low-rank minimization problem for learning multitask features can be achieved by a few sequences of efficient closed form update process. Since the proposed method attempts to perform the feature learning problem in both multitask and low-rank manner, it can not only reduce the dimension but also improve the tracking performance without drift. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods for tracking objects in challenging image sequences.
Academic rankings: an approach to a Portuguese ranking
Bernardino, Pedro; Marques,Rui
2009-01-01
The academic rankings are a controversial subject in higher education. However, despite all the criticism, academic rankings are here to stay and more and more different stakeholders use rankings to obtain information about the institutions’ performance. The two most well-known rankings, The Times and the Shanghai Jiao Tong University rankings have different methodologies. The Times ranking is based on peer review, whereas the Shanghai ranking has only quantitative indicators and is mainly ba...
Statistical inference of Minimum Rank Factor Analysis
Shapiro, A; Ten Berge, JMF
For any given number of factors, Minimum Rank Factor Analysis yields optimal communalities for an observed covariance matrix in the sense that the unexplained common variance with that number of factors is minimized, subject to the constraint that both the diagonal matrix of unique variances and the
Directory of Open Access Journals (Sweden)
Pedro Bernardino
2010-03-01
Full Text Available The academic rankings are a controversial subject in higher education. However, despite all the criticism, academic rankings are here to stay and more and more different stakeholders use rankings to obtain information about the institutions' performance. The two most well-known rankings, The Times and the Shanghai Jiao Tong University rankings have different methodologies. The Times ranking is based on peer review, whereas the Shanghai ranking has only quantitative indicators and is mainly based on research outputs. In Germany, the CHE ranking uses a different methodology from the traditional rankings, allowing the users to choose criteria and weights. The Portuguese higher education institutions are performing below their European peers, and the Government believes that an academic ranking could improve both performance and competitiveness between institutions. The purpose of this paper is to analyse the advantages and problems of academic rankings and provide guidance to a new Portuguese ranking.Los rankings académicos son un tema muy contradictorio en la enseñanza superior. Todavía, además de todas las críticas los rankings están para quedarse entre nosotros. Y cada vez más, diferentes stakeholders utilizan los rankings para obtener información sobre el desempeño de las instituciones. Dos de los rankings más conocidos, el The Times y el ranking de la universidad de Shangai Jiao Tong tienen métodos distintos. El The Times se basa en la opinión de expertos mientras el ranking de la universidad de Shangai presenta solamente indicadores cuantitativos y mayoritariamente basados en los resultados de actividades de investigación. En Alemania el ranking CHE usa un método distinto permitiendo al utilizador elegir los criterios y su importancia. Las instituciones de enseñanza superior portuguesas tienen un desempeño abajo de las europeas y el gobierno cree que un ranking académico podría contribuir para mejorar su desempeño y
Castillo, Edward; Castillo, Richard; Fuentes, David; Guerrero, Thomas
2014-04-01
Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimall1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download atwww.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match generated estimates. Thus, the framework
Ranking Economic History Journals
DEFF Research Database (Denmark)
Di Vaio, Gianfranco; Weisdorf, Jacob Louis
This study ranks - for the first time - 12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We...... also compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential...
Ranking economic history journals
DEFF Research Database (Denmark)
Di Vaio, Gianfranco; Weisdorf, Jacob Louis
2010-01-01
This study ranks-for the first time-12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We also...... compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential for economic...
Recurrent fuzzy ranking methods
Hajjari, Tayebeh
2012-11-01
With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.
Directory of Open Access Journals (Sweden)
Gbemileke A. Ogunranti
2016-09-01
Full Text Available Purpose: The main objective of this study is to develop a model for solving the one dimensional cutting stock problem in the wood working industry, and develop a program for its implementation. Design/methodology/approach: This study adopts the pattern oriented approach in the formulation of the cutting stock model. A pattern generation algorithm was developed and coded using Visual basic.NET language. The cutting stock model developed is a Linear Programming (LP Model constrained by numerous feasible patterns. A LP solver was integrated with the pattern generation algorithm program to develop a one - dimensional cutting stock model application named GB Cutting Stock Program. Findings and Originality/value: Applying the model to a real life optimization problem significantly reduces material waste (off-cuts and minimizes the total stock used. The result yielded about 30.7% cost savings for company-I when the total stock materials used is compared with the former cutting plan. Also, to evaluate the efficiency of the application, Case I problem was solved using two top commercial 1D-cutting stock software. The results show that the GB program performs better when related results were compared. Research limitations/implications: This study round up the linear programming solution for the number of pattern to cut. Practical implications: From Managerial perspective, implementing optimized cutting plans increases productivity by eliminating calculating errors and drastically reducing operator mistakes. Also, financial benefits that can annually amount to millions in cost savings can be achieved through significant material waste reduction. Originality/value: This paper developed a linear programming one dimensional cutting stock model based on a pattern generation algorithm to minimize waste in the wood working industry. To implement the model, the algorithm was coded using VisualBasic.net and linear programming solver called lpsolvedll (dynamic
A Hybrid Distance-Based Ideal-Seeking Consensus Ranking Model
Tavana, Madjid; LoPinto, Frank; Smither, James W.
2007-01-01
Ordinal consensus ranking problems have received much attention in the management science literature. A problem arises in situations where a group of k decision makers (DMs) is asked to rank order n alternatives. The question is how to combine the DM rankings into one consensus ranking. Several different approaches have been suggested to aggregate DM responses into a compromise or consensus ranking; however, the similarity of consensus rankings generated by the differe...
Higher Education Ranking and Leagues Tables: Lessons Learned from Benchmarking
Proulx, Roland
2007-01-01
The paper intends to contribute to the debate on ranking and league tables by adopting a critical approach to ranking methodologies from the point of view of a university benchmarking exercise. The absence of a strict benchmarking exercise in the ranking process has been, in the opinion of the author, one of the major problems encountered in the…
Soh, Kay Cheng
2012-01-01
University ranking has become ritualistic in higher education. Ranking results are taken as bona fide by rank users. Ranking systems usually use large data sets from highly heterogeneous universities of varied backgrounds. This poses the problem of Simpson's Paradox and the lurking variables causing it. Using QS 2011-2012 Ranking data, the dual…
Adaptive low-rank subspace learning with online optimization for robust visual tracking.
Liu, Risheng; Wang, Di; Han, Yuzhuo; Fan, Xin; Luo, Zhongxuan
2017-04-01
In recent years, sparse and low-rank models have been widely used to formulate appearance subspace for visual tracking. However, most existing methods only consider the sparsity or low-rankness of the coefficients, which is not sufficient enough for appearance subspace learning on complex video sequences. Moreover, as both the low-rank and the column sparse measures are tightly related to all the samples in the sequences, it is challenging to incrementally solve optimization problems with both nuclear norm and column sparse norm on sequentially obtained video data. To address above limitations, this paper develops a novel low-rank subspace learning with adaptive penalization (LSAP) framework for subspace based robust visual tracking. Different from previous work, which often simply decomposes observations as low-rank features and sparse errors, LSAP simultaneously learns the subspace basis, low-rank coefficients and column sparse errors to formulate appearance subspace. Within LSAP framework, we introduce a Hadamard production based regularization to incorporate rich generative/discriminative structure constraints to adaptively penalize the coefficients for subspace learning. It is shown that such adaptive penalization can significantly improve the robustness of LSAP on severely corrupted dataset. To utilize LSAP for online visual tracking, we also develop an efficient incremental optimization scheme for nuclear norm and column sparse norm minimizations. Experiments on 50 challenging video sequences demonstrate that our tracker outperforms other state-of-the-art methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
Ranking scientific publications: the effect of nonlinearity.
Yao, Liyang; Wei, Tian; Zeng, An; Fan, Ying; Di, Zengru
2014-10-17
Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.
Ranking scientific publications: the effect of nonlinearity
Yao, Liyang; Wei, Tian; Zeng, An; Fan, Ying; di, Zengru
2014-10-01
Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.
Partial Kernelization for Rank Aggregation: Theory and Experiments
Betzler, Nadja; Bredereck, Robert; Niedermeier, Rolf
Rank Aggregation is important in many areas ranging from web search over databases to bioinformatics. The underlying decision problem Kemeny Score is NP-complete even in case of four input rankings to be aggregated into a "median ranking". We study efficient polynomial-time data reduction rules that allow us to find optimal median rankings. On the theoretical side, we improve a result for a "partial problem kernel" from quadratic to linear size. On the practical side, we provide encouraging experimental results with data based on web search and sport competitions, e.g., computing optimal median rankings for real-world instances with more than 100 candidates within milliseconds.
On low-rank updates to the singular value and Tucker decompositions
Energy Technology Data Exchange (ETDEWEB)
O' Hara, M J
2009-10-06
The singular value decomposition is widely used in signal processing and data mining. Since the data often arrives in a stream, the problem of updating matrix decompositions under low-rank modification has been widely studied. Brand developed a technique in 2006 that has many advantages. However, the technique does not directly approximate the updated matrix, but rather its previous low-rank approximation added to the new update, which needs justification. Further, the technique is still too slow for large information processing problems. We show that the technique minimizes the change in error per update, so if the error is small initially it remains small. We show that an updating algorithm for large sparse matrices should be sub-linear in the matrix dimension in order to be practical for large problems, and demonstrate a simple modification to the original technique that meets the requirements.
Asset ranking manager (ranking index of components)
Energy Technology Data Exchange (ETDEWEB)
Maloney, S.M.; Engle, A.M.; Morgan, T.A. [Applied Reliability, Maracor Software and Engineering (United States)
2004-07-01
The Ranking Index of Components (RIC) is an Asset Reliability Manager (ARM), which itself is a Web Enabled front end where plant database information fields from several disparate databases are combined. That information is used to create a specific weighted number (Ranking Index) relating to that components health and risk to the site. The higher the number, the higher priority that any work associated with that component receives. ARM provides site Engineering, Maintenance and Work Control personnel with a composite real time - (current condition) look at the components 'risk of not working' to the plant. Information is extracted from the existing Computerized Maintenance management System (CMMS) and specific site applications and processed nightly. ARM helps to ensure that the most important work is placed into the workweeks and the non value added work is either deferred, frequency changed or deleted. This information is on the web, updated each night, and available for all employees to use. This effort assists the work management specialist when allocating limited resources to the most important work. The use of this tool has maximized resource usage, performing the most critical work with available resources. The ARM numbers are valued inputs into work scoping for the workweek managers. System and Component Engineers are using ARM to identify the components that are at 'risk of failure' and therefore should be placed into the appropriate work week schedule.
Sparse subspace clustering for data with missing entries and high-rank matrix completion.
Fan, Jicong; Chow, Tommy W S
2017-09-01
Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
Robust Visual Tracking Via Consistent Low-Rank Sparse Learning
Zhang, Tianzhu
2014-06-19
Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.
Reduced Rank Adaptive Filtering in Impulsive Noise Environments
Soury, Hamza
2014-01-06
An impulsive noise environment is used in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction. The minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each algorithm is discussed.
Directory of Open Access Journals (Sweden)
Arda Halu
Full Text Available Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.
Halu, Arda; Mondragón, Raúl J; Panzarasa, Pietro; Bianconi, Ginestra
2013-01-01
Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.
Ranking of Rankings: Benchmarking Twenty-Five Higher Education Ranking Systems in Europe
Stolz, Ingo; Hendel, Darwin D.; Horn, Aaron S.
2010-01-01
The purpose of this study is to evaluate the ranking practices of 25 European higher education ranking systems (HERSs). Ranking practices were assessed with 14 quantitative measures derived from the Berlin Principles on Ranking of Higher Education Institutions (BPs). HERSs were then ranked according to their degree of congruence with the BPs.…
An algorithm for ranking assignments using reoptimization
DEFF Research Database (Denmark)
Pedersen, Christian Roed; Nielsen, Lars Relund; Andersen, Kim Allan
2008-01-01
We consider the problem of ranking assignments according to cost in the classical linear assignment problem. An algorithm partitioning the set of possible assignments, as suggested by Murty, is presented where, for each partition, the optimal assignment is calculated using a new reoptimization...... technique. Computational results for the new algorithm are presented...
Kirch, Darrell G; Prescott, John E
2013-08-01
Since the 1980s, school ranking systems have been a topic of discussion among leaders of higher education. Various ranking systems are based on inadequate data that fail to illustrate the complex nature and special contributions of the institutions they purport to rank, including U.S. medical schools, each of which contributes uniquely to meeting national health care needs. A study by Tancredi and colleagues in this issue of Academic Medicine illustrates the limitations of rankings specific to primary care training programs. This commentary discusses, first, how each school's mission and strengths, as well as the impact it has on the community it serves, are distinct, and, second, how these schools, which are each unique, are poorly represented by overly subjective ranking methodologies. Because academic leaders need data that are more objective to guide institutional development, the Association of American Medical Colleges (AAMC) has been developing tools to provide valid data that are applicable to each medical school. Specifically, the AAMC's Medical School Admissions Requirements and its Missions Management Tool each provide a comprehensive assessment of medical schools that leaders are using to drive institutional capacity building. This commentary affirms the importance of mission while challenging the leaders of medical schools, teaching hospitals, and universities to use reliable data to continually improve the quality of their training programs to improve the health of all.
Low-rank quadratic semidefinite programming
Yuan, Ganzhao
2013-04-01
Low rank matrix approximation is an attractive model in large scale machine learning problems, because it can not only reduce the memory and runtime complexity, but also provide a natural way to regularize parameters while preserving learning accuracy. In this paper, we address a special class of nonconvex quadratic matrix optimization problems, which require a low rank positive semidefinite solution. Despite their non-convexity, we exploit the structure of these problems to derive an efficient solver that converges to their local optima. Furthermore, we show that the proposed solution is capable of dramatically enhancing the efficiency and scalability of a variety of concrete problems, which are of significant interest to the machine learning community. These problems include the Top-k Eigenvalue problem, Distance learning and Kernel learning. Extensive experiments on UCI benchmarks have shown the effectiveness and efficiency of our proposed method. © 2012.
Linear Subspace Ranking Hashing for Cross-Modal Retrieval.
Li, Kai; Qi, Guo-Jun; Ye, Jun; Hua, Kien A
2017-09-01
Hashing has attracted a great deal of research in recent years due to its effectiveness for the retrieval and indexing of large-scale high-dimensional multimedia data. In this paper, we propose a novel ranking-based hashing framework that maps data from different modalities into a common Hamming space where the cross-modal similarity can be measured using Hamming distance. Unlike existing cross-modal hashing algorithms where the learned hash functions are binary space partitioning functions, such as the sign and threshold function, the proposed hashing scheme takes advantage of a new class of hash functions closely related to rank correlation measures which are known to be scale-invariant, numerically stable, and highly nonlinear. Specifically, we jointly learn two groups of linear subspaces, one for each modality, so that features' ranking orders in different linear subspaces maximally preserve the cross-modal similarities. We show that the ranking-based hash function has a natural probabilistic approximation which transforms the original highly discontinuous optimization problem into one that can be efficiently solved using simple gradient descent algorithms. The proposed hashing framework is also flexible in the sense that the optimization procedures are not tied up to any specific form of loss function, which is typical for existing cross-modal hashing methods, but rather we can flexibly accommodate different loss functions with minimal changes to the learning steps. We demonstrate through extensive experiments on four widely-used real-world multimodal datasets that the proposed cross-modal hashing method can achieve competitive performance against several state-of-the-arts with only moderate training and testing time.
Diversifying customer review rankings.
Krestel, Ralf; Dokoohaki, Nima
2015-06-01
E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything. Current approaches to display reviews to users or recommend an individual review for a product are based on the recency or helpfulness of each review. In this paper, we present a framework to rank product reviews by optimizing the coverage of the ranking with respect to sentiment or aspects, or by summarizing all reviews with the top-K reviews in the ranking. To accomplish this, we make use of the assigned star rating for a product as an indicator for a review's sentiment polarity and compare bag-of-words (language model) with topic models (latent Dirichlet allocation) as a mean to represent aspects. Our evaluation on manually annotated review data from a commercial review Web site demonstrates the effectiveness of our approach, outperforming plain recency ranking by 30% and obtaining best results by combining language and topic model representations. Copyright © 2015 Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Müller, Emmanuel; Assent, Ira; Steinhausen, Uwe
2008-01-01
Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed...
Block models and personalized PageRank.
Kloumann, Isabel M; Ugander, Johan; Kleinberg, Jon
2017-01-03
Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods through the "seed set expansion problem": given a subset [Formula: see text] of nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate in the space of "landing probabilities" of a random walk rooted at the seed set, ranking nodes according to weighted sums of landing probabilities of different length walks. Both schemes, however, lack an a priori relationship to the seed set objective. In this work, we develop a principled framework for evaluating ranking methods by studying seed set expansion applied to the stochastic block model. We derive the optimal gradient for separating the landing probabilities of two classes in a stochastic block model and find, surprisingly, that under reasonable assumptions the gradient is asymptotically equivalent to personalized PageRank for a specific choice of the PageRank parameter [Formula: see text] that depends on the block model parameters. This connection provides a formal motivation for the success of personalized PageRank in seed set expansion and node ranking generally. We use this connection to propose more advanced techniques incorporating higher moments of landing probabilities; our advanced methods exhibit greatly improved performance, despite being simple linear classification rules, and are even competitive with belief propagation.
Tensor Factorization for Low-Rank Tensor Completion.
Zhou, Pan; Lu, Canyi; Lin, Zhouchen; Zhang, Chao
2018-03-01
Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem. Our method preserves the low-rank structure of a tensor by factorizing it into the product of two tensors of smaller sizes. In the optimization process, our method only needs to update two smaller tensors, which can be more efficiently conducted than computing t-SVD. Furthermore, we prove that the proposed alternating minimization algorithm can converge to a Karush-Kuhn-Tucker point. Experimental results on the synthetic data recovery, image and video inpainting tasks clearly demonstrate the superior performance and efficiency of our developed method over state-of-the-arts including the TNN and matricization methods.
Assembly line balancing with resource constraints using new rank-based crossovers
Kamarudin, N. H.; Rashid, M. F. F. Ab.
2017-10-01
Assembly line balancing (ALB) is about distributing the assembly tasks into workstations with the almost equal workload. Recently, researchers started to consider the resource constraints in ALB such as machine and worker, to make the assembly layout more efficient. This paper presents an ALB with resource constraints (ALB-RC) to minimize the workstation, machine and worker. For the optimization purpose, genetic algorithm (GA) with two new crossovers is introduced. The crossovers are developed using ranking approach and known as rank-based crossover type I and type II (RBC-I and RBC-II). These crossovers are tested against popular combinatorial crossovers using 17 benchmark problems. The computational experiment results indicated that the RBC-II has better overall performance because of the balance between divergence and guidance in the reproduction process. In future, the RBC-I and RBC-II will be tested for different variant of ALB problems.
Improving Ranking Using Quantum Probability
Melucci, Massimo
2011-01-01
The paper shows that ranking information units by quantum probability differs from ranking them by classical probability provided the same data used for parameter estimation. As probability of detection (also known as recall or power) and probability of false alarm (also known as fallout or size) measure the quality of ranking, we point out and show that ranking by quantum probability yields higher probability of detection than ranking by classical probability provided a given probability of ...
Research of Subgraph Estimation Page Rank Algorithm for Web Page Rank
Directory of Open Access Journals (Sweden)
LI Lan-yin
2017-04-01
Full Text Available The traditional PageRank algorithm can not efficiently perform large data Webpage scheduling problem. This paper proposes an accelerated algorithm named topK-Rank，which is based on PageRank on the MapReduce platform. It can find top k nodes efficiently for a given graph without sacrificing accuracy. In order to identify top k nodes，topK-Rank algorithm prunes unnecessary nodes and edges in each iteration to dynamically construct subgraphs，and iteratively estimates lower/upper bounds of PageRank scores through subgraphs. Theoretical analysis shows that this method guarantees result exactness. Experiments show that topK-Rank algorithm can find k nodes much faster than the existing approaches.
Improving the Incoherence of a Learned Dictionary via Rank Shrinkage.
Ubaru, Shashanka; Seghouane, Abd-Krim; Saad, Yousef
2017-01-01
This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first update the dictionary using the method of optimal directions (MOD) and then apply a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of the column-normalized least squares estimate of the dictionary obtained from the MOD step. We then shrink the rank of this learned dictionary by transforming the problem of reducing the rank to a nonnegative garrotte estimation problem and solving it using a path-wise coordinate descent approach. We establish theoretical results that show that the rank shrinkage step included will reduce the coherence of the dictionary, which is further validated by experimental results. Numerical experiments illustrating the performance of the proposed algorithm in comparison to various other well-known dictionary learning algorithms are also presented.
Fractional cointegration rank estimation
DEFF Research Database (Denmark)
Lasak, Katarzyna; Velasco, Carlos
We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for further long-run equilibrium relations with possibly different persistence levels. The fi…rst step consists in estimating the parame......We consider cointegration rank estimation for a p-dimensional Fractional Vector Error Correction Model. We propose a new two-step procedure which allows testing for further long-run equilibrium relations with possibly different persistence levels. The fi…rst step consists in estimating...... to control for stochastic trend estimation effects from the first step. The critical values of the tests proposed depend only on the number of common trends under the null, p - r, and on the interval of the cointegration degrees b allowed, but not on the true cointegration degree b0. Hence, no additional...
Low Rank Approximation Algorithms, Implementation, Applications
Markovsky, Ivan
2012-01-01
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory. Applications described include: system and control theory: approximate realization, model reduction, output error, and errors-in-variables identification; signal processing: harmonic retrieval, sum-of-damped exponentials, finite impulse response modeling, and array processing; machine learning: multidimensional scaling and recommender system; computer vision: algebraic curve fitting and fundamental matrix estimation; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; ...
Efficient completion for corrupted low-rank images via alternating direction method
Li, Wei; Zhao, Lei; Xu, Duanqing; Lu, Dongming
2014-05-01
We propose an efficient and easy-to-implement method to settle the inpainting problem for low-rank images following the recent studies about low-rank matrix completion. In general, our method has three steps: first, corresponding to the three channels of RGB color space, an incomplete image is split into three incomplete matrices; second, each matrix is restored by solving a convex problem derived from the nuclear norm relaxation; at last, the three recovered matrices are merged to produce the final output. During the process, in order to efficiently solve the nuclear norm minimization problem, we employ the alternating direction method. Except for the basic image inpainting problem, we also enable our method to handle cases where corrupted images not only have missing values but also have noisy entries. Our experiments show that our method outperforms the existing inpainting techniques both quantitatively and qualitatively. We also demonstrate that our method is capable of processing many other situations, including block-wise low-rank image completion, large-scale image restoration, and object removal.
Can College Rankings Be Believed?
Directory of Open Access Journals (Sweden)
Meredith Davis
Full Text Available The article summarizes literature on college and university rankings worldwide and the strategies used by various ranking organizations, including those of government and popular media. It traces the history of national and global rankings, indicators used by ranking systems, and the effect of rankings on academic programs and their institutions. Although ranking systems employ diverse criteria and most weight certain indicators over others, there is considerable skepticism that most actually measure educational quality. At the same time, students and their families increasingly consult these evaluations when making college decisions, and sponsors of faculty research consider reputation when forming academic partnerships. While there are serious concerns regarding the validity of ranking institutions when so little data can support differences between one institution and another, college rankings appear to be here to stay.
Ranking images based on aesthetic qualities.
Gaur, Aarushi
2015-01-01
The qualitative assessment of image content and aesthetic impression is affected by various image attributes and relations between the attributes. Modelling of such assessments in the form of objective rankings and learning image representations based on them is not a straightforward problem. The criteria can be varied with different levels of complexity for various applications. A highly-complex problem could involve a large number of interrelated attributes and features alongside varied rul...
Ranking Baltic States Researchers
Directory of Open Access Journals (Sweden)
Gyula Mester
2017-10-01
Full Text Available In this article, using the h-index and the total number of citations, the best 10 Lithuanian, Latvian and Estonian researchers from several disciplines are ranked. The list may be formed based on the h-index and the total number of citations, given in Web of Science, Scopus, Publish or Perish Program and Google Scholar database. Data for the first 10 researchers are presented. Google Scholar is the most complete. Therefore, to define a single indicator, h-index calculated by Google Scholar may be a good and simple one. The author chooses the Google Scholar database as it is the broadest one.
2015-04-28
eigenvector of the associated Laplacian matrix (i.e., the Fiedler vector) matches that of the variables. In other words, this approach (reminiscent of...S1), i.e., Dii = ∑n j=1Gi,j is the degree of node i in the measurement graph G. 3: Compute the Fiedler vector of S (eigenvector corresponding to the...smallest nonzero eigenvalue of LS). 4: Output the ranking induced by sorting the Fiedler vector of S, with the global ordering (increasing or decreasing
Ranking agricultural, environmental and natural resource economics journals: A note
Halkos, George; Tzeremes, Nickolaos
2012-01-01
This paper by applying Data Envelopment Analysis (DEA) ranks for the first time Economics journals in the field of Agricultural, Environmental and Natural Resource. Specifically, by using one composite input and one composite output the paper ranks 32 journals. In addition for the first time three different quality ranking reports have been incorporated to the DEA modelling problem in order to classify the journals into four categories (‘A’ to ‘D’). The results reveal that the journals with t...
Rankings from Fuzzy Pairwise Comparisons
van den Broek, P.M.; Noppen, J.A.R.; Mohammadian, M.
2006-01-01
We propose a new method for deriving rankings from fuzzy pairwise comparisons. It is based on the observation that quantification of the uncertainty of the pairwise comparisons should be used to obtain a better crisp ranking, instead of a fuzzified version of the ranking obtained from crisp pairwise
African Journals Online (AJOL)
maths/stats
INTRODUCTION. PageRank is Google's system for ranking web pages. A page with a higher PageRank is deemed more important and is more likely to be listed above a ... Felix U. Ogban, Department of Mathematics/Statistics and Computer Science, Faculty of Science, University of ..... probability, 2004, 41, (3): 721-734.
University Rankings and Social Science
Marginson, Simon
2014-01-01
University rankings widely affect the behaviours of prospective students and their families, university executive leaders, academic faculty, governments and investors in higher education. Yet the social science foundations of global rankings receive little scrutiny. Rankings that simply recycle reputation without any necessary connection to real…
Sequential rank agreement methods for comparison of ranked lists
DEFF Research Database (Denmark)
Ekstrøm, Claus Thorn; Gerds, Thomas Alexander; Jensen, Andreas Kryger
2015-01-01
The comparison of alternative rankings of a set of items is a general and prominent task in applied statistics. Predictor variables are ranked according to magnitude of association with an outcome, prediction models rank subjects according to the personalized risk of an event, and genetic studies...... are illustrated using gene rankings, and using data from two Danish ovarian cancer studies where we assess the within and between agreement of different statistical classification methods.......The comparison of alternative rankings of a set of items is a general and prominent task in applied statistics. Predictor variables are ranked according to magnitude of association with an outcome, prediction models rank subjects according to the personalized risk of an event, and genetic studies...
Hosking, Michael Robert
This dissertation improves an analyst's use of simulation by offering improvements in the utilization of kriging metamodels. There are three main contributions. First an analysis is performed of what comprises good experimental designs for practical (non-toy) problems when using a kriging metamodel. Second is an explanation and demonstration of how reduced rank decompositions can improve the performance of kriging, now referred to as reduced rank kriging. Third is the development of an extension of reduced rank kriging which solves an open question regarding the usage of reduced rank kriging in practice. This extension is called omni-rank kriging. Finally these results are demonstrated on two case studies. The first contribution focuses on experimental design. Sequential designs are generally known to be more efficient than "one shot" designs. However, sequential designs require some sort of pilot design from which the sequential stage can be based. We seek to find good initial designs for these pilot studies, as well as designs which will be effective if there is no following sequential stage. We test a wide variety of designs over a small set of test-bed problems. Our findings indicate that analysts should take advantage of any prior information they have about their problem's shape and/or their goals in metamodeling. In the event of a total lack of information we find that Latin hypercube designs are robust default choices. Our work is most distinguished by its attention to the higher levels of dimensionality. The second contribution introduces and explains an alternative method for kriging when there is noise in the data, which we call reduced rank kriging. Reduced rank kriging is based on using a reduced rank decomposition which artificially smoothes the kriging weights similar to a nugget effect. Our primary focus will be showing how the reduced rank decomposition propagates through kriging empirically. In addition, we show further evidence for our
A multivariate rank test for comparing mass size distributions
Lombard, F.
2012-04-01
Particle size analyses of a raw material are commonplace in the mineral processing industry. Knowledge of particle size distributions is crucial in planning milling operations to enable an optimum degree of liberation of valuable mineral phases, to minimize plant losses due to an excess of oversize or undersize material or to attain a size distribution that fits a contractual specification. The problem addressed in the present paper is how to test the equality of two or more underlying size distributions. A distinguishing feature of these size distributions is that they are not based on counts of individual particles. Rather, they are mass size distributions giving the fractions of the total mass of a sampled material lying in each of a number of size intervals. As such, the data are compositional in nature, using the terminology of Aitchison [1] that is, multivariate vectors the components of which add to 100%. In the literature, various versions of Hotelling\\'s T 2 have been used to compare matched pairs of such compositional data. In this paper, we propose a robust test procedure based on ranks as a competitor to Hotelling\\'s T 2. In contrast to the latter statistic, the power of the rank test is not unduly affected by the presence of outliers or of zeros among the data. © 2012 Copyright Taylor and Francis Group, LLC.
Ranking schools on external knowledge tests results
Directory of Open Access Journals (Sweden)
Gašper Cankar
2007-01-01
Full Text Available The paper discusses the use of external knowledge test results for school ranking and the implicit effect of such ranking. A question of validity is raised and a review of research literature and main known problems are presented. In many western countries publication of school results is a common practice and a similar trend can be observed in Slovenia. Experiences of other countries help to predict positive and negative aspects of such publication. Results of external knowledge tests produce very limited information about school quality—if we use other sources of information our ranking of schools can be very different. Nevertheless, external knowledge tests can yield useful information. If we want to improve quality in schools, we must allow schools to use this information themselves and improve from within. Broad public scrutiny is unnecessary and problematic—it moves the focus of school efforts from real improvement of quality to mere improvement of the school public image.
Moving object detection via low-rank total variation regularization
Wang, Pengcheng; Chen, Qian; Shao, Na
2016-09-01
Moving object detection is a challenging task in video surveillance. Recently proposed Robust Principal Component Analysis (RPCA) can recover the outlier patterns from the low-rank data under some mild conditions. However, the l-penalty in RPCA doesn't work well in moving object detection because the irrepresentable condition is often not satisfied. In this paper, a method based on total variation (TV) regularization scheme is proposed. In our model, image sequences captured with a static camera are highly related, which can be described using a low-rank matrix. Meanwhile, the low-rank matrix can absorb background motion, e.g. periodic and random perturbation. The foreground objects in the sequence are usually sparsely distributed and drifting continuously, and can be treated as group outliers from the highly-related background scenes. Instead of l-penalty, we exploit the total variation of the foreground. By minimizing the total variation energy, the outliers tend to collapse and finally converge to be the exact moving objects. The TV-penalty is superior to the l-penalty especially when the outlier is in the majority for some pixels, and our method can estimate the outlier explicitly with less bias but higher variance. To solve the problem, a joint optimization function is formulated and can be effectively solved through the inexact Augmented Lagrange Multiplier (ALM) method. We evaluate our method along with several state-of-the-art approaches in MATLAB. Both qualitative and quantitative results demonstrate that our proposed method works effectively on a large range of complex scenarios.
Non-Convex Sparse and Low-Rank Based Robust Subspace Segmentation for Data Mining.
Cheng, Wenlong; Zhao, Mingbo; Xiong, Naixue; Chui, Kwok Tai
2017-07-15
Parsimony, including sparsity and low-rank, has shown great importance for data mining in social networks, particularly in tasks such as segmentation and recognition. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with convex l₁-norm or nuclear norm constraints. However, the obtained results by convex optimization are usually suboptimal to solutions of original sparse or low-rank problems. In this paper, a novel robust subspace segmentation algorithm has been proposed by integrating lp-norm and Schatten p-norm constraints. Our so-obtained affinity graph can better capture local geometrical structure and the global information of the data. As a consequence, our algorithm is more generative, discriminative and robust. An efficient linearized alternating direction method is derived to realize our model. Extensive segmentation experiments are conducted on public datasets. The proposed algorithm is revealed to be more effective and robust compared to five existing algorithms.
Ranking nodes in growing networks: When PageRank fails.
Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng
2015-11-10
PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm's efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank's performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.
Probabilistic Properties of Rectilinear Steiner Minimal Trees
Directory of Open Access Journals (Sweden)
V. N. Salnikov
2015-01-01
Full Text Available This work concerns the properties of Steiner minimal trees for the manhattan plane in the context of introducing a probability measure. This problem is important because exact algorithms to solve the Steiner problem are computationally expensive (NP-hard and the solution (especially in the case of big number of points to be connected has a diversity of practical applications. That is why the work considers a possibility to rank the possible topologies of the minimal trees with respect to a probability of their usage. For this, the known facts about the structural properties of minimal trees for selected metrics have been analyzed to see their usefulness for the problem in question. For the small amount of boundary (fixed vertices, the paper offers a way to introduce a probability measure as a corollary of proved theorem about some structural properties of the minimal trees.This work is considered to further the previous similar activity concerning a problem of searching for minimal fillings, and it is a door opener to the more general (complicated task. The stated method demonstrates the possibility to reach the final result analytically, which gives a chance of its applicability to the case of the bigger number of boundary vertices (probably, with the use of computer engineering.The introducing definition of an essential Steiner point allowed a considerable restriction of the ambiguity of initial problem solution and, at the same time, comparison of such an approach with more classical works in the field concerned. The paper also lists main barriers of classical approaches, preventing their use for the task of introducing a probability measure.In prospect, application areas of the described method are expected to be wider both in terms of system enlargement (the number of boundary vertices and in terms of other metric spaces (the Euclidean case is of especial interest. The main interest is to find the classes of topologies with significantly
Reduced rank adaptive filtering in impulsive noise environments
Soury, Hamza
2014-11-01
An impulsive noise environment is considered in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction, while the minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each method is discussed. © 2014 IEEE.
Using incomplete citation data for MEDLINE results ranking.
Herskovic, Jorge R; Bernstam, Elmer V
2005-01-01
Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts from large result sets with decreasing citation information. We found that PageRank performs better than simple citation counts, but both algorithms are surprisingly robust to information loss. We conclude that even an incomplete citation database is likely to be effective for importance ranking.
The Friedman Two-Way Analysis of Variance as a Test for Ranking Error
Wagner, Edwin E.
1976-01-01
The problem of bias in rankings due to the initial position of entities when presented to judges is discussed. A modification of the Friedman Two-Way Analysis of Variance to test "ranking error" is presented. (JKS)
Wikipedia ranking of world universities
Lages, José; Patt, Antoine; Shepelyansky, Dima L.
2016-03-01
We use the directed networks between articles of 24 Wikipedia language editions for producing the wikipedia ranking of world Universities (WRWU) using PageRank, 2DRank and CheiRank algorithms. This approach allows to incorporate various cultural views on world universities using the mathematical statistical analysis independent of cultural preferences. The Wikipedia ranking of top 100 universities provides about 60% overlap with the Shanghai university ranking demonstrating the reliable features of this approach. At the same time WRWU incorporates all knowledge accumulated at 24 Wikipedia editions giving stronger highlights for historically important universities leading to a different estimation of efficiency of world countries in university education. The historical development of university ranking is analyzed during ten centuries of their history.
Energy Technology Data Exchange (ETDEWEB)
Weber, G. F.; Laudal, D. L.
1989-01-01
This work is a compilation of reports on ongoing research at the University of North Dakota. Topics include: Control Technology and Coal Preparation Research (SO{sub x}/NO{sub x} control, waste management), Advanced Research and Technology Development (turbine combustion phenomena, combustion inorganic transformation, coal/char reactivity, liquefaction reactivity of low-rank coals, gasification ash and slag characterization, fine particulate emissions), Combustion Research (fluidized bed combustion, beneficiation of low-rank coals, combustion characterization of low-rank coal fuels, diesel utilization of low-rank coals), Liquefaction Research (low-rank coal direct liquefaction), and Gasification Research (hydrogen production from low-rank coals, advanced wastewater treatment, mild gasification, color and residual COD removal from Synfuel wastewaters, Great Plains Gasification Plant, gasifier optimization).
Kirschner, P.A.; Sweller, J.; Clark, R.E
2006-01-01
Evidence for the superiority of guided instruction is explained in the context of our knowledge of human cognitive architecture, expert–novice differences, and cognitive load. Although unguided or minimally guided instructional approaches are very popular and intuitively appealing, the point is
CNN-based ranking for biomedical entity normalization.
Li, Haodi; Chen, Qingcai; Tang, Buzhou; Wang, Xiaolong; Xu, Hua; Wang, Baohua; Huang, Dong
2017-10-03
Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.
Statistical methods for ranking data
Alvo, Mayer
2014-01-01
This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.
Probabilistic Low-Rank Multitask Learning.
Kong, Yu; Shao, Ming; Li, Kang; Fu, Yun
2017-01-04
In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multitask learning (MTL) that can automatically balance between low-rank and sparsity constraints. The former assumes a low-rank structure of the underlying predictive hypothesis space to explicitly capture the relationship of different tasks and the latter learns the incoherent sparse patterns private to each task. We derive and perform inference via variational Bayesian methods. Experimental results on both regression and classification tasks on real-world applications demonstrate the effectiveness of the proposed method in dealing with the MTL problems.
Trachomatous Scar Ranking: A Novel Outcome for Trachoma Studies.
Baldwin, Angela; Ryner, Alexander M; Tadesse, Zerihun; Shiferaw, Ayalew; Callahan, Kelly; Fry, Dionna M; Zhou, Zhaoxia; Lietman, Thomas M; Keenan, Jeremy D
2017-06-01
AbstractWe evaluated a new trachoma scarring ranking system with potential use in clinical research. The upper right tarsal conjunctivas of 427 individuals from Ethiopian villages with hyperendemic trachoma were photographed. An expert grader first assigned a scar grade to each photograph using the 1981 World Health Organization (WHO) grading system. Then, all photographs were ranked from least (rank = 1) to most scarring (rank = 427). Photographic grading found 79 (18.5%) conjunctivae without scarring (C0), 191 (44.7%) with minimal scarring (C1), 105 (24.6%) with moderate scarring (C2), and 52 (12.2%) with severe scarring (C3). The ranking method demonstrated good internal validity, exhibiting a monotonic increase in the median rank across the levels of the 1981 WHO grading system. Intrarater repeatability was better for the ranking method (intraclass correlation coefficient = 0.84, 95% CI = 0.74-0.94). Exhibiting better internal and external validity, this ranking method may be useful for evaluating the difference in scarring between groups of individuals.
Ranking nodes in growing networks: When PageRank fails
Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng
2015-11-01
PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm’s efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank’s performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.
Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation
Directory of Open Access Journals (Sweden)
Pandiarajan K.
2014-09-01
Full Text Available This paper presents an effective method of network overload management in power systems. The three competing objectives 1 generation cost 2 transmission line overload and 3 real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO and Differential evolution (DE. Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem
Dermísek, Radovan; Gunion, John F
2005-07-22
We demonstrate that the next to minimal supersymmetric model can have small fine-tuning and modest top-squark mass while still evading all experimental constraints. For small tan(beta (large tan(beta), the relevant scenarios are such that there is always (often) a standard-model-like Higgs boson that decays to two lighter--possibly much lighter--Higgs pseudoscalars.
Martin, Brian
2016-01-01
Brian Martin describes a difficult committee meeting he once attended which consisted of one representative from each department. When the meeting ended it left a bitter taste for many who participated. Having learned from this experience, Martin became a chair of the committee and tried a new system that overcame many of the previous problems.…
University Rankings in Critical Perspective
Pusser, Brian; Marginson, Simon
2013-01-01
This article addresses global postsecondary ranking systems by using critical-theoretical perspectives on power. This research suggests rankings are at once a useful lens for studying power in higher education and an important instrument for the exercise of power in service of dominant norms in global higher education. (Contains 1 table and 1…
University Ranking as Social Exclusion
Amsler, Sarah S.; Bolsmann, Chris
2012-01-01
In this article we explore the dual role of global university rankings in the creation of a new, knowledge-identified, transnational capitalist class and in facilitating new forms of social exclusion. We examine how and why the practice of ranking universities has become widely defined by national and international organisations as an important…
Minimizing Costs Can Be Costly
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Rasmus Rasmussen
2010-01-01
Full Text Available A quite common practice, even in academic literature, is to simplify a decision problem and model it as a cost-minimizing problem. In fact, some type of models has been standardized to minimization problems, like Quadratic Assignment Problems (QAPs, where a maximization formulation would be treated as a “generalized” QAP and not solvable by many of the specially designed softwares for QAP. Ignoring revenues when modeling a decision problem works only if costs can be separated from the decisions influencing revenues. More often than we think this is not the case, and minimizing costs will not lead to maximized profit. This will be demonstrated using spreadsheets to solve a small example. The example is also used to demonstrate other pitfalls in network models: the inability to generally balance the problem or allocate costs in advance, and the tendency to anticipate a specific type of solution and thereby make constraints too limiting when formulating the problem.
Universal scaling in sports ranking
Deng, Weibing; Li, Wei; Cai, Xu; Bulou, Alain; Wang, Qiuping A.
2012-09-01
Ranking is a ubiquitous phenomenon in human society. On the web pages of Forbes, one may find all kinds of rankings, such as the world's most powerful people, the world's richest people, the highest-earning tennis players, and so on and so forth. Herewith, we study a specific kind—sports ranking systems in which players' scores and/or prize money are accrued based on their performances in different matches. By investigating 40 data samples which span 12 different sports, we find that the distributions of scores and/or prize money follow universal power laws, with exponents nearly identical for most sports. In order to understand the origin of this universal scaling we focus on the tennis ranking systems. By checking the data we find that, for any pair of players, the probability that the higher-ranked player tops the lower-ranked opponent is proportional to the rank difference between the pair. Such a dependence can be well fitted to a sigmoidal function. By using this feature, we propose a simple toy model which can simulate the competition of players in different matches. The simulations yield results consistent with the empirical findings. Extensive simulation studies indicate that the model is quite robust with respect to the modifications of some parameters.
Universal scaling in sports ranking
Deng, Weibing; Cai, Xu; Bulou, Alain; Wang, Qiuping A
2011-01-01
Ranking is a ubiquitous phenomenon in the human society. By clicking the web pages of Forbes, you may find all kinds of rankings, such as world's most powerful people, world's richest people, top-paid tennis stars, and so on and so forth. Herewith, we study a specific kind, sports ranking systems in which players' scores and prize money are calculated based on their performances in attending various tournaments. A typical example is tennis. It is found that the distributions of both scores and prize money follow universal power laws, with exponents nearly identical for most sports fields. In order to understand the origin of this universal scaling we focus on the tennis ranking systems. By checking the data we find that, for any pair of players, the probability that the higher-ranked player will top the lower-ranked opponent is proportional to the rank difference between the pair. Such a dependence can be well fitted to a sigmoidal function. By using this feature, we propose a simple toy model which can simul...
Fioriti, Vincenzo
2014-01-01
Recent advances in graph theory suggest that is possible to identify the oldest nodes of a network using only the graph topology. Here we report on applications to heterogeneous real world networks. To this end, and in order to gain new insights, we propose the theoretical framework of the Estrada communicability. We apply it to two technological networks (an underground, the diffusion of a software worm in a LAN) and to a third network representing a cholera outbreak. In spite of errors introduced in the adjacency matrix of their graphs, the identification of the oldest nodes is feasible, within a small margin of error, and extremely simple. Utilizations include the search of the initial disease-spreader (patient zero problem), rumors in social networks, malware in computer networks, triggering events in blackouts, oldest urban sites recognition.
Frahm, K. M.; Chepelianskii, A. D.; Shepelyansky, D. L.
2012-10-01
We up a directed network tracing links from a given integer to its divisors and analyze the properties of the Google matrix of this network. The PageRank vector of this matrix is computed numerically and it is shown that its probability is approximately inversely proportional to the PageRank index thus being similar to the Zipf law and the dependence established for the World Wide Web. The spectrum of the Google matrix of integers is characterized by a large gap and a relatively small number of nonzero eigenvalues. A simple semi-analytical expression for the PageRank of integers is derived that allows us to find this vector for matrices of billion size. This network provides a new PageRank order of integers.
Ogunranti, Gbemileke A.; Oluleye, Ayodeji E.
2016-01-01
Purpose: The main objective of this study is to develop a model for solving the one dimensional cutting stock problem in the wood working industry, and develop a program for its implementation. Design/methodology/approach: This study adopts the pattern oriented approach in the formulation of the cutting stock model. A pattern generation algorithm was developed and coded using Visual basic.NET language. The cutting stock model developed is a Linear Programming (LP) Model constrained by num...
Rank Awareness in Group-Sparse Recovery of Multi-Echo MR Images
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Rabab Ward
2013-03-01
Full Text Available This work addresses the problem of recovering multi-echo T1 or T2 weighted images from their partial K-space scans. Recent studies have shown that the best results are obtained when all the multi-echo images are reconstructed by simultaneously exploiting their intra-image spatial redundancy and inter-echo correlation. The aforesaid studies either stack the vectorised images (formed by row or columns concatenation as columns of a Multiple Measurement Vector (MMV matrix or concatenate them as a long vector. Owing to the inter-image correlation, the thus formed MMV matrix or the long concatenated vector is row-sparse or group-sparse respectively in a transform domain (wavelets. Consequently the reconstruction problem was formulated as a row-sparse MMV recovery or a group-sparse vector recovery. In this work we show that when the multi-echo images are arranged in the MMV form, the thus formed matrix is low-rank. We show that better reconstruction accuracy can be obtained when the information about rank-deficiency is incorporated into the row/group sparse recovery problem. Mathematically, this leads to a constrained optimization problem where the objective function promotes the signal’s groups-sparsity as well as its rank-deficiency; the objective function is minimized subject to data fidelity constraints. The experiments were carried out on ex vivo and in vivo T2 weighted images of a rat's spinal cord. Results show that this method yields considerably superior results than state-of-the-art reconstruction techniques.
RANK and RANK ligand expression in primary human osteosarcoma
Directory of Open Access Journals (Sweden)
Daniel Branstetter
2015-09-01
Our results demonstrate RANKL expression was observed in the tumor element in 68% of human OS using IHC. However, the staining intensity was relatively low and only 37% (29/79 of samples exhibited≥10% RANKL positive tumor cells. RANK expression was not observed in OS tumor cells. In contrast, RANK expression was clearly observed in other cells within OS samples, including the myeloid osteoclast precursor compartment, osteoclasts and in giant osteoclast cells. The intensity and frequency of RANKL and RANK staining in OS samples were substantially less than that observed in GCTB samples. The observation that RANKL is expressed in OS cells themselves suggests that these tumors may mediate an osteoclastic response, and anti-RANKL therapy may potentially be protective against bone pathologies in OS. However, the absence of RANK expression in primary human OS cells suggests that any autocrine RANKL/RANK signaling in human OS tumor cells is not operative, and anti-RANKL therapy would not directly affect the tumor.
Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction
Niu, Shanzhou; Yu, Gaohang; Ma, Jianhua; Wang, Jing
2018-02-01
Spectral computed tomography (CT) has been a promising technique in research and clinics because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often affected by serious quantum noise because of the limited number of photons used in the corresponding energy bin. To address this problem, we present an iterative reconstruction method for spectral CT using nonlocal low-rank and sparse matrix decomposition (NLSMD), which exploits the self-similarity of patches that are collected in multi-energy images. Specifically, each set of patches can be decomposed into a low-rank component and a sparse component, and the low-rank component represents the stationary background over different energy bins, while the sparse component represents the rest of the different spectral features in individual energy bins. Subsequently, an effective alternating optimization algorithm was developed to minimize the associated objective function. To validate and evaluate the NLSMD method, qualitative and quantitative studies were conducted by using simulated and real spectral CT data. Experimental results show that the NLSMD method improves spectral CT images in terms of noise reduction, artifact suppression and resolution preservation.
Ranking structures and Rank-Rank Correlations of Countries. The FIFA and UEFA cases
Ausloos, Marcel; Gadomski, Adam; Vitanov, Nikolay K
2014-01-01
Ranking of agents competing with each other in complex systems may lead to paradoxes according to the pre-chosen different measures. A discussion is presented on such rank-rank, similar or not, correlations based on the case of European countries ranked by UEFA and FIFA from different soccer competitions. The first question to be answered is whether an empirical and simple law is obtained for such (self-) organizations of complex sociological systems with such different measuring schemes. It is found that the power law form is not the best description contrary to many modern expectations. The stretched exponential is much more adequate. Moreover, it is found that the measuring rules lead to some inner structures, in both cases.
Ranking structures and rank-rank correlations of countries: The FIFA and UEFA cases
Ausloos, Marcel; Cloots, Rudi; Gadomski, Adam; Vitanov, Nikolay K.
2014-04-01
Ranking of agents competing with each other in complex systems may lead to paradoxes according to the pre-chosen different measures. A discussion is presented on such rank-rank, similar or not, correlations based on the case of European countries ranked by UEFA and FIFA from different soccer competitions. The first question to be answered is whether an empirical and simple law is obtained for such (self-) organizations of complex sociological systems with such different measuring schemes. It is found that the power law form is not the best description contrary to many modern expectations. The stretched exponential is much more adequate. Moreover, it is found that the measuring rules lead to some inner structures in both cases.
Discovery of DNA methylation markers in cervical cancer using relaxation ranking
Directory of Open Access Journals (Sweden)
van Criekinge Wim
2008-11-01
Full Text Available Abstract Background To discover cancer specific DNA methylation markers, large-scale screening methods are widely used. The pharmacological unmasking expression microarray approach is an elegant method to enrich for genes that are silenced and re-expressed during functional reversal of DNA methylation upon treatment with demethylation agents. However, such experiments are performed in in vitro (cancer cell lines, mostly with poor relevance when extrapolating to primary cancers. To overcome this problem, we incorporated data from primary cancer samples in the experimental design. A strategy to combine and rank data from these different data sources is essential to minimize the experimental work in the validation steps. Aim To apply a new relaxation ranking algorithm to enrich DNA methylation markers in cervical cancer. Results The application of a new sorting methodology allowed us to sort high-throughput microarray data from both cervical cancer cell lines and primary cervical cancer samples. The performance of the sorting was analyzed in silico. Pathway and gene ontology analysis was performed on the top-selection and gives a strong indication that the ranking methodology is able to enrich towards genes that might be methylated. Terms like regulation of progression through cell cycle, positive regulation of programmed cell death as well as organ development and embryonic development are overrepresented. Combined with the highly enriched number of imprinted and X-chromosome located genes, and increased prevalence of known methylation markers selected from cervical (the highest-ranking known gene is CCNA1 as well as from other cancer types, the use of the ranking algorithm seems to be powerful in enriching towards methylated genes. Verification of the DNA methylation state of the 10 highest-ranking genes revealed that 7/9 (78% gene promoters showed DNA methylation in cervical carcinomas. Of these 7 genes, 3 (SST, HTRA3 and NPTX1 are not
Brill, K
1997-06-17
Adolescents represent a particularly difficult group with respect to compliance. Not only is incorrect pill intake a common problem, but unnecessary discontinuation also occurs regularly. Reasons for poor compliance are varied, but inadequate information and problems with cycle control and weight gain are particularly important. Choosing a well-tolerated oral contraceptive can help to improve compliance, and clinical experience from a large, multicenter trial suggests that monophasic gestodene (75 micrograms gestodene/30 micrograms ethinylestradiol) is a suitable preparation for this group of women. An investigation of 5,602 adolescents with an average age of 16.4 years found good contraceptive reliability and excellent cycle control. The incidence of spotting and breakthrough bleeding was low and declined during the course of the study. The preparation was tolerated well, and the incidence of adverse events was low, with only 4.4% of women withdrawing from the study due to adverse events. An increase in body weight was uncommon. At the end of the study, 85.0% of adolescents rated monophasic gestodene as good and 9.6% as satisfactory.
Barrera, Gabriela N; León, Alberto E; Ribotta, Pablo D
2016-05-01
During wheat milling, starch granules can experience mechanical damage, producing damaged starch. High levels of damaged starch modify the physicochemical properties of wheat flour, negatively affecting the dough behavior as well as the flour quality and cookie and bread making quality. The aim of this work was to evaluate the effect of α-amylase, maltogenic amylase and amyloglucosidase on dough rheology in order to propose alternatives to reduce the issues related to high levels of damaged starch. The dough with a high level of damaged starch became more viscous and resistant to deformations as well as less elastic and extensible. The soluble fraction of the doughs influenced the rheological behavior of the systems. The α-amylase and amyloglucosidase reduced the negative effects of high damaged starch contents, improving the dough rheological properties modified by damaged starch. The rheological behavior of dough with the higher damaged-starch content was related to a more open gluten network arrangement as a result of the large size of the swollen damaged starch granules. We can conclude that the dough rheological properties of systems with high damaged starch content changed positively as a result of enzyme action, particularly α-amylase and amyloglucosidase additions, allowing the use of these amylases and mixtures of them as corrective additives. Little information was reported about amyloglucosidase activity alone or combined with α-amylase. The combinations of these two enzymes are promising to minimize the negative effects caused by high levels of damaged starch on product quality. More research needs to be done on bread quality combining these two enzymes. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.
University Ranking Systems; Criteria and Critiques
Saka, Yavuz; YAMAN, Süleyman
2011-01-01
The purpose of this paper is to explore international university ranking systems. As a compilation study this paper provides specific criteria that each ranking system uses and main critiques regarding these ranking systems. Since there are many ranking systems in this area of research, this study focused on only most cited and referred ranking systems. As there is no consensus in terms of the criteria that these systems use, this paper has no intention of identifying the best ranking system ...
Ranking species in mutualistic networks.
Domínguez-García, Virginia; Muñoz, Miguel A
2015-02-02
Understanding the architectural subtleties of ecological networks, believed to confer them enhanced stability and robustness, is a subject of outmost relevance. Mutualistic interactions have been profusely studied and their corresponding bipartite networks, such as plant-pollinator networks, have been reported to exhibit a characteristic "nested" structure. Assessing the importance of any given species in mutualistic networks is a key task when evaluating extinction risks and possible cascade effects. Inspired in a recently introduced algorithm--similar in spirit to Google's PageRank but with a built-in non-linearity--here we propose a method which--by exploiting their nested architecture--allows us to derive a sound ranking of species importance in mutualistic networks. This method clearly outperforms other existing ranking schemes and can become very useful for ecosystem management and biodiversity preservation, where decisions on what aspects of ecosystems to explicitly protect need to be made.
University rankings in computer science
DEFF Research Database (Denmark)
Ehret, Philip; Zuccala, Alesia Ann; Gipp, Bela
2017-01-01
This is a research-in-progress paper concerning two types of institutional rankings, the Leiden and QS World ranking, and their relationship to a list of universities’ ‘geo-based’ impact scores, and Computing Research and Education Conference (CORE) participation scores in the field of computer...... science. A ‘geo-based’ impact measure examines the geographical distribution of incoming citations to a particular university’s journal articles for a specific period of time. It takes into account both the number of citations and the geographical variability in these citations. The CORE participation...... score is calculated on the basis of the number of weighted proceedings papers that a university has contributed to either an A*, A, B, or C conference as ranked by the Computing Research and Education Association of Australasia. In addition to calculating the correlations between the distinct university...
A Study of Rank Defect and Network Effect in Processing the CMONOC Network on Bernese
Directory of Open Access Journals (Sweden)
Weiwei Wu
2018-02-01
Full Text Available High-precision GPS data processing on Bernese has been employed to routinely resolve daily position solutions of GPS stations in the Crustal Movement Observation Network of China (CMONOC. The rank-deficient problems of the normal equation (NEQ system and the network effect on the frame alignment of NEQs in the processing of CMONOC data on Bernese still present difficulties. In this study, we diagnose the rank-deficient problems of the original NEQ, review the efficiency of the controlled datum removal (CDR method in filtering out the three frame-origin-related datum contents, investigate the reliabilities of the inherited frame orientation and scale information from the fixation of the GPS satellite orbits and the Earth rotation parameters in establishing the NEQ of the CMONOC network on Bernese, and analyze the impact of the network effect on the position time series of GPS stations. Our results confirm the nonsingularity of the original NEQ and the efficiency of the CDR filtering in resolving the rank-deficient problems; show that the frame origin parameters are weakly defined and should be stripped off, while the frame orientation and scale parameters should be retained due to their insufficient redefinition from the minimal constraint (MC implementation through inhomogeneous and asymmetrical fiducial networks; and reveal the superiority of a globally distributed fiducial network for frame alignment of the reconstructed NEQs via No-Net-Translation (NNT MC conditions. Finally, we attribute the two apparent discontinuities in the position time series to the terrestrial reference frame (TRF conversions of the GPS satellite orbits, and identify it as the orbit TRF effect.
Rank distributions: Frequency vs. magnitude.
Velarde, Carlos; Robledo, Alberto
2017-01-01
We examine the relationship between two different types of ranked data, frequencies and magnitudes. We consider data that can be sorted out either way, through numbers of occurrences or size of the measures, as it is the case, say, of moon craters, earthquakes, billionaires, etc. We indicate that these two types of distributions are functional inverses of each other, and specify this link, first in terms of the assumed parent probability distribution that generates the data samples, and then in terms of an analog (deterministic) nonlinear iterated map that reproduces them. For the particular case of hyperbolic decay with rank the distributions are identical, that is, the classical Zipf plot, a pure power law. But their difference is largest when one displays logarithmic decay and its counterpart shows the inverse exponential decay, as it is the case of Benford law, or viceversa. For all intermediate decay rates generic differences appear not only between the power-law exponents for the midway rank decline but also for small and large rank. We extend the theoretical framework to include thermodynamic and statistical-mechanical concepts, such as entropies and configuration.
Rankings Methodology Hurts Public Institutions
Van Der Werf, Martin
2007-01-01
In the 1980s, when the "U.S. News & World Report" rankings of colleges were based solely on reputation, the nation's public universities were well represented at the top. However, as soon as the magazine began including its "measures of excellence," statistics intended to define quality, public universities nearly disappeared from the top. As the…
Let Us Rank Journalism Programs
Weber, Joseph
2014-01-01
Unlike law, business, and medical schools, as well as universities in general, journalism schools and journalism programs have rarely been ranked. Publishers such as "U.S. News & World Report," "Forbes," "Bloomberg Businessweek," and "Washington Monthly" do not pay them much mind. What is the best…
Weighted Discriminative Dictionary Learning based on Low-rank Representation
Chang, Heyou; Zheng, Hao
2017-01-01
Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.
An ensemble rank learning approach for gene prioritization.
Lee, Po-Feng; Soo, Von-Wun
2013-01-01
Several different computational approaches have been developed to solve the gene prioritization problem. We intend to use the ensemble boosting learning techniques to combine variant computational approaches for gene prioritization in order to improve the overall performance. In particular we add a heuristic weighting function to the Rankboost algorithm according to: 1) the absolute ranks generated by the adopted methods for a certain gene, and 2) the ranking relationship between all gene-pairs from each prioritization result. We select 13 known prostate cancer genes in OMIM database as training set and protein coding gene data in HGNC database as test set. We adopt the leave-one-out strategy for the ensemble rank boosting learning. The experimental results show that our ensemble learning approach outperforms the four gene-prioritization methods in ToppGene suite in the ranking results of the 13 known genes in terms of mean average precision, ROC and AUC measures.
RUSSIAN UNIVERSITIES IN THE LOOP OF THE WORLD EDUCATION RANKINGS
Directory of Open Access Journals (Sweden)
Inozemtseva Ekaterina Sergeevna
2013-02-01
Full Text Available Purpose: a research on different sociological and economic aspects of world education rankings (THE, ARWU, QS, evaluation of their role and impact on the world education market’s main consumers (i.e. students and academic staff as a subject to discussion in terms of the customers’ preferences and choice motivators. Methodology: general scientific research tools were applied throughout the research: analysis, synthesis, deduction. Results: world ranking approach and methodology was assessed, defined and systemized, a unique general ranking of the countries was developed and performed (based on the researched body – the US ranked #1, Russia #30, expert recommendations for Russian universities have been developed and concluded. Practical implications: the main statements could be used within learning courses on the internationalization of higher education and applied in sociological and economic research dedicated to macroeconomic problems and issues analysis.
RUSSIAN UNIVERSITIES IN THE LOOP OF THE WORLD EDUCATION RANKINGS
Directory of Open Access Journals (Sweden)
Екатерина Сергеевна Иноземцева
2013-04-01
Full Text Available Purpose: a research on different sociological and economic aspects of world education rankings (THE, ARWU, QS, evaluation of their role and impact on the world education market’s main consumers (i.e. students and academic staff as a subject to discussion in terms of the customers’ preferences and choice motivators. Methodology: general scientific research tools were applied throughout the research: analysis, synthesis, deduction.Results: world ranking approach and methodology was assessed, defined and systemized, a unique general ranking of the countries was developed and performed (based on the researched body – the US ranked #1, Russia #30, expert recommendations for Russian universities have been developed and concluded.Practical implications: the main statements could be used within learning courses on the internationalization of higher education and applied in sociological and economic research dedicated to macroeconomic problems and issues analysis.DOI: http://dx.doi.org/10.12731/2218-7405-2013-2-18
Ranking industries using a hybrid of DEA-TOPSIS
Directory of Open Access Journals (Sweden)
Amir Mehdiabadi
2013-10-01
Full Text Available Ranking industry normally helps find hot sectors and attract potential investors to invest in appropriate plans. Ranking various industries is also a multiple criteria decision making problem. In this paper, we present an empirical investigation to rank different industries using the art of data envelopment analysis (DEA. The inputs of our proposed DEA model include capital, employment and importance coefficient and outputs are exports, ecological effects and added value. In addition, exports, value added and environmental investment are used as outputs of DEA method. Since the results of DEA may consider more than one efficient unit, so we implement Technique for Order Preference by Similarity to Ideal Solution (TOPSIS technique to rank efficient units. In our case study, there were 15 different sectors from various industries and the implementation of DEA technique recommends 8 efficient units. The implementation of TOPSIS among these efficient units has suggested that Chemical industry could be considered as the most attracting industry for investment.
Analysis of convergence performance of neural networks ranking algorithm.
Zhang, Yongquan; Cao, Feilong
2012-10-01
The ranking problem is to learn a real-valued function which gives rise to a ranking over an instance space, which has gained much attention in machine learning in recent years. This article gives analysis of the convergence performance of neural networks ranking algorithm by means of the given samples and approximation property of neural networks. The upper bounds of convergence rate provided by our results can be considerably tight and independent of the dimension of input space when the target function satisfies some smooth condition. The obtained results imply that neural networks are able to adapt to ranking function in the instance space. Hence the obtained results are able to circumvent the curse of dimensionality on some smooth condition. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.
The Globalization of College and University Rankings
Altbach, Philip G.
2012-01-01
In the era of globalization, accountability, and benchmarking, university rankings have achieved a kind of iconic status. The major ones--the Academic Ranking of World Universities (ARWU, or the "Shanghai rankings"), the QS (Quacquarelli Symonds Limited) World University Rankings, and the "Times Higher Education" World…
Motif discovery in ranked lists of sequences
DEFF Research Database (Denmark)
Nielsen, Morten Muhlig; Tataru, Paula; Madsen, Tobias
2016-01-01
. These features make Regmex well suited for a range of biological sequence analysis problems related to motif discovery, exemplified by microRNA seed enrichment, but also including enrichment problems involving complex motifs and combinations of motifs. We demonstrate a number of usage scenarios that take......Motif analysis has long been an important method to characterize biological functionality and the current growth of sequencing-based genomics experiments further extends its potential. These diverse experiments often generate sequence lists ranked by some functional property. There is therefore...... a growing need for motif analysis methods that can exploit this coupled data structure and be tailored for specific biological questions. Here, we present an exploratory motif analysis tool, Regmex (REGular expression Motif EXplorer), which offers several methods to evaluate the correlation of motifs...
Soh, Kaycheng
2013-01-01
Recent research into university ranking methodologies uncovered several methodological problems among the systems currently in vogue. One of these is the discrepancy between the nominal and attained weights. The problem is the summation of unstandardized indicators for the total scores used in ranking. It is demonstrated that weight discrepancy…
Mirror, Mirror on the Wall: A Closer Look at the Top Ten in University Rankings
Cheng, Soh Kay
2011-01-01
Notwithstanding criticisms and discussions on methodological grounds, much attention has been and still will be paid to university rankings for various reasons. The present paper uses published information of the 10 top-ranking universities of the world and demonstrates the problem of spurious precision. In view of the problem of measurement error…
Time evolution of Wikipedia network ranking
Eom, Young-Ho; Frahm, Klaus M.; Benczúr, András; Shepelyansky, Dima L.
2013-12-01
We study the time evolution of ranking and spectral properties of the Google matrix of English Wikipedia hyperlink network during years 2003-2011. The statistical properties of ranking of Wikipedia articles via PageRank and CheiRank probabilities, as well as the matrix spectrum, are shown to be stabilized for 2007-2011. A special emphasis is done on ranking of Wikipedia personalities and universities. We show that PageRank selection is dominated by politicians while 2DRank, which combines PageRank and CheiRank, gives more accent on personalities of arts. The Wikipedia PageRank of universities recovers 80% of top universities of Shanghai ranking during the considered time period.
Auto insurance fraud detection using unsupervised spectral ranking for anomaly
Directory of Open Access Journals (Sweden)
Ke Nian
2016-03-01
Full Text Available For many data mining problems, obtaining labels is costly and time consuming, if not practically infeasible. In addition, unlabeled data often includes categorical or ordinal features which, compared with numerical features, can present additional challenges. We propose a new unsupervised spectral ranking method for anomaly (SRA. We illustrate that the spectral optimization in SRA can be viewed as a relaxation of an unsupervised SVM problem. We demonstrate that the first non-principal eigenvector of a Laplacian matrix is linked to a bi-class classification strength measure which can be used to rank anomalies. Using the first non-principal eigenvector of the Laplacian matrix directly, the proposed SRA generates an anomaly ranking either with respect to the majority class or with respect to two main patterns. The choice of the ranking reference can be made based on whether the cardinality of the smaller class (positive or negative is sufficiently large. Using an auto insurance claim data set but ignoring labels when generating ranking, we show that our proposed SRA significantly surpasses existing outlier-based fraud detection methods. Finally we demonstrate that, while proposed SRA yields good performance for a few similarity measures for the auto insurance claim data, notably ones based on the Hamming distance, choosing appropriate similarity measures for a fraud detection problem remains crucial.
s-Goodness for Low-Rank Matrix Recovery
Directory of Open Access Journals (Sweden)
Lingchen Kong
2013-01-01
to linear transformations in LMR. Using the two characteristic s-goodness constants, γs and γ^s, of a linear transformation, we derive necessary and sufficient conditions for a linear transformation to be s-good. Moreover, we establish the equivalence of s-goodness and the null space properties. Therefore, s-goodness is a necessary and sufficient condition for exact s-rank matrix recovery via the nuclear norm minimization.
Validating rankings in soccer championships
Directory of Open Access Journals (Sweden)
Annibal Parracho Sant'Anna
2012-08-01
Full Text Available The final ranking of a championship is determined by quality attributes combined with other factors which should be filtered out of any decision on relegation or draft for upper level tournaments. Factors like referees' mistakes and difficulty of certain matches due to its accidental importance to the opponents should have their influence reduced. This work tests approaches to combine classification rules considering the imprecision of the number of points as a measure of quality and of the variables that provide reliable explanation for it. Two home-advantage variables are tested and shown to be apt to enter as explanatory variables. Independence between the criteria is checked against the hypothesis of maximal correlation. The importance of factors and of composition rules is evaluated on the basis of correlation between rank vectors, number of classes and number of clubs in tail classes. Data from five years of the Brazilian Soccer Championship are analyzed.
A Hybrid Distance-Based Ideal-Seeking Consensus Ranking Model
Directory of Open Access Journals (Sweden)
Madjid Tavana
2007-01-01
Full Text Available Ordinal consensus ranking problems have received much attention in the management science literature. A problem arises in situations where a group of k decision makers (DMs is asked to rank order n alternatives. The question is how to combine the DM rankings into one consensus ranking. Several different approaches have been suggested to aggregate DM responses into a compromise or consensus ranking; however, the similarity of consensus rankings generated by the different algorithms is largely unknown. In this paper, we propose a new hybrid distance-based ideal-seeking consensus ranking model (DCM. The proposed hybrid model combines parts of the two commonly used consensus ranking techniques of Beck and Lin (1983 and Cook and Kress (1985 into an intuitive and computationally simple model. We illustrate our method and then run a Monte Carlo simulation across a range of k and n to compare the similarity of the consensus rankings generated by our method with the best-known method of Borda and Kendall (Kendall 1962 and the two methods proposed by Beck and Lin (1983 and Cook and Kress (1985. DCM and Beck and Lin's method yielded the most similar consensus rankings, whereas the Cook-Kress method and the Borda-Kendall method yielded the least similar consensus rankings.
Minkowski metrics in creating universal ranking algorithms
Directory of Open Access Journals (Sweden)
Andrzej Ameljańczyk
2014-06-01
Full Text Available The paper presents a general procedure for creating the rankings of a set of objects, while the relation of preference based on any ranking function. The analysis was possible to use the ranking functions began by showing the fundamental drawbacks of commonly used functions in the form of a weighted sum. As a special case of the ranking procedure in the space of a relation, the procedure based on the notion of an ideal element and generalized Minkowski distance from the element was proposed. This procedure, presented as universal ranking algorithm, eliminates most of the disadvantages of ranking functions in the form of a weighted sum.[b]Keywords[/b]: ranking functions, preference relation, ranking clusters, categories, ideal point, universal ranking algorithm
Iacovacci, Jacopo; Rahmede, Christoph; Arenas, Alex; Bianconi, Ginestra
2016-10-01
Recently it has been recognized that many complex social, technological and biological networks have a multilayer nature and can be described by multiplex networks. Multiplex networks are formed by a set of nodes connected by links having different connotations forming the different layers of the multiplex. Characterizing the centrality of the nodes in a multiplex network is a challenging task since the centrality of the node naturally depends on the importance associated to links of a certain type. Here we propose to assign to each node of a multiplex network a centrality called Functional Multiplex PageRank that is a function of the weights given to every different pattern of connections (multilinks) existent in the multiplex network between any two nodes. Since multilinks distinguish all the possible ways in which the links in different layers can overlap, the Functional Multiplex PageRank can describe important non-linear effects when large relevance or small relevance is assigned to multilinks with overlap. Here we apply the Functional Page Rank to the multiplex airport networks, to the neuronal network of the nematode C. elegans, and to social collaboration and citation networks between scientists. This analysis reveals important differences existing between the most central nodes of these networks, and the correlations between their so-called pattern to success.
A new mutually reinforcing network node and link ranking algorithm.
Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E
2015-10-23
This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.
A new mutually reinforcing network node and link ranking algorithm
Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.
2015-10-01
This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.
On the Rank of Cutting-Plane Proof Systems
Pokutta, Sebastian; Schulz, Andreas S.
We introduce a natural abstraction of propositional proof systems that are based on cutting planes. This new class of proof systems includes well-known operators such as Gomory-Chvátal cuts, lift-and-project cuts, Sherali-Adams cuts (for a fixed hierarchy level d), and split cuts. The rank of such a proof system corresponds to the number of rounds needed to show the nonexistence of integral solutions. We exhibit a family of polytopes without integral points contained in the n-dimensional 0/1-cube that has rank Ω(n/logn) for any proof system in our class. In fact, we show that whenever a specific cutting-plane based proof system has (maximal) rank n on a particular family of instances, then any cutting-plane proof system in our class has rank Ω(n/logn) for this family. This shows that the rank complexity of worst-case instances is intrinsic to the problem, and does not depend on specific cutting-plane proof systems, except for log factors. We also construct a new cutting-plane proof system that has worst-case rank O(n/logn) for any polytope without integral points, implying that the universal lower bound is essentially tight.
25 CFR 181.5 - How are applications ranked?
2010-04-01
... 25 Indians 1 2010-04-01 2010-04-01 false How are applications ranked? 181.5 Section 181.5 Indians BUREAU OF INDIAN AFFAIRS, DEPARTMENT OF THE INTERIOR LAND AND WATER INDIAN HIGHWAY SAFETY PROGRAM § 181.5... resolution of the identified highway safety problem. (4) The number of traffic accidents occurring within the...
UTV Expansion Pack: Special-Purpose Rank-Revealing Algorithms
DEFF Research Database (Denmark)
Fierro, Ricardo D.; Hansen, Per Christian
2005-01-01
-revealing VSV decompositions, we expand the algorithms for the ULLV decomposition of a matrix pair to handle interference-type problems with a rank-deficient covariance matrix, and we provide a robust and reliable Lanczos algorithm which - despite its simplicity - is able to capture all the dominant singular...
Low-rank sparse learning for robust visual tracking
Zhang, Tianzhu
2012-01-01
In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.
Diagrammatic perturbation methods in networks and sports ranking combinatorics
Park, Juyong
2010-04-01
Analytic and computational tools developed in statistical physics are being increasingly applied to the study of complex networks. Here we present recent developments in the diagrammatic perturbation methods for the exponential random graph models, and apply them to the combinatoric problem of determining the ranking of nodes in directed networks that represent pairwise competitions.
Reduced-Rank Regression: A Useful Determinant Identity
DEFF Research Database (Denmark)
Hansen, Peter Reinhard
We derive an identity for the determinant of a product involving non-squared matrices. The identity can be used to derive the maximum likelihood estimator in reduced-rank regres- sions with Gaussian innovations. Furthermore, the identity sheds light on the structure of the estimation problem...
Low-Rank Sparse Coding for Image Classification
Zhang, Tianzhu
2013-12-01
In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.
Ranking network of a captive rhesus macaque society: a sophisticated corporative kingdom.
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Hsieh Fushing
2011-03-01
Full Text Available We develop a three-step computing approach to explore a hierarchical ranking network for a society of captive rhesus macaques. The computed network is sufficiently informative to address the question: Is the ranking network for a rhesus macaque society more like a kingdom or a corporation? Our computations are based on a three-step approach. These steps are devised to deal with the tremendous challenges stemming from the transitivity of dominance as a necessary constraint on the ranking relations among all individual macaques, and the very high sampling heterogeneity in the behavioral conflict data. The first step simultaneously infers the ranking potentials among all network members, which requires accommodation of heterogeneous measurement error inherent in behavioral data. Our second step estimates the social rank for all individuals by minimizing the network-wide errors in the ranking potentials. The third step provides a way to compute confidence bounds for selected empirical features in the social ranking. We apply this approach to two sets of conflict data pertaining to two captive societies of adult rhesus macaques. The resultant ranking network for each society is found to be a sophisticated mixture of both a kingdom and a corporation. Also, for validation purposes, we reanalyze conflict data from twenty longhorn sheep and demonstrate that our three-step approach is capable of correctly computing a ranking network by eliminating all ranking error.
Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights
Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd
2017-11-01
This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.
Liu, Ryan Wen; Shi, Lin; Yu, Simon Chun Ho; Xiong, Naixue; Wang, Defeng
2017-03-03
Dynamic magnetic resonance imaging (MRI) has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t)-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM) is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments.
Directory of Open Access Journals (Sweden)
Ryan Wen Liu
2017-03-01
Full Text Available Dynamic magnetic resonance imaging (MRI has been extensively utilized for enhancing medical living environment visualization, however, in clinical practice it often suffers from long data acquisition times. Dynamic imaging essentially reconstructs the visual image from raw (k,t-space measurements, commonly referred to as big data. The purpose of this work is to accelerate big medical data acquisition in dynamic MRI by developing a non-convex minimization framework. In particular, to overcome the inherent speed limitation, both non-convex low-rank and sparsity constraints were combined to accelerate the dynamic imaging. However, the non-convex constraints make the dynamic reconstruction problem difficult to directly solve through the commonly-used numerical methods. To guarantee solution efficiency and stability, a numerical algorithm based on Alternating Direction Method of Multipliers (ADMM is proposed to solve the resulting non-convex optimization problem. ADMM decomposes the original complex optimization problem into several simple sub-problems. Each sub-problem has a closed-form solution or could be efficiently solved using existing numerical methods. It has been proven that the quality of images reconstructed from fewer measurements can be significantly improved using non-convex minimization. Numerous experiments have been conducted on two in vivo cardiac datasets to compare the proposed method with several state-of-the-art imaging methods. Experimental results illustrated that the proposed method could guarantee the superior imaging performance in terms of quantitative and visual image quality assessments.
Asynchronous Gossip for Averaging and Spectral Ranking
Borkar, Vivek S.; Makhijani, Rahul; Sundaresan, Rajesh
2014-08-01
We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.
Ranking Visualizations of Correlation Using Weber's Law.
Harrison, Lane; Yang, Fumeng; Franconeri, Steven; Chang, Remco
2014-12-01
Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n=1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law. The results of this experiment contribute to our understanding of information visualization by establishing that: (1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber's law, (2) correlation judgment precision showed striking variation between negatively and positively correlated data, and (3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization.
Visual Tracking Using L2 Minimization
Directory of Open Access Journals (Sweden)
Pei Zhijun
2016-01-01
Full Text Available Visual tracking has been an active research topic in the computer vision applications. By modeling the target appearance with a sparse approximation over a template set, sparse representation has been applied to the visual tracker, which called L1 tracker. Due to the need to solve the L1 norm related minimization problem for many times, this L1 tracker is very computationally demanding. Although various fast numerical solver is developed to solve the resulting L1 norm related minimization problem, the framework is still a L1 norm related minimization model. Similar to the face recognition problem, sparse approximations may not deliver the desired robustness and a simple L2 approach to the visual tracking problem is not only robust, but also much faster. It may be possible to apply the L2 minimization, instead of L1 minimization, to the visual tracking problems, which has been verified by experiments on challenging sequences in the paper.
Yang, Yongchao; Nagarajaiah, Satish
2016-06-01
Randomly missing data of structural vibration responses time history often occurs in structural dynamics and health monitoring. For example, structural vibration responses are often corrupted by outliers or erroneous measurements due to sensor malfunction; in wireless sensing platforms, data loss during wireless communication is a common issue. Besides, to alleviate the wireless data sampling or communication burden, certain accounts of data are often discarded during sampling or before transmission. In these and other applications, recovery of the randomly missing structural vibration responses from the available, incomplete data, is essential for system identification and structural health monitoring; it is an ill-posed inverse problem, however. This paper explicitly harnesses the data structure itself-of the structural vibration responses-to address this (inverse) problem. What is relevant is an empirical, but often practically true, observation, that is, typically there are only few modes active in the structural vibration responses; hence a sparse representation (in frequency domain) of the single-channel data vector, or, a low-rank structure (by singular value decomposition) of the multi-channel data matrix. Exploiting such prior knowledge of data structure (intra-channel sparse or inter-channel low-rank), the new theories of ℓ1-minimization sparse recovery and nuclear-norm-minimization low-rank matrix completion enable recovery of the randomly missing or corrupted structural vibration response data. The performance of these two alternatives, in terms of recovery accuracy and computational time under different data missing rates, is investigated on a few structural vibration response data sets-the seismic responses of the super high-rise Canton Tower and the structural health monitoring accelerations of a real large-scale cable-stayed bridge. Encouraging results are obtained and the applicability and limitation of the presented methods are discussed.
The Privilege of Ranking: Google Plays Ball.
Wiggins, Richard
2003-01-01
Discussion of ranking systems used in various settings, including college football and academic admissions, focuses on the Google search engine. Explains the PageRank mathematical formula that scores Web pages by connecting the number of links; limitations, including authenticity and accuracy of ranked Web pages; relevancy; adjusting algorithms;…
Methodology, Meaning and Usefulness of Rankings
Williams, Ross
2008-01-01
University rankings are having a profound effect on both higher education systems and individual universities. In this paper we outline these effects, discuss the desirable characteristics of a good ranking methodology and document existing practice, with an emphasis on the two main international rankings (Shanghai Jiao Tong and THES-QS). We take…
Directory of Open Access Journals (Sweden)
Antônio Augusto Chaves
2012-01-01
Full Text Available O problema de minimização de troca de ferramentas (MTSP busca uma sequência de processamento de um conjunto de tarefas, de modo a minimizar o número de trocas de ferramentas requeridas. Este trabalho apresenta uma nova heurística para o MTSP, capaz de produzir bons limitantes superiores para um algoritmo enumerativo. Esta heurística possui duas fases: uma fase construtiva que é baseada em um grafo em que os vértices correspondem a ferramentas e existe um arco k = (i, j que liga os vértices i e j se e somente se as ferramentas i e j são necessárias para a execução de alguma tarefa k; e uma fase de refinamento baseada na meta-heurística Busca Local Iterativa. Resultados computacionais mostram que a heurística proposta tem um bom desempenho para os problemas testados, contribuindo para uma redução significativa no número de nós gerados de um algoritmo enumerativo.The minimization of tool switches problem (MTSP seeks a sequence to process a set of jobs so that the number of tool switches required is minimized. This study presents a new heuristic for the MTSP. This heuristic has two phases: a constructive phase, based on a graph where the vertices correspond to tools and there is an arc k = (i, j linking vertices i and j if and only if the tools i and j are required to execute some job; and an improvement phase, based on an Iterated Local Search. Computational results show that the proposed heuristic has a good performance on the instances tested contributing to a significant reduction in the number of nodes generated by an enumerative algorithm.
Ranking DMUs by Comparing DEA Cross-Efficiency Intervals Using Entropy Measures
Directory of Open Access Journals (Sweden)
Tim Lu
2016-12-01
Full Text Available Cross-efficiency evaluation, an extension of data envelopment analysis (DEA, can eliminate unrealistic weighing schemes and provide a ranking for decision making units (DMUs. In the literature, the determination of input and output weights uniquely receives more attentions. However, the problem of choosing the aggressive (minimal or benevolent (maximal formulation for decision-making might still remain. In this paper, we develop a procedure to perform cross-efficiency evaluation without the need to make any specific choice of DEA weights. The proposed procedure takes into account the aggressive and benevolent formulations at the same time, and the choice of DEA weights can then be avoided. Consequently, a number of cross-efficiency intervals is obtained for each DMU. The entropy, which is based on information theory, is an effective tool to measure the uncertainty. We then utilize the entropy to construct a numerical index for DMUs with cross-efficiency intervals. A mathematical program is proposed to find the optimal entropy values of DMUs for comparison. With the derived entropy value, we can rank DMUs accordingly. Two examples are illustrated to show the effectiveness of the idea proposed in this paper.
Tool for Ranking Research Options
Ortiz, James N.; Scott, Kelly; Smith, Harold
2005-01-01
Tool for Research Enhancement Decision Support (TREDS) is a computer program developed to assist managers in ranking options for research aboard the International Space Station (ISS). It could likely also be adapted to perform similar decision-support functions in industrial and academic settings. TREDS provides a ranking of the options, based on a quantifiable assessment of all the relevant programmatic decision factors of benefit, cost, and risk. The computation of the benefit for each option is based on a figure of merit (FOM) for ISS research capacity that incorporates both quantitative and qualitative inputs. Qualitative inputs are gathered and partly quantified by use of the time-tested analytical hierarchical process and used to set weighting factors in the FOM corresponding to priorities determined by the cognizant decision maker(s). Then by use of algorithms developed specifically for this application, TREDS adjusts the projected benefit for each option on the basis of levels of technical implementation, cost, and schedule risk. Based partly on Excel spreadsheets, TREDS provides screens for entering cost, benefit, and risk information. Drop-down boxes are provided for entry of qualitative information. TREDS produces graphical output in multiple formats that can be tailored by users.
Issue Management Risk Ranking Systems
Energy Technology Data Exchange (ETDEWEB)
Novack, Steven David; Marshall, Frances Mc Clellan; Stromberg, Howard Merion; Grant, Gary Michael
1999-06-01
Thousands of safety issues have been collected on-line at the Idaho National Engineering and Environmental Laboratory (INEEL) as part of the Issue Management Plan. However, there has been no established approach to prioritize collected and future issues. The authors developed a methodology, based on hazards assessment, to identify and risk rank over 5000 safety issues collected at INEEL. This approach required that it was easily applied and understandable for site adaptation and commensurate with the Integrated Safety Plan. High-risk issues were investigated and mitigative/preventive measures were suggested and ranked based on a cost-benefit scheme to provide risk-informed safety measures. This methodology was consistent with other integrated safety management goals and tasks providing a site-wide risk informed decision tool to reduce hazardous conditions and focus resources on high-risk safety issues. As part of the issue management plan, this methodology was incorporated at the issue collection level and training was provided to management to better familiarize decision-makers with concepts of safety and risk. This prioritization methodology and issue dissemination procedure will be discussed. Results of issue prioritization and training efforts will be summarized. Difficulties and advantages of the process will be reported. Development and incorporation of this process into INEELs lessons learned reporting and the site-wide integrated safety management program will be shown with an emphasis on establishing self reliance and ownership of safety issues.
Energy Technology Data Exchange (ETDEWEB)
Chala, Mikael [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Valencia Univ. (Spain). Dept. de Fisica Teorica y IFIC; Durieux, Gauthier; Matsedonskyi, Oleksii [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Grojean, Christophe [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Humboldt-Univ. Berlin (Germany). Inst. fuer Physik; Lima, Leonardo de [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Univ. Estadual Paulista, Sao Paulo (Brazil). Inst. de Fisica Teorica
2017-03-15
Higgs boson compositeness is a phenomenologically viable scenario addressing the hierarchy problem. In minimal models, the Higgs boson is the only degree of freedom of the strong sector below the strong interaction scale. We present here the simplest extension of such a framework with an additional composite spin-zero singlet. To this end, we adopt an effective field theory approach and develop a set of rules to estimate the size of the various operator coefficients, relating them to the parameters of the strong sector and its structural features. As a result, we obtain the patterns of new interactions affecting both the new singlet and the Higgs boson's physics. We identify the characteristics of the singlet field which cause its effects on Higgs physics to dominate over the ones inherited from the composite nature of the Higgs boson. Our effective field theory construction is supported by comparisons with explicit UV models.
Fabbrichesi, Marco
2016-01-01
We show how the Higgs boson mass is protected from the potentially large corrections due to the introduction of minimal dark matter if the new physics sector is made supersymmetric. The fermionic dark matter candidate (a 5-plet of $SU(2)_L$) is accompanied by a scalar state. The weak gauge sector is made supersymmetric and the Higgs boson is embedded in a supersymmetric multiplet. The remaining standard model states are non-supersymmetric. Non vanishing corrections to the Higgs boson mass only appear at three-loop level and the model is natural for dark matter masses up to 15 TeV--a value larger than the one required by the cosmological relic density. The construction presented stands as an example of a general approach to naturalness that solves the little hierarchy problem which arises when new physics is added beyond the standard model at an energy scale around 10 TeV.
International Conference on Robust Rank-Based and Nonparametric Methods
McKean, Joseph
2016-01-01
The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with r...
Joint Sport Classification of Ukraine and ranking standards in powerlifting
Directory of Open Access Journals (Sweden)
Stetsenko A.I.
2010-01-01
Full Text Available Realization of motion potential of a human happens more often in the process of his competitive activity. Criteria for the assessment of such activity's success are sport trophies (medals, cups, certificates, diplomas and correspondence of competitive results to sport ranks and categories. Joint sport classification of Ukraine is an important regulatory enactment of the physical education and sports sphere of country's life activity, that's why requirements and regulations of sport ranks and categories conferment require scientific-pragmatic approach. Performance of powerlifters, who are divided into weight categories, has progressive and advanced character. These and other factories complicate solving of the problem according the definition of logical algorithm of the table forming of ranking standards.
Two-dimensional ranking of Wikipedia articles
Zhirov, A. O.; Zhirov, O. V.; Shepelyansky, D. L.
2010-10-01
The Library of Babel, described by Jorge Luis Borges, stores an enormous amount of information. The Library exists ab aeterno. Wikipedia, a free online encyclopaedia, becomes a modern analogue of such a Library. Information retrieval and ranking of Wikipedia articles become the challenge of modern society. While PageRank highlights very well known nodes with many ingoing links, CheiRank highlights very communicative nodes with many outgoing links. In this way the ranking becomes two-dimensional. Using CheiRank and PageRank we analyze the properties of two-dimensional ranking of all Wikipedia English articles and show that it gives their reliable classification with rich and nontrivial features. Detailed studies are done for countries, universities, personalities, physicists, chess players, Dow-Jones companies and other categories.
A document clustering and ranking system for exploring MEDLINE citations.
Lin, Yongjing; Li, Wenyuan; Chen, Keke; Liu, Ying
2007-01-01
A major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group. A text mining system framework for automatic document clustering and ranking organized MEDLINE citations following simple PubMed queries. The system grouped the retrieved citations, ranked the citations in each cluster, and generated a set of keywords and MeSH terms to describe the common theme of each cluster. Several possible ranking functions were compared, including citation count per year (CCPY), citation count (CC), and journal impact factor (JIF). We evaluated this framework by identifying as "important" those articles selected by the Surgical Oncology Society. Our results showed that CCPY outperforms CC and JIF, i.e., CCPY better ranked important articles than did the others. Furthermore, our text clustering and knowledge extraction strategy grouped the retrieval results into informative clusters as revealed by the keywords and MeSH terms extracted from the documents in each cluster. The text mining system studied effectively integrated text clustering, text summarization, and text ranking and organized MEDLINE retrieval results into different topical groups.
Comparison of a Class of Rank-Score Tests in Two-Factor Designs ...
African Journals Online (AJOL)
The empirical Type I error rate and power of these test statistics on the rank scores were determined using Monte Carlo simulation to investigate the robustness of the tests. The results show that there are problems of inflation in the Type I error rate using asymptotic ƒÓ2 test for all the rank score functions, especially for small ...
Individual Preference Rankings Compatible with Prices, Income Distributions and Total Resources
DEFF Research Database (Denmark)
Balasko, Yves; Tvede, Mich
We consider the problem of determining the individual preference rankings that are necessarily implied by a dataset consisting of prices, income distributions and total resources. We show the equivalence between the compatibility with individual preference rankings and the existence of a solution...
Where to stop reading a ranked list? Threshold optimization using truncated score distributions
Arampatzis, A.; Kamps, J.; Robertson, S.; Sanderson, M.; Zhai, C.; Zobel, J.; Allan, J.; Aslam, J.A.
2009-01-01
Ranked retrieval has a particular disadvantage in comparison with traditional Boolean retrieval: there is no clear cut-off point where to stop consulting results. This is a serious problem in some setups. We investigate and further develop methods to select the rank cut-off value which optimizes a
The multiplication map for global sections of line bundles and rank 1 ...
Indian Academy of Sciences (India)
Let be an integral projective curve and ∈ Pic(), ∈ Pic() with ℎ1(, ) = ℎ1(, ) = 0 and , general. Here we study the rank of the multiplication map ,:0(, ) ⊗ 0(, ) → 0(, ⊗ ). We also study the same problem when and are rank 1 torsion free sheaves on . Most of our results are for ...
Santoro, M.; Sorichetta, A.; Roglia, E.; Quaglia, A.; Craglia, M.; Nativi, S.
2013-12-01
The vision of the Global Earth Observation System of Systems (GEOSS) is the achievement of societal benefits through voluntary contribution and sharing of resources to better understand the relationships between the society and the environment where we live. To address complex issues in the field of geosciences a combined effort from many disciplines, ranging from physical to social sciences and including humanities, is required. The introduction of the Discovery and Access Broker (DAB) in the GEOSS Common Infrastructure (GCI) allowed to lower significantly the entry barriers for data users and producers, and thus to increase the order of magnitude of discoverable resources in the GCI, from hundreds of thousands to millions. This is a major step forward but from discovery to access, the road is still long! Either missing accessibility information in the metadata or broken links represent the major issue that prevents the real exploitation of the GCI resources. This is a remarkable problem for users attempting to exploit services and datasets obtained through a DAB query. This issue can be minimized providing the user with a ranked list of results that takes into account the real availability and accessibility of resources. We present in this work a methodology that overcomes the problem described above by improving the ranking algorithm, which is currently applied to the result set of a query to the DAB. The proposed methodology is based on the following steps: 1) Verify if information related to the accessibility of resources is described in the metadata provided by GEOSS contributors; 2) If accessibility information is provided, identify the type of resources (e.g. services, datasets) and produce modified and standardized accessibility information in a consistent manner; 3) Use standardized information to test accessibility and availability of resources using a probing approach; 4) Use the results returned in the ranking algorithm to assign the correct weight to
Directory of Open Access Journals (Sweden)
Mahidin Mahidin
2012-12-01
Full Text Available NOx and N2O emissions from coal combustion are claimed as the major contributors for the acid rain, photochemical smog, green house and ozone depletion problems. Based on the facts, study on those emissions formation is interest topic in the combustion area. In this paper, theoretical study by modeling and simulation on NOx and N2O formation in co-combustion of low-rank coal and palm kernel shell has been done. Combustion model was developed by using the principle of chemical-reaction equilibrium. Simulation on the model in order to evaluate the composition of the flue gas was performed by minimization the Gibbs free energy. The results showed that by introduced of biomass in coal combustion can reduce the NOx concentration in considerably level. Maximum NO level in co-combustion of low-rank coal and palm kernel shell with fuel composition 1:1 is 2,350 ppm, low enough compared to single low-rank coal combustion up to 3,150 ppm. Moreover, N2O is less than 0.25 ppm in all cases. Keywords: low-rank coal, N2O emission, NOx emission, palm kernel shell
Multirelational Social Recommendations via Multigraph Ranking.
Mao, Mingsong; Lu, Jie; Zhang, Guangquan; Zhang, Jinlong
2017-12-01
Recommender systems aim to identify relevant items for particular users in large-scale online applications. The historical rating data of users is a valuable input resource for many recommendation models such as collaborative filtering (CF), but these models are known to suffer from the rating sparsity problem when the users or items under consideration have insufficient rating records. With the continued growth of online social networks, the increased user-to-user relationships are reported to be helpful and can alleviate the CF rating sparsity problem. Although researchers have developed a range of social network-based recommender systems, there is no unified model to handle multirelational social networks. To address this challenge, this paper represents different user relationships in a multigraph and develops a multigraph ranking model to identify and recommend the nearest neighbors of particular users in high-order environments. We conduct empirical experiments on two real-world datasets: 1) Epinions and 2) Last.fm, and the comprehensive comparison with other approaches demonstrates that our model improves recommendation performance in terms of both recommendation coverage and accuracy, especially when the rating data are sparse.
Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering.
Gao, Shan; Guo, Guibing; Li, Runzhi; Wang, Zongmin
2017-01-01
Nowadays, providing high-quality recommendation services to users is an essential component in web applications, including shopping, making friends, and healthcare. This can be regarded either as a problem of estimating users' preference by exploiting explicit feedbacks (numerical ratings), or as a problem of collaborative ranking with implicit feedback (e.g., purchases, views, and clicks). Previous works for solving this issue include pointwise regression methods and pairwise ranking methods. The emerging healthcare websites and online medical databases impose a new challenge for medical service recommendation. In this paper, we develop a model, MBPR (Medical Bayesian Personalized Ranking over multiple users' actions), based on the simple observation that users tend to assign higher ranks to some kind of healthcare services that are meanwhile preferred in users' other actions. Experimental results on the real-world datasets demonstrate that MBPR achieves more accurate recommendations than several state-of-the-art methods and shows its generality and scalability via experiments on the datasets from one mobile shopping app.
Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering
Directory of Open Access Journals (Sweden)
Shan Gao
2017-01-01
Full Text Available Nowadays, providing high-quality recommendation services to users is an essential component in web applications, including shopping, making friends, and healthcare. This can be regarded either as a problem of estimating users’ preference by exploiting explicit feedbacks (numerical ratings, or as a problem of collaborative ranking with implicit feedback (e.g., purchases, views, and clicks. Previous works for solving this issue include pointwise regression methods and pairwise ranking methods. The emerging healthcare websites and online medical databases impose a new challenge for medical service recommendation. In this paper, we develop a model, MBPR (Medical Bayesian Personalized Ranking over multiple users’ actions, based on the simple observation that users tend to assign higher ranks to some kind of healthcare services that are meanwhile preferred in users’ other actions. Experimental results on the real-world datasets demonstrate that MBPR achieves more accurate recommendations than several state-of-the-art methods and shows its generality and scalability via experiments on the datasets from one mobile shopping app.
Low-rank approximation pursuit for matrix completion
Xu, An-Bao; Xie, Dongxiu
2017-10-01
We consider the matrix completion problem that aims to construct a low rank matrix X that approximates a given large matrix Y from partially known sample data in Y . In this paper we introduce an efficient greedy algorithm for such matrix completions. The greedy algorithm generalizes the orthogonal rank-one matrix pursuit method (OR1MP) by creating s ⩾ 1 candidates per iteration by low-rank matrix approximation. Due to selecting s ⩾ 1 candidates in each iteration step, our approach uses fewer iterations than OR1MP to achieve the same results. Our algorithm is a randomized low-rank approximation method which makes it computationally inexpensive. The algorithm comes in two forms, the standard one which uses the Lanzcos algorithm to find partial SVDs, and another that uses a randomized approach for this part of its work. The storage complexity of this algorithm can be reduced by using an weight updating rule as an economic version algorithm. We prove that all our algorithms are linearly convergent. Numerical experiments on image reconstruction and recommendation problems are included that illustrate the accuracy and efficiency of our algorithms.
A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.
Wei, Wei; Gao, Bin; Liu, Tie-Yan; Wang, Taifeng; Li, Guohui; Li, Hang
2016-04-01
Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.
Calculating PageRank in a changing network with added or removed edges
Engström, Christopher; Silvestrov, Sergei
2017-01-01
PageRank was initially developed by S. Brinn and L. Page in 1998 to rank homepages on the Internet using the stationary distribution of a Markov chain created using the web graph. Due to the large size of the web graph and many other real world networks fast methods to calculate PageRank is needed and even if the original way of calculating PageRank using a Power iterations is rather fast, many other approaches have been made to improve the speed further. In this paper we will consider the problem of recalculating PageRank of a changing network where the PageRank of a previous version of the network is known. In particular we will consider the special case of adding or removing edges to a single vertex in the graph or graph component.
Rank Modulation for Translocation Error Correction
Farnoud, Farzad; Milenkovic, Olgica
2012-01-01
We consider rank modulation codes for flash memories that allow for handling arbitrary charge drop errors. Unlike classical rank modulation codes used for correcting errors that manifest themselves as swaps of two adjacently ranked elements, the proposed \\emph{translocation rank codes} account for more general forms of errors that arise in storage systems. Translocations represent a natural extension of the notion of adjacent transpositions and as such may be analyzed using related concepts in combinatorics and rank modulation coding. Our results include tight bounds on the capacity of translocation rank codes, construction techniques for asymptotically good codes, as well as simple decoding methods for one class of structured codes. As part of our exposition, we also highlight the close connections between the new code family and permutations with short common subsequences, deletion and insertion error-correcting codes for permutations and permutation arrays.
Dynamics of Ranking Processes in Complex Systems
Blumm, Nicholas; Ghoshal, Gourab; Forró, Zalán; Schich, Maximilian; Bianconi, Ginestra; Bouchaud, Jean-Philippe; Barabási, Albert-László
2012-09-01
The world is addicted to ranking: everything, from the reputation of scientists, journals, and universities to purchasing decisions is driven by measured or perceived differences between them. Here, we analyze empirical data capturing real time ranking in a number of systems, helping to identify the universal characteristics of ranking dynamics. We develop a continuum theory that not only predicts the stability of the ranking process, but shows that a noise-induced phase transition is at the heart of the observed differences in ranking regimes. The key parameters of the continuum theory can be explicitly measured from data, allowing us to predict and experimentally document the existence of three phases that govern ranking stability.
Error analysis of stochastic gradient descent ranking.
Chen, Hong; Tang, Yi; Li, Luoqing; Yuan, Yuan; Li, Xuelong; Tang, Yuanyan
2013-06-01
Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.
Optimality Certificates for Convex Minimization and Helly Numbers
2016-10-20
Optimality certificates for convex minimization and Helly numbers Amitabh Basu Michele Conforti Gérard Cornuéjols Robert Weismantel Stefan Weltge...October 20, 2016 Abstract We consider the problem of minimizing a convex function over a subset of Rn that is not necessarily convex ( minimization of a...problem. Moreover, the inner optimization problem (2) of minimizing on the boundary of C can be very hard if C has no structure other than being S-free
Increasingly minimal bias routing
Bataineh, Abdulla; Court, Thomas; Roweth, Duncan
2017-02-21
A system and algorithm configured to generate diversity at the traffic source so that packets are uniformly distributed over all of the available paths, but to increase the likelihood of taking a minimal path with each hop the packet takes. This is achieved by configuring routing biases so as to prefer non-minimal paths at the injection point, but increasingly prefer minimal paths as the packet proceeds, referred to herein as Increasing Minimal Bias (IMB).
Ausloos, Marcel
2016-01-01
A mere hyperbolic law, like the Zipf's law power function, is often inadequate to describe rank-size relationships. An alternative theoretical distribution is proposed based on theoretical physics arguments starting from the Yule-Simon distribution. A modeling is proposed leading to a universal form. A theoretical suggestion for the "best (or optimal) distribution", is provided through an entropy argument. The ranking of areas through the number of cities in various countries and some sport competition ranking serves for the present illustrations.
Methodology for ranking restoration options
Energy Technology Data Exchange (ETDEWEB)
Hedemann Jensen, Per
1999-04-01
The work described in this report has been performed as a part of the RESTRAT Project FI4P-CT95-0021a (PL 950128) co-funded by the Nuclear Fission Safety Programme of the European Commission. The RESTRAT project has the overall objective of developing generic methodologies for ranking restoration techniques as a function of contamination and site characteristics. The project includes analyses of existing remediation methodologies and contaminated sites, and is structured in the following steps: characterisation of relevant contaminated sites; identification and characterisation of relevant restoration techniques; assessment of the radiological impact; development and application of a selection methodology for restoration options; formulation of generic conclusions and development of a manual. The project is intended to apply to situations in which sites with nuclear installations have been contaminated with radioactive materials as a result of the operation of these installations. The areas considered for remedial measures include contaminated land areas, rivers and sediments in rivers, lakes, and sea areas. Five contaminated European sites have been studied. Various remedial measures have been envisaged with respect to the optimisation of the protection of the populations being exposed to the radionuclides at the sites. Cost-benefit analysis and multi-attribute utility analysis have been applied for optimisation. Health, economic and social attributes have been included and weighting factors for the different attributes have been determined by the use of scaling constants. (au)
Ranking documents with a thesaurus.
Rada, R; Bicknell, E
1989-09-01
This article reports on exploratory experiments in evaluating and improving a thesaurus through studying its effect on retrieval. A formula called DISTANCE was developed to measure the conceptual distance between queries and documents encoded as sets of thesaurus terms. DISTANCE references MeSH (Medical Subject Headings) and assesses the degree of match between a MeSH-encoded query and document. The performance of DISTANCE on MeSH is compared to the performance of people in the assessment of conceptual distance between queries and documents, and is found to simulate with surprising accuracy the human performance. The power of the computer simulation stems both from the tendency of people to rely heavily on broader-than (BT) relations in making decisions about conceptual distance and from the thousands of accurate BT relations in MeSH. One source for discrepancy between the algorithms' measurement of closeness between query and document and people's measurement of closeness between query and document is occasional inconsistency in the BT relations. Our experiments with adding non-BT relations to MeSH showed how these non-BT non-BT relations to MeSH showed how these non-BT relations could improve document ranking, if DISTANCE were also appropriately revised to treat these relations differently from BT relations.
Citation graph based ranking in Invenio
Marian, Ludmila; Rajman, Martin; Vesely, Martin
2010-01-01
Invenio is the web-based integrated digital library system developed at CERN. Within this framework, we present four types of ranking models based on the citation graph that complement the simple approach based on citation counts: time-dependent citation counts, a relevancy ranking which extends the PageRank model, a time-dependent ranking which combines the freshness of citations with PageRank and a ranking that takes into consideration the external citations. We present our analysis and results obtained on two main data sets: Inspire and CERN Document Server. Our main contributions are: (i) a study of the currently available ranking methods based on the citation graph; (ii) the development of new ranking methods that correct some of the identified limitations of the current methods such as treating all citations of equal importance, not taking time into account or considering the citation graph complete; (iii) a detailed study of the key parameters for these ranking methods. (The original publication is ava...
LANL environmental restoration site ranking system: System description. Final report
Energy Technology Data Exchange (ETDEWEB)
Merkhofer, L.; Kann, A.; Voth, M. [Applied Decision Analysis, Inc., Menlo Park, CA (United States)
1992-10-13
The basic structure of the LANL Environmental Restoration (ER) Site Ranking System and its use are described in this document. A related document, Instructions for Generating Inputs for the LANL ER Site Ranking System, contains detailed descriptions of the methods by which necessary inputs for the system will be generated. LANL has long recognized the need to provide a consistent basis for comparing the risks and other adverse consequences associated with the various waste problems at the Lab. The LANL ER Site Ranking System is being developed to help address this need. The specific purpose of the system is to help improve, defend, and explain prioritization decisions at the Potential Release Site (PRS) and Operable Unit (OU) level. The precise relationship of the Site Ranking System to the planning and overall budget processes is yet to be determined, as the system is still evolving. Generally speaking, the Site Ranking System will be used as a decision aid. That is, the system will be used to aid in the planning and budgetary decision-making process. It will never be used alone to make decisions. Like all models, the system can provide only a partial and approximate accounting of the factors important to budget and planning decisions. Decision makers at LANL will have to consider factors outside of the formal system when making final choices. Some of these other factors are regulatory requirements, DOE policy, and public concern. The main value of the site ranking system, therefore, is not the precise numbers it generates, but rather the general insights it provides.
Asymptotic geometry in higher products of rank one Hadamard spaces
Link, Gabriele
2013-01-01
Given a product X of locally compact rank one Hadamard spaces, we study asymptotic properties of certain discrete isometry groups. First we give a detailed description of the structure of the geometric limit set and relate it to the limit cone; moreover, we show that the action of the group on a quotient of the regular geometric boundary of X is minimal and proximal. This is completely analogous to the case of Zariski dense discrete subgroups of semi-simple Lie groups acting on the associated...
VisualRank: applying PageRank to large-scale image search.
Jing, Yushi; Baluja, Shumeet
2008-11-01
Because of the relative ease in understanding and processing text, commercial image-search systems often rely on techniques that are largely indistinguishable from text-search. Recently, academic studies have demonstrated the effectiveness of employing image-based features to provide alternative or additional signals. However, it remains uncertain whether such techniques will generalize to a large number of popular web queries, and whether the potential improvement to search quality warrants the additional computational cost. In this work, we cast the image-ranking problem into the task of identifying "authority" nodes on an inferred visual similarity graph and propose VisualRank to analyze the visual link structures among images. The images found to be "authorities" are chosen as those that answer the image-queries well. To understand the performance of such an approach in a real system, we conducted a series of large-scale experiments based on the task of retrieving images for 2000 of the most popular products queries. Our experimental results show significant improvement, in terms of user satisfaction and relevancy, in comparison to the most recent Google Image Search results. Maintaining modest computational cost is vital to ensuring that this procedure can be used in practice; we describe the techniques required to make this system practical for large scale deployment in commercial search engines.
Flattening the inflaton potential beyond minimal gravity
Lee, Hyun Min
2018-01-01
We review the status of the Starobinsky-like models for inflation beyond minimal gravity and discuss the unitarity problem due to the presence of a large non-minimal gravity coupling. We show that the induced gravity models allow for a self-consistent description of inflation and discuss the implications of the inflaton couplings to the Higgs field in the Standard Model.
Topological Rankings in Communication Networks
DEFF Research Database (Denmark)
Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Træholt, Chresten
2015-01-01
In the theory of communication the central problem is to study how agents exchange information. This problem may be studied using the theory of connected spaces in topology, since a communication network can be modelled as a topological space such that agents can communicate if and only...... if they belong to the same path connected component of that space. In order to study combinatorial properties of such a communication network, notions from algebraic topology are applied. This makes it possible to determine the shape of a network by concrete invariants, e.g. the number of connected components...
Optimizing Processes to Minimize Risk
Loyd, David
2017-01-01
NASA, like the other hazardous industries, has suffered very catastrophic losses. Human error will likely never be completely eliminated as a factor in our failures. When you can't eliminate risk, focus on mitigating the worst consequences and recovering operations. Bolstering processes to emphasize the role of integration and problem solving is key to success. Building an effective Safety Culture bolsters skill-based performance that minimizes risk and encourages successful engagement.
Robust Tracking with Discriminative Ranking Middle-Level Patches
Directory of Open Access Journals (Sweden)
Hong Liu
2014-04-01
Full Text Available The appearance model has been shown to be essential for robust visual tracking since it is the basic criterion to locating targets in video sequences. Though existing tracking-by-detection algorithms have shown to be greatly promising, they still suffer from the drift problem, which is caused by updating appearance models. In this paper, we propose a new appearance model composed of ranking middle-level patches to capture more object distinctiveness than traditional tracking-by-detection models. Targets and backgrounds are represented by both low-level bottom-up features and high-level top-down patches, which can compensate each other. Bottom-up features are defined at the pixel level, and each feature gets its discrimination score through selective feature attention mechanism. In top-down feature extraction, rectangular patches are ranked according to their bottom-up discrimination scores, by which all of them are clustered into irregular patches, named ranking middle-level patches. In addition, at the stage of classifier training, the online random forests algorithm is specially refined to reduce drifting problems. Experiments on challenging public datasets and our test videos demonstrate that our approach can effectively prevent the tracker drifting problem and obtain competitive performance in visual tracking.
Ranked Conservation Opportunity Areas for Region 7 (ECO_RES.RANKED_OAS)
U.S. Environmental Protection Agency — The RANKED_OAS are all the Conservation Opportunity Areas identified by MoRAP that have subsequently been ranked by patch size, landform representation, and the...
Consistent ranking of volatility models
DEFF Research Database (Denmark)
Hansen, Peter Reinhard; Lunde, Asger
2006-01-01
result in an inferior model being chosen as "best" with a probability that converges to one as the sample size increases. We document the practical relevance of this problem in an empirical application and by simulation experiments. Our results provide an additional argument for using the realized...
Entity Ranking using Wikipedia as a Pivot
R. Kaptein; P. Serdyukov; A.P. de Vries (Arjen); J. Kamps
2010-01-01
htmlabstractIn this paper we investigate the task of Entity Ranking on the Web. Searchers looking for entities are arguably better served by presenting a ranked list of entities directly, rather than a list of web pages with relevant but also potentially redundant information about
Entity ranking using Wikipedia as a pivot
Kaptein, R.; Serdyukov, P.; de Vries, A.; Kamps, J.; Huang, X.J.; Jones, G.; Koudas, N.; Wu, X.; Collins-Thompson, K.
2010-01-01
In this paper we investigate the task of Entity Ranking on the Web. Searchers looking for entities are arguably better served by presenting a ranked list of entities directly, rather than a list of web pages with relevant but also potentially redundant information about these entities. Since
Mining Feedback in Ranking and Recommendation Systems
Zhuang, Ziming
2009-01-01
The amount of online information has grown exponentially over the past few decades, and users become more and more dependent on ranking and recommendation systems to address their information seeking needs. The advance in information technologies has enabled users to provide feedback on the utilities of the underlying ranking and recommendation…
Using centrality to rank web snippets
Jijkoun, V.; de Rijke, M.; Peters, C.; Jijkoun, V.; Mandl, T.; Müller, H.; Oard, D.W.; Peñas, A.; Petras, V.; Santos, D.
2008-01-01
We describe our participation in the WebCLEF 2007 task, targeted at snippet retrieval from web data. Our system ranks snippets based on a simple similarity-based centrality, inspired by the web page ranking algorithms. We experimented with retrieval units (sentences and paragraphs) and with the
Generating and ranking of Dyck words
Kasa, Zoltan
2010-01-01
A new algorithm to generate all Dyck words is presented, which is used in ranking and unranking Dyck words. We emphasize the importance of using Dyck words in encoding objects related to Catalan numbers. As a consequence of formulas used in the ranking algorithm we can obtain a recursive formula for the nth Catalan number.
Relevancy Ranking of Satellite Dataset Search Results
Lynnes, Christopher; Quinn, Patrick; Norton, James
2017-01-01
As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.
Alberto Baccini; Antono Banfi; Giuseppe De Nicolao; Paola Galimberti
2015-01-01
University rankings represent a controversial issue in the debate about higher education policy. One of the best known university ranking is the Quacquarelli Symonds World University Rankings (QS), published annually since 2004 by Quacquarelli Symonds ltd, a company founded in 1990 and headquartered in London. QS provides a ranking based on a score calculated by weighting six different indicators. The 2015 edition, published in October 2015, introduced major methodological innovations and, as...
Integrated inventory ranking system for oilfield equipment industry
Directory of Open Access Journals (Sweden)
Jalel Ben Hmida
2014-01-01
Full Text Available Purpose: This case study is motivated by the subcontracting problem in an oilfield equipment and service company where the management needs to decide which parts to manufacture in-house when the capacity is not enough to make all required parts. Currently the company is making subcontracting decisions based on management’s experience. Design/methodology/approach: Working with the management, a decision support system (DSS is developed to rank parts by integrating three inventory classification methods considering both quantitative factors such as cost and demand, and qualitative factors such as functionality, efficiency, and quality. The proposed integrated inventory ranking procedure will make use of three classification methods: ABC, FSN, and VED. Findings: An integration mechanism using weights is developed to rank the parts based on the total priority scores. The ranked list generated by the system helps management to identify about 50 critical parts to manufacture in-house. Originality/value: The integration of all three inventory classification techniques into a single system is a unique feature of this research. This is important as it provides a more inclusive, big picture view of the DSS for management’s use in making business decisions.
Rank-Constrained Beamforming for MIMO Cognitive Interference Channel
Directory of Open Access Journals (Sweden)
Duoying Zhang
2016-01-01
Full Text Available This paper considers the spectrum sharing multiple-input multiple-output (MIMO cognitive interference channel, in which multiple primary users (PUs coexist with multiple secondary users (SUs. Interference alignment (IA approach is introduced that guarantees that secondary users access the licensed spectrum without causing harmful interference to the PUs. A rank-constrained beamforming design is proposed where the rank of the interferences and the desired signals is concerned. The standard interferences metric for the primary link, that is, interference temperature, is investigated and redesigned. The work provides a further improvement that optimizes the dimension of the interferences in the cognitive interference channel, instead of the power of the interference leakage. Due to the nonconvexity of the rank, the developed optimization problems are further approximated as convex form and are solved via choosing the transmitter precoder and receiver subspace iteratively. Numerical results show that the proposed designs can improve the achievable degree of freedom (DoF of the primary links and provide the considerable sum rate for both secondary and primary transmissions under the rank constraints.
Low-rank regularization for learning gene expression programs.
Ye, Guibo; Tang, Mengfan; Cai, Jian-Feng; Nie, Qing; Xie, Xiaohui
2013-01-01
Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.
Universal emergence of PageRank
Energy Technology Data Exchange (ETDEWEB)
Frahm, K M; Georgeot, B; Shepelyansky, D L, E-mail: frahm@irsamc.ups-tlse.fr, E-mail: georgeot@irsamc.ups-tlse.fr, E-mail: dima@irsamc.ups-tlse.fr [Laboratoire de Physique Theorique du CNRS, IRSAMC, Universite de Toulouse, UPS, 31062 Toulouse (France)
2011-11-18
The PageRank algorithm enables us to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter {alpha} Element-Of ]0, 1[. Using extensive numerical simulations of large web networks, with a special accent on British University networks, we determine numerically and analytically the universal features of the PageRank vector at its emergence when {alpha} {yields} 1. The whole network can be divided into a core part and a group of invariant subspaces. For {alpha} {yields} 1, PageRank converges to a universal power-law distribution on the invariant subspaces whose size distribution also follows a universal power law. The convergence of PageRank at {alpha} {yields} 1 is controlled by eigenvalues of the core part of the Google matrix, which are extremely close to unity, leading to large relaxation times as, for example, in spin glasses. (paper)
Comparing classical and quantum PageRanks
Loke, T.; Tang, J. W.; Rodriguez, J.; Small, M.; Wang, J. B.
2017-01-01
Following recent developments in quantum PageRanking, we present a comparative analysis of discrete-time and continuous-time quantum-walk-based PageRank algorithms. Relative to classical PageRank and to different extents, the quantum measures better highlight secondary hubs and resolve ranking degeneracy among peripheral nodes for all networks we studied in this paper. For the discrete-time case, we investigated the periodic nature of the walker's probability distribution for a wide range of networks and found that the dominant period does not grow with the size of these networks. Based on this observation, we introduce a new quantum measure using the maximum probabilities of the associated walker during the first couple of periods. This is particularly important, since it leads to a quantum PageRanking scheme that is scalable with respect to network size.
Reliability of journal impact factor rankings
Greenwood, Darren C
2007-01-01
Background Journal impact factors and their ranks are used widely by journals, researchers, and research assessment exercises. Methods Based on citations to journals in research and experimental medicine in 2005, Bayesian Markov chain Monte Carlo methods were used to estimate the uncertainty associated with these journal performance indicators. Results Intervals representing plausible ranges of values for journal impact factor ranks indicated that most journals cannot be ranked with great precision. Only the top and bottom few journals could place any confidence in their rank position. Intervals were wider and overlapping for most journals. Conclusion Decisions placed on journal impact factors are potentially misleading where the uncertainty associated with the measure is ignored. This article proposes that caution should be exercised in the interpretation of journal impact factors and their ranks, and specifically that a measure of uncertainty should be routinely presented alongside the point estimate. PMID:18005435
Reliability of journal impact factor rankings
Directory of Open Access Journals (Sweden)
Greenwood Darren C
2007-11-01
Full Text Available Abstract Background Journal impact factors and their ranks are used widely by journals, researchers, and research assessment exercises. Methods Based on citations to journals in research and experimental medicine in 2005, Bayesian Markov chain Monte Carlo methods were used to estimate the uncertainty associated with these journal performance indicators. Results Intervals representing plausible ranges of values for journal impact factor ranks indicated that most journals cannot be ranked with great precision. Only the top and bottom few journals could place any confidence in their rank position. Intervals were wider and overlapping for most journals. Conclusion Decisions placed on journal impact factors are potentially misleading where the uncertainty associated with the measure is ignored. This article proposes that caution should be exercised in the interpretation of journal impact factors and their ranks, and specifically that a measure of uncertainty should be routinely presented alongside the point estimate.
Cointegration rank testing under conditional heteroskedasticity
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, Robert M.
2010-01-01
(martingale difference) innovations. We first demonstrate that the limiting null distributions of the rank statistics coincide with those derived by previous authors who assume either independent and identically distributed (i.i.d.) or (strict and covariance) stationary martingale difference innovations. We...... then propose wild bootstrap implementations of the cointegrating rank tests and demonstrate that the associated bootstrap rank statistics replicate the first-order asymptotic null distributions of the rank statistics. We show that the same is also true of the corresponding rank tests based on the i.......i.d. bootstrap of Swensen (2006, Econometrica 74, 1699-1714). The wild bootstrap, however, has the important property that, unlike the i.i.d. bootstrap, it preserves in the resampled data the pattern of heteroskedasticity present in the original shocks. Consistent with this, numerical evidence suggests that...
Ranks of a Constrained Hermitian Matrix Expression with Applications
Directory of Open Access Journals (Sweden)
Shao-Wen Yu
2013-01-01
Full Text Available We establish the formulas of the maximal and minimal ranks of the quaternion Hermitian matrix expression C4−A4XA4∗ where X is a Hermitian solution to quaternion matrix equations A1X=C1, XB1=C2, and A3XA3*=C3. As applications, we give a new necessary and sufficient condition for the existence of Hermitian solution to the system of matrix equations A1X=C1, XB1=C2, A3XA3*=C3, and A4XA4*=C4, which was investigated by Wang and Wu, 2010, by rank equalities. In addition, extremal ranks of the generalized Hermitian Schur complement C4−A4A3~A4∗ with respect to a Hermitian g-inverse A3~ of A3, which is a common solution to quaternion matrix equations A1X=C1 and XB1=C2, are also considered.
PageRank and rank-reversal dependence on the damping factor
Son, S.-W.; Christensen, C.; Grassberger, P.; Paczuski, M.
2012-12-01
PageRank (PR) is an algorithm originally developed by Google to evaluate the importance of web pages. Considering how deeply rooted Google's PR algorithm is to gathering relevant information or to the success of modern businesses, the question of rank stability and choice of the damping factor (a parameter in the algorithm) is clearly important. We investigate PR as a function of the damping factor d on a network obtained from a domain of the World Wide Web, finding that rank reversal happens frequently over a broad range of PR (and of d). We use three different correlation measures, Pearson, Spearman, and Kendall, to study rank reversal as d changes, and we show that the correlation of PR vectors drops rapidly as d changes from its frequently cited value, d0=0.85. Rank reversal is also observed by measuring the Spearman and Kendall rank correlation, which evaluate relative ranks rather than absolute PR. Rank reversal happens not only in directed networks containing rank sinks but also in a single strongly connected component, which by definition does not contain any sinks. We relate rank reversals to rank pockets and bottlenecks in the directed network structure. For the network studied, the relative rank is more stable by our measures around d=0.65 than at d=d0.
PageRank and rank-reversal dependence on the damping factor.
Son, S-W; Christensen, C; Grassberger, P; Paczuski, M
2012-12-01
PageRank (PR) is an algorithm originally developed by Google to evaluate the importance of web pages. Considering how deeply rooted Google's PR algorithm is to gathering relevant information or to the success of modern businesses, the question of rank stability and choice of the damping factor (a parameter in the algorithm) is clearly important. We investigate PR as a function of the damping factor d on a network obtained from a domain of the World Wide Web, finding that rank reversal happens frequently over a broad range of PR (and of d). We use three different correlation measures, Pearson, Spearman, and Kendall, to study rank reversal as d changes, and we show that the correlation of PR vectors drops rapidly as d changes from its frequently cited value, d_{0}=0.85. Rank reversal is also observed by measuring the Spearman and Kendall rank correlation, which evaluate relative ranks rather than absolute PR. Rank reversal happens not only in directed networks containing rank sinks but also in a single strongly connected component, which by definition does not contain any sinks. We relate rank reversals to rank pockets and bottlenecks in the directed network structure. For the network studied, the relative rank is more stable by our measures around d=0.65 than at d=d_{0}.
A tilting approach to ranking influence
Genton, Marc G.
2014-12-01
We suggest a new approach, which is applicable for general statistics computed from random samples of univariate or vector-valued or functional data, to assessing the influence that individual data have on the value of a statistic, and to ranking the data in terms of that influence. Our method is based on, first, perturbing the value of the statistic by ‘tilting’, or reweighting, each data value, where the total amount of tilt is constrained to be the least possible, subject to achieving a given small perturbation of the statistic, and, then, taking the ranking of the influence of data values to be that which corresponds to ranking the changes in data weights. It is shown, both theoretically and numerically, that this ranking does not depend on the size of the perturbation, provided that the perturbation is sufficiently small. That simple result leads directly to an elegant geometric interpretation of the ranks; they are the ranks of the lengths of projections of the weights onto a ‘line’ determined by the first empirical principal component function in a generalized measure of covariance. To illustrate the generality of the method we introduce and explore it in the case of functional data, where (for example) it leads to generalized boxplots. The method has the advantage of providing an interpretable ranking that depends on the statistic under consideration. For example, the ranking of data, in terms of their influence on the value of a statistic, is different for a measure of location and for a measure of scale. This is as it should be; a ranking of data in terms of their influence should depend on the manner in which the data are used. Additionally, the ranking recognizes, rather than ignores, sign, and in particular can identify left- and right-hand ‘tails’ of the distribution of a random function or vector.
Leck, Kira
2006-10-01
Researchers have associated minimal dating with numerous factors. The present author tested shyness, introversion, physical attractiveness, performance evaluation, anxiety, social skill, social self-esteem, and loneliness to determine the nature of their relationships with 2 measures of self-reported minimal dating in a sample of 175 college students. For women, shyness, introversion, physical attractiveness, self-rated anxiety, social self-esteem, and loneliness correlated with 1 or both measures of minimal dating. For men, physical attractiveness, observer-rated social skill, social self-esteem, and loneliness correlated with 1 or both measures of minimal dating. The patterns of relationships were not identical for the 2 indicators of minimal dating, indicating the possibility that minimal dating is not a single construct as researchers previously believed. The present author discussed implications and suggestions for future researchers.
Minimally invasive orthognathic surgery.
Resnick, Cory M; Kaban, Leonard B; Troulis, Maria J
2009-02-01
Minimally invasive surgery is defined as the discipline in which operative procedures are performed in novel ways to diminish the sequelae of standard surgical dissections. The goals of minimally invasive surgery are to reduce tissue trauma and to minimize bleeding, edema, and injury, thereby improving the rate and quality of healing. In orthognathic surgery, there are two minimally invasive techniques that can be used separately or in combination: (1) endoscopic exposure and (2) distraction osteogenesis. This article describes the historical developments of the fields of orthognathic surgery and minimally invasive surgery, as well as the integration of the two disciplines. Indications, techniques, and the most current outcome data for specific minimally invasive orthognathic surgical procedures are presented.
Hierarchical low-rank approximation for high dimensional approximation
Nouy, Anthony
2016-01-07
Tensor methods are among the most prominent tools for the numerical solution of high-dimensional problems where functions of multiple variables have to be approximated. Such high-dimensional approximation problems naturally arise in stochastic analysis and uncertainty quantification. In many practical situations, the approximation of high-dimensional functions is made computationally tractable by using rank-structured approximations. In this talk, we present algorithms for the approximation in hierarchical tensor format using statistical methods. Sparse representations in a given tensor format are obtained with adaptive or convex relaxation methods, with a selection of parameters using crossvalidation methods.
DEFF Research Database (Denmark)
Antola, M.; Di Chiara, S.; Sannino, F.
2011-01-01
We introduce novel extensions of the Standard Model featuring a supersymmetric technicolor sector (supertechnicolor). As the first minimal conformal supertechnicolor model we consider N=4 Super Yang-Mills which breaks to N=1 via the electroweak interactions. This is a well defined, economical......, between unparticle physics and Minimal Walking Technicolor. We consider also other N =1 extensions of the Minimal Walking Technicolor model. The new models allow all the standard model matter fields to acquire a mass....
Robust noise attenuation based on nuclear norm minimization and a trace prediction strategy
Zhou, Yatong; Zhang, Shili
2017-12-01
Rejecting noise in seismic data while not affecting the amplitude of useful signals is a long standing problem in seismic data processing. Seismic noise attenuation can be formulated as a nuclear norm minimization (NNM) problem. To meet the assumption that seismic data should have low nuclear norm, we first map the seismic data into a low-rank matrix based on a trace prediction strategy. We provide detailed algorithm workflow and mathematical analysis of the trace prediction method. The seismic data after trace rearrangement is demonstrated to be locally low-rank. The NNM problem is then solved via the singular value thresholding (SVT) algorithm. The effectiveness of the proposed method is validated via both synthetic and field data examples. We also test the robustness of the proposed method with respect to random noise, spiky noise, and blending interference. Compared with the state-of-the-art predictive filtering method, median filtering method, singular spectrum analysis method, and curvelet thresholding method, the proposed method obtains an obviously better performance in compromising signal preservation and noise removal.
Directory of Open Access Journals (Sweden)
Alberto Baccini
2015-10-01
Full Text Available University rankings represent a controversial issue in the debate about higher education policy. One of the best known university ranking is the Quacquarelli Symonds World University Rankings (QS, published annually since 2004 by Quacquarelli Symonds ltd, a company founded in 1990 and headquartered in London. QS provides a ranking based on a score calculated by weighting six different indicators. The 2015 edition, published in October 2015, introduced major methodological innovations and, as a consequence, many universities worldwide underwent major changes of their scores and ranks. Ben Sowter, head of division of intelligence unit of Quacquarelli Symonds, responds to 15 questions about the new QS methodology.
Adiabatic quantum algorithm for search engine ranking.
Garnerone, Silvano; Zanardi, Paolo; Lidar, Daniel A
2012-06-08
We propose an adiabatic quantum algorithm for generating a quantum pure state encoding of the PageRank vector, the most widely used tool in ranking the relative importance of internet pages. We present extensive numerical simulations which provide evidence that this algorithm can prepare the quantum PageRank state in a time which, on average, scales polylogarithmically in the number of web pages. We argue that the main topological feature of the underlying web graph allowing for such a scaling is the out-degree distribution. The top-ranked log(n) entries of the quantum PageRank state can then be estimated with a polynomial quantum speed-up. Moreover, the quantum PageRank state can be used in "q-sampling" protocols for testing properties of distributions, which require exponentially fewer measurements than all classical schemes designed for the same task. This can be used to decide whether to run a classical update of the PageRank.
Ranking Adverse Drug Reactions With Crowdsourcing
Gottlieb, Assaf
2015-03-23
Background: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. Results: There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. Conclusions: ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.
Adiabatic Quantum Algorithm for Search Engine Ranking
Garnerone, Silvano; Zanardi, Paolo; Lidar, Daniel A.
2012-06-01
We propose an adiabatic quantum algorithm for generating a quantum pure state encoding of the PageRank vector, the most widely used tool in ranking the relative importance of internet pages. We present extensive numerical simulations which provide evidence that this algorithm can prepare the quantum PageRank state in a time which, on average, scales polylogarithmically in the number of web pages. We argue that the main topological feature of the underlying web graph allowing for such a scaling is the out-degree distribution. The top-ranked log(n) entries of the quantum PageRank state can then be estimated with a polynomial quantum speed-up. Moreover, the quantum PageRank state can be used in “q-sampling” protocols for testing properties of distributions, which require exponentially fewer measurements than all classical schemes designed for the same task. This can be used to decide whether to run a classical update of the PageRank.
Ranking adverse drug reactions with crowdsourcing.
Gottlieb, Assaf; Hoehndorf, Robert; Dumontier, Michel; Altman, Russ B
2015-03-23
There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. The intent of the study was to rank ADRs according to severity. We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.
Augmenting the Deliberative Method for Ranking Risks.
Susel, Irving; Lasley, Trace; Montezemolo, Mark; Piper, Joel
2016-01-01
The Department of Homeland Security (DHS) characterized and prioritized the physical cross-border threats and hazards to the nation stemming from terrorism, market-driven illicit flows of people and goods (illegal immigration, narcotics, funds, counterfeits, and weaponry), and other nonmarket concerns (movement of diseases, pests, and invasive species). These threats and hazards pose a wide diversity of consequences with very different combinations of magnitudes and likelihoods, making it very challenging to prioritize them. This article presents the approach that was used at DHS to arrive at a consensus regarding the threats and hazards that stand out from the rest based on the overall risk they pose. Due to time constraints for the decision analysis, it was not feasible to apply multiattribute methodologies like multiattribute utility theory or the analytic hierarchy process. Using a holistic approach was considered, such as the deliberative method for ranking risks first published in this journal. However, an ordinal ranking alone does not indicate relative or absolute magnitude differences among the risks. Therefore, the use of the deliberative method for ranking risks is not sufficient for deciding whether there is a material difference between the top-ranked and bottom-ranked risks, let alone deciding what the stand-out risks are. To address this limitation of ordinal rankings, the deliberative method for ranking risks was augmented by adding an additional step to transform the ordinal ranking into a ratio scale ranking. This additional step enabled the selection of stand-out risks to help prioritize further analysis. © 2015 Society for Risk Analysis.
Enhancing collaborative filtering by user interest expansion via personalized ranking.
Liu, Qi; Chen, Enhong; Xiong, Hui; Ding, Chris H Q; Chen, Jian
2012-02-01
Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.
Constrained low-rank gamut completion for robust illumination estimation
Zhou, Jianshen; Yuan, Jiazheng; Liu, Hongzhe
2017-02-01
Illumination estimation is an important component of color constancy and automatic white balancing. According to recent survey and evaluation work, the supervised methods with a learning phase are competitive for illumination estimation. However, the robustness and performance of any supervised algorithm suffer from an incomplete gamut in training image sets because of limited reflectance surfaces in a scene. In order to address this problem, we present a constrained low-rank gamut completion algorithm, which can replenish gamut from limited surfaces in an image, for robust illumination estimation. In the proposed algorithm, we first discuss why the gamut completion is actually a low-rank matrix completion problem. Then a constrained low-rank matrix completion framework is proposed by adding illumination similarities among the training images as an additional constraint. An optimization algorithm is also given out by extending the augmented Lagrange multipliers. Finally, the completed gamut based on the proposed algorithm is fed into the support vector regression (SVR)-based illumination estimation method to evaluate the effect of gamut completion. The experimental results on both synthetic and real-world image sets show that the proposed gamut completion model not only can effectively improve the performance of the original SVR method but is also robust to the surface insufficiency in training samples.
Evaluation of treatment effects by ranking
DEFF Research Database (Denmark)
Halekoh, U; Kristensen, K
2008-01-01
In crop experiments measurements are often made by a judge evaluating the crops' conditions after treatment. In the present paper an analysis is proposed for experiments where plots of crops treated differently are mutually ranked. In the experimental layout the crops are treated on consecutive...... plots usually placed side by side in one or more rows. In the proposed method a judge ranks several neighbouring plots, say three, by ranking them from best to worst. For the next observation the judge moves on by no more than two plots, such that up to two plots will be re-evaluated again...
DEFF Research Database (Denmark)
2010-01-01
Disclosed herein are techniques, systems, and methods relating to minimizing mutual coupling between a first antenna and a second antenna.......Disclosed herein are techniques, systems, and methods relating to minimizing mutual coupling between a first antenna and a second antenna....
Minimally invasive lumbar fusion.
Foley, Kevin T; Holly, Langston T; Schwender, James D
2003-08-01
Review article. To provide an overview of current techniques for minimally invasive lumbar fusion. Minimally invasive techniques have revolutionized the management of pathologic conditions in various surgical disciplines. Although these same principles have been used in the treatment of lumbar disc disease for many years, minimally invasive lumbar fusion procedures have only recently been developed. The goals of these procedures are to reduce the approach-related morbidity associated with traditional lumbar fusion, yet allow the surgery to be performed in an effective and safe manner. The authors' clinical experience with minimally invasive lumbar fusion was reviewed, and the pertinent literature was surveyed. Minimally invasive approaches have been developed for common lumbar procedures such as anterior and posterior interbody fusion, posterolateral onlay fusion, and internal fixation. As with all new surgical techniques, minimally invasive lumbar fusion has a learning curve. As well, there are benefits and disadvantages associated with each technique. However, because these techniques are new and evolving, evidence to support their potential benefits is largely anecdotal. Additionally, there are few long-term studies to document clinical outcomes. Preliminary clinical results suggest that minimally invasive lumbar fusion will have a beneficial impact on the care of patients with spinal disorders. Outcome studies with long-term follow-up will be necessary to validate its success and allow minimally invasive lumbar fusion to become more widely accepted.
Sugeno integral ranking of release scenarios in a low and intermediate waste repository
Energy Technology Data Exchange (ETDEWEB)
Kim, S. Ho; Kim, Tae Woon; Ha, Jae Joo [Korea Atomic Energy Reserach Institute, Taejon (Korea, Republic of)
2004-11-15
In the present study, a multi criteria decision-making (MCDM) problem of ranking of important radionuclide release scenarios in a low and intermediate radioactive waste repository is to treat on the basis of {lambda}-fuzzy measures and Sugeno integral. Ranking of important scenarios can lead to the provision of more effective safety measure in a design stage of the repository. The ranking is determined by a relative degree of appropriateness of scenario alternatives. To demonstrate a validation of the proposed approach to ranking of release scenarios, results of the previous AHP study are used and compared with them of the present SIAHP approach. Since the AHP approach uses importance weight based on additive probability measures, the interaction among criteria is ignored. The comparison of scenarios ranking obtained from these two approaches enables us to figure out the effect of different models for interaction among criteria.
The effect of uncertainties in distance-based ranking methods for multi-criteria decision making
Jaini, Nor I.; Utyuzhnikov, Sergei V.
2017-08-01
Data in the multi-criteria decision making are often imprecise and changeable. Therefore, it is important to carry out sensitivity analysis test for the multi-criteria decision making problem. The paper aims to present a sensitivity analysis for some ranking techniques based on the distance measures in multi-criteria decision making. Two types of uncertainties are considered for the sensitivity analysis test. The first uncertainty is related to the input data, while the second uncertainty is towards the Decision Maker preferences (weights). The ranking techniques considered in this study are TOPSIS, the relative distance and trade-off ranking methods. TOPSIS and the relative distance method measure a distance from an alternative to the ideal and antiideal solutions. In turn, the trade-off ranking calculates a distance of an alternative to the extreme solutions and other alternatives. Several test cases are considered to study the performance of each ranking technique in both types of uncertainties.
GRASP to minimize total weighted tardiness in a permutation flow shop environment
Directory of Open Access Journals (Sweden)
Lina Paola Molina-Sánchez
2016-01-01
Full Text Available This paper addresses the scheduling problem in a Permutation Flow Shop (PFS environment, which is associated with many types of industries such as chemical, petrochemical, automobile manufacturing, metallurgical, textile, etc. Thus, this work intends to solve a PFS scheduling problem in order to minimize the total weighted tardiness, since it is an important sequencing criterion not only for on time delivery jobs but also for customer satisfaction. To solve the problem, GRASP (Greedy Randomized Adaptive Search Procedure metaheuristic is proposed as a solution, which has shown competitive results compared with other combinatorial problems. In addition, two utility functions called Weighted Modified Due Date (WMDD and Apparent Tardiness Cost (ATC are proposed to develop GRASP. These are based on dynamic dispatching rules and also known for solving the problem of total weighted tardiness for single machine scheduling problem. Next, an experimental design was carried out for comparing the GRASP performance with both utility functions and against the WEDD dispatching rule results. The results indicate that GRASP-WMDD could improve the total weighted tardiness in 47.8% compared with WEDD results. Finally, the GRASP-WMDD performance for the PFS total tardiness problem was evaluated, obtaining a relative deviation index of 13.89% and ranking the method over 26 heuristics and metaheuristics.
Google and the mind: predicting fluency with PageRank.
Griffiths, Thomas L; Steyvers, Mark; Firl, Alana
2007-12-01
Human memory and Internet search engines face a shared computational problem, needing to retrieve stored pieces of information in response to a query. We explored whether they employ similar solutions, testing whether we could predict human performance on a fluency task using PageRank, a component of the Google search engine. In this task, people were shown a letter of the alphabet and asked to name the first word beginning with that letter that came to mind. We show that PageRank, computed on a semantic network constructed from word-association data, outperformed word frequency and the number of words for which a word is named as an associate as a predictor of the words that people produced in this task. We identify two simple process models that could support this apparent correspondence between human memory and Internet search, and relate our results to previous rational models of memory.
EFFICIENCY OF RANKED SET SAMPLING IN HORTICULTURAL SURVEYS
Directory of Open Access Journals (Sweden)
M Iqbal Jeelani
2015-12-01
Full Text Available DOI: 10.12957/cadest.2015.19114 Abstract In this paper, we explore the feasibility of using RSS (Ranked Set Sampling in improving the estimates of the population mean in comparison to SRS (Simple Random Sampling in Horticultural research. We use an experience developed with a survey of apples in India. The numerical results suggest that RSS procedure results in a substantial reduction of standard errors, and thus provides more efficient estimates than SRS, in the specific Horticultural Survey studied, using the same sample size. Then it is recommended as an easy-to-use accurate method to management of this Horticulture problem. Key-words: Ranked Set Sampling, Simple Random Sampling, Standard Error, Accuracy.
Analysis of some methods for reduced rank Gaussian process regression
DEFF Research Database (Denmark)
Quinonero-Candela, J.; Rasmussen, Carl Edward
2005-01-01
proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank......While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent...... Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning...
Sparse reduced-rank regression with covariance estimation
Chen, Lisha
2014-12-08
Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.
Hou, Angela Yung-Chi; Morse, Robert; Shao, Yueh-jen E.
2012-01-01
In order to help students make well-informed choices, reliable college ranking systems with comparable information about higher education institutions worldwide have been welcomed by many students. Because traditional college rankings had many methodological problems, a new type of user-based ranking, called "personalized college…
Who's bigger? where historical figures really rank
Skiena, Steven
2014-01-01
Is Hitler bigger than Napoleon? Washington bigger than Lincoln? Picasso bigger than Einstein? Quantitative analysts are rapidly finding homes in social and cultural domains, from finance to politics. What about history? In this fascinating book, Steve Skiena and Charles Ward bring quantitative analysis to bear on ranking and comparing historical reputations. They evaluate each person by aggregating the traces of millions of opinions, just as Google ranks webpages. The book includes a technical discussion for readers interested in the details of the methods, but no mathematical or computational background is necessary to understand the rankings or conclusions. Along the way, the authors present the rankings of more than one thousand of history's most significant people in science, politics, entertainment, and all areas of human endeavor. Anyone interested in history or biography can see where their favorite figures place in the grand scheme of things.
Ranking Forestry Investments With Parametric Linear Programming
Paul A. Murphy
1976-01-01
Parametric linear programming is introduced as a technique for ranking forestry investments under multiple constraints; it combines the advantages of simple tanking and linear programming as capital budgeting tools.
Superfund Hazard Ranking System Training Course
The Hazard Ranking System (HRS) training course is a four and ½ day, intermediate-level course designed for personnel who are required to compile, draft, and review preliminary assessments (PAs), site inspections (SIs), and HRS documentation records/packag
A cognitive model for aggregating people's rankings
National Research Council Canada - National Science Library
Lee, Michael D; Steyvers, Mark; Miller, Brent
2014-01-01
.... Applications of the model to 23 data sets, dealing with general knowledge and prediction tasks, show that the model performs well in producing an aggregate ranking that is often close to the ground...
Rank rigidity for CAT(0) cube complexes
Caprace, Pierre-Emmanuel; Sageev, Michah
2010-01-01
We prove that any group acting essentially without a fixed point at infinity on an irreducible finite-dimensional CAT(0) cube complex contains a rank one isometry. This implies that the Rank Rigidity Conjecture holds for CAT(0) cube complexes. We derive a number of other consequences for CAT(0) cube complexes, including a purely geometric proof of the Tits Alternative, an existence result for regular elements in (possibly non-uniform) lattices acting on cube complexes, and a characterization ...
NUCLEAR POWER PLANTS SAFETY IMPROVEMENT PROJECTS RANKING
Григорян, Анна Сергеевна; Тигран Георгиевич ГРИГОРЯН; Квасневский, Евгений Анатольевич
2013-01-01
The ranking nuclear power plants safety improvement projects is the most important task for ensuring the efficiency of NPP project management office work. Total amount of projects in NPP portfolio may reach more than 400. Features of the nuclear power plants safety improvement projects ranking in NPP portfolio determine the choice of the decision verbal analysis as a method of decision-making, as it allows to quickly compare the number of alternatives that are not available at the time of con...
Ranking Music Data by Relevance and Importance
DEFF Research Database (Denmark)
Ruxanda, Maria Magdalena; Nanopoulos, Alexandros; Jensen, Christian Søndergaard
2008-01-01
Due to the rapidly increasing availability of audio files on the Web, it is relevant to augment search engines with advanced audio search functionality. In this context, the ranking of the retrieved music is an important issue. This paper proposes a music ranking method capable of flexibly fusing...... the relevance and importance of music. The proposed method may support users with diverse needs when searching for music....
Rank distributions: A panoramic macroscopic outlook
Eliazar, Iddo I.; Cohen, Morrel H.
2014-01-01
This paper presents a panoramic macroscopic outlook of rank distributions. We establish a general framework for the analysis of rank distributions, which classifies them into five macroscopic "socioeconomic" states: monarchy, oligarchy-feudalism, criticality, socialism-capitalism, and communism. Oligarchy-feudalism is shown to be characterized by discrete macroscopic rank distributions, and socialism-capitalism is shown to be characterized by continuous macroscopic size distributions. Criticality is a transition state between oligarchy-feudalism and socialism-capitalism, which can manifest allometric scaling with multifractal spectra. Monarchy and communism are extreme forms of oligarchy-feudalism and socialism-capitalism, respectively, in which the intrinsic randomness vanishes. The general framework is applied to three different models of rank distributions—top-down, bottom-up, and global—and unveils each model's macroscopic universality and versatility. The global model yields a macroscopic classification of the generalized Zipf law, an omnipresent form of rank distributions observed across the sciences. An amalgamation of the three models establishes a universal rank-distribution explanation for the macroscopic emergence of a prevalent class of continuous size distributions, ones governed by unimodal densities with both Pareto and inverse-Pareto power-law tails.
Hierarchical Rank Aggregation with Applications to Nanotoxicology.
Patel, Trina; Telesca, Donatello; Rallo, Robert; George, Saji; Xia, Tian; Nel, André E
2013-06-01
The development of high throughput screening (HTS) assays in the field of nanotoxicology provide new opportunities for the hazard assessment and ranking of engineered nanomaterials (ENMs). It is often necessary to rank lists of materials based on multiple risk assessment parameters, often aggregated across several measures of toxicity and possibly spanning an array of experimental platforms. Bayesian models coupled with the optimization of loss functions have been shown to provide an effective framework for conducting inference on ranks. In this article we present various loss-function-based ranking approaches for comparing ENM within experiments and toxicity parameters. Additionally, we propose a framework for the aggregation of ranks across different sources of evidence while allowing for differential weighting of this evidence based on its reliability and importance in risk ranking. We apply these methods to high throughput toxicity data on two human cell-lines, exposed to eight different nanomaterials, and measured in relation to four cytotoxicity outcomes. This article has supplementary material online.
RANWAR: rank-based weighted association rule mining from gene expression and methylation data.
Mallik, Saurav; Mukhopadhyay, Anirban; Maulik, Ujjwal
2015-01-01
Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported.
Younes, Mohammad K.; Nopiah, Z. M.; Basri, N. E. Ahmad; Basri, H.
2015-02-01
Integrating environmental, social, political, and economical attributes enhances the decision making process. Multi criteria decision making (MCDM) involves ambiguity and uncertainty due to various preferences. This study presents a model to minimize the uncertainty and ambiguity of human judgments by means of integrating the counter stakeholders with median ranked sample set (MRSS) and Analytic hierarchy process (AHP). The model uses landfill site selection as a MCDM problem. Sixteen experts belong to four clusters that are government, private, institution, and non-governmental organisations participated and their preferences were ranked in four by four matrix. Then the MRSS and the AHP were used to obtain the priorities of landfill siting criteria. Environmental criteria have the highest priority that equals to 48.1% and the distance from surface water, and the faults zones are the most important factors with priorities equal to 18% and 13.7% respectively. In conclusion, the hybrid approach that integrates counter stakeholders MRSS, and AHP is capable of being applied to complex decision making process and its outputs are justified.
An introduction to nonlinear programming. IV - Numerical methods for constrained minimization
Sorenson, H. W.; Koble, H. M.
1976-01-01
An overview is presented of the numerical solution of constrained minimization problems. Attention is given to both primal and indirect (linear programs and unconstrained minimizations) methods of solution.
A proof that the maximal rank for plane quartics is seven
Directory of Open Access Journals (Sweden)
Alessandro De Paris
2015-12-01
Full Text Available At the time of writing, the general problem of finding the maximal Waring rank for homogeneous polynomials of fixed degree and number of variables (or, equivalently, the maximal symmetric rank for symmetric tensors of fixed order and in fixed dimension is still unsolved. To our knowledge, the answer for ternary quartics is not widely known and can only be found among the results of a master's thesis by Johannes Kleppe at the University of Oslo (1999. In the present work we give a (direct proof that the maximal rank for plane quartics is seven, following the elementary geometric idea of splitting power sum decompositions along three suitable lines.
Ranking Silent Nodes in Information Networks: A Quantitative Approach and Applications
Interdonato, Roberto; Tagarelli, Andrea
This paper overviews recent research findings concerning a new challenging problem in information networks, namely identifying and ranking silent nodes. We present three case studies which show how silent nodes' behavior maps to different situations in computer networks, online social networks, and online collaboration networks, and we discuss major benefits in identifying and ranking silent nodes in such networks. We also provide an overview of our proposed approach, which relies on a new eigenvector- centrality graph-based ranking method built on a silent-oriented network model.
Ruled Laguerre minimal surfaces
Skopenkov, Mikhail
2011-10-30
A Laguerre minimal surface is an immersed surface in ℝ 3 being an extremal of the functional ∫ (H 2/K-1)dA. In the present paper, we prove that the only ruled Laguerre minimal surfaces are up to isometry the surfaces ℝ (φλ) = (Aφ, Bφ, Cφ + D cos 2φ) + λ(sin φ, cos φ, 0), where A,B,C,D ε ℝ are fixed. To achieve invariance under Laguerre transformations, we also derive all Laguerre minimal surfaces that are enveloped by a family of cones. The methodology is based on the isotropic model of Laguerre geometry. In this model a Laguerre minimal surface enveloped by a family of cones corresponds to a graph of a biharmonic function carrying a family of isotropic circles. We classify such functions by showing that the top view of the family of circles is a pencil. © 2011 Springer-Verlag.
Hubbard, Guy
2002-01-01
Provides background information on the art movement called "Minimalism" discussing why it started and its characteristics. Includes learning activities and information on the artist, Donald Judd. Includes a reproduction of one of his art works and discusses its content. (CMK)
ARWU vs. Alternative ARWU Ranking: What are the Consequences for Lower Ranked Universities?
Directory of Open Access Journals (Sweden)
Milica Maričić
2017-05-01
Full Text Available The ARWU ranking has been a source of academic debate since its development in 2003, but the same does not account for the Alternative ARWU ranking. Namely, the Alternative ARWU ranking attempts to reduce the influence of the prestigious indicators Alumni and Award which are based on the number of received Nobel Prizes and Fields Medals by alumni or university staff. However, the consequences of the reduction of the two indicators have not been scrutinized in detail. Therefore, we propose a statistical approach to the comparison of the two rankings and an in-depth analysis of the Alternative ARWU groups. The obtained results, which are based on the official data, can provide new insights into the nature of the Alternative ARWU ranking. The presented approach might initiate further research on the Alternative ARWU ranking and on the impact of university ranking’s list length. JEL Classification: C10, C38, I23
Minimizing makespan in flowshops with pallet requirements: computational complexity
M. Wang (Michael); S. Sethi (Suresh); C. Sriskandarajah (Chelliah); S.L. van de Velde (Steef)
1997-01-01
textabstractStudies the minimization in flowshops with pallet requirements. Importance of pallets in automated or flexible manufacturing environments; Mounting and dismounting of work pieces; Planning problems involved.
A Class of Weighted Low Rank Approximation of the Positive Semidefinite Hankel Matrix
Directory of Open Access Journals (Sweden)
Jianchao Bai
2015-01-01
Full Text Available We consider the weighted low rank approximation of the positive semidefinite Hankel matrix problem arising in signal processing. By using the Vandermonde representation, we firstly transform the problem into an unconstrained optimization problem and then use the nonlinear conjugate gradient algorithm with the Armijo line search to solve the equivalent unconstrained optimization problem. Numerical examples illustrate that the new method is feasible and effective.
Efficient Top-k Search for PageRank
National Research Council Canada - National Science Library
Fujiwara, Yasuhiro; Nakatsuji, Makoto; Shiokawa, Hiroaki; Mishima, Takeshi; Onizuka, Makoto
2015-01-01
In AI communities, many applications utilize PageRank. To obtain high PageRank score nodes, the original approach iteratively computes the PageRank score of each node until convergence from the whole graph...
PageRank as a method to rank biomedical literature by importance.
Yates, Elliot J; Dixon, Louise C
2015-01-01
Optimal ranking of literature importance is vital in overcoming article overload. Existing ranking methods are typically based on raw citation counts, giving a sum of 'inbound' links with no consideration of citation importance. PageRank, an algorithm originally developed for ranking webpages at the search engine, Google, could potentially be adapted to bibliometrics to quantify the relative importance weightings of a citation network. This article seeks to validate such an approach on the freely available, PubMed Central open access subset (PMC-OAS) of biomedical literature. On-demand cloud computing infrastructure was used to extract a citation network from over 600,000 full-text PMC-OAS articles. PageRanks and citation counts were calculated for each node in this network. PageRank is highly correlated with citation count (R = 0.905, P PageRank can be trivially computed on commodity cluster hardware and is linearly correlated with citation count. Given its putative benefits in quantifying relative importance, we suggest it may enrich the citation network, thereby overcoming the existing inadequacy of citation counts alone. We thus suggest PageRank as a feasible supplement to, or replacement of, existing bibliometric ranking methods.
RANK/RANK-Ligand/OPG: Ein neuer Therapieansatz in der Osteoporosebehandlung
Directory of Open Access Journals (Sweden)
Preisinger E
2007-01-01
Full Text Available Die Erforschung der Kopplungsmechanismen zur Osteoklastogenese, Knochenresorption und Remodellierung eröffnete neue mögliche Therapieansätze in der Behandlung der Osteoporose. Eine Schlüsselrolle beim Knochenabbau spielt der RANK- ("receptor activator of nuclear factor (NF- κB"- Ligand (RANKL. Durch die Bindung von RANKL an den Rezeptor RANK wird die Knochenresorption eingeleitet. OPG (Osteoprotegerin sowie der für den klinischen Gebrauch entwickelte humane monoklonale Antikörper (IgG2 Denosumab blockieren die Bindung von RANK-Ligand an RANK und verhindern den Knochenabbau.
Monte Carlo methods for top-k personalized PageRank lists and name disambiguation
Avrachenkov, Konstatin; Litvak, Nelli; Nemirovsky, Danil; Smirnova, Elena; Sokol, Marina
We study a problem of quick detection of top-k Personalized PageRank lists. This problem has a number of important applications such as finding local cuts in large graphs, estimation of similarity distance and name disambiguation. In particular, we apply our results to construct efficient algorithms
The Seven Deadly Sins of World University Ranking: A Summary from Several Papers
Soh, Kaycheng
2017-01-01
World university rankings use the weight-and-sum approach to process data. Although this seems to pass the common sense test, it has statistical problems. In recent years, seven such problems have been uncovered: spurious precision, weight discrepancies, assumed mutual compensation, indictor redundancy, inter-system discrepancy, negligence of…
Energy Technology Data Exchange (ETDEWEB)
Zhang, Zhenyue [Zhejiang Univ., Hangzhou (People' s Republic of China); Zha, Hongyuan [Pennsylvania State Univ., University Park, PA (United States); Simon, Horst [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2006-07-31
In this paper, we developed numerical algorithms for computing sparse low-rank approximations of matrices, and we also provided a detailed error analysis of the proposed algorithms together with some numerical experiments. The low-rank approximations are constructed in a certain factored form with the degree of sparsity of the factors controlled by some user-specified parameters. In this paper, we cast the sparse low-rank approximation problem in the framework of penalized optimization problems. We discuss various approximation schemes for the penalized optimization problem which are more amenable to numerical computations. We also include some analysis to show the relations between the original optimization problem and the reduced one. We then develop a globally convergent discrete Newton-like iterative method for solving the approximate penalized optimization problems. We also compare the reconstruction errors of the sparse low-rank approximations computed by our new methods with those obtained using the methods in the earlier paper and several other existing methods for computing sparse low-rank approximations. Numerical examples show that the penalized methods are more robust and produce approximations with factors which have fewer columns and are sparser.
Ranking Fuzzy Numbers and Its Application to Products Attributes Preferences
Abdullah, Lazim; Fauzee, Nor Nashrah Ahmad
2011-01-01
Ranking is one of the widely used methods in fuzzy decision making environment. The recent ranking fuzzy numbers proposed by Wang and Li is claimed to be the improved version in ranking. However, the method was never been simplified and tested in real life application. This paper presents a four-step computation of ranking fuzzy numbers and its application in ranking attributes of selected chocolate products. The four steps algorithm was formulated to rank fuzzy numbers and followed by a tes...
Flattening the inflaton potential beyond minimal gravity
Directory of Open Access Journals (Sweden)
Lee Hyun Min
2018-01-01
Full Text Available We review the status of the Starobinsky-like models for inflation beyond minimal gravity and discuss the unitarity problem due to the presence of a large non-minimal gravity coupling. We show that the induced gravity models allow for a self-consistent description of inflation and discuss the implications of the inflaton couplings to the Higgs field in the Standard Model.
Social class rank, essentialism, and punitive judgment.
Kraus, Michael W; Keltner, Dacher
2013-08-01
Recent evidence suggests that perceptions of social class rank influence a variety of social cognitive tendencies, from patterns of causal attribution to moral judgment. In the present studies we tested the hypotheses that upper-class rank individuals would be more likely to endorse essentialist lay theories of social class categories (i.e., that social class is founded in genetically based, biological differences) than would lower-class rank individuals and that these beliefs would decrease support for restorative justice--which seeks to rehabilitate offenders, rather than punish unlawful action. Across studies, higher social class rank was associated with increased essentialism of social class categories (Studies 1, 2, and 4) and decreased support for restorative justice (Study 4). Moreover, manipulated essentialist beliefs decreased preferences for restorative justice (Study 3), and the association between social class rank and class-based essentialist theories was explained by the tendency to endorse beliefs in a just world (Study 2). Implications for how class-based essentialist beliefs potentially constrain social opportunity and mobility are discussed.
A network-based dynamical ranking system
Motegi, Shun
2012-01-01
Ranking players or teams in sports is of practical interests. From the viewpoint of networks, a ranking system is equivalent a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score (i.e., strength) of a player, for example, depends on time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. Our ranking system, also interpreted as a centrality measure for directed temporal networks, has two parameters. One parameter represents the exponential decay rate of the past score, and the other parameter controls the effect of indirect wins on the score. We derive a set of linear online update equ...
Global network centrality of university rankings
Guo, Weisi; Del Vecchio, Marco; Pogrebna, Ganna
2017-10-01
Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct, to our knowledge, the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also found that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing that global connectivity distance is more important than the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.
A Cognitive Model for Aggregating People's Rankings
Lee, Michael D.; Steyvers, Mark; Miller, Brent
2014-01-01
We develop a cognitive modeling approach, motivated by classic theories of knowledge representation and judgment from psychology, for combining people's rankings of items. The model makes simple assumptions about how individual differences in knowledge lead to observed ranking data in behavioral tasks. We implement the cognitive model as a Bayesian graphical model, and use computational sampling to infer an aggregate ranking and measures of the individual expertise. Applications of the model to 23 data sets, dealing with general knowledge and prediction tasks, show that the model performs well in producing an aggregate ranking that is often close to the ground truth and, as in the “wisdom of the crowd” effect, usually performs better than most of individuals. We also present some evidence that the model outperforms the traditional statistical Borda count method, and that the model is able to infer people's relative expertise surprisingly well without knowing the ground truth. We discuss the advantages of the cognitive modeling approach to combining ranking data, and in wisdom of the crowd research generally, as well as highlighting a number of potential directions for future model development. PMID:24816733
A Review of Outcomes of Seven World University Ranking Systems
National Research Council Canada - National Science Library
Mahmood Khosrowjerdi; Neda Zeraatkar
2012-01-01
There are many national and international ranking systems rank the universities and higher education institutions of the world, nationally or internationally, based on the same or different criteria...
Codes in Permutations and Error Correction for Rank Modulation
Barg, Alexander
2009-01-01
Codes for rank modulation have been recently proposed as a means of protecting flash memory devices from errors. We study basic coding theoretic problems for such codes, representing them as subsets of the set of permutations of $n$ elements equipped with the Kendall tau distance. We derive several lower and upper bounds on the size of codes. These bounds enable us to establish the exact scaling of the size of optimal codes for large values of $n$. We also show the existence of codes whose size is within a constant factor of the sphere packing bound for any fixed number of errors.
Hyper-local, directions-based ranking of places
DEFF Research Database (Denmark)
Venetis, Petros; Gonzalez, Hector; Jensen, Christian S.
2011-01-01
, including the frequency with which places have been referred to in directions queries. Next, the paper proposes an algorithm and accompanying data structures capable of ranking places in response to hyper-local web queries. Finally, an empirical study with very large directions query logs offers insight......, enables so-called hyper-local web querying where the location of a user is accurate at a much finer granularity than with IP-based positioning. This paper addresses the problem of determining the importance of points of interest, or places, in local-search results. In doing so, the paper proposes...
Multifeature Extreme Ordinal Ranking Machine for Facial Age Estimation
Directory of Open Access Journals (Sweden)
Wei Zhao
2015-01-01
Full Text Available Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking machine (MFEORM for facial age estimation. Experimental results clearly demonstrate that the proposed approach can sharply reduce the runtime (even up to nearly one hundred times faster while achieving comparable or better estimation performances than the state-of-the-art approaches. The inner properties of MFEORM are further explored with more advantages.
Theories of minimalism in architecture: Post scriptum
Directory of Open Access Journals (Sweden)
Stevanović Vladimir
2012-01-01
Full Text Available Owing to the period of intensive development in the last decade of XX century, architectural phenomenon called Minimalism in Architecture was remembered as the Style of the Nineties, which is characterized, morphologically speaking, by simplicity and formal reduction. Simultaneously with its development in practice, on a theoretical level several dominant interpretative models were able to establish themselves. The new millennium and time distance bring new problems; therefore this paper represents a discussion on specific theorization related to Minimalism in Architecture that can bear the designation of post scriptum, because their development starts after the constitutional period of architectural minimalist discourse. In XXI century theories, the problem of definition of minimalism remains important topic, approached by theorists through resolving on the axis: Modernism - Minimal Art - Postmodernism - Minimalism in Architecture. With regard to this, analyzed texts can be categorized in two groups: 1 texts of affirmative nature and historical-associative approach in which minimalism is identified with anything that is simple and reduced, in an idealizing manner, relied mostly on the existing hypotheses; 2 critically oriented texts, in which authors reconsider adequacy of the very term 'minimalism' in the context of architecture and take a metacritical attitude towards previous texts.
Ranking of delay factors in construction projects after Egyptian revolution
Directory of Open Access Journals (Sweden)
Remon Fayek Aziz
2013-09-01
Full Text Available Time is one of the major considerations throughout project management life cycle and can be regarded as one of the most important parameters of a project and the driving force of project success. Time delay is a very frequent phenomenon and is almost associated with nearly all constructing projects. However, little effort has been made to curtail the phenomenon, this research work attempts to identify, investigate, and rank factors perceived to affect delays in the Egyptian construction projects with respect to their relative importance so as to proffer possible ways of coping with this phenomenon. To achieve this objective, researcher invited practitioners and experts, comprising a statistically representative sample to participate in a structured questionnaire survey. Brain storming was taken into consideration, through which a number of delay factors were identified in construction projects. Totally, ninety-nine (99 factors were short-listed to be made part of the questionnaire survey and were identified and categorized into nine (9 major categories. The survey was conducted with experts and representatives from private, public, and local general construction firms. The data were analyzed using Relative Importance Index (RII, ranking and simple percentages. Ranking of factors and categories was demonstrated according to their importance level on delay, especially after 25/1/2011 (Egyptian revolution. According to the case study results, the most contributing factors and categories (those need attention to delays were discussed, and some recommendations were made in order to minimize and control delays in construction projects. Also, this paper can serve as a guide for all construction parties with effective management in construction projects to achieve a competitive level of quality and a time effective project.
Approximate error conjugation gradient minimization methods
Kallman, Jeffrey S
2013-05-21
In one embodiment, a method includes selecting a subset of rays from a set of all rays to use in an error calculation for a constrained conjugate gradient minimization problem, calculating an approximate error using the subset of rays, and calculating a minimum in a conjugate gradient direction based on the approximate error. In another embodiment, a system includes a processor for executing logic, logic for selecting a subset of rays from a set of all rays to use in an error calculation for a constrained conjugate gradient minimization problem, logic for calculating an approximate error using the subset of rays, and logic for calculating a minimum in a conjugate gradient direction based on the approximate error. In other embodiments, computer program products, methods, and systems are described capable of using approximate error in constrained conjugate gradient minimization problems.
Resolution of ranking hierarchies in directed networks
Barucca, Paolo; Lillo, Fabrizio
2018-01-01
Identifying hierarchies and rankings of nodes in directed graphs is fundamental in many applications such as social network analysis, biology, economics, and finance. A recently proposed method identifies the hierarchy by finding the ordered partition of nodes which minimises a score function, termed agony. This function penalises the links violating the hierarchy in a way depending on the strength of the violation. To investigate the resolution of ranking hierarchies we introduce an ensemble of random graphs, the Ranked Stochastic Block Model. We find that agony may fail to identify hierarchies when the structure is not strong enough and the size of the classes is small with respect to the whole network. We analytically characterise the resolution threshold and we show that an iterated version of agony can partly overcome this resolution limit. PMID:29394278
Ranking beta sheet topologies of proteins
DEFF Research Database (Denmark)
Fonseca, Rasmus; Helles, Glennie; Winter, Pawel
2010-01-01
One of the challenges of protein structure prediction is to identify long-range interactions between amino acids. To reliably predict such interactions, we enumerate, score and rank all beta-topologies (partitions of beta-strands into sheets, orderings of strands within sheets and orientations...... of paired strands) of a given protein. We show that the beta-topology corresponding to the native structure is, with high probability, among the top-ranked. Since full enumeration is very time-consuming, we also suggest a method to deal with proteins with many beta-strands. The results reported...... in this paper are highly relevant for ab initio protein structure prediction methods based on decoy generation. The top-ranked beta-topologies can be used to find initial conformations from which conformational searches can be started. They can also be used to filter decoys by removing those with poorly...
Adaptive distributional extensions to DFR ranking
DEFF Research Database (Denmark)
Petersen, Casper; Simonsen, Jakob Grue; Järvelin, Kalervo
2016-01-01
Divergence From Randomness (DFR) ranking models assume that informative terms are distributed in a corpus differently than non-informative terms. Different statistical models (e.g. Poisson, geometric) are used to model the distribution of non-informative terms, producing different DFR models....... An informative term is then detected by measuring the divergence of its distribution from the distribution of non-informative terms. However, there is little empirical evidence that the distributions of non-informative terms used in DFR actually fit current datasets. Practically this risks providing a poor...... separation between informative and non-informative terms, thus compromising the discriminative power of the ranking model. We present a novel extension to DFR, which first detects the best-fitting distribution of non-informative terms in a collection, and then adapts the ranking computation to this best...
Sign rank versus Vapnik-Chervonenkis dimension
Alon, N.; Moran, Sh; Yehudayoff, A.
2017-12-01
This work studies the maximum possible sign rank of sign (N × N)-matrices with a given Vapnik-Chervonenkis dimension d. For d=1, this maximum is three. For d=2, this maximum is \\widetilde{\\Theta}(N1/2). For d >2, similar but slightly less accurate statements hold. The lower bounds improve on previous ones by Ben-David et al., and the upper bounds are novel. The lower bounds are obtained by probabilistic constructions, using a theorem of Warren in real algebraic topology. The upper bounds are obtained using a result of Welzl about spanning trees with low stabbing number, and using the moment curve. The upper bound technique is also used to: (i) provide estimates on the number of classes of a given Vapnik-Chervonenkis dimension, and the number of maximum classes of a given Vapnik-Chervonenkis dimension--answering a question of Frankl from 1989, and (ii) design an efficient algorithm that provides an O(N/log(N)) multiplicative approximation for the sign rank. We also observe a general connection between sign rank and spectral gaps which is based on Forster's argument. Consider the adjacency (N × N)-matrix of a Δ-regular graph with a second eigenvalue of absolute value λ and Δ ≤ N/2. We show that the sign rank of the signed version of this matrix is at least Δ/λ. We use this connection to prove the existence of a maximum class C\\subseteq\\{+/- 1\\}^N with Vapnik-Chervonenkis dimension 2 and sign rank \\widetilde{\\Theta}(N1/2). This answers a question of Ben-David et al. regarding the sign rank of large Vapnik-Chervonenkis classes. We also describe limitations of this approach, in the spirit of the Alon-Boppana theorem. We further describe connections to communication complexity, geometry, learning theory, and combinatorics. Bibliography: 69 titles.
Pulling Rank: A Plan to Help Students with College Choice in an Age of Rankings
Thacker, Lloyd
2008-01-01
Colleges and universities are "ranksteering"--driving under the influence of popular college rankings systems like "U.S. News and World Report's" Best Colleges. This article examines the criticisms of college rankings and describes how a group of education leaders is honing a plan to end the tyranny of the ratings game and better help students and…
Minimalism and Speakers’ Intuitions
Directory of Open Access Journals (Sweden)
Matías Gariazzo
2011-08-01
Full Text Available Minimalism proposes a semantics that does not account for speakers’ intuitions about the truth conditions of a range of sentences or utterances. Thus, a challenge for this view is to offer an explanation of how its assignment of semantic contents to these sentences is grounded in their use. Such an account was mainly offered by Soames, but also suggested by Cappelen and Lepore. The article criticizes this explanation by presenting four kinds of counterexamples to it, and arrives at the conclusion that minimalism has not successfully answered the above-mentioned challenge.
DEFF Research Database (Denmark)
Frandsen, Mads Toudal
2007-01-01
I report on our construction and analysis of the effective low energy Lagrangian for the Minimal Walking Technicolor (MWT) model. The parameters of the effective Lagrangian are constrained by imposing modified Weinberg sum rules and by imposing a value for the S parameter estimated from the under......I report on our construction and analysis of the effective low energy Lagrangian for the Minimal Walking Technicolor (MWT) model. The parameters of the effective Lagrangian are constrained by imposing modified Weinberg sum rules and by imposing a value for the S parameter estimated from...
Ranking Entities in Networks via Lefschetz Duality
DEFF Research Database (Denmark)
Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Poulsen, Bjarne
2014-01-01
In the theory of communication it is essential that agents are able to exchange information. This fact is closely related to the study of connected spaces in topology. A communication network may be modelled as a topological space such that agents can communicate if and only if they belong...... then be ranked according to how essential their positions are in the network by considering the effect of their respective absences. Defining a ranking of a network which takes the individual position of each entity into account has the purpose of assigning different roles to the entities, e.g. agents...
Compressed Sensing with Rank Deficient Dictionaries
DEFF Research Database (Denmark)
Hansen, Thomas Lundgaard; Johansen, Daniel Højrup; Jørgensen, Peter Bjørn
2012-01-01
In compressed sensing it is generally assumed that the dictionary matrix constitutes a (possibly overcomplete) basis of the signal space. In this paper we consider dictionaries that do not span the signal space, i.e. rank deficient dictionaries. We show that in this case the signal-to-noise ratio...... (SNR) in the compressed samples can be increased by selecting the rows of the measurement matrix from the column space of the dictionary. As an example application of compressed sensing with a rank deficient dictionary, we present a case study of compressed sensing applied to the Coarse Acquisition (C...
Energy Technology Data Exchange (ETDEWEB)
Stenner, R.D.; Peloquin, R.A.; Hawley, K.A.
1986-11-01
The mHRS/HRS software package was developed by the Pacific Northwest Laboratory (PNL) under contract with the Department of Energy (DOE) to provide a uniform method for DOE facilities to use in performing their Conservation Environmental Response Compensation and Liability Act (CERCLA) Phase I Modified Hazard Ranking System or Hazard Ranking System evaluations. The program is designed to remove the tedium and potential for error associated with the performing of hand calculations and the interpreting of information on tables and in reference books when performing an evaluation. The software package is designed to operate on a microcomputer (IBM PC, PC/XT, or PC/AT, or a compatible system) using either a dual floppy disk drive or a hard disk storage system. It is written in the dBASE III language and operates using the dBASE III system. Although the mHRS/HRS software package was developed for use at DOE facilities, it has direct applicability to the performing of CERCLA Phase I evaluations for any facility contaminated by hazardous waste. The software can perform evaluations using either the modified hazard ranking system methodology developed by DOE/PNL, the hazard ranking system methodology developed by EPA/MITRE Corp., or a combination of the two. This document is a companion manual to the mHRS/HRS user manual. It is intended for the programmer who must maintain the software package and for those interested in the computer implementation. This manual documents the system logic, computer programs, and data files that comprise the package. Hardware and software implementation requirements are discussed. In addition, hand calculations of three sample situations (problems) with associated computer runs used for the verification of program calculations are included.
On the MSE Performance and Optimization of Regularized Problems
Alrashdi, Ayed
2016-11-01
The amount of data that has been measured, transmitted/received, and stored in the recent years has dramatically increased. So, today, we are in the world of big data. Fortunately, in many applications, we can take advantages of possible structures and patterns in the data to overcome the curse of dimensionality. The most well known structures include sparsity, low-rankness, block sparsity. This includes a wide range of applications such as machine learning, medical imaging, signal processing, social networks and computer vision. This also led to a specific interest in recovering signals from noisy compressed measurements (Compressed Sensing (CS) problem). Such problems are generally ill-posed unless the signal is structured. The structure can be captured by a regularizer function. This gives rise to a potential interest in regularized inverse problems, where the process of reconstructing the structured signal can be modeled as a regularized problem. This thesis particularly focuses on finding the optimal regularization parameter for such problems, such as ridge regression, LASSO, square-root LASSO and low-rank Generalized LASSO. Our goal is to optimally tune the regularizer to minimize the mean-squared error (MSE) of the solution when the noise variance or structure parameters are unknown. The analysis is based on the framework of the Convex Gaussian Min-max Theorem (CGMT) that has been used recently to precisely predict performance errors.
DEFF Research Database (Denmark)
David, Alexandre; Håkansson, John; G. Larsen, Kim
In this paper we present an algorithm to compute DBM substractions with a guaranteed minimal number of splits and disjoint DBMs to avoid any redundance. The substraction is one of the few operations that result in a non-convex zone, and thus, requires splitting. It is of prime importance to reduce...
Minimal constrained supergravity
Energy Technology Data Exchange (ETDEWEB)
Cribiori, N. [Dipartimento di Fisica e Astronomia “Galileo Galilei”, Università di Padova, Via Marzolo 8, 35131 Padova (Italy); INFN, Sezione di Padova, Via Marzolo 8, 35131 Padova (Italy); Dall' Agata, G., E-mail: dallagat@pd.infn.it [Dipartimento di Fisica e Astronomia “Galileo Galilei”, Università di Padova, Via Marzolo 8, 35131 Padova (Italy); INFN, Sezione di Padova, Via Marzolo 8, 35131 Padova (Italy); Farakos, F. [Dipartimento di Fisica e Astronomia “Galileo Galilei”, Università di Padova, Via Marzolo 8, 35131 Padova (Italy); INFN, Sezione di Padova, Via Marzolo 8, 35131 Padova (Italy); Porrati, M. [Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003 (United States)
2017-01-10
We describe minimal supergravity models where supersymmetry is non-linearly realized via constrained superfields. We show that the resulting actions differ from the so called “de Sitter” supergravities because we consider constraints eliminating directly the auxiliary fields of the gravity multiplet.
Minimally invasive periodontal therapy.
Dannan, Aous
2011-10-01
Minimally invasive dentistry is a concept that preserves dentition and supporting structures. However, minimally invasive procedures in periodontal treatment are supposed to be limited within periodontal surgery, the aim of which is to represent alternative approaches developed to allow less extensive manipulation of surrounding tissues than conventional procedures, while accomplishing the same objectives. In this review, the concept of minimally invasive periodontal surgery (MIPS) is firstly explained. An electronic search for all studies regarding efficacy and effectiveness of MIPS between 2001 and 2009 was conducted. For this purpose, suitable key words from Medical Subject Headings on PubMed were used to extract the required studies. All studies are demonstrated and important results are concluded. Preliminary data from case cohorts and from many studies reveal that the microsurgical access flap, in terms of MIPS, has a high potential to seal the healing wound from the contaminated oral environment by achieving and maintaining primary closure. Soft tissues are mostly preserved and minimal gingival recession is observed, an important feature to meet the demands of the patient and the clinician in the esthetic zone. However, although the potential efficacy of MIPS in the treatment of deep intrabony defects has been proved, larger studies are required to confirm and extend the reported positive preliminary outcomes.
Minimal constrained supergravity
Directory of Open Access Journals (Sweden)
N. Cribiori
2017-01-01
Full Text Available We describe minimal supergravity models where supersymmetry is non-linearly realized via constrained superfields. We show that the resulting actions differ from the so called “de Sitter” supergravities because we consider constraints eliminating directly the auxiliary fields of the gravity multiplet.
DEFF Research Database (Denmark)
Foadi, Roshan; Frandsen, Mads Toudal; A. Ryttov, T.
2007-01-01
, pseudoscalars, vector mesons and other fields predicted by the minimal walking theory. We construct their self-interactions and interactions with standard model fields. Using the Weinberg sum rules, opportunely modified to take into account the walking behavior of the underlying gauge theory, we find...
Frames for exact inversion of the rank order coder.
Masmoudi, Khaled; Antonini, Marc; Kornprobst, Pierre
2012-02-01
Our goal is to revisit rank order coding by proposing an original exact decoding procedure for it. Rank order coding was proposed by Thorpe et al. who stated that the order in which the retina cells are activated encodes for the visual stimulus. Based on this idea, the authors proposed in [1] a rank order coder/decoder associated to a retinal model. Though, it appeared that the decoding procedure employed yields reconstruction errors that limit the model bit-cost/quality performances when used as an image codec. The attempts made in the literature to overcome this issue are time consuming and alter the coding procedure, or are lacking mathematical support and feasibility for standard size images. Here we solve this problem in an original fashion by using the frames theory, where a frame of a vector space designates an extension for the notion of basis. Our contribution is twofold. First, we prove that the analyzing filter bank considered is a frame, and then we define the corresponding dual frame that is necessary for the exact image reconstruction. Second, to deal with the problem of memory overhead, we design a recursive out-of-core blockwise algorithm for the computation of this dual frame. Our work provides a mathematical formalism for the retinal model under study and defines a simple and exact reverse transform for it with over than 265 dB of increase in the peak signal-to-noise ratio quality compared to [1]. Furthermore, the framework presented here can be extended to several models of the visual cortical areas using redundant representations.
TrustRank: a Cold-Start tolerant recommender system
Zou, Haitao; Gong, Zhiguo; Zhang, Nan; Zhao, Wei; Guo, Jingzhi
2015-02-01
The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. User- and item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are known to have Cold-Start problems, i.e., they are unable to effectively handle Cold-Start users who have an extremely limited number of purchase records. In this paper, we develop TrustRank, a novel recommender system which handles the Cold-Start problem by leveraging the user-trust networks which are commonly available for e-commerce applications. A user-trust network is formed by friendships or trust relationships that users specify among them. While it is straightforward to conjecture that a user-trust network is helpful for improving the accuracy of recommendations, a key challenge for using user-trust network to facilitate Cold-Start users is that these users also tend to have a very limited number of trust relationships. To address this challenge, we propose a pre-processing propagation of the Cold-Start users' trust network. In particular, by applying the personalised PageRank algorithm, we expand the friends of a given user to include others with similar purchase records to his/her original friends. To make this propagation algorithm scalable to a large amount of users, as required by real-world recommender systems, we devise an iterative computation algorithm of the original personalised TrustRank which can incrementally compute trust vectors for Cold-Start users. We conduct extensive experiments to demonstrate the consistently improvement provided by our proposed algorithm over the existing recommender algorithms on the accuracy of recommendations for Cold-Start users.
GoDec+: Fast and Robust Low-Rank Matrix Decomposition Based on Maximum Correntropy.
Guo, Kailing; Liu, Liu; Xu, Xiangmin; Xu, Dong; Tao, Dacheng
2017-04-24
GoDec is an efficient low-rank matrix decomposition algorithm. However, optimal performance depends on sparse errors and Gaussian noise. This paper aims to address the problem that a matrix is composed of a low-rank component and unknown corruptions. We introduce a robust local similarity measure called correntropy to describe the corruptions and, in doing so, obtain a more robust and faster low-rank decomposition algorithm: GoDec+. Based on half-quadratic optimization and greedy bilateral paradigm, we deliver a solution to the maximum correntropy criterion (MCC)-based low-rank decomposition problem. Experimental results show that GoDec+ is efficient and robust to different corruptions including Gaussian noise, Laplacian noise, salt & pepper noise, and occlusion on both synthetic and real vision data. We further apply GoDec+ to more general applications including classification and subspace clustering. For classification, we construct an ensemble subspace from the low-rank GoDec+ matrix and introduce an MCC-based classifier. For subspace clustering, we utilize GoDec+ values low-rank matrix for MCC-based self-expression and combine it with spectral clustering. Face recognition, motion segmentation, and face clustering experiments show that the proposed methods are effective and robust. In particular, we achieve the state-of-the-art performance on the Hopkins 155 data set and the first 10 subjects of extended Yale B for subspace clustering.
SOUTH AFRICAN ARMY RANKS AND INSIGNIA
African Journals Online (AJOL)
major, cap- tain, lieutenant;. Other Ranks : Warrant officer, staff sergeant, sergeant, corporal, lance-cor- poral, private.' We apparently had no need for second lieuten- ants at that time, and they were introduced only .... Army warrant officers can also hold the cmmon serv- ice posts of Sergeant-Major of Special Forces.
Kinesiology Faculty Citations across Academic Rank
Knudson, Duane
2015-01-01
Citations to research reports are used as a measure for the influence of a scholar's research line when seeking promotion, grants, and awards. The current study documented the distributions of citations to kinesiology scholars of various academic ranks. Google Scholar Citations was searched for user profiles using five research interest areas…
Biomechanics Scholar Citations across Academic Ranks
Directory of Open Access Journals (Sweden)
Knudson Duane
2015-11-01
Full Text Available Study aim: citations to the publications of a scholar have been used as a measure of the quality or influence of their research record. A world-wide descriptive study of the citations to the publications of biomechanics scholars of various academic ranks was conducted.
Ranking Workplace Competencies: Student and Graduate Perceptions.
Rainsbury, Elizabeth; Hodges, Dave; Burchell, Noel; Lay, Mark
2002-01-01
New Zealand business students and graduates made similar rankings of the five most important workplace competencies: computer literacy, customer service orientation, teamwork and cooperation, self-confidence, and willingness to learn. Graduates placed greater importance on most of the 24 competencies, resulting in a statistically significant…
Subject Gateway Sites and Search Engine Ranking.
Thelwall, Mike
2002-01-01
Discusses subject gateway sites and commercial search engines for the Web and presents an explanation of Google's PageRank algorithm. The principle question addressed is the conditions under which a gateway site will increase the likelihood that a target page is found in search engines. (LRW)
Ranking related entities: components and analyses
Bron, M.; Balog, K.; de Rijke, M.
2010-01-01
Related entity finding is the task of returning a ranked list of homepages of relevant entities of a specified type that need to engage in a given relationship with a given source entity. We propose a framework for addressing this task and perform a detailed analysis of four core components;
Low-rank coal oil agglomeration
Knudson, C.L.; Timpe, R.C.
1991-07-16
A low-rank coal oil agglomeration process is described. High mineral content, a high ash content subbituminous coals are effectively agglomerated with a bridging oil which is partially water soluble and capable of entering the pore structure, and is usually coal-derived.
An evaluation and critique of current rankings
Federkeil, Gero; Westerheijden, Donald F.; van Vught, Franciscus A.; Ziegele, Frank
2012-01-01
This chapter raises the question of whether university league tables deliver relevant information to one of their key target groups – students. It examines the inherent biases and weaknesses in the methodologies of the major rankings and argues that the concentration on a single indicator of
World University Ranking Methodologies: Stability and Variability
Fidler, Brian; Parsons, Christine
2008-01-01
There has been a steady growth in the number of national university league tables over the last 25 years. By contrast, "World University Rankings" are a more recent development and have received little serious academic scrutiny in peer-reviewed publications. Few researchers have evaluated the sources of data and the statistical…
Alternative Class Ranks Using Z-Scores
Brown, Philip H.; Van Niel, Nicholas
2012-01-01
Grades at US colleges and universities have increased precipitously over the last 50 years, suggesting that their signalling power has become attenuated. Moreover, average grades have risen disproportionately in some departments, implying that weak students in departments with high grades may obtain better class ranks than strong students in…
City Life: Rankings (Livability) versus Perceptions (Satisfaction)
Okulicz-Kozaryn, Adam
2013-01-01
I investigate the relationship between the popular Mercer city ranking (livability) and survey data (satisfactions). Livability aims to capture "objective" quality of life such as infrastructure. Survey items capture "subjective" quality of life such as satisfaction with city. The relationship between objective measures of quality of life and…
Matrices with high completely positive semidefinite rank
de Laat, David; Gribling, Sander; Laurent, Monique
2017-01-01
A real symmetric matrix M is completely positive semidefinite if it admits a Gram representation by (Hermitian) positive semidefinite matrices of any size d. The smallest such d is called the (complex) completely positive semidefinite rank of M , and it is an open question whether there exists an
Ranking health between countries in international comparisons
DEFF Research Database (Denmark)
Brønnum-Hansen, Henrik
2014-01-01
Cross-national comparisons and ranking of summary measures of population health sometimes give rise to inconsistent and diverging conclusions. In order to minimise confusion, international comparative studies ought to be based on well-harmonised data with common standards of definitions...
Comparing survival curves using rank tests
Albers, Willem/Wim
1990-01-01
Survival times of patients can be compared using rank tests in various experimental setups, including the two-sample case and the case of paired data. Attention is focussed on two frequently occurring complications in medical applications: censoring and tail alternatives. A review is given of the
Smooth rank one perturbations of selfadjoint operators
Hassi, Seppo; Snoo, H.S.V. de; Willemsma, A.D.I.
Let A be a selfadjoint operator in a Hilbert space aleph with inner product [.,.]. The rank one perturbations of A have the form A+tau [.,omega]omega, tau epsilon R, for some element omega epsilon aleph. In this paper we consider smooth perturbations, i.e. we consider omega epsilon dom \\A\\(k/2) for
Primate Innovation: Sex, Age and Social Rank
Reader, S.M.; Laland, K.N.
2001-01-01
Analysis of an exhaustive survey of primate behavior collated from the published literature revealed significant variation in rates of innovation among individuals of different sex, age and social rank. We searched approximately 1,000 articles in four primatology journals, together with other
Ouderdom, omvang en citatiescores: rankings nader bekeken
van Rooij, Jules
2017-01-01
By comparing the Top-300 lists of four global university rankings (ARWU, THE, QS, Leiden), three hypotheses are tested: 1) position correlates with size in the ARWU more than in the THE and QS; 2) given their strong dependency on reputation scores, position will be correlated more with a
Returns to Tenure: Time or Rank?
DEFF Research Database (Denmark)
Buhai, Ioan Sebastian
-specific investment, efficiency-wages or adverse-selection models. However, rent extracting arguments as suggested by the theory of internal labor markets, indicate that the relative position of the worker in the seniority hierarchy of the firm, her 'seniority rank', may also explain part of the observed returns...
transportation cost minimization of a manufacturing firm using
African Journals Online (AJOL)
user
Nigeria with a view to minimizing the total transportation cost and obtaining an optimal schedule or schedules using transportation ... Keywords: genetic algorithm, transportation problem, minimization, manufacturing firm, optimal schedules. 1. INTRODUCTION. ... one of the sub-classes of Linear Programming. Problems in ...
Using Metric Distance Ranking Method to Find Intuitionistic Fuzzy Critical Path
Directory of Open Access Journals (Sweden)
P. Jayagowri
2015-01-01
Full Text Available Network analysis is a technique which determines the various sequences of activities concerning a project and the project completion time. The popular methods of this technique which is widely used are the critical path method and program evaluation and review techniques. The aim of this paper is to present an analytical method for measuring the criticality in an (Atanassov intuitionistic fuzzy project network. Vague parameters in the project network are represented by (Atanassov intuitionistic trapezoidal fuzzy numbers. A metric distance ranking method for (Atanassov intuitionistic fuzzy numbers to a critical path method is proposed. (Atanassov Intuitionistic fuzzy critical length of the project network is found without converting the (Atanassov intuitionistic fuzzy activity times to classical numbers. The fuzzified conversion of the problem has been discussed with the numerical example. We also apply four different ranking procedures and we compare it with metric distance ranking method. Comparison reveals that the proposed ranking method is better than other raking procedures.
Systemic testing on Bradley-Terry model against nonlinear ranking hierarchy.
Shev, Aaron; Fujii, Kevin; Hsieh, Fushing; McCowan, Brenda
2014-01-01
We take a system point of view toward constructing any power or ranking hierarchy onto a society of human or animal players. The most common hierarchy is the linear ranking, which is habitually used in nearly all real-world problems. A stronger version of linear ranking via increasing and unvarying winning potentials, known as Bradley-Terry model, is particularly popular. Only recently non-linear ranking hierarchy is discussed and developed through recognition of dominance information contents beyond direct dyadic win-and-loss. We take this development further by rigorously arguing for the necessity of accommodating system's global pattern information contents, and then introducing a systemic testing on Bradley-Terry model. Our test statistic with an ensemble based empirical distribution favorably compares with the Deviance test equipped with a Chi-squared asymptotic approximation. Several simulated and real data sets are analyzed throughout our development.
Semilattices of finitely generated ideals of exchange rings with finite stable rank
Wehrung, F
2004-01-01
We find a distributive (v, 0, 1)-semilattice S of size $ aleph\\_1$ that is not isomorphic to the maximal semilattice quotient of any Riesz monoid endowed with an order-unit of finite stable rank. We thus obtain solutions to various open problems in ring theory and in lattice theory. In particular: - There is no exchange ring (thus, no von Neumann regular ring and no C*-algebra of real rank zero) with finite stable rank whose semilattice of finitely generated, idempotent-generated two-sided ideals is isomorphic to S. - There is no locally finite, modular lattice whose semilattice of finitely generated congruences is isomorphic to S. These results are established by constructing an infinitary statement, denoted here by URPsr, that holds in the maximal semilattice quotient of every Riesz monoid endowed with an order-unit of finite stable rank, but not in the semilattice S.
Probabilistic relation between In-Degree and PageRank
Litvak, Nelli; Scheinhardt, Willem R.W.; Volkovich, Y.
2008-01-01
This paper presents a novel stochastic model that explains the relation between power laws of In-Degree and PageRank. PageRank is a popularity measure designed by Google to rank Web pages. We model the relation between PageRank and In-Degree through a stochastic equation, which is inspired by the
The effect of new links on Google PageRank
Avrachenkov, Konstatin; Litvak, Nelli
2004-01-01
PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as a frequency of visiting a Web page by a random surfer and thus it reflects the popularity of a Web page. We study the effect of newly created links on Google PageRank. We discuss to
Generalized Reduced Rank Tests using the Singular Value Decomposition
Kleibergen, F.R.; Paap, R.
2006-01-01
We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson [Annals of Mathematical Statistics (1951), 22, 327-351] sensitivity to the
Some upper and lower bounds on PSD-rank
T. J. Lee (Troy); Z. Wei (Zhaohui); R. M. de Wolf (Ronald)
2014-01-01
textabstractPositive semidefinite rank (PSD-rank) is a relatively new quantity with applications to combinatorial optimization and communication complexity. We first study several basic properties of PSD-rank, and then develop new techniques for showing lower bounds on the PSD-rank. All of these
Some upper and lower bounds on PSD-rank
Lee, T.; Wei, Z.; de Wolf, R.
Positive semidefinite rank (PSD-rank) is a relatively new complexity measure on matrices, with applications to combinatorial optimization and communication complexity. We first study several basic properties of PSD-rank, and then develop new techniques for showing lower bounds on the PSD-rank. All
Regularization Reconstruction Method for Imaging Problems in Electrical Capacitance Tomography
Chu, Pan; Lei, Jing
2017-11-01
The electrical capacitance tomography (ECT) is deemed to be a powerful visualization measurement technique for the parametric measurement in a multiphase flow system. The inversion task in the ECT technology is an ill-posed inverse problem, and seeking for an efficient numerical method to improve the precision of the reconstruction images is important for practical measurements. By the introduction of the Tikhonov regularization (TR) methodology, in this paper a loss function that emphasizes the robustness of the estimation and the low rank property of the imaging targets is put forward to convert the solution of the inverse problem in the ECT reconstruction task into a minimization problem. Inspired by the split Bregman (SB) algorithm, an iteration scheme is developed for solving the proposed loss function. Numerical experiment results validate that the proposed inversion method not only reconstructs the fine structures of the imaging targets, but also improves the robustness.
Biology of RANK, RANKL, and osteoprotegerin
Boyce, Brendan F; Xing, Lianping
2007-01-01
The discovery of the receptor activator of nuclear factor-κB ligand (RANKL)/RANK/osteoprotegerin (OPG) system and its role in the regulation of bone resorption exemplifies how both serendipity and a logic-based approach can identify factors that regulate cell function. Before this discovery in the mid to late 1990s, it had long been recognized that osteoclast formation was regulated by factors expressed by osteoblast/stromal cells, but it had not been anticipated that members of the tumor necrosis factor superfamily of ligands and receptors would be involved or that the factors involved would have extensive functions beyond bone remodeling. RANKL/RANK signaling regulates the formation of multinucleated osteoclasts from their precursors as well as their activation and survival in normal bone remodeling and in a variety of pathologic conditions. OPG protects the skeleton from excessive bone resorption by binding to RANKL and preventing it from binding to its receptor, RANK. Thus, RANKL/OPG ratio is an important determinant of bone mass and skeletal integrity. Genetic studies in mice indicate that RANKL/RANK signaling is also required for lymph node formation and mammary gland lactational hyperplasia, and that OPG also protects arteries from medial calcification. Thus, these tumor necrosis factor superfamily members have important functions outside bone. Although our understanding of the mechanisms whereby they regulate osteoclast formation has advanced rapidly during the past 10 years, many questions remain about their roles in health and disease. Here we review our current understanding of the role of the RANKL/RANK/OPG system in bone and other tissues. PMID:17634140
Image restoration via patch orientation-based low-rank matrix approximation and nonlocal means
Zhang, Di; He, Jiazhong; Du, Minghui
2016-03-01
Low-rank matrix approximation and nonlocal means (NLM) are two popular techniques for image restoration. Although the basic principle for applying these two techniques is the same, i.e., similar image patches are abundant in the image, previously published related algorithms use either low-rank matrix approximation or NLM because they manipulate the information of similar patches in different ways. We propose a method for image restoration by jointly using low-rank matrix approximation and NLM in a unified minimization framework. To improve the accuracy of determining similar patches, we also propose a patch similarity measurement based on curvelet transform. Extensive experiments on image deblurring and compressive sensing image recovery validate that the proposed method achieves better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception.
VaRank: a simple and powerful tool for ranking genetic variants
Directory of Open Access Journals (Sweden)
Véronique Geoffroy
2015-03-01
Full Text Available Background. Most genetic disorders are caused by single nucleotide variations (SNVs or small insertion/deletions (indels. High throughput sequencing has broadened the catalogue of human variation, including common polymorphisms, rare variations or disease causing mutations. However, identifying one variation among hundreds or thousands of others is still a complex task for biologists, geneticists and clinicians.Results. We have developed VaRank, a command-line tool for the ranking of genetic variants detected by high-throughput sequencing. VaRank scores and prioritizes variants annotated either by Alamut Batch or SnpEff. A barcode allows users to quickly view the presence/absence of variants (with homozygote/heterozygote status in analyzed samples. VaRank supports the commonly used VCF input format for variants analysis thus allowing it to be easily integrated into NGS bioinformatics analysis pipelines. VaRank has been successfully applied to disease-gene identification as well as to molecular diagnostics setup for several hundred patients.Conclusions. VaRank is implemented in Tcl/Tk, a scripting language which is platform-independent but has been tested only on Unix environment. The source code is available under the GNU GPL, and together with sample data and detailed documentation can be downloaded from http://www.lbgi.fr/VaRank/.
Multidimensional Rank Reduction Estimator for Parametric MIMO Channel Models
Directory of Open Access Journals (Sweden)
Marius Pesavento
2004-08-01
Full Text Available A novel algebraic method for the simultaneous estimation of MIMO channel parameters from channel sounder measurements is developed. We consider a parametric multipath propagation model with P discrete paths where each path is characterized by its complex path gain, its directions of arrival and departure, time delay, and Doppler shift. This problem is treated as a special case of the multidimensional harmonic retrieval problem. While the well-known ESPRIT-type algorithms exploit shift-invariance between specific partitions of the signal matrix, the rank reduction estimator (RARE algorithm exploits their internal Vandermonde structure. A multidimensional extension of the RARE algorithm is developed, analyzed, and applied to measurement data recorded with the RUSK vector channel sounder in the 2 GHz band.
Ranking of microRNA target prediction scores by Pareto front analysis.
Sahoo, Sudhakar; Albrecht, Andreas A
2010-12-01
Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure, which encourages further research towards a higher-dimensional analysis of Pareto fronts. Copyright © 2010 Elsevier Ltd. All rights reserved.
Ranked Conservation Opportunity Areas for Region 7 (ECO_RES.RANKED_OAS)
The RANKED_OAS are all the Conservation Opportunity Areas identified by MoRAP that have subsequently been ranked by patch size, landform representation, and the targeted land cover class (highest rank for conservation management = 1 [LFRANK_NOR]). The OAs designate areas with potential for forest or grassland conservation because they are areas of natural or semi-natural land cover that are at least 75 meters away from roads and away from patch edges. The OAs were modeled by creating distance grids using the National Land Cover Database and the Census Bureau's TIGER roads files.
UNIVERSITY RANKINGS BY COST OF LIVING ADJUSTED FACULTY COMPENSATION
Terrance Jalbert; Mercedes Jalbert; Karla Hayashi
2010-01-01
In this paper we rank 574 universities based on compensation paid to their faculty. The analysis examines universities both on a raw basis and on a cost of living adjusted basis. Rankings based on salary data and benefit data are presented. In addition rankings based on total compensation are presented. Separate rankings are provided for universities offering different degrees. The results indicate that rankings of universities based on raw and cost of living adjusted data are markedly differ...
DEFF Research Database (Denmark)
Channuie, Phongpichit; Jark Joergensen, Jakob; Sannino, Francesco
2011-01-01
We investigate models in which the inflaton emerges as a composite field of a four dimensional, strongly interacting and nonsupersymmetric gauge theory featuring purely fermionic matter. We show that it is possible to obtain successful inflation via non-minimal coupling to gravity, and that the u......We investigate models in which the inflaton emerges as a composite field of a four dimensional, strongly interacting and nonsupersymmetric gauge theory featuring purely fermionic matter. We show that it is possible to obtain successful inflation via non-minimal coupling to gravity......, and that the underlying dynamics is preferred to be near conformal. We discover that the compositeness scale of inflation is of the order of the grand unified energy scale....
Minimally symmetric Higgs boson
Energy Technology Data Exchange (ETDEWEB)
Low, Ian
2015-06-01
Models addressing the naturalness of a light Higgs boson typically employ symmetries, either bosonic or fermionic, to stabilize the Higgs mass. We consider a setup with the minimal amount of symmetries: four shift symmetries acting on the four components of the Higgs doublet, subject to the constraints of linearly realized SU(2)(L) x U(1)(Y) electroweak symmetry. Up to terms that explicitly violate the shift symmetries, the effective Lagrangian can be derived, irrespective of the spontaneously broken group G in the ultraviolet, and is universal among all models where the Higgs arises as a pseudo-Nambu-Goldstone boson. Very high energy scatterings of vector bosons could provide smoking gun signals of a minimally symmetric Higgs boson.
Minimal complexity control law synthesis
Bernstein, Dennis S.; Haddad, Wassim M.; Nett, Carl N.
1989-01-01
A paradigm for control law design for modern engineering systems is proposed: Minimize control law complexity subject to the achievement of a specified accuracy in the face of a specified level of uncertainty. Correspondingly, the overall goal is to make progress towards the development of a control law design methodology which supports this paradigm. Researchers achieve this goal by developing a general theory of optimal constrained-structure dynamic output feedback compensation, where here constrained-structure means that the dynamic-structure (e.g., dynamic order, pole locations, zero locations, etc.) of the output feedback compensation is constrained in some way. By applying this theory in an innovative fashion, where here the indicated iteration occurs over the choice of the compensator dynamic-structure, the paradigm stated above can, in principle, be realized. The optimal constrained-structure dynamic output feedback problem is formulated in general terms. An elegant method for reducing optimal constrained-structure dynamic output feedback problems to optimal static output feedback problems is then developed. This reduction procedure makes use of star products, linear fractional transformations, and linear fractional decompositions, and yields as a byproduct a complete characterization of the class of optimal constrained-structure dynamic output feedback problems which can be reduced to optimal static output feedback problems. Issues such as operational/physical constraints, operating-point variations, and processor throughput/memory limitations are considered, and it is shown how anti-windup/bumpless transfer, gain-scheduling, and digital processor implementation can be facilitated by constraining the controller dynamic-structure in an appropriate fashion.
The bicriterion multimodal assignment problem
DEFF Research Database (Denmark)
Pedersen, Christian Roed; Nielsen, Lars Relund; Andersen, Kim Allan
2008-01-01
We consider the bicriterion multimodal assignment problem, which is a new generalization of the classical linear assignment problem. A two-phase solution method using an effective ranking scheme is presented. The algorithm is valid for generating all nondominated criterion points or an approximat......We consider the bicriterion multimodal assignment problem, which is a new generalization of the classical linear assignment problem. A two-phase solution method using an effective ranking scheme is presented. The algorithm is valid for generating all nondominated criterion points...
The minimal flavour violating axion
Arias-Aragón, F.; Merlo, L.
2017-10-01
The solution to the Strong CP problem is analysed within the Minimal Flavour Violation (MFV) context. An Abelian factor of the complete flavour symmetry of the fermionic kinetic terms may play the role of the Peccei-Quinn symmetry in traditional axion models. Its spontaneous breaking, due to the addition of a complex scalar field to the Standard Model scalar spectrum, generates the MFV axion, which may redefine away the QCD theta parameter. It differs from the traditional QCD axion for its couplings that are governed by the fermion charges under the axial Abelian symmetry. It is also distinct from the so-called Axiflavon, as the MFV axion does not describe flavour violation, while it does induce flavour non-universality effects. The MFV axion phenomenology is discussed considering astrophysical, collider and flavour data.
Strategies to Minimize Antibiotic Resistance
Directory of Open Access Journals (Sweden)
Sang Hee Lee
2013-09-01
Full Text Available Antibiotic resistance can be reduced by using antibiotics prudently based on guidelines of antimicrobial stewardship programs (ASPs and various data such as pharmacokinetic (PK and pharmacodynamic (PD properties of antibiotics, diagnostic testing, antimicrobial susceptibility testing (AST, clinical response, and effects on the microbiota, as well as by new antibiotic developments. The controlled use of antibiotics in food animals is another cornerstone among efforts to reduce antibiotic resistance. All major resistance-control strategies recommend education for patients, children (e.g., through schools and day care, the public, and relevant healthcare professionals (e.g., primary-care physicians, pharmacists, and medical students regarding unique features of bacterial infections and antibiotics, prudent antibiotic prescribing as a positive construct, and personal hygiene (e.g., handwashing. The problem of antibiotic resistance can be minimized only by concerted efforts of all members of society for ensuring the continued efficiency of antibiotics.
Strategies to minimize antibiotic resistance.
Lee, Chang-Ro; Cho, Ill Hwan; Jeong, Byeong Chul; Lee, Sang Hee
2013-09-12
Antibiotic resistance can be reduced by using antibiotics prudently based on guidelines of antimicrobial stewardship programs (ASPs) and various data such as pharmacokinetic (PK) and pharmacodynamic (PD) properties of antibiotics, diagnostic testing, antimicrobial susceptibility testing (AST), clinical response, and effects on the microbiota, as well as by new antibiotic developments. The controlled use of antibiotics in food animals is another cornerstone among efforts to reduce antibiotic resistance. All major resistance-control strategies recommend education for patients, children (e.g., through schools and day care), the public, and relevant healthcare professionals (e.g., primary-care physicians, pharmacists, and medical students) regarding unique features of bacterial infections and antibiotics, prudent antibiotic prescribing as a positive construct, and personal hygiene (e.g., handwashing). The problem of antibiotic resistance can be minimized only by concerted efforts of all members of society for ensuring the continued efficiency of antibiotics.
Pulling Rank: Military Rank Affects Hormone Levels and Fairness in an Allocation Experiment
Directory of Open Access Journals (Sweden)
Benjamin Siart
2016-11-01
Full Text Available Status within social hierarchies has great effects on the lives of socially organized mammals. Its effects on human behavior and related physiology however is relatively little studied. The present study investigated the impact of military rank on fairness and behavior in relation to salivary cortisol (C and testosterone (T levels in male soldiers. For this purpose 180 members of the Austrian Armed Forces belonging to two distinct rank groups participated in two variations of a computer-based guard duty allocation experiment. The rank groups were 1 warrant officers (High Rank, HR and 2 enlisted men (Low Rank, LR. One soldier from each rank group participated in every experiment. At the beginning of the experiment, one participant was assigned to start standing guard and the other participant at rest. The participant who started at rest could choose if and when to relieve his fellow soldier and therefore had control over the experiment. In order to trigger perception of unfair behavior, an additional experiment was conducted which was manipulated by the experimenter. In the manipulated version both soldiers started in the standing guard position and were never relieved, believing that their opponent was at rest, not relieving them. Our aim was to test whether unfair behavior causes a physiological reaction. Saliva samples for hormone analysis were collected at regular intervals throughout the experiment.We found that in the un-manipulated setup high-ranking soldiers spent less time standing guard than lower ranking individuals. Rank was a significant predictor for C but not for T levels during the experiment. C levels in the HR group were higher than in LR group. C levels were also elevated in the manipulated experiment compared to the un-manipulated experiment, especially in LR. We assume that the elevated C levels in HR were caused by HR feeling their status challenged by the situation of having to negotiate with an individual of lower military
Pulling Rank: Military Rank Affects Hormone Levels and Fairness in an Allocation Experiment.
Siart, Benjamin; Pflüger, Lena S; Wallner, Bernard
2016-01-01
Status within social hierarchies has great effects on the lives of socially organized mammals. Its effects on human behavior and related physiology, however, is relatively little studied. The present study investigated the impact of military rank on fairness and behavior in relation to salivary cortisol (C) and testosterone (T) levels in male soldiers. For this purpose 180 members of the Austrian Armed Forces belonging to two distinct rank groups participated in two variations of a computer-based guard duty allocation experiment. The rank groups were (1) warrant officers (high rank, HR) and (2) enlisted men (low rank, LR). One soldier from each rank group participated in every experiment. At the beginning of the experiment, one participant was assigned to start standing guard and the other participant at rest. The participant who started at rest could choose if and when to relieve his fellow soldier and therefore had control over the experiment. In order to trigger perception of unfair behavior, an additional experiment was conducted which was manipulated by the experimenter. In the manipulated version both soldiers started in the standing guard position and were never relieved, believing that their opponent was at rest, not relieving them. Our aim was to test whether unfair behavior causes a physiological reaction. Saliva samples for hormone analysis were collected at regular intervals throughout the experiment. We found that in the un-manipulated setup high-ranking soldiers spent less time standing guard than lower ranking individuals. Rank was a significant predictor for C but not for T levels during the experiment. C levels in the HR group were higher than in the LR group. C levels were also elevated in the manipulated experiment compared to the un-manipulated experiment, especially in LR. We assume that the elevated C levels in HR were caused by HR feeling their status challenged by the situation of having to negotiate with an individual of lower military rank
Social Bookmarking Induced Active Page Ranking
Takahashi, Tsubasa; Kitagawa, Hiroyuki; Watanabe, Keita
Social bookmarking services have recently made it possible for us to register and share our own bookmarks on the web and are attracting attention. The services let us get structured data: (URL, Username, Timestamp, Tag Set). And these data represent user interest in web pages. The number of bookmarks is a barometer of web page value. Some web pages have many bookmarks, but most of those bookmarks may have been posted far in the past. Therefore, even if a web page has many bookmarks, their value is not guaranteed. If most of the bookmarks are very old, the page may be obsolete. In this paper, by focusing on the timestamp sequence of social bookmarkings on web pages, we model their activation levels representing current values. Further, we improve our previously proposed ranking method for web search by introducing the activation level concept. Finally, through experiments, we show effectiveness of the proposed ranking method.
Regression Estimator Using Double Ranked Set Sampling
Directory of Open Access Journals (Sweden)
Hani M. Samawi
2002-06-01
Full Text Available The performance of a regression estimator based on the double ranked set sample (DRSS scheme, introduced by Al-Saleh and Al-Kadiri (2000, is investigated when the mean of the auxiliary variable X is unknown. Our primary analysis and simulation indicates that using the DRSS regression estimator for estimating the population mean substantially increases relative efficiency compared to using regression estimator based on simple random sampling (SRS or ranked set sampling (RSS (Yu and Lam, 1997 regression estimator. Moreover, the regression estimator using DRSS is also more efficient than the naïve estimators of the population mean using SRS, RSS (when the correlation coefficient is at least 0.4 and DRSS for high correlation coefficient (at least 0.91. The theory is illustrated using a real data set of trees.
Classification of rank 2 cluster varieties
DEFF Research Database (Denmark)
Mandel, Travis
We classify rank 2 cluster varieties (those whose corresponding skew-form has rank 2) according to the deformation type of a generic fiber U of their X-spaces, as defined by Fock and Goncharov. Our approach is based on the work of Gross, Hacking, and Keel for cluster varieties and log Calabi......-Yau surfaces. We find, for example, that U is "positive" (i.e., nearly affine) and either finite-type or non-acyclic (in the cluster sense) if and only if the monodromy of the tropicalization of U is one of Kodaira's matrices for the monodromy of an ellpitic fibration. In the positive cases, we also describe...... the action of the cluster modular group on the tropicalization of U....
Deep Impact: Unintended consequences of journal rank
Brembs, Björn
2013-01-01
Much has been said about the increasing bureaucracy in science, stifling innovation, hampering the creativity of researchers and incentivizing misconduct, even outright fraud. Many anecdotes have been recounted, observations described and conclusions drawn about the negative impact of impact assessment on scientists and science. However, few of these accounts have drawn their conclusions from data, and those that have typically relied on a few studies. In this review, we present the most recent and pertinent data on the consequences that our current scholarly communication system has had on various measures of scientific quality (such as utility/citations, methodological soundness, expert ratings and retractions). These data confirm previous suspicions: using journal rank as an assessment tool is bad scientific practice. Moreover, the data lead us to argue that any journal rank (not only the currently-favored Impact Factor) would have this negative impact. Therefore, we suggest that abandoning journals altoge...
Robust ranks of true associations in genome-wide case-control association studies.
Zheng, Gang; Joo, Jungnam; Lin, Jing-Ping; Stylianou, Mario; Waclawiw, Myron A; Geller, Nancy L
2007-01-01
In whole-genome association studies, at the first stage, all markers are tested for association and their test statistics or p-values are ranked. At the second stage, some most significant markers are further analyzed by more powerful statistical methods. This helps reduce the number of hypotheses to be corrected for in multiple testing. Ranks of true associations in genome-wide scans using a single test statistic have been studied. In a case-control design for association, the trend test has been proposed. However, three different trend tests, optimal for the recessive, additive, and dominant models, respectively, are available for each marker. Because the true genetic model is unknown, we rank markers based on multiple test statistics or test statistics robust to model mis-specification. We studied this problem with application to Problem 3 of Genetic Analysis Workshop 15. An independent simulation study was also conducted to further evaluate the proposed procedure.
Ranking agility factors affecting hospitals in Iran
M. Abdi Talarposht; GH. Mahmodi; MA. Jahani
2017-01-01
Background: Agility is an effective response to the changing and unpredictable environment and using these changes as opportunities for organizational improvement. Objective: The aim of the present study was to rank the factors affecting agile supply chain of hospitals of Iran. Methods: This applied study was conducted by cross sectional-descriptive method at some point of 2015 for one year. The research population included managers, administrators, faculty members and experts were sele...
Homological characterisation of Lambda-ranks
Howson, Susan
1999-01-01
If G is a pro-p, p-adic, Lie group and if $\\Lambda(G)$ denotes the Iwasawa algebra of G then we present a formula for determining the $\\Lambda(G)$-rank of a finitely generated $\\Lambda(G)$-module. This is given in terms of the G homology groups of the module. We explore some consequences of this for the structure of $\\Lambda(G)$-modules.
Domain Generalization and Adaptation using Low Rank Exemplar SVMs.
Li, Wen; Xu, Zheng; Xu, Dong; Dai, Dengxin; Van Gool, Luc
2017-05-16
Domain adaptation between diverse source and target domains is a challenging research problem, especially in the real-world visual recognition tasks where the images and videos consist of significant variations in viewpoints, illuminations, qualities, etc. In this paper, we propose a new approach for domain generalization and domain adaptation based on exemplar SVMs. Specifically, we decompose the source domain into many subdomains, each of which contains only one positive training sample and all negative samples. Each subdomain is relatively less diverse, and is expected to have a simpler distribution. By training one exemplar SVM for each subdomain, we obtain a set of exemplar SVMs. To further exploit the inherent structure of source domain, we introduce a nuclear-norm based regularizer into the objective function in order to enforce the exemplar SVMs to produce a low-rank output on training samples. In the prediction process, the confident exemplar SVM classifiers are selected and reweigted according to the distribution mismatch between each subdomain and the test sample in the target domain. We formulate our approach based on the logistic regression and least square SVM algorithms, which are referred to as low rank exemplar SVMs (LRE-SVMs) and low rank exemplar least square SVMs (LRE-LSSVMs), respectively. A fast algorithm is also developed for accelerating the training of LRE-LSSVMs. We further extend Domain Adaptation Machine (DAM) to learn an optimal target classifier for domain adaptation, and show that our approach can also be applied to domain adaptation with evolving target domain, where the target data distribution is gradually changing. The comprehensive experiments for object recognition and action recognition demonstrate the effectiveness of our approach for domain generalization and domain adaptation with fixed and evolving target domains.
Citation ranking versus peer evaluation of senior faculty research performance
DEFF Research Database (Denmark)
Meho, Lokman I.; Sonnenwald, Diane H.
2000-01-01
indicator of research performance of senior faculty members? Citation data, book reviews, and peer ranking were compiled and examined for faculty members specializing in Kurdish studies. Analysis shows that normalized citation ranking and citation content analysis data yield identical ranking results....... Analysis also shows that normalized citation ranking and citation content analysis, book reviews, and peer ranking perform similarly (i.e., are highly correlated) for high-ranked and low-ranked senior scholars. Additional evaluation methods and measures that take into account the context and content......The purpose of this study is to analyze the relationship between citation ranking and peer evaluation in assessing senior faculty research performance. Other studies typically derive their peer evaluation data directly from referees, often in the form of ranking. This study uses two additional...
Estimation of rank correlation for clustered data.
Rosner, Bernard; Glynn, Robert J
2017-06-30
It is well known that the sample correlation coefficient (Rxy ) is the maximum likelihood estimator of the Pearson correlation (ρxy ) for independent and identically distributed (i.i.d.) bivariate normal data. However, this is not true for ophthalmologic data where X (e.g., visual acuity) and Y (e.g., visual field) are available for each eye and there is positive intraclass correlation for both X and Y in fellow eyes. In this paper, we provide a regression-based approach for obtaining the maximum likelihood estimator of ρxy for clustered data, which can be implemented using standard mixed effects model software. This method is also extended to allow for estimation of partial correlation by controlling both X and Y for a vector U_ of other covariates. In addition, these methods can be extended to allow for estimation of rank correlation for clustered data by (i) converting ranks of both X and Y to the probit scale, (ii) estimating the Pearson correlation between probit scores for X and Y, and (iii) using the relationship between Pearson and rank correlation for bivariate normally distributed data. The validity of the methods in finite-sized samples is supported by simulation studies. Finally, two examples from ophthalmology and analgesic abuse are used to illustrate the methods. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Higher-rank fields and currents
Energy Technology Data Exchange (ETDEWEB)
Gelfond, O.A. [Institute of System Research of Russian Academy of Sciences,Nakhimovsky prospect 36-1, 117218, Moscow (Russian Federation); I.E.Tamm Department of Theoretical Physics, Lebedev Physical Institute,Leninsky prospect 53, 119991, Moscow (Russian Federation); Vasiliev, M.A. [I.E.Tamm Department of Theoretical Physics, Lebedev Physical Institute,Leninsky prospect 53, 119991, Moscow (Russian Federation)
2016-10-13
Sp(2M) invariant field equations in the space M{sub M} with symmetric matrix coordinates are classified. Analogous results are obtained for Minkowski-like subspaces of M{sub M} which include usual 4d Minkowski space as a particular case. The constructed equations are associated with the tensor products of the Fock (singleton) representation of Sp(2M) of any rank r. The infinite set of higher-spin conserved currents multilinear in rank-one fields in M{sub M} is found. The associated conserved charges are supported by (rM−((r(r−1))/2))-dimensional differential forms in M{sub M}, that are closed by virtue of the rank-2r field equations. The cohomology groups H{sup p}(σ{sub −}{sup r}) with all p and r, which determine the form of appropriate gauge fields and their field equations, are found both for M{sub M} and for its Minkowski-like subspace.
Association between Metabolic Syndrome and Job Rank.
Mehrdad, Ramin; Pouryaghoub, Gholamreza; Moradi, Mahboubeh
2018-01-01
The occupation of the people can influence the development of metabolic syndrome. To determine the association between metabolic syndrome and its determinants with the job rank in workers of a large car factory in Iran. 3989 male workers at a large car manufacturing company were invited to participate in this cross-sectional study. Demographic and anthropometric data of the participants, including age, height, weight, and abdominal circumference were measured. Blood samples were taken to measure lipid profile and blood glucose level. Metabolic syndrome was diagnosed in each participant based on ATPIII 2001 criteria. The workers were categorized based on their job rank into 3 groups of (1) office workers, (2) workers with physical exertion, and (3) workers with chemical exposure. The study characteristics, particularly the frequency of metabolic syndrome and its determinants were compared among the study groups. The prevalence of metabolic syndrome in our study was 7.7% (95% CI 6.9 to 8.5). HDL levels were significantly lower in those who had chemical exposure (p=0.045). Diastolic blood pressure was significantly higher in those who had mechanical exertion (p=0.026). The frequency of metabolic syndrome in the office workers, workers with physical exertion, and workers with chemical exposure was 7.3%, 7.9%, and 7.8%, respectively (p=0.836). Seemingly, there is no association between metabolic syndrome and job rank.
[Ranke and modern surgery in Groningen].
van Gijn, Jan; Gijselhart, Joost P
2012-01-01
Hans Rudolph Ranke (1849-1887) studied medicine in Halle, located in the eastern part of Germany, where he also trained as a surgeon under Richard von Volkmann (1830-1889), during which time he became familiar with the new antiseptic technique that had been introduced by Joseph Lister (1827-1912). In 1878 he was appointed head of the department of surgery in Groningen, the Netherlands, where his predecessor had been chronically indisposed and developments were flagging. Within a few months, Ranke had introduced disinfection by using carbolic acid both before and during operations. For the disinfection of wound dressings, he replaced carbolic acid with thymol as this was less pungent and foul-smelling. The rate of postoperative infections dropped to a minimum despite the inadequate housing and living conditions of the patients with infectious diseases. In 1887, at the age of 37, Ranke died after a brief illness - possibly glomerulonephritis - only eight years after he had assumed office. A street in the city of Groningen near its present-day University Medical Centre has been named after him.
Ranking agility factors affecting hospitals in Iran
Directory of Open Access Journals (Sweden)
M. Abdi Talarposht
2017-04-01
Full Text Available Background: Agility is an effective response to the changing and unpredictable environment and using these changes as opportunities for organizational improvement. Objective: The aim of the present study was to rank the factors affecting agile supply chain of hospitals of Iran. Methods: This applied study was conducted by cross sectional-descriptive method at some point of 2015 for one year. The research population included managers, administrators, faculty members and experts were selected hospitals. A total of 260 people were selected as sample from the health centers. The construct validity of the questionnaire was approved by confirmatory factor analysis test and its reliability was approved by Cronbach's alpha (α=0.97. All data were analyzed by Kolmogorov-Smirnov, Chi-square and Friedman tests. Findings: The development of staff skills, the use of information technology, the integration of processes, appropriate planning, and customer satisfaction and product quality had a significant impact on the agility of public hospitals of Iran (P<0.001. New product introductions had earned the highest ranking and the development of staff skills earned the lowest ranking. Conclusion: The new product introduction, market responsiveness and sensitivity, reduce costs, and the integration of organizational processes, ratings better to have acquired agility hospitals in Iran. Therefore, planners and officials of hospitals have to, through the promotion quality and variety of services customer-oriented, providing a basis for investing in the hospital and etc to apply for agility supply chain public hospitals of Iran.
Minimally Invasive Parathyroidectomy
Directory of Open Access Journals (Sweden)
Lee F. Starker
2011-01-01
Full Text Available Minimally invasive parathyroidectomy (MIP is an operative approach for the treatment of primary hyperparathyroidism (pHPT. Currently, routine use of improved preoperative localization studies, cervical block anesthesia in the conscious patient, and intraoperative parathyroid hormone analyses aid in guiding surgical therapy. MIP requires less surgical dissection causing decreased trauma to tissues, can be performed safely in the ambulatory setting, and is at least as effective as standard cervical exploration. This paper reviews advances in preoperative localization, anesthetic techniques, and intraoperative management of patients undergoing MIP for the treatment of pHPT.
Minimally Actuated Serial Robot
Mann, Moshe P.; Damti, Lior; Zarrouk, David
2017-01-01
In this paper, we propose a novel type of serial robot with minimal actuation. The robot is a serial rigid structure consisting of multiple links connected by passive joints and of movable actuators. The novelty of this robot is that the actuators travel over the links to a given joint and adjust the relative angle between the two adjacent links. The joints passively preserve their angles until one of the actuators moves them again. This actuation can be applied to any serial robot with two o...
Patch-Based Image Inpainting via Two-Stage Low Rank Approximation.
Guo, Qiang; Gao, Shanshan; Zhang, Xiaofeng; Yin, Yilong; Zhang, Caiming
2017-05-09
To recover the corrupted pixels, traditional inpainting methods based on low-rank priors generally need to solve a convex optimization problem by an iterative singular value shrinkage algorithm. In this paper, we propose a simple method for image inpainting using low rank approximation, which avoids the time-consuming iterative shrinkage. Specifically, if similar patches of a corrupted image are identified and reshaped as vectors, then a patch matrix can be constructed by collecting these similar patch-vectors. Due to its columns being highly linearly correlated, this patch matrix is low-rank. Instead of using an iterative singular value shrinkage scheme, the proposed method utilizes low rank approximation with truncated singular values to derive a closed-form estimate for each patch matrix. Depending upon an observation that there exists a distinct gap in the singular spectrum of patch matrix, the rank of each patch matrix is empirically determined by a heuristic procedure. Inspired by the inpainting algorithms with component decomposition, a two-stage low rank approximation (TSLRA) scheme is designed to recover image structures and refine texture details of corrupted images. Experimental results on various inpainting tasks demonstrate that the proposed method is comparable and even superior to some state-of-the-art inpainting algorithms.
Ranking Fuzzy Numbers and Its Application to Products Attributes Preferences
Lazim Abdullah; Nor Nashrah Ahmad Fauzee
2011-01-01
Ranking is one of the widely used methods in fuzzy decision making environment. The recent ranking fuzzy numbers proposed by Wang and Li is claimed to be the improved version in ranking. However, the method was never been simplified and tested in real life application. This paper presents a four-step computation of ranking fuzzy numbers and its application in ranking attributes of selected chocolate products. The four steps algorithm was formulated to rank fuzzy numbers and followed by a tes...
Why does Income Relate to Depressive Symptoms? Testing the Income Rank Hypothesis Longitudinally.
Osafo Hounkpatin, Hilda; Wood, Alex M; Brown, Gordon D A; Dunn, Graham
This paper reports a test of the relative income rank hypothesis of depression, according to which it is the rank position of an individual's income amongst a comparison group, rather than the individual's absolute income, that will be associated with depressive symptoms. A new methodology is developed to test between psychosocial and material explanations of why income relates to well-being. This method was used to test the income rank hypothesis as applied to depressive symptoms. We used data from a cohort of 10,317 individuals living in Wisconsin who completed surveys in 1992 and 2003. The utility assumed to arise from income was represented with a constant relative risk aversion function to overcome limitations of previous work in which inadequate specification of the relationship between absolute income and well-being may have inappropriately favoured relative income specifications. We compared models in which current and future depressive symptoms were predicted from: (a) income utility alone, (b) income rank alone, (c) the transformed difference between the individual's income and the mean income of a comparison group and (d) income utility, income rank and distance from the mean jointly. Model comparison overcomes problems involving multi-collinearity amongst the predictors. A rank-only model was consistently supported. Similar results were obtained for the association between depressive symptoms and wealth and rank of wealth in a cohort of 32,900 British individuals who completed surveys in 2002 and 2008. We conclude that it is the rank of a person's income or wealth within a social comparison group, rather than income or wealth themselves or their deviations from the mean within a reference group, that is more strongly associated with depressive symptoms.
Low-Rank Coal Grinding Performance Versus Power Plant Performance
Energy Technology Data Exchange (ETDEWEB)
Rajive Ganguli; Sukumar Bandopadhyay
2008-12-31
The intent of this project was to demonstrate that Alaskan low-rank coal, which is high in volatile content, need not be ground as fine as bituminous coal (typically low in volatile content) for optimum combustion in power plants. The grind or particle size distribution (PSD), which is quantified by percentage of pulverized coal passing 74 microns (200 mesh), affects the pulverizer throughput in power plants. The finer the grind, the lower the throughput. For a power plant to maintain combustion levels, throughput needs to be high. The problem of particle size is compounded for Alaskan coal since it has a low Hardgrove grindability index (HGI); that is, it is difficult to grind. If the thesis of this project is demonstrated, then Alaskan coal need not be ground to the industry standard, thereby alleviating somewhat the low HGI issue (and, hopefully, furthering the salability of Alaskan coal). This project studied the relationship between PSD and power plant efficiency, emissions, and mill power consumption for low-rank high-volatile-content Alaskan coal. The emissions studied were CO, CO{sub 2}, NO{sub x}, SO{sub 2}, and Hg (only two tests). The tested PSD range was 42 to 81 percent passing 76 microns. Within the tested range, there was very little correlation between PSD and power plant efficiency, CO, NO{sub x}, and SO{sub 2}. Hg emissions were very low and, therefore, did not allow comparison between grind sizes. Mill power consumption was lower for coarser grinds.
Ranking Biomedical Annotations with Annotator’s Semantic Relevancy
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Aihua Wu
2014-01-01
Full Text Available Biomedical annotation is a common and affective artifact for researchers to discuss, show opinion, and share discoveries. It becomes increasing popular in many online research communities, and implies much useful information. Ranking biomedical annotations is a critical problem for data user to efficiently get information. As the annotator’s knowledge about the annotated entity normally determines quality of the annotations, we evaluate the knowledge, that is, semantic relationship between them, in two ways. The first is extracting relational information from credible websites by mining association rules between an annotator and a biomedical entity. The second way is frequent pattern mining from historical annotations, which reveals common features of biomedical entities that an annotator can annotate with high quality. We propose a weighted and concept-extended RDF model to represent an annotator, a biomedical entity, and their background attributes and merge information from the two ways as the context of an annotator. Based on that, we present a method to rank the annotations by evaluating their correctness according to user’s vote and the semantic relevancy between the annotator and the annotated entity. The experimental results show that the approach is applicable and efficient even when data set is large.
Ranking Schools' Academic Performance Using a Fuzzy VIKOR
Musani, Suhaina; Aziz Jemain, Abdul
2015-06-01
Determination rank is structuring alternatives in order of priority. It is based on the criteria determined for each alternative involved. Evaluation criteria are performed and then a composite index composed of each alternative for the purpose of arranging in order of preference alternatives. This practice is known as multiple criteria decision making (MCDM). There are several common approaches to MCDM, one of the practice is known as VIKOR (Multi-criteria Optimization and Compromise Solution). The objective of this study is to develop a rational method for school ranking based on linguistic information of a criterion. The school represents an alternative, while the results for a number of subjects as the criterion. The results of the examination for a course, is given according to the student percentage of each grade. Five grades of excellence, honours, average, pass and fail is used to indicate a level of achievement in linguistics. Linguistic variables are transformed to fuzzy numbers to form a composite index of school performance. Results showed that fuzzy set theory can solve the limitations of using MCDM when there is uncertainty problems exist in the data.
Low-Rank Linear Dynamical Systems for Motor Imagery EEG
Tan, Chuanqi; Liu, Shaobo
2016-01-01
The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from “BCI Competition III Dataset IVa” and “BCI Competition IV Database 2a.” The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP. PMID:28096809
Low-Rank Linear Dynamical Systems for Motor Imagery EEG
Directory of Open Access Journals (Sweden)
Wenchang Zhang
2016-01-01
Full Text Available The common spatial pattern (CSP and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from “BCI Competition III Dataset IVa” and “BCI Competition IV Database 2a.” The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP.
Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models
El Gharamti, Mohamad
2010-12-01
Understanding the geology and the hydrology of the subsurface is important to model the fluid flow and the behavior of the contaminant. It is essential to have an accurate knowledge of the movement of the contaminants in the porous media in order to track them and later extract them from the aquifer. A two-dimensional flow model is studied and then applied on a linear contaminant transport model in the same porous medium. Because of possible different sources of uncertainties, the deterministic model by itself cannot give exact estimations for the future contaminant state. Incorporating observations in the model can guide it to the true state. This is usually done using the Kalman filter (KF) when the system is linear and the extended Kalman filter (EKF) when the system is nonlinear. To overcome the high computational cost required by the KF, we use the singular evolutive Kalman filter (SEKF) and the singular evolutive extended Kalman filter (SEEKF) approximations of the KF operating with low-rank covariance matrices. The SEKF can be implemented on large dimensional contaminant problems while the usage of the KF is not possible. Experimental results show that with perfect and imperfect models, the low rank filters can provide as much accurate estimates as the full KF but at much less computational cost. Localization can help the filter analysis as long as there are enough neighborhood data to the point being analyzed. Estimating the permeabilities of the aquifer is successfully tackled using both the EKF and the SEEKF.
Optimal data collection for informative rankings expose well-connected graphs
Osting, Braxton; Brune, Christoph; Osher, Stanley J.
2014-01-01
Given a graph where vertices represent alternatives and arcs represent pairwise comparison data, the statistical ranking problem is to find a potential function, defined on the vertices, such that the gradient of the potential function agrees with the pairwise comparisons. Our goal in this paper is
Iteratively reweighted generalized rank annihilation method 1. Improved handling of prediction bias
Faber, N.M.; Ferre, J.; Boque, R.
2001-01-01
The generalized rank annihilation method (GRAM) is a method for curve resolution and calibration that uses two bilinear matrices simultaneously, i.e., one for the unknown and one for the calibration sample. A GRAM calculation amounts to solving an eigenvalue problem for which the eigenvalues are
Akbudak, Kadir
2017-05-11
Covariance matrices are ubiquitous in computational science and engineering. In particular, large covariance matrices arise from multivariate spatial data sets, for instance, in climate/weather modeling applications to improve prediction using statistical methods and spatial data. One of the most time-consuming computational steps consists in calculating the Cholesky factorization of the symmetric, positive-definite covariance matrix problem. The structure of such covariance matrices is also often data-sparse, in other words, effectively of low rank, though formally dense. While not typically globally of low rank, covariance matrices in which correlation decays with distance are nearly always hierarchically of low rank. While symmetry and positive definiteness should be, and nearly always are, exploited for performance purposes, exploiting low rank character in this context is very recent, and will be a key to solving these challenging problems at large-scale dimensions. The authors design a new and flexible tile row rank Cholesky factorization and propose a high performance implementation using OpenMP task-based programming model on various leading-edge manycore architectures. Performance comparisons and memory footprint saving on up to 200K×200K covariance matrix size show a gain of more than an order of magnitude for both metrics, against state-of-the-art open-source and vendor optimized numerical libraries, while preserving the numerical accuracy fidelity of the original model. This research represents an important milestone in enabling large-scale simulations for covariance-based scientific applications.
The Quest for Minimal Quotients for Probabilistic Automata
DEFF Research Database (Denmark)
Eisentraut, Christian; Hermanns, Holger; Schuster, Johann
2013-01-01
One of the prevailing ideas in applied concurrency theory and verification is the concept of automata minimization with respect to strong or weak bisimilarity. The minimal automata can be seen as canonical representations of the behaviour modulo the bisimilarity considered. Together with congruence...... results wrt. process algebraic operators, this can be exploited to alleviate the notorious state space explosion problem. In this paper, we aim at identifying minimal automata and canonical representations for concurrent probabilistic models. We present minimality and canonicity results for probabilistic...... automata wrt. strong and weak bisimilarity, together with polynomial time minimization algorithms....
Directory of Open Access Journals (Sweden)
Carlos-Roberto Peña-Barrera
2011-08-01
Full Text Available Los principales objetivos de esta investigación son los siguientes: (1 que la comunidad científica nacional e internacional y la sociedad en general co-nozcan los resultados del Ranking U-Sapiens Colombia 2010_2, el cual clasifica a cada institución de educación superior colombiana según puntaje, posición y cuartil; (2 destacar los movimientos más importantes al comparar los resultados del ranking 2010_1 con los del 2010_2; (3 publicar las respuestas de algunos actores de la academia nacional con respecto a la dinámica de la investigación en el país; (4 reconocer algunas instituciones, medios de comunicación e investigadores que se han interesado a modo de reflexión, referenciación o citación por esta investigación; y (5 dar a conocer el «Sello Ranking U-Sapiens Colombia» para las IES clasificadas. El alcance de este estudio en cuanto a actores abordó todas y cada una de las IES nacionales (aunque solo algunas lograran entrar al ranking y en cuanto a tiempo, un periodo referido al primer semestre de 2010 con respecto a: (1 los resultados 2010-1 de revistas indexadas en Publindex, (2 los programas de maestrías y doctorados activos durante 2010-1 según el Ministerio de Educación Nacional, y (3 los resultados de grupos de investigación clasificados para 2010 según Colciencias. El método empleado para esta investigación es el mismo que para el ranking 2010_1, salvo por una especificación aún más detallada en uno de los pasos del modelo (las variables α, β, γ; es completamente cuantitativo y los datos de las variables que fundamentan sus resultados provienen de Colciencias y el Ministerio de Educación Nacional; y en esta ocasión se darán a conocer los resultados por variable para 2010_1 y 2010_2. Los resultados más relevantes son estos: (1 entraron 8 IES al ranking y salieron 3; (2 las 3 primeras IES son públicas; (3 en total hay 6 instituciones universitarias en el ranking; (4 7 de las 10 primeras IES son
Transanal Minimally Invasive Surgery
deBeche-Adams, Teresa; Nassif, George
2015-01-01
Transanal minimally invasive surgery (TAMIS) was first described in 2010 as a crossover between single-incision laparoscopic surgery and transanal endoscopic microsurgery (TEM) to allow access to the proximal and mid-rectum for resection of benign and early-stage malignant rectal lesions. The TAMIS technique can also be used for noncurative intent surgery of more advanced lesions in patients who are not candidates for radical surgery. Proper workup and staging should be done before surgical decision-making. In addition to the TAMIS port, instrumentation and set up include readily available equipment found in most operating suites. TAMIS has proven its usefulness in a wide range of applications outside of local excision, including repair of rectourethral fistula, removal of rectal foreign body, control of rectal hemorrhage, and as an adjunct in total mesorectal excision for rectal cancer. TAMIS is an easily accessible, technically feasible, and cost-effective alternative to TEM. PMID:26491410
Minimal asymmetric dark matter
Directory of Open Access Journals (Sweden)
Sofiane M. Boucenna
2015-09-01
Full Text Available In the early Universe, any particle carrying a conserved quantum number and in chemical equilibrium with the thermal bath will unavoidably inherit a particle–antiparticle asymmetry. A new particle of this type, if stable, would represent a candidate for asymmetric dark matter (DM with an asymmetry directly related to the baryon asymmetry. We study this possibility for a minimal DM sector constituted by just one (generic SU(2L multiplet χ carrying hypercharge, assuming that at temperatures above the electroweak phase transition an effective operator enforces chemical equilibrium between χ and the Higgs boson. We argue that limits from DM direct detection searches severely constrain this scenario, leaving as the only possibilities scalar or fermion multiplets with hypercharge y=1, preferentially quintuplets or larger SU(2 representations, and with a mass in the few TeV range.
Directory of Open Access Journals (Sweden)
Iris Iddaly Mendez Gurrola
2011-03-01
Full Text Available The proper detection of patient level of dementia is important to offer the suitable treatment. The diagnosis is based on certain criteria, reflected in the clinical examinations. From these examinations emerge the limitations and the degree in which each patient is in. In order to reduce the total of limitations to be evaluated, we used the rough set theory, this theory has been applied in areas of the artificial intelligence such as decision analysis, expert systems, knowledge discovery, classification with multiple attributes. In our case this theory is applied to find the minimal limitations set or reduct that generate the same classification that considering all the limitations, to fulfill this purpose we development an algorithm GRASP (Greedy Randomized Adaptive Search Procedure.
Ranking the Online Documents Based on Relative Credibility Measures
Directory of Open Access Journals (Sweden)
Ahmad Dahlan
2009-05-01
Full Text Available Information searching is the most popular activity in Internet. Usually the search engine provides the search results ranked by the relevance. However, for a certain purpose that concerns with information credibility, particularly citing information for scientific works, another approach of ranking the search engine results is required. This paper presents a study on developing a new ranking method based on the credibility of information. The method is built up upon two well-known algorithms, PageRank and Citation Analysis. The result of the experiment that used Spearman Rank Correlation Coefficient to compare the proposed rank (generated by the method with the standard rank (generated manually by a group of experts showed that the average Spearman 0 < rS < critical value. It means that the correlation was proven but it was not significant. Hence the proposed rank does not satisfy the standard but the performance could be improved.
Ranking the Online Documents Based on Relative Credibility Measures
Directory of Open Access Journals (Sweden)
Ahmad Dahlan
2013-09-01
Full Text Available Information searching is the most popular activity in Internet. Usually the search engine provides the search results ranked by the relevance. However, for a certain purpose that concerns with information credibility, particularly citing information for scientific works, another approach of ranking the search engine results is required. This paper presents a study on developing a new ranking method based on the credibility of information. The method is built up upon two well-known algorithms, PageRank and Citation Analysis. The result of the experiment that used Spearman Rank Correlation Coefficient to compare the proposed rank (generated by the method with the standard rank (generated manually by a group of experts showed that the average Spearman 0 < rS < critical value. It means that the correlation was proven but it was not significant. Hence the proposed rank does not satisfy the standard but the performance could be improved.
On the Nonnegative Rank of Euclidean Distance Matrices.
Lin, Matthew M; Chu, Moody T
2010-09-01
The Euclidean distance matrix for n distinct points in ℝ r is generically of rank r + 2. It is shown in this paper via a geometric argument that its nonnegative rank for the case r = 1 is generically n.
Algebraic and computational aspects of real tensor ranks
Sakata, Toshio; Miyazaki, Mitsuhiro
2016-01-01
This book provides comprehensive summaries of theoretical (algebraic) and computational aspects of tensor ranks, maximal ranks, and typical ranks, over the real number field. Although tensor ranks have been often argued in the complex number field, it should be emphasized that this book treats real tensor ranks, which have direct applications in statistics. The book provides several interesting ideas, including determinant polynomials, determinantal ideals, absolutely nonsingular tensors, absolutely full column rank tensors, and their connection to bilinear maps and Hurwitz-Radon numbers. In addition to reviews of methods to determine real tensor ranks in details, global theories such as the Jacobian method are also reviewed in details. The book includes as well an accessible and comprehensive introduction of mathematical backgrounds, with basics of positive polynomials and calculations by using the Groebner basis. Furthermore, this book provides insights into numerical methods of finding tensor ranks through...
Implicit Block Diagonal Low-Rank Representation.
Xie, Xingyu; Guo, Xianglin; Liu, Guangcan; Wang, Jun
2017-10-17
While current block diagonal constrained subspace clustering methods are performed explicitly on the original data space, in practice it is often more desirable to embed the block diagonal prior into the reproducing kernel Hilbert feature space by kernelization techniques, as the underlying data structure in reality is usually nonlinear. However, it is still unknown how to carry out the embedding and kernelization in the models with block diagonal constraints. In this work, we shall take a step in this direction. First, we establish a novel model termed Implicit Block Diagonal Low-Rank Representation (IBDLR), by incorporating the implicit feature representation and block diagonal prior into the prevalent Low-Rank Representation (LRR) method. Second, mostly important, we show that the model in IBDLR could be kernelized by making use of a smoothed dual representation and the specifics of a proximal gradient based optimization algorithm. Finally, we provide some theoretical analyses for the convergence of our optimization algorithm. Comprehensive experiments on synthetic and realworld datasets demonstrate the superiorities of our IBDLR over state-of-the-art methods.While current block diagonal constrained subspace clustering methods are performed explicitly on the original data space, in practice it is often more desirable to embed the block diagonal prior into the reproducing kernel Hilbert feature space by kernelization techniques, as the underlying data structure in reality is usually nonlinear. However, it is still unknown how to carry out the embedding and kernelization in the models with block diagonal constraints. In this work, we shall take a step in this direction. First, we establish a novel model termed Implicit Block Diagonal Low-Rank Representation (IBDLR), by incorporating the implicit feature representation and block diagonal prior into the prevalent Low-Rank Representation (LRR) method. Second, mostly important, we show that the model in IBDLR could be
Tecer sobe no ranking da Capes
Directory of Open Access Journals (Sweden)
José Aparecido
2013-11-01
Full Text Available Surpresa ainda maior foi verificar que prosseguimos no rumo da consolidação, crescendo no ranking – chegando a B3 em alguns campos, como pode ser visto no portal de buscas do Qualis Capes http://qualis.capes.gov.br/webqualis/principal.seamhttp://qualis.capes.gov, que apresenta nossa classificação abaixo: B3 ADMINISTRAÇÃO, CIÊNCIAS CONTÁBEIS E TURISMO B4 CIÊNCIAS SOCIAIS APLICADAS I B4 EDUCAÇÃO B4 INTERDISCIPLINAR B5 DIREITO B5 HISTÓRIA C CIÊNCIA DA COMPUTAÇÃO
Allanach, B. C.; Athron, P.; Tunstall, Lewis C.; Voigt, A.; Williams, A. G.
2014-09-01
We describe an extension to the SOFTSUSY program that provides for the calculation of the sparticle spectrum in the Next-to-Minimal Supersymmetric Standard Model (NMSSM), where a chiral superfield that is a singlet of the Standard Model gauge group is added to the Minimal Supersymmetric Standard Model (MSSM) fields. Often, a Z3 symmetry is imposed upon the model. SOFTSUSY can calculate the spectrum in this case as well as the case where general Z3 violating (denoted as =) terms are added to the soft supersymmetry breaking terms and the superpotential. The user provides a theoretical boundary condition for the couplings and mass terms of the singlet. Radiative electroweak symmetry breaking data along with electroweak and CKM matrix data are used as weak-scale boundary conditions. The renormalisation group equations are solved numerically between the weak scale and a high energy scale using a nested iterative algorithm. This paper serves as a manual to the NMSSM mode of the program, detailing the approximations and conventions used. Catalogue identifier: ADPM_v4_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADPM_v4_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 154886 No. of bytes in distributed program, including test data, etc.: 1870890 Distribution format: tar.gz Programming language: C++, fortran. Computer: Personal computer. Operating system: Tested on Linux 3.x. Word size: 64 bits Classification: 11.1, 11.6. Does the new version supersede the previous version?: Yes Catalogue identifier of previous version: ADPM_v3_0 Journal reference of previous version: Comput. Phys. Comm. 183 (2012) 785 Nature of problem: Calculating supersymmetric particle spectrum and mixing parameters in the next-to-minimal supersymmetric standard model. The solution to the
On Stein's unbiased risk estimate for reduced rank estimators
DEFF Research Database (Denmark)
Hansen, Niels Richard
2018-01-01
Stein's unbiased risk estimate (SURE) is considered for matrix valued observables with low rank means. It is shown that SURE is applicable to a class of spectral function estimators including the reduced rank estimator.......Stein's unbiased risk estimate (SURE) is considered for matrix valued observables with low rank means. It is shown that SURE is applicable to a class of spectral function estimators including the reduced rank estimator....
A study of serial ranks via random graphs
Haeusler, Erich; Mason, David M.; Turova, Tatyana S.
2000-01-01
Serial ranks have long been used as the basis for nonparametric tests of independence in time series analysis. We shall study the underlying graph structure of serial ranks. This will lead us to a basic martingale which will allow us to construct a weighted approximation to a serial rank process. To show the applicability of this approximation, we will use it to prove two very general central limit theorems for Wald-Wolfowitz-type serial rank statistics.
Do PageRank-based author rankings outperform simple citation counts?
Fiala, Dalibor; Žitnik, Slavko; Bajec, Marko
2015-01-01
The basic indicators of a researcher's productivity and impact are still the number of publications and their citation counts. These metrics are clear, straightforward, and easy to obtain. When a ranking of scholars is needed, for instance in grant, award, or promotion procedures, their use is the fastest and cheapest way of prioritizing some scientists over others. However, due to their nature, there is a danger of oversimplifying scientific achievements. Therefore, many other indicators have been proposed including the usage of the PageRank algorithm known for the ranking of webpages and its modifications suited to citation networks. Nevertheless, this recursive method is computationally expensive and even if it has the advantage of favouring prestige over popularity, its application should be well justified, particularly when compared to the standard citation counts. In this study, we analyze three large datasets of computer science papers in the categories of artificial intelligence, software engineering,...