LogDet Rank Minimization with Application to Subspace Clustering
Directory of Open Access Journals (Sweden)
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.
Zero forcing parameters and minimum rank problems
Barioli, F.; Barrett, W.; Fallat, S.M.; Hall, H.T.; Hogben, L.; Shader, B.L.; Driessche, van den P.; Holst, van der H.
2010-01-01
The zero forcing number Z(G), which is the minimum number of vertices in a zero forcing set of a graph G, is used to study the maximum nullity/minimum rank of the family of symmetric matrices described by G. It is shown that for a connected graph of order at least two, no vertex is in every zero
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.
Learning Preference Models from Data: On the Problem of Label Ranking and Its Variants
Hüllermeier, Eyke; Fürnkranz, Johannes
The term “preference learning” refers to the application of machine learning methods for inducing preference models from empirical data. In the recent literature, corresponding problems appear in various guises. After a brief overview of the field, this work focuses on a particular learning scenario called label ranking where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a ranking function, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data, using a natural extension of pairwise classification. A ranking is then derived from this relation by means of a ranking procedure. This paper elaborates on a key advantage of such an approach, namely the fact that our learner can be adapted to different loss functions by using different ranking procedures on the same underlying order relations. In particular, the Spearman rank correlation is minimized by using a simple weighted voting procedure. Moreover, we discuss a loss function suitable for settings where candidate labels must be tested successively until a target label is found. In this context, we propose the idea of “empirical conditioning” of class probabilities. A related ranking procedure, called “ranking through iterated choice”, is investigated experimentally.
A Generalized Robust Minimization Framework for Low-Rank Matrix Recovery
Directory of Open Access Journals (Sweden)
Wen-Ze Shao
2014-01-01
Full Text Available This paper considers the problem of recovering low-rank matrices which are heavily corrupted by outliers or large errors. To improve the robustness of existing recovery methods, the problem is solved by formulating it as a generalized nonsmooth nonconvex minimization functional via exploiting the Schatten p-norm (0 < p ≤1 and Lq(0 < q ≤1 seminorm. Two numerical algorithms are provided based on the augmented Lagrange multiplier (ALM and accelerated proximal gradient (APG methods as well as efficient root-finder strategies. Experimental results demonstrate that the proposed generalized approach is more inclusive and effective compared with state-of-the-art methods, either convex or nonconvex.
METHOD FOR SOLVING FUZZY ASSIGNMENT PROBLEM USING MAGNITUDE RANKING TECHNIQUE
D. Selvi; R. Queen Mary; G. Velammal
2017-01-01
Assignment problems have various applications in the real world because of their wide applicability in industry, commerce, management science, etc. Traditional classical assignment problems cannot be successfully used for real life problem, hence the use of fuzzy assignment problems is more appropriate. In this paper, the fuzzy assignment problem is formulated to crisp assignment problem using Magnitude Ranking technique and Hungarian method has been applied to find an optimal solution. The N...
One-dimensional Gromov minimal filling problem
International Nuclear Information System (INIS)
Ivanov, Alexandr O; Tuzhilin, Alexey A
2012-01-01
The paper is devoted to a new branch in the theory of one-dimensional variational problems with branching extremals, the investigation of one-dimensional minimal fillings introduced by the authors. On the one hand, this problem is a one-dimensional version of a generalization of Gromov's minimal fillings problem to the case of stratified manifolds. On the other hand, this problem is interesting in itself and also can be considered as a generalization of another classical problem, the Steiner problem on the construction of a shortest network connecting a given set of terminals. Besides the statement of the problem, we discuss several properties of the minimal fillings and state several conjectures. Bibliography: 38 titles.
Gravitino problem in minimal supergravity inflation
Directory of Open Access Journals (Sweden)
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.
Separability problem for multipartite states of rank at most 4
International Nuclear Information System (INIS)
Chen, Lin; Đoković, Dragomir Ž
2013-01-01
One of the most important problems in quantum information is the separability problem, which asks whether a given quantum state is separable. We investigate multipartite states of rank at most 4 which are PPT (i.e., all their partial transposes are positive semidefinite). We show that any PPT state of rank 2 or 3 is separable and has length at most 4. For separable states of rank 4, we show that they have length at most 6. It is six only for some qubit–qutrit or multiqubit states. It turns out that any PPT entangled state of rank 4 is necessarily supported on a 3⊗3 or a 2⊗2⊗2 subsystem. We obtain a very simple criterion for the separability problem of the PPT states of rank at most 4: such a state is entangled if and only if its range contains no product vectors. This criterion can be easily applied since a four-dimensional subspace in the 3⊗3 or 2⊗2⊗2 system contains a product vector if and only if its Plücker coordinates satisfy a homogeneous polynomial equation (the Chow form of the corresponding Segre variety). We have computed an explicit determinantal expression for the Chow form in the former case, while such an expression was already known in the latter case. (paper)
An Improved Approach to the PageRank Problems
Directory of Open Access Journals (Sweden)
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.
Learning of Rule Ensembles for Multiple Attribute Ranking Problems
Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin
In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.
Ngastiti, P. T. B.; Surarso, Bayu; Sutimin
2018-05-01
Transportation issue of the distribution problem such as the commodity or goods from the supply tothe demmand is to minimize the transportation costs. Fuzzy transportation problem is an issue in which the transport costs, supply and demand are in the form of fuzzy quantities. Inthe case study at CV. Bintang Anugerah Elektrik, a company engages in the manufacture of gensets that has more than one distributors. We use the methods of zero point and zero suffix to investigate the transportation minimum cost. In implementing both methods, we use robust ranking techniques for the defuzzification process. The studyresult show that the iteration of zero suffix method is less than that of zero point method.
Directory of Open Access Journals (Sweden)
Torres-Salinas, Daniel
2015-12-01
Full Text Available We present the results of the Bibliometric Indicators for Publishers project (also known as BiPublishers. This project represents the first attempt to systematically develop bibliometric publisher rankings. The data for this project was derived from the Book Citation Index and the study time period was 2009-2013. We have developed 42 rankings: 4 by fields and 38 by disciplines. We display six indicators for publishers divided into three types: output, impact and publisher’s profile. The aim is to capture different characteristics of the research performance of publishers. 254 publishers were processed and classified according to publisher type: commercial publishers and university presses. We present the main publishers by field and then discuss the principal challenges presented when developing this type of tool. The BiPublishers ranking is an on-going project which aims to develop and explore new data sources and indicators to better capture and define the research impact of publishers.Presentamos los resultados del proyecto Bibliometric Indicators for Publishers (BiPublishers. Es el primer proyecto que desarrolla de manera sistemática rankings bibliométricos de editoriales. La fuente de datos empleada es el Book Citation Index y el periodo de análisis 2009-2013. Se presentan 42 rankings: 4 por áreas y 38 por disciplinas. Mostramos seis indicadores por editorial divididos según su tipología: producción, impacto y características editoriales. Se procesaron 254 editoriales y se clasificaron según el tipo: comerciales y universitarias. Se presentan las principales editoriales por áreas. Después, se discuten los principales retos a superar en el desarrollo de este tipo de herramientas. El ranking Bipublishers es un proyecto en desarrollo que persigue analizar y explorar nuevas fuentes de datos e indicadores para captar y definir el impacto de las editoriales académicas.
Rank deficiency and Tikhonov regularization in the inverse problem for gravitational-wave bursts
International Nuclear Information System (INIS)
Rakhmanov, M
2006-01-01
Coherent techniques for searches of gravitational-wave bursts effectively combine data from several detectors, taking into account differences in their responses. The efforts are now focused on the maximum likelihood principle as the most natural way to combine data, which can also be used without prior knowledge of the signal. Recent studies however have shown that straightforward application of the maximum likelihood method to gravitational waves with unknown waveforms can lead to inconsistencies and unphysical results such as discontinuity in the residual functional, or divergence of the variance of the estimated waveforms for some locations in the sky. So far the solutions to these problems have been based on rather different physical arguments. Following these investigations, we now find that all these inconsistencies stem from the rank deficiency of the underlying network response matrix. In this paper we show that the detection of gravitational-wave bursts with a network of interferometers belongs to the category of ill-posed problems. We then apply the method of Tikhonov regularization to resolve the rank deficiency and introduce a minimal regulator which yields a well-conditioned solution to the inverse problem for all locations on the sky
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.
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...... sparsity, and low rank of the BES matrix. The method is based on the factorization of the BES matrix as a product of a sparse coding matrix and a dense latent source matrix. The structured sparse-low-rank structure is enforced by minimizing a regularized functional that includes the ℓ21-norm of the coding...... 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...
Global Sufficient Optimality Conditions for a Special Cubic Minimization Problem
Directory of Open Access Journals (Sweden)
Xiaomei Zhang
2012-01-01
Full Text Available We present some sufficient global optimality conditions for a special cubic minimization problem with box constraints or binary constraints by extending the global subdifferential approach proposed by V. Jeyakumar et al. (2006. The present conditions generalize the results developed in the work of V. Jeyakumar et al. where a quadratic minimization problem with box constraints or binary constraints was considered. In addition, a special diagonal matrix is constructed, which is used to provide a convenient method for justifying the proposed sufficient conditions. Then, the reformulation of the sufficient conditions follows. It is worth noting that this reformulation is also applicable to the quadratic minimization problem with box or binary constraints considered in the works of V. Jeyakumar et al. (2006 and Y. Wang et al. (2010. Finally some examples demonstrate that our optimality conditions can effectively be used for identifying global minimizers of the certain nonconvex cubic minimization problem.
OPTIM, Minimization of Band-Width of Finite Elements Problems
International Nuclear Information System (INIS)
Huart, M.
1977-01-01
1 - Nature of the physical problem solved: To minimize the band-width of finite element problems. 2 - Method of solution: A surface is constructed from the x-y-coordinates of each node using its node number as z-value. This surface consists of triangles. Nodes are renumbered in such a way as to minimize the surface area. 3 - Restrictions on the complexity of the problem: This program is applicable to 2-D problems. It is dimensioned for a maximum of 1000 elements
Minimization under entropy conditions, with applications in lower bound problems
International Nuclear Information System (INIS)
Toft, Joachim
2004-01-01
We minimize the functional f->∫ afdμ under the entropy condition E(f)=-∫ f log fdμ≥E, ∫ fdμ=1 and f≥0, where E is a member of R is fixed. We prove that the minimum is attained for f=e -sa /∫ e -sa dμ, where s is a member of R is chosen such that E(f)=E. We apply the result on minimizing problems in pseudodifferential calculus, where we minimize the harmonic oscillator
Minimization In Digital Design As A Meta-Planning Problem
Ho, William P. C.; Wu, Jung-Gen
1987-05-01
In our model-based expert system for automatic digital system design, we formalize the design process into three sub-processes - compiling high-level behavioral specifications into primitive behavioral operations, grouping primitive operations into behavioral functions, and grouping functions into modules. Consideration of design minimization explicitly controls decision-making in the last two subprocesses. Design minimization, a key task in the automatic design of digital systems, is complicated by the high degree of interaction among the time sequence and content of design decisions. In this paper, we present an AI approach which directly addresses these interactions and their consequences by modeling the minimization prob-lem as a planning problem, and the management of design decision-making as a meta-planning problem.
Minimal surfaces, stratified multivarifolds, and the plateau problem
Thi, Dao Trong; Primrose, E J F; Silver, Ben
1991-01-01
Plateau's problem is a scientific trend in modern mathematics that unites several different problems connected with the study of minimal surfaces. In its simplest version, Plateau's problem is concerned with finding a surface of least area that spans a given fixed one-dimensional contour in three-dimensional space--perhaps the best-known example of such surfaces is provided by soap films. From the mathematical point of view, such films are described as solutions of a second-order partial differential equation, so their behavior is quite complicated and has still not been thoroughly studied. Soap films, or, more generally, interfaces between physical media in equilibrium, arise in many applied problems in chemistry, physics, and also in nature. In applications, one finds not only two-dimensional but also multidimensional minimal surfaces that span fixed closed "contours" in some multidimensional Riemannian space. An exact mathematical statement of the problem of finding a surface of least area or volume requir...
Optimal solution of full fuzzy transportation problems using total integral ranking
Sam’an, M.; Farikhin; Hariyanto, S.; Surarso, B.
2018-03-01
Full fuzzy transportation problem (FFTP) is a transportation problem where transport costs, demand, supply and decision variables are expressed in form of fuzzy numbers. To solve fuzzy transportation problem, fuzzy number parameter must be converted to a crisp number called defuzzyfication method. In this new total integral ranking method with fuzzy numbers from conversion of trapezoidal fuzzy numbers to hexagonal fuzzy numbers obtained result of consistency defuzzyfication on symmetrical fuzzy hexagonal and non symmetrical type 2 numbers with fuzzy triangular numbers. To calculate of optimum solution FTP used fuzzy transportation algorithm with least cost method. From this optimum solution, it is found that use of fuzzy number form total integral ranking with index of optimism gives different optimum value. In addition, total integral ranking value using hexagonal fuzzy numbers has an optimal value better than the total integral ranking value using trapezoidal fuzzy numbers.
Iterative Schemes for Convex Minimization Problems with Constraints
Directory of Open Access Journals (Sweden)
Lu-Chuan Ceng
2014-01-01
Full Text Available We first introduce and analyze one implicit iterative algorithm for finding a solution of the minimization problem for a convex and continuously Fréchet differentiable functional, with constraints of several problems: the generalized mixed equilibrium problem, the system of generalized equilibrium problems, and finitely many variational inclusions 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 implicit iterative algorithm for finding a fixed point of infinitely many nonexpansive mappings with the same constraints, and derive its strong convergence under mild assumptions.
Ranking periodic ordering models on the basis of minimizing total inventory cost
Directory of Open Access Journals (Sweden)
Mohammadali Keramati
2015-06-01
Full Text Available This paper aims to provide proper policies for inventory under uncertain conditions by comparing different inventory policies. To review the efficiency of these algorithms it is necessary to specify the area in which each of them is applied. Therefore, each of the models has been reviewed under different forms of retailing and they are ranked in terms of their expenses. According to the high values of inventories and their impacts on the costs of the companies, the ranking of various models using the simulation annealing algorithm are presented, which indicates that the proposed model of this paper could perform better than other alternative ones. The results also indicate that the suggested algorithm could save from 4 to 29 percent on costs of inventories.
Complexity of the positive semidefinite matrix completion problem with a rank constraint
Nagy, M.; Laurent, M.; Varvitsiotis, A.; Bezdek, K.; Deza, A.; Ye, Y.
2013-01-01
We consider the decision problem asking whether a partial rational symmetric matrix with an all-ones diagonal can be completed to a full positive semidefinite matrix of rank at most k. We show that this problem is NP-hard for any fixed integer k ≥ 2. In other words, for k ≥ 2, it is NP-hard to test
Complexity of the positive semidefinite matrix completion problem with a rank constraint.
M. Eisenberg-Nagy (Marianna); M. Laurent (Monique); A. Varvitsiotis (Antonios); K. Bezdek; A. Deza; Y. Ye
2013-01-01
htmlabstractWe consider the decision problem asking whether a partial rational symmetric matrix with an all-ones diagonal can be completed to a full positive semidefinite matrix of rank at most k. We show that this problem is NP-hard for any fixed integer k ≥ 2. Equivalently, for k ≥ 2, it is
Complexity of the positive semidefinite matrix completion problem with a rank constraint.
M. Eisenberg-Nagy (Marianna); M. Laurent (Monique); A. Varvitsiotis (Antonios)
2012-01-01
htmlabstractWe consider the decision problem asking whether a partial rational symmetric matrix with an all-ones diagonal can be completed to a full positive semidefinite matrix of rank at most k. We show that this problem is NP-hard for any fixed integer k ≥ 2. Equivalently, for k ≥ 2, it is
Convergence of Inner-Iteration GMRES Methods for Rank-Deficient Least Squares Problems
Czech Academy of Sciences Publication Activity Database
Morikuni, Keiichi; Hayami, K.
2015-01-01
Roč. 36, č. 1 (2015), s. 225-250 ISSN 0895-4798 Institutional support: RVO:67985807 Keywords : least squares problem * iterative methods * preconditioner * inner-outer iteration * GMRES method * stationary iterative method * rank-deficient problem Subject RIV: BA - General Mathematics Impact factor: 1.883, year: 2015
NP-hardness of the cluster minimization problem revisited
Adib, Artur B.
2005-10-01
The computational complexity of the 'cluster minimization problem' is revisited (Wille and Vennik 1985 J. Phys. A: Math. Gen. 18 L419). It is argued that the original NP-hardness proof does not apply to pairwise potentials of physical interest, such as those that depend on the geometric distance between the particles. A geometric analogue of the original problem is formulated, and a new proof for such potentials is provided by polynomial time transformation from the independent set problem for unit disk graphs. Limitations of this formulation are pointed out, and new subproblems that bear more direct consequences to the numerical study of clusters are suggested.
NP-hardness of the cluster minimization problem revisited
International Nuclear Information System (INIS)
Adib, Artur B
2005-01-01
The computational complexity of the 'cluster minimization problem' is revisited (Wille and Vennik 1985 J. Phys. A: Math. Gen. 18 L419). It is argued that the original NP-hardness proof does not apply to pairwise potentials of physical interest, such as those that depend on the geometric distance between the particles. A geometric analogue of the original problem is formulated, and a new proof for such potentials is provided by polynomial time transformation from the independent set problem for unit disk graphs. Limitations of this formulation are pointed out, and new subproblems that bear more direct consequences to the numerical study of clusters are suggested
NP-hardness of the cluster minimization problem revisited
Energy Technology Data Exchange (ETDEWEB)
Adib, Artur B [Physics Department, Brown University, Providence, RI 02912 (United States)
2005-10-07
The computational complexity of the 'cluster minimization problem' is revisited (Wille and Vennik 1985 J. Phys. A: Math. Gen. 18 L419). It is argued that the original NP-hardness proof does not apply to pairwise potentials of physical interest, such as those that depend on the geometric distance between the particles. A geometric analogue of the original problem is formulated, and a new proof for such potentials is provided by polynomial time transformation from the independent set problem for unit disk graphs. Limitations of this formulation are pointed out, and new subproblems that bear more direct consequences to the numerical study of clusters are suggested.
Free time minimizers for the three-body problem
Moeckel, Richard; Montgomery, Richard; Sánchez Morgado, Héctor
2018-03-01
Free time minimizers of the action (called "semi-static" solutions by Mañe in International congress on dynamical systems in Montevideo (a tribute to Ricardo Mañé), vol 362, pp 120-131, 1996) play a central role in the theory of weak KAM solutions to the Hamilton-Jacobi equation (Fathi in Weak KAM Theorem in Lagrangian Dynamics Preliminary Version Number 10, 2017). We prove that any solution to Newton's three-body problem which is asymptotic to Lagrange's parabolic homothetic solution is eventually a free time minimizer. Conversely, we prove that every free time minimizer tends to Lagrange's solution, provided the mass ratios lie in a certain large open set of mass ratios. We were inspired by the work of Da Luz and Maderna (Math Proc Camb Philos Soc 156:209-227, 1980) which showed that every free time minimizer for the N-body problem is parabolic and therefore must be asymptotic to the set of central configurations. We exclude being asymptotic to Euler's central configurations by a second variation argument. Central configurations correspond to rest points for the McGehee blown-up dynamics. The large open set of mass ratios are those for which the linearized dynamics at each Euler rest point has a complex eigenvalue.
A Hybrid ACO Approach to the Matrix Bandwidth Minimization Problem
Pintea, Camelia-M.; Crişan, Gloria-Cerasela; Chira, Camelia
The evolution of the human society raises more and more difficult endeavors. For some of the real-life problems, the computing time-restriction enhances their complexity. The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous permutation of the rows and the columns of a square matrix in order to keep its nonzero entries close to the main diagonal. The MBMP is a highly investigated {NP}-complete problem, as it has broad applications in industry, logistics, artificial intelligence or information recovery. This paper describes a new attempt to use the Ant Colony Optimization framework in tackling MBMP. The introduced model is based on the hybridization of the Ant Colony System technique with new local search mechanisms. Computational experiments confirm a good performance of the proposed algorithm for the considered set of MBMP instances.
Directory of Open Access Journals (Sweden)
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.
Quantum N-body problem with a minimal length
International Nuclear Information System (INIS)
Buisseret, Fabien
2010-01-01
The quantum N-body problem is studied in the context of nonrelativistic quantum mechanics with a one-dimensional deformed Heisenberg algebra of the form [x,p]=i(1+βp 2 ), leading to the existence of a minimal observable length √(β). For a generic pairwise interaction potential, analytical formulas are obtained that allow estimation of the ground-state energy of the N-body system by finding the ground-state energy of a corresponding two-body problem. It is first shown that in the harmonic oscillator case, the β-dependent term grows faster with increasing N than the β-independent term. Then, it is argued that such a behavior should also be observed with generic potentials and for D-dimensional systems. Consequently, quantum N-body bound states might be interesting places to look at nontrivial manifestations of a minimal length, since the more particles that are present, the more the system deviates from standard quantum-mechanical predictions.
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).
Analogue of Pontryagin's maximum principle for multiple integrals minimization problems
Mikhail, Zelikin
2016-01-01
The theorem like Pontryagin's maximum principle for multiple integrals is proved. Unlike the usual maximum principle, the maximum should be taken not over all matrices, but only on matrices of rank one. Examples are given.
Directory of Open Access Journals (Sweden)
Yi-hua Zhong
2013-01-01
Full Text Available Recently, various methods have been developed for solving linear programming problems with fuzzy number, such as simplex method and dual simplex method. But their computational complexities are exponential, which is not satisfactory for solving large-scale fuzzy linear programming problems, especially in the engineering field. A new method which can solve large-scale fuzzy number linear programming problems is presented in this paper, which is named a revised interior point method. Its idea is similar to that of interior point method used for solving linear programming problems in crisp environment before, but its feasible direction and step size are chosen by using trapezoidal fuzzy numbers, linear ranking function, fuzzy vector, and their operations, and its end condition is involved in linear ranking function. Their correctness and rationality are proved. Moreover, choice of the initial interior point and some factors influencing the results of this method are also discussed and analyzed. The result of algorithm analysis and example study that shows proper safety factor parameter, accuracy parameter, and initial interior point of this method may reduce iterations and they can be selected easily according to the actual needs. Finally, the method proposed in this paper is an alternative method for solving fuzzy number linear programming problems.
APPLYING ROBUST RANKING METHOD IN TWO PHASE FUZZY OPTIMIZATION LINEAR PROGRAMMING PROBLEMS (FOLPP
Directory of Open Access Journals (Sweden)
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.
Block-accelerated aggregation multigrid for Markov chains with application to PageRank problems
Shen, Zhao-Li; Huang, Ting-Zhu; Carpentieri, Bruno; Wen, Chun; Gu, Xian-Ming
2018-06-01
Recently, the adaptive algebraic aggregation multigrid method has been proposed for computing stationary distributions of Markov chains. This method updates aggregates on every iterative cycle to keep high accuracies of coarse-level corrections. Accordingly, its fast convergence rate is well guaranteed, but often a large proportion of time is cost by aggregation processes. In this paper, we show that the aggregates on each level in this method can be utilized to transfer the probability equation of that level into a block linear system. Then we propose a Block-Jacobi relaxation that deals with the block system on each level to smooth error. Some theoretical analysis of this technique is presented, meanwhile it is also adapted to solve PageRank problems. The purpose of this technique is to accelerate the adaptive aggregation multigrid method and its variants for solving Markov chains and PageRank problems. It also attempts to shed some light on new solutions for making aggregation processes more cost-effective for aggregation multigrid methods. Numerical experiments are presented to illustrate the effectiveness of this technique.
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.
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.
Multiple graph regularized protein domain ranking
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin
2012-01-01
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.
Multiple graph regularized protein domain ranking.
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin
2012-11-19
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. 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. 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
Directory of Open Access Journals (Sweden)
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.
Contribution of Fuzzy Minimal Cost Flow Problem by Possibility Programming
S. Fanati Rashidi; A. A. Noora
2010-01-01
Using the concept of possibility proposed by zadeh, luhandjula ([4,8]) and buckley ([1]) have proposed the possibility programming. The formulation of buckley results in nonlinear programming problems. Negi [6]re-formulated the approach of Buckley by the use of trapezoidal fuzzy numbers and reduced the problem into fuzzy linear programming problem. Shih and Lee ([7]) used the Negi approach to solve a minimum cost flow problem, whit fuzzy costs and the upper and lower bound. ...
Contribution of Fuzzy Minimal Cost Flow Problem by Possibility Programming
Directory of Open Access Journals (Sweden)
S. Fanati Rashidi
2010-06-01
Full Text Available Using the concept of possibility proposed by zadeh, luhandjula ([4,8] and buckley ([1] have proposed the possibility programming. The formulation of buckley results in nonlinear programming problems. Negi [6]re-formulated the approach of Buckley by the use of trapezoidal fuzzy numbers and reduced the problem into fuzzy linear programming problem. Shih and Lee ([7] used the Negi approach to solve a minimum cost flow problem, whit fuzzy costs and the upper and lower bound. In this paper we shall consider the general form of this problem where all of the parameters and variables are fuzzy and also a model for solving is proposed
Radiological terrorism: problems of prevention and minimization of consequences
International Nuclear Information System (INIS)
Bolshov, Leonid; Arutyunyan, Rafael; Pavlovski, Oleg
2008-01-01
This paper gives a review of the key factors defining the extent of potential hazard caused by ionizing radiation sources for the purpose of radiological terrorism and the key areas of activities in the field of counteractions and minimization of possible consequences of such acts. The importance of carrying out system analysis of the practical experience of response to radiation accidents and elimination of their consequences is emphasized. The need to develop scientific approaches, methods and software to realistically analyze possible scenarios and predict the scale of consequences of the acts of terrorism involving radioactive materials is pointed out. The importance of improvement of radioactive materials accounting, control and monitoring systems, especially in non-nuclear areas, as well as improvement of the legal and regulatory framework governing all aspects of radiation source application in the national economy is of particular importance. (author)
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.
Aihong Ren
2016-01-01
This paper is concerned with a class of fully fuzzy bilevel linear programming problems where all the coefficients and decision variables of both objective functions and the constraints are fuzzy numbers. A new approach based on deviation degree measures and a ranking function method is proposed to solve these problems. We first introduce concepts of the feasible region and the fuzzy optimal solution of a fully fuzzy bilevel linear programming problem. In order to obtain a fuzzy optimal solut...
Canonical Primal-Dual Method for Solving Non-convex Minimization Problems
Wu, Changzhi; Li, Chaojie; Gao, David Yang
2012-01-01
A new primal-dual algorithm is presented for solving a class of non-convex minimization problems. This algorithm is based on canonical duality theory such that the original non-convex minimization problem is first reformulated as a convex-concave saddle point optimization problem, which is then solved by a quadratically perturbed primal-dual method. %It is proved that the popular SDP method is indeed a special case of the canonical duality theory. Numerical examples are illustrated. Comparing...
Sparse structure regularized ranking
Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin
2014-01-01
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
Directory of Open Access Journals (Sweden)
Aihong Ren
2016-01-01
Full Text Available This paper is concerned with a class of fully fuzzy bilevel linear programming problems where all the coefficients and decision variables of both objective functions and the constraints are fuzzy numbers. A new approach based on deviation degree measures and a ranking function method is proposed to solve these problems. We first introduce concepts of the feasible region and the fuzzy optimal solution of a fully fuzzy bilevel linear programming problem. In order to obtain a fuzzy optimal solution of the problem, we apply deviation degree measures to deal with the fuzzy constraints and use a ranking function method of fuzzy numbers to rank the upper and lower level fuzzy objective functions. Then the fully fuzzy bilevel linear programming problem can be transformed into a deterministic bilevel programming problem. Considering the overall balance between improving objective function values and decreasing allowed deviation degrees, the computational procedure for finding a fuzzy optimal solution is proposed. Finally, a numerical example is provided to illustrate the proposed approach. The results indicate that the proposed approach gives a better optimal solution in comparison with the existing method.
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.
Minimizers of a Class of Constrained Vectorial Variational Problems: Part I
Hajaiej, Hichem; Markowich, Peter A.; Trabelsi, Saber
2014-01-01
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 minimizers of calculus of variations problems in Hilbert spaces
Gomes, Diogo A.; Nurbekyan, Levon
2014-01-01
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.
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
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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.
Numerical solution of large nonlinear boundary value problems by quadratic minimization techniques
International Nuclear Information System (INIS)
Glowinski, R.; Le Tallec, P.
1984-01-01
The objective of this paper is to describe the numerical treatment of large highly nonlinear two or three dimensional boundary value problems by quadratic minimization techniques. In all the different situations where these techniques were applied, the methodology remains the same and is organized as follows: 1) derive a variational formulation of the original boundary value problem, and approximate it by Galerkin methods; 2) transform this variational formulation into a quadratic minimization problem (least squares methods) or into a sequence of quadratic minimization problems (augmented lagrangian decomposition); 3) solve each quadratic minimization problem by a conjugate gradient method with preconditioning, the preconditioning matrix being sparse, positive definite, and fixed once for all in the iterative process. This paper will illustrate the methodology above on two different examples: the description of least squares solution methods and their application to the solution of the unsteady Navier-Stokes equations for incompressible viscous fluids; the description of augmented lagrangian decomposition techniques and their application to the solution of equilibrium problems in finite elasticity
2014-01-01
Berth allocation is the forefront operation performed when ships arrive at a port and is a critical task in container port optimization. Minimizing the time ships spend at berths constitutes an important objective of berth allocation problems. This study focuses on the discrete dynamic berth allocation problem (discrete DBAP), which aims to minimize total service time, and proposes an iterated greedy (IG) algorithm to solve it. The proposed IG algorithm is tested on three benchmark problem sets. Experimental results show that the proposed IG algorithm can obtain optimal solutions for all test instances of the first and second problem sets and outperforms the best-known solutions for 35 out of 90 test instances of the third problem set. PMID:25295295
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Shih-Wei Lin
2014-01-01
Full Text Available Berth allocation is the forefront operation performed when ships arrive at a port and is a critical task in container port optimization. Minimizing the time ships spend at berths constitutes an important objective of berth allocation problems. This study focuses on the discrete dynamic berth allocation problem (discrete DBAP, which aims to minimize total service time, and proposes an iterated greedy (IG algorithm to solve it. The proposed IG algorithm is tested on three benchmark problem sets. Experimental results show that the proposed IG algorithm can obtain optimal solutions for all test instances of the first and second problem sets and outperforms the best-known solutions for 35 out of 90 test instances of the third problem set.
Solving Minimal Covering Location Problems with Single and Multiple Node Coverage
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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.
International Nuclear Information System (INIS)
Horesh, L; Haber, E
2009-01-01
The l 1 minimization problem has been studied extensively in the past few years. Recently, there has been a growing interest in its application for inverse problems. Most studies have concentrated in devising ways for sparse representation of a solution using a given prototype dictionary. Very few studies have addressed the more challenging problem of optimal dictionary construction, and even these were primarily devoted to the simplistic sparse coding application. In this paper, sensitivity analysis of the inverse solution with respect to the dictionary is presented. This analysis reveals some of the salient features and intrinsic difficulties which are associated with the dictionary design problem. Equipped with these insights, we propose an optimization strategy that alleviates these hurdles while utilizing the derived sensitivity relations for the design of a locally optimal dictionary. Our optimality criterion is based on local minimization of the Bayesian risk, given a set of training models. We present a mathematical formulation and an algorithmic framework to achieve this goal. The proposed framework offers the design of dictionaries for inverse problems that incorporate non-trivial, non-injective observation operators, where the data and the recovered parameters may reside in different spaces. We test our algorithm and show that it yields improved dictionaries for a diverse set of inverse problems in geophysics and medical imaging
Horesh, L.; Haber, E.
2009-09-01
The ell1 minimization problem has been studied extensively in the past few years. Recently, there has been a growing interest in its application for inverse problems. Most studies have concentrated in devising ways for sparse representation of a solution using a given prototype dictionary. Very few studies have addressed the more challenging problem of optimal dictionary construction, and even these were primarily devoted to the simplistic sparse coding application. In this paper, sensitivity analysis of the inverse solution with respect to the dictionary is presented. This analysis reveals some of the salient features and intrinsic difficulties which are associated with the dictionary design problem. Equipped with these insights, we propose an optimization strategy that alleviates these hurdles while utilizing the derived sensitivity relations for the design of a locally optimal dictionary. Our optimality criterion is based on local minimization of the Bayesian risk, given a set of training models. We present a mathematical formulation and an algorithmic framework to achieve this goal. The proposed framework offers the design of dictionaries for inverse problems that incorporate non-trivial, non-injective observation operators, where the data and the recovered parameters may reside in different spaces. We test our algorithm and show that it yields improved dictionaries for a diverse set of inverse problems in geophysics and medical imaging.
Limit behavior of mass critical Hartree minimization problems with steep potential wells
Guo, Yujin; Luo, Yong; Wang, Zhi-Qiang
2018-06-01
We consider minimizers of the following mass critical Hartree minimization problem: eλ(N ) ≔inf {u ∈H1(Rd ) , ‖u‖2 2=N } Eλ(u ) , where d ≥ 3, λ > 0, and the Hartree energy functional Eλ(u) is defined by Eλ(u ) ≔∫Rd|∇u (x ) |2d x +λ ∫Rdg (x ) u2(x ) d x -1/2 ∫Rd∫Rdu/2(x ) u2(y ) |x -y |2 d x d y . Here the steep potential g(x) satisfies 0 =g (0 ) =infRdg (x ) ≤g (x ) ≤1 and 1 -g (x ) ∈Ld/2(Rd ) . We prove that there exists a constant N* > 0, independent of λg(x), such that if N ≥ N*, then eλ(N) does not admit minimizers for any λ > 0; if 0 N N*, then there exists a constant λ*(N) > 0 such that eλ(N) admits minimizers for any λ > λ*(N) and eλ(N) does not admit minimizers for 0 N). For any given 0 N N*, the limit behavior of positive minimizers for eλ(N) is also studied as λ → ∞, where the mass concentrates at the bottom of g(x).
A novel discrete PSO algorithm for solving job shop scheduling problem to minimize makespan
Rameshkumar, K.; Rajendran, C.
2018-02-01
In this work, a discrete version of PSO algorithm is proposed to minimize the makespan of a job-shop. A novel schedule builder has been utilized to generate active schedules. The discrete PSO is tested using well known benchmark problems available in the literature. The solution produced by the proposed algorithms is compared with best known solution published in the literature and also compared with hybrid particle swarm algorithm and variable neighborhood search PSO algorithm. The solution construction methodology adopted in this study is found to be effective in producing good quality solutions for the various benchmark job-shop scheduling problems.
Mathematical models for a batch scheduling problem to minimize earliness and tardiness
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Basar Ogun
2018-05-01
Full Text Available Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem is addressed to provide on-time completion of customer orders in the environment of lean manufacturing. The problem is to optimize partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components. Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid from inventory of final products. The first model is a non-linear integer programming model while the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented. Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times comparing to the other two models. It was also showed that the alternative model can solve moderate sized real-world problems. Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature since it includes new circumstances which may arise in real-world applications. This research, also, contributes the literature of batch scheduling problem by presenting new optimization models.
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.
Directory of Open Access Journals (Sweden)
Amir Salehipour
2012-01-01
Full Text Available This paper presents a novel application of operations research to support decision making in blood distribution management. The rapid and dynamic increasing demand, criticality of the product, storage, handling, and distribution requirements, and the different geographical locations of hospitals and medical centers have made blood distribution a complex and important problem. In this study, a real blood distribution problem containing 24 hospitals was tackled by the authors, and an exact approach was presented. The objective of the problem is to distribute blood and its products among hospitals and medical centers such that the total waiting time of those requiring the product is minimized. Following the exact solution, a hybrid heuristic algorithm is proposed. Computational experiments showed the optimal solutions could be obtained for medium size instances, while for larger instances the proposed hybrid heuristic is very competitive.
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
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.
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.
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.
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
Zubakov, V. A.
2011-02-01
An international scientific conflict has arisen around the International Stratigraphic Scale, the main document that regulates the rules of reading of geological records and, hence, concerns all Earth sciences. The matter of debate is the geological time scale of 2004, developed by the International Commission on Stratigraphy, where the Quaternary system was abandoned. This ICS decision triggered a protest among Quaternary geologists, members of INQUA, and became the subject of much controversy. This article provides a comprehensive analysis of the Quaternary problem and proposes a reasonable scientific solution that may be appropriate for both parties. The subject of Late Cenozoic geology is discussed: glaciations, human evolution, and recent deposits. In contrast to Charles Lyell's definition of the Plio-Pleistocene according to the percentage of modern mollusk species, it is defined here as a blanket formation, which is correlative to the topography and consists of mapped stratogens hosting fossils of modern biogeocenoses. Features of the description of the Plio-Pleistocene in terms of gravitational orbital tuning are considered. Four paleogeographic phases of modern environment evolution are recognized and ranked as stages: (1) The Messinian evolutionary explosion involved the appearance of many biogeocenoses and the bipedal walking of our extinct ancestors armed with sticks. It was a consequence of the Early Greenland (7.6 Ma BP) and Patagonian (6.7 Ma BP) hyperglaciations. (2) The Zanclean age is marked by climatic and hydrological but not evolutionary boundaries. (3) The appearance of the Villafranchian animal assemblage and Australopithecus, who used stones as weapon: 4.0-3.6 Ma BP. Orogeny and isolation of the Arctic Ocean changed the global climate dramatically. (4) The sexual revolution became the third evolutionary jump: the appearance of the first woman, "Eve", and the genus Homo with her: 1.9 Ma BP. According to the current view, the Plio
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
Nader Ghaffari-Nasab
2010-07-01
Full Text Available During the past two decades, there have been increasing interests on permutation flow shop with different types of objective functions such as minimizing the makespan, the weighted mean flow-time etc. The permutation flow shop is formulated as a mixed integer programming and it is classified as NP-Hard problem. Therefore, a direct solution is not available and meta-heuristic approaches need to be used to find the near-optimal solutions. In this paper, we present a new discrete firefly meta-heuristic to minimize the makespan for the permutation flow shop scheduling problem. The results of implementation of the proposed method are compared with other existing ant colony optimization technique. The preliminary results indicate that the new proposed method performs better than the ant colony for some well known benchmark problems.
Directory of Open Access Journals (Sweden)
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.
Liu, Xian; Xu, Yuan; Li, Shanshan; Wang, Yulan; Peng, Jianlong; Luo, Cheng; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang
2014-01-01
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. 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. 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.
Robust Visual Tracking Via Consistent Low-Rank Sparse Learning
Zhang, Tianzhu; Liu, Si; Ahuja, Narendra; Yang, Ming-Hsuan; Ghanem, Bernard
2014-01-01
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
A minimally-resolved immersed boundary model for reaction-diffusion problems
Pal Singh Bhalla, A; Griffith, BE; Patankar, NA; Donev, A
2013-01-01
We develop an immersed boundary approach to modeling reaction-diffusion processes in dispersions of reactive spherical particles, from the diffusion-limited to the reaction-limited setting. We represent each reactive particle with a minimally-resolved "blob" using many fewer degrees of freedom per particle than standard discretization approaches. More complicated or more highly resolved particle shapes can be built out of a collection of reactive blobs. We demonstrate numerically that the blo...
Directory of Open Access Journals (Sweden)
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.
Du, Shouqiang; Chen, Miao
2018-01-01
We consider a kind of nonsmooth optimization problems with [Formula: see text]-norm minimization, which has many applications in compressed sensing, signal reconstruction, and the related engineering problems. Using smoothing approximate techniques, this kind of nonsmooth optimization problem can be transformed into a general unconstrained optimization problem, which can be solved by the proposed smoothing modified three-term conjugate gradient method. The smoothing modified three-term conjugate gradient method is based on Polak-Ribière-Polyak conjugate gradient method. For the Polak-Ribière-Polyak conjugate gradient method has good numerical properties, the proposed method possesses the sufficient descent property without any line searches, and it is also proved to be globally convergent. Finally, the numerical experiments show the efficiency of the proposed method.
Waste minimization and control: a review of problems and available technologies
International Nuclear Information System (INIS)
Butt, W.M.
1999-01-01
A country's environmental problems are affected by the level of its economic development, the availability of national resources, and the socio-economic level of this population. Poverty presents special problems for a heavily populated country with limited resources. environmental problems in Pakistan have become serious and should no longer be neglected. These relate air and water pollution particularly in metropolitan and industrial zones, degradation of common property sources which affect the poor adversely due to the degradation of their life support system, threat to biodiversity, inadequate system of solid waste disposal and sanitation with consequent adverse impact on health, infant mortality, birth rate. These problems impose a serious cost on society although it is impossible to comprehend the extent of these on costs. (author)
International Nuclear Information System (INIS)
Tselios, Kostas; Simos, T.E.
2007-01-01
In this Letter a new explicit fourth-order seven-stage Runge-Kutta method with a combination of minimal dispersion and dissipation error and maximal accuracy and stability limit along the imaginary axes, is developed. This method was produced by a general function that was constructed to satisfy all the above requirements and, from which, all the existing fourth-order six-stage RK methods can be produced. The new method is more efficient than the other optimized methods, for acoustic computations
An Adaptive Reordered Method for Computing PageRank
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
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.
The numerical solution of total variation minimization problems in image processing
Energy Technology Data Exchange (ETDEWEB)
Vogel, C.R.; Oman, M.E. [Montana State Univ., Bozeman, MT (United States)
1994-12-31
Consider the minimization of penalized least squares functionals of the form: f(u) = 1/2 ({parallel}Au {minus} z{parallel}){sup 2} + {alpha}{integral}{sub {Omega}}{vert_bar}{del}u{vert_bar}dx. Here A is a bounded linear operator, z represents data, {parallel} {center_dot} {parallel} is a Hilbert space norm, {alpha} is a positive parameter, {integral}{sub {Omega}}{vert_bar}{del}u{vert_bar} dx represents the total variation (TV) of a function u {element_of} BV ({Omega}), the class of functions of bounded variation on a bounded region {Omega}, and {vert_bar} {center_dot} {vert_bar} denotes Euclidean norm. In image processing, u represents an image which is to be recovered from noisy data z. Certain {open_quotes}blurring processes{close_quotes} may be represented by the action of an operator A on the image u.
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)
University Rankings: The Web Ranking
Aguillo, Isidro F.
2012-01-01
The publication in 2003 of the Ranking of Universities by Jiao Tong University of Shanghai has revolutionized not only academic studies on Higher Education, but has also had an important impact on the national policies and the individual strategies of the sector. The work gathers the main characteristics of this and other global university…
A Fast and Accurate Algorithm for l1 Minimization Problems in Compressive Sampling (Preprint)
2013-01-22
However, updating uk+1 via the formulation of Step 2 in Algorithm 1 can be implemented through the use of the component-wise Gauss - Seidel iteration which...may accelerate the rate of convergence of the algorithm and therefore reduce the total CPU-time consumed. The efficiency of component-wise Gauss - Seidel ...Micchelli, L. Shen, and Y. Xu, A proximity algorithm accelerated by Gauss - Seidel iterations for L1/TV denoising models, Inverse Problems, 28 (2012), p
Dierkes, Ulrich; Sauvigny, Friedrich; Jakob, Ruben; Kuster, Albrecht
2010-01-01
Minimal Surfaces is the first volume of a three volume treatise on minimal surfaces (Grundlehren Nr. 339-341). Each volume can be read and studied independently of the others. The central theme is boundary value problems for minimal surfaces. The treatise is a substantially revised and extended version of the monograph Minimal Surfaces I, II (Grundlehren Nr. 295 & 296). The first volume begins with an exposition of basic ideas of the theory of surfaces in three-dimensional Euclidean space, followed by an introduction of minimal surfaces as stationary points of area, or equivalently
Hierarchical partial order ranking
International Nuclear Information System (INIS)
Carlsen, Lars
2008-01-01
Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritisation of polluted sites is given. - Hierarchical partial order ranking of polluted sites has been developed for prioritization based on a large number of parameters
On the fine-tuning problem in minimal SO(10) SUSY-GUT
International Nuclear Information System (INIS)
Hempfling, R.
1994-05-01
In grand unified theories (GUT) based on SO(10) all fermions of one generation are embedded in a single representation. As a result, the top quark, the bottom quark, and the τ lepton have the same Yukawa coupling at the GUT scale. This implies a very large ratio of Higgs vacuum expectation values, tanβ≅m t /m b . In this letter we show that GUT threshold correction to the universal Higgs mass parameter can solve the fine-tuning problem associated with such large values of tan β. (orig.)
Padua, Átila Andrade
2015-01-01
With the June movements of 2013 was fostered the proposal to convene a "mini constituent" as a possibility to minimize the problems experienced by Brazilian society. Considering the constitutional work of the influx Portuguese José Joaquim Gomes Canotilho this constitutional model and the breaking of paradigms that represented the turgid Brazilian and Portuguese constitutions, dedicated special attention to the differentiation of program standards with the constitutional di...
Ranking Specific Sets of Objects.
Maly, Jan; Woltran, Stefan
2017-01-01
Ranking sets of objects based on an order between the single elements has been thoroughly studied in the literature. In particular, it has been shown that it is in general impossible to find a total ranking - jointly satisfying properties as dominance and independence - on the whole power set of objects. However, in many applications certain elements from the entire power set might not be required and can be neglected in the ranking process. For instance, certain sets might be ruled out due to hard constraints or are not satisfying some background theory. In this paper, we treat the computational problem whether an order on a given subset of the power set of elements satisfying different variants of dominance and independence can be found, given a ranking on the elements. We show that this problem is tractable for partial rankings and NP-complete for total rankings.
Levin, Michael E; Krafft, Jennifer; Levin, Crissa
2018-01-01
This study examined whether self-help (books, websites, mobile apps) increases help seeking for mental health problems among college students by minimizing stigma as a barrier. A survey was conducted with 200 college students reporting elevated distress from February to April 2017. Intentions to use self-help were low, but a significant portion of students unwilling to see mental health professionals intended to use self-help. Greater self-stigma related to lower intentions to seek professional help, but was unrelated to seeking self-help. Similarly, students who only used self-help in the past reported higher self-stigma than those who sought professional treatment in the past. Although stigma was not a barrier for self-help, alternate barriers were identified. Offering self-help may increase rates of students receiving help for mental health problems, possibly by offering an alternative for students unwilling to seek in-person therapy due to stigma concerns.
Ebrahimnejad, Ali
2015-08-01
There are several methods, in the literature, for solving fuzzy variable linear programming problems (fuzzy linear programming in which the right-hand-side vectors and decision variables are represented by trapezoidal fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings a new method based on the bounded dual simplex method is proposed to determine the fuzzy optimal solution of that kind of fuzzy variable linear programming problems in which some or all variables are restricted to lie within lower and upper bounds. To illustrate the proposed method, an application example is solved and the obtained results are given. The advantages of the proposed method over existing methods are discussed. Also, one application of this algorithm in solving bounded transportation problems with fuzzy supplies and demands is dealt with. The proposed method is easy to understand and to apply for determining the fuzzy optimal solution of bounded fuzzy variable linear programming problems occurring in real-life situations.
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.
Directory of Open Access Journals (Sweden)
Antonio Costa
2014-07-01
Full Text Available Production processes in Cellular Manufacturing Systems (CMS often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs and Biased Random Sampling (BRS search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation.
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....
Directory of Open Access Journals (Sweden)
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.
Energy Technology Data Exchange (ETDEWEB)
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.
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.
Tang, Dunbing; Dai, Min
2015-09-01
The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production planning and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed smalland large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.
Ranking Queries on Uncertain Data
Hua, Ming
2011-01-01
Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorith
Energy Technology Data Exchange (ETDEWEB)
Gyllenhammar, Marianne; Herstad Svaerd, Solvie; Davidsson, Kent; Aamand, Lars-Erik; Steenari, Britt-Marie; Folkeson, Nicklas; Pettersson, Jesper; Svensson, Jan-Erik; Boss, Anna; Johansson, Linda; Kassman, Haakan
2007-12-15
Combustion of an increasing amount of biofuel and waste woods has resulted in certain environmental advantages, including decreased emissions of fossil CO{sub 2}, SO{sub 2} and metals. On the other hand, a number of chloride and alkali related operational problems have occurred which are related to combustion of these fuels. Alkali related operational problems have been studied in a project consisting of two parts. The overall scope has been to characterise the operational problems and to study measures to minimise them. The first part was reported in Vaermeforsk report 997. In part two, additional measures have been included in the test plan and initial corrosion has been studied linked to the different measures. The tests have also in part two been carried out at the 12 MW CFB boiler at Chalmers. The effect of the selected measures has been investigated concerning both deposit formation and bed agglomeration, and at the same time emissions and other operational conditions were characterised. The second part of the project has among other things focused on: To investigate measures which decrease the content of alkali and chloride in the deposits, and consequently decrease the risk for corrosion (by investigating the initial corrosion). Focus was also on trying to explain favourable effects. To investigate if it is possible to combine a rather low dosage of kaolin and injection of ammonium sulphate. This was done in order to reduce both bed agglomeration and problems from deposits during combustion of fuels rich in chlorine. To investigate if co-combustion with sewage sludge, de-inking sludge or peat with high ash content, could give similar advantages as conventional additives. Investigate if ash from PFBC (coal ash and dolomite) is possible to use as an alternative bed material. In the reference case, straw pellets were co-combusted together with wood pellets. This fuel mixture gave high alkali and chlorine contents. Alkali was in surplus of chlorine. The
Ranking Support Vector Machine with Kernel Approximation
Directory of Open Access Journals (Sweden)
Kai Chen
2017-01-01
Full Text Available 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.
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
Directory of Open Access Journals (Sweden)
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.
International Nuclear Information System (INIS)
Marrakchi, A.E.L.; Tapia, V.
1992-05-01
Some cosmological implications of the recently proposed fourth-rank theory of gravitation are studied. The model exhibits the possibility of being free from the horizon and flatness problems at the price of introducing a negative pressure. The field equations we obtain are compatible with k obs =0 and Ω obs t clas approx. 10 20 t Planck approx. 10 -23 s. When interpreted at the light of General Relativity the treatment is shown to be almost equivalent to that of the standard model of cosmology combined with the inflationary scenario. Hence, an interpretation of the negative pressure hypothesis is provided. (author). 8 refs
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.
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...
Subtracting a best rank-1 approximation may increase tensor rank
Stegeman, Alwin; Comon, Pierre
2010-01-01
It has been shown that a best rank-R approximation of an order-k tensor may not exist when R >= 2 and k >= 3. This poses a serious problem to data analysts using tensor decompositions it has been observed numerically that, generally, this issue cannot be solved by consecutively computing and
Model of Decision Making through Consensus in Ranking Case
Tarigan, Gim; Darnius, Open
2018-01-01
The basic problem to determine ranking consensus is a problem to combine some rankings those are decided by two or more Decision Maker (DM) into ranking consensus. DM is frequently asked to present their preferences over a group of objects in terms of ranks, for example to determine a new project, new product, a candidate in a election, and so on. The problem in ranking can be classified into two major categories; namely, cardinal and ordinal rankings. The objective of the study is to obtin the ranking consensus by appying some algorithms and methods. The algorithms and methods used in this study were partial algorithm, optimal ranking consensus, BAK (Borde-Kendal)Model. A method proposed as an alternative in ranking conssensus is a Weighted Distance Forward-Backward (WDFB) method, which gave a little difference i ranking consensus result compare to the result oethe example solved by Cook, et.al (2005).
Vercruysse, Gary A; Friese, Randall S; Khalil, Mazhar; Ibrahim-Zada, Irada; Zangbar, Bardiya; Hashmi, Ammar; Tang, Andrew; O'Keeffe, Terrence; Kulvatunyou, Narong; Green, Donald J; Gries, Lynn; Joseph, Bellal; Rhee, Peter M
2015-03-01
Mortality benefit has been demonstrated for trauma patients transported via helicopter but at great cost. This study identified patients who did not benefit from helicopter transport to our facility and demonstrates potential cost savings when transported instead by ground. We performed a 6-year (2007-2013) retrospective analysis of all trauma patients presenting to our center. Patients with a known mode of transfer were included in the study. Patients with missing data and those who were dead on arrival were excluded from the study. Patients were then dichotomized into helicopter transfer and ground transfer groups. A subanalysis was performed between minimally injured patients (ISS helicopter and 76.7% (3,992) were transferred via ground transport. Helicopter-transferred patients had longer hospital (p = 0.001) and intensive care unit (p = 0.001) stays. There was no difference in mortality between the groups (p = 0.6).On subanalysis of minimally injured patients there was no difference in hospital length of stay (p = 0.1) and early discharge (p = 0.6) between the helicopter transfer and ground transfer group. Average helicopter transfer cost at our center was $18,000, totaling $4,860,000 for 270 minimally injured helicopter-transferred patients. Nearly one third of patients transported by helicopter were minimally injured. Policies to identify patients who do not benefit from helicopter transport should be developed. Significant reduction in transport cost can be made by judicious selection of patients. Education to physicians calling for transport and identification of alternate means of transportation would be both safe and financially beneficial to our system. Epidemiologic study, level III. Therapeutic study, level IV.
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
Statistical Optimality in Multipartite Ranking and Ordinal Regression.
Uematsu, Kazuki; Lee, Yoonkyung
2015-05-01
Statistical optimality in multipartite ranking is investigated as an extension of bipartite ranking. We consider the optimality of ranking algorithms through minimization of the theoretical risk which combines pairwise ranking errors of ordinal categories with differential ranking costs. The extension shows that for a certain class of convex loss functions including exponential loss, the optimal ranking function can be represented as a ratio of weighted conditional probability of upper categories to lower categories, where the weights are given by the misranking costs. This result also bridges traditional ranking methods such as proportional odds model in statistics with various ranking algorithms in machine learning. Further, the analysis of multipartite ranking with different costs provides a new perspective on non-smooth list-wise ranking measures such as the discounted cumulative gain and preference learning. We illustrate our findings with simulation study and real data analysis.
Consistent ranking of volatility models
DEFF Research Database (Denmark)
Hansen, Peter Reinhard; Lunde, Asger
2006-01-01
We show that the empirical ranking of volatility models can be inconsistent for the true ranking if the evaluation is based on a proxy for the population measure of volatility. For example, the substitution of a squared return for the conditional variance in the evaluation of ARCH-type models can...... variance in out-of-sample evaluations rather than the squared return. We derive the theoretical results in a general framework that is not specific to the comparison of volatility models. Similar problems can arise in comparisons of forecasting models whenever the predicted variable is a latent variable....
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,…
Neural Ranking Models with Weak Supervision
Dehghani, M.; Zamani, H.; Severyn, A.; Kamps, J.; Croft, W.B.
2017-01-01
Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from
Beyond Low Rank: A Data-Adaptive Tensor Completion Method
Zhang, Lei; Wei, Wei; Shi, Qinfeng; Shen, Chunhua; Hengel, Anton van den; Zhang, Yanning
2017-01-01
Low rank tensor representation underpins much of recent progress in tensor completion. In real applications, however, this approach is confronted with two challenging problems, namely (1) tensor rank determination; (2) handling real tensor data which only approximately fulfils the low-rank requirement. To address these two issues, we develop a data-adaptive tensor completion model which explicitly represents both the low-rank and non-low-rank structures in a latent tensor. Representing the no...
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.
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....
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)
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.
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.
Directory of Open Access Journals (Sweden)
Yongping Zhang
2017-11-01
Full Text Available Coal reservoirs in the Qinshui Basin are characterized by high thermal evolution degree, low permeability, low reservoir pressure, lower gas saturation and strong heterogeneity, so its coalbed methane (CBM development is quite difficult. In this paper, the development practice of high-rank CBM horizontal wells in the Fanzhuang–Zhengzhuang Block was analyzed in terms of geological and engineering factors to clarify the productivity influencing factors, suitable geological conditions and potential tapping countermeasures of multi-lateral horizontal wells. It is shown that the reasons for the low development efficiency of multi-lateral horizontal wells are divided into three types. The first one is geological factor, such as encountering low CBM content areas or faults. The second is engineering factor, such as drilling fluid plugging, drilling collapse, drainage collapse and dust coal blockage. The third is the combination of both factors. It is concluded that encountering low CBM content areas and faults, collapse and blockage are the main reasons for the low production of multi-lateral horizontal wells in the Zhengzhuang–Fanzhuang Block, with the CBM content higher than 20 m3/t, the ratio of critical desorption pressure and reservoir pressure higher than 0.7 and vitrinite reflectance (Ro higher than 3.8. The prerequisite for an open-hole horizontal well in this area to produce CBM at a high rate is that the well lies in the tensile stress zone. Finally, the countermeasures to tap the potential of some inefficient wells were put forward. First, the inefficient wells which are blocked with dust coal or collapsed in the later stage should be stimulated based on classifications. And second, it is necessary to explore new types of horizontal wells so as to deal with borehole collapse and continue the operation in the later stage by using tree-like roof horizontal wells, single-lateral horizontal wells with casing or screen completion and fish
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
Block models and personalized PageRank
Kloumann, Isabel M.; Ugander, Johan; Kleinberg, Jon
2016-01-01
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 though the seed set expansion problem: given a subset $S$ 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...
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.
Directory of Open Access Journals (Sweden)
Chowdhury Molhammad SR
2000-01-01
Full Text Available Results are obtained on existence theorems of generalized bi-quasi-variational inequalities for quasi-semi-monotone and bi-quasi-semi-monotone operators in both compact and non-compact settings. We shall use the concept of escaping sequences introduced by Border (Fixed Point Theorem with Applications to Economics and Game Theory, Cambridge University Press, Cambridge, 1985 to obtain results in non-compact settings. Existence theorems on non-compact generalized bi-complementarity problems for quasi-semi-monotone and bi-quasi-semi-monotone operators are also obtained. Moreover, as applications of some results of this paper on generalized bi-quasi-variational inequalities, we shall obtain existence of solutions for some kind of minimization problems with quasi- semi-monotone and bi-quasi-semi-monotone operators.
Hoede, C.
In this paper the concept of page rank for the world wide web is discussed. The possibility of describing the distribution of page rank by an exponential law is considered. It is shown that the concept is essentially equal to that of status score, a centrality measure discussed already in 1953 by
Dobbs, David E.
2012-01-01
This note explains how Emil Artin's proof that row rank equals column rank for a matrix with entries in a field leads naturally to the formula for the nullity of a matrix and also to an algorithm for solving any system of linear equations in any number of variables. This material could be used in any course on matrix theory or linear algebra.
Chapman, David W.
2008-01-01
Recently, Samford University was ranked 27th in the nation in a report released by "Forbes" magazine. In this article, the author relates how the people working at Samford University were surprised at its ranking. Although Samford is the largest privately institution in Alabama, its distinguished academic achievements aren't even…
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.
A Ranking Approach to Genomic Selection.
Blondel, Mathieu; Onogi, Akio; Iwata, Hiroyoshi; Ueda, Naonori
2015-01-01
Genomic selection (GS) is a recent selective breeding method which uses predictive models based on whole-genome molecular markers. Until now, existing studies formulated GS as the problem of modeling an individual's breeding value for a particular trait of interest, i.e., as a regression problem. To assess predictive accuracy of the model, the Pearson correlation between observed and predicted trait values was used. In this paper, we propose to formulate GS as the problem of ranking individuals according to their breeding value. Our proposed framework allows us to employ machine learning methods for ranking which had previously not been considered in the GS literature. To assess ranking accuracy of a model, we introduce a new measure originating from the information retrieval literature called normalized discounted cumulative gain (NDCG). NDCG rewards more strongly models which assign a high rank to individuals with high breeding value. Therefore, NDCG reflects a prerequisite objective in selective breeding: accurate selection of individuals with high breeding value. We conducted a comparison of 10 existing regression methods and 3 new ranking methods on 6 datasets, consisting of 4 plant species and 25 traits. Our experimental results suggest that tree-based ensemble methods including McRank, Random Forests and Gradient Boosting Regression Trees achieve excellent ranking accuracy. RKHS regression and RankSVM also achieve good accuracy when used with an RBF kernel. Traditional regression methods such as Bayesian lasso, wBSR and BayesC were found less suitable for ranking. Pearson correlation was found to correlate poorly with NDCG. Our study suggests two important messages. First, ranking methods are a promising research direction in GS. Second, NDCG can be a useful evaluation measure for GS.
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
On rank 2 Seiberg-Witten equations
International Nuclear Information System (INIS)
Massamba, F.; Thompson, G.
2004-02-01
We introduce and study a set of rank 2 Seiberg-Witten equations. We show that the moduli space of solutions is a compact, orientational and smooth manifold. For minimal surfaces of general type we are able to determine the basic classes. (author)
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)
Win-Chin Lin
2018-01-01
Full Text Available Two-stage production process and its applications appear in many production environments. Job processing times are usually assumed to be constant throughout the process. In fact, the learning effect accrued from repetitive work experiences, which leads to the reduction of actual job processing times, indeed exists in many production environments. However, the issue of learning effect is rarely addressed in solving a two-stage assembly scheduling problem. Motivated by this observation, the author studies a two-stage three-machine assembly flow shop problem with a learning effect based on sum of the processing times of already processed jobs to minimize the makespan criterion. Because this problem is proved to be NP-hard, a branch-and-bound method embedded with some developed dominance propositions and a lower bound is employed to search for optimal solutions. A cloud theory-based simulated annealing (CSA algorithm and an iterated greedy (IG algorithm with four different local search methods are used to find near-optimal solutions for small and large number of jobs. The performances of adopted algorithms are subsequently compared through computational experiments and nonparametric statistical analyses, including the Kruskal–Wallis test and a multiple comparison procedure.
Regularity of Minimal Surfaces
Dierkes, Ulrich; Tromba, Anthony J; Kuster, Albrecht
2010-01-01
"Regularity of Minimal Surfaces" begins with a survey of minimal surfaces with free boundaries. Following this, the basic results concerning the boundary behaviour of minimal surfaces and H-surfaces with fixed or free boundaries are studied. In particular, the asymptotic expansions at interior and boundary branch points are derived, leading to general Gauss-Bonnet formulas. Furthermore, gradient estimates and asymptotic expansions for minimal surfaces with only piecewise smooth boundaries are obtained. One of the main features of free boundary value problems for minimal surfaces is t
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…
Ranking as parameter estimation
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav; Guy, Tatiana Valentine
2009-01-01
Roč. 4, č. 2 (2009), s. 142-158 ISSN 1745-7645 R&D Projects: GA MŠk 2C06001; GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ranking * Bayesian estimation * negotiation * modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/AS/karny- ranking as parameter estimation.pdf
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.
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.
Communities in Large Networks: Identification and Ranking
DEFF Research Database (Denmark)
Olsen, Martin
2008-01-01
show that the problem of deciding whether a non trivial community exists is NP complete. Nevertheless, experiments show that a very simple greedy approach can identify members of a community in the Danish part of the web graph with time complexity only dependent on the size of the found community...... 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....
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.
Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging.
Directory of Open Access Journals (Sweden)
Xingjian Yu
Full Text Available In dynamic Positron Emission Tomography (PET, an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data statistical properties. It limits the speed of data acquisition, besides, it is unable to afford the separated information about the structure and the variation of shape and rate of metabolism which play a major role in improving the visualization of contrast for some requirement of the diagnosing in application. This paper presents a novel low rank-based activity map reconstruction scheme from emission sinograms of dynamic PET, termed as SLCR representing Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging. In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse. The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods. In this paper, the linearized alternating direction method is applied. The effectiveness of the proposed scheme is illustrated on three data sets.
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.
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.
Reduced Rank Adaptive Filtering in Impulsive Noise Environments
Soury, Hamza; Abed-Meraim, Karim; Alouini, Mohamed-Slim
2014-01-01
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.
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.
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.
Preference Learning and Ranking by Pairwise Comparison
Fürnkranz, Johannes; Hüllermeier, Eyke
This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.
When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores
Wang, Jim Jing-Yan; Cui, Xuefeng; Yu, Ge; Guo, Lili; Gao, Xin
2017-01-01
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
Ranking Very Many Typed Entities on Wikipedia
Zaragoza, Hugo; Rode, H.; Mika, Peter; Atserias, Jordi; Ciaramita, Massimiliano; Attardi, Guiseppe
2007-01-01
We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine
A note on ranking assignments using reoptimization
DEFF Research Database (Denmark)
Pedersen, Christian Roed; Nielsen, L.R.; Andersen, K.A.
2005-01-01
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...
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...
Groundwater contaminant plume ranking
International Nuclear Information System (INIS)
1988-08-01
Containment plumes at Uranium Mill Tailings Remedial Action (UMTRA) Project sites were ranked to assist in Subpart B (i.e., restoration requirements of 40 CFR Part 192) compliance strategies for each site, to prioritize aquifer restoration, and to budget future requests and allocations. The rankings roughly estimate hazards to the environment and human health, and thus assist in determining for which sites cleanup, if appropriate, will provide the greatest benefits for funds available. The rankings are based on the scores that were obtained using the US Department of Energy's (DOE) Modified Hazard Ranking System (MHRS). The MHRS and HRS consider and score three hazard modes for a site: migration, fire and explosion, and direct contact. The migration hazard mode score reflects the potential for harm to humans or the environment from migration of a hazardous substance off a site by groundwater, surface water, and air; it is a composite of separate scores for each of these routes. For ranking the containment plumes at UMTRA Project sites, it was assumed that each site had been remediated in compliance with the EPA standards and that relict contaminant plumes were present. Therefore, only the groundwater route was scored, and the surface water and air routes were not considered. Section 2.0 of this document describes the assumptions and procedures used to score the groundwater route, and Section 3.0 provides the resulting scores for each site. 40 tabs
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...... history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....
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...... for economic history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....
Ranking multivariate GARCH models by problem dimension
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2010-01-01
textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. The two most widely known and used are the Scalar BEKK model of Engle and Kroner (1995) and Ding and Engle (2001), and the DCC model of Engle (2002). Some recent research has begun to
Learning to rank for information retrieval and natural language processing
Li, Hang
2014-01-01
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work.The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as tw
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.
College Rankings. ERIC Digest.
Holub, Tamara
The popularity of college ranking surveys published by "U.S. News and World Report" and other magazines is indisputable, but the methodologies used to measure the quality of higher education institutions have come under fire by scholars and college officials. Criticisms have focused on methodological flaws, such as failure to consider…
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...
Scalable Faceted Ranking in Tagging Systems
Orlicki, José I.; Alvarez-Hamelin, J. Ignacio; Fierens, Pablo I.
Nowadays, web collaborative tagging systems which allow users to upload, comment on and recommend contents, are growing. Such systems can be represented as graphs where nodes correspond to users and tagged-links to recommendations. In this paper we analyze the problem of computing a ranking of users with respect to a facet described as a set of tags. A straightforward solution is to compute a PageRank-like algorithm on a facet-related graph, but it is not feasible for online computation. We propose an alternative: (i) a ranking for each tag is computed offline on the basis of tag-related subgraphs; (ii) a faceted order is generated online by merging rankings corresponding to all the tags in the facet. Based on the graph analysis of YouTube and Flickr, we show that step (i) is scalable. We also present efficient algorithms for step (ii), which are evaluated by comparing their results with two gold standards.
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.
Low-rank quadratic semidefinite programming
Yuan, Ganzhao; Zhang, Zhenjie; Ghanem, Bernard; Hao, Zhifeng
2013-01-01
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.
Directory of Open Access Journals (Sweden)
Angela Hsiang-Ling Chen
2016-09-01
Full Text Available Modeling and optimizing organizational processes, such as the one represented by the Resource-Constrained Project Scheduling Problem (RCPSP, improve outcomes. Based on assumptions and simplification, this model tackles the allocation of resources so that organizations can continue to generate profits and reinvest in future growth. Nonetheless, despite all of the research dedicated to solving the RCPSP and its multi-mode variations, there is no standardized procedure that can guide project management practitioners in their scheduling tasks. This is mainly because many of the proposed approaches are either based on unrealistic/oversimplified scenarios or they propose solution procedures not easily applicable or even feasible in real-life situations. In this study, we solve a more true-to-life and complex model, Multimode RCPSP with minimal and maximal time lags (MRCPSP/max. The complexity of the model solved is presented, and the practicality of the proposed approach is justified depending on only information that is available for every project regardless of its industrial context. The results confirm that it is possible to determine a robust makespan and to calculate an execution time-frame with gaps lower than 11% between their lower and upper bounds. In addition, in many instances, the solved lower bound obtained was equal to the best-known optimum.
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.
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 ...
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.
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.
Generalization Performance of Regularized Ranking With Multiscale Kernels.
Zhou, Yicong; Chen, Hong; Lan, Rushi; Pan, Zhibin
2016-05-01
The regularized kernel method for the ranking problem has attracted increasing attentions in machine learning. The previous regularized ranking algorithms are usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we go beyond this framework by investigating the generalization performance of the regularized ranking with multiscale kernels. A novel ranking algorithm with multiscale kernels is proposed and its representer theorem is proved. We establish the upper bound of the generalization error in terms of the complexity of hypothesis spaces. It shows that the multiscale ranking algorithm can achieve satisfactory learning rates under mild conditions. Experiments demonstrate the effectiveness of the proposed method for drug discovery and recommendation tasks.
Low rank approach to computing first and higher order derivatives using automatic differentiation
International Nuclear Information System (INIS)
Reed, J. A.; Abdel-Khalik, H. S.; Utke, J.
2012-01-01
This manuscript outlines a new approach for increasing the efficiency of applying automatic differentiation (AD) to large scale computational models. By using the principles of the Efficient Subspace Method (ESM), low rank approximations of the derivatives for first and higher orders can be calculated using minimized computational resources. The output obtained from nuclear reactor calculations typically has a much smaller numerical rank compared to the number of inputs and outputs. This rank deficiency can be exploited to reduce the number of derivatives that need to be calculated using AD. The effective rank can be determined according to ESM by computing derivatives with AD at random inputs. Reduced or pseudo variables are then defined and new derivatives are calculated with respect to the pseudo variables. Two different AD packages are used: OpenAD and Rapsodia. OpenAD is used to determine the effective rank and the subspace that contains the derivatives. Rapsodia is then used to calculate derivatives with respect to the pseudo variables for the desired order. The overall approach is applied to two simple problems and to MATWS, a safety code for sodium cooled reactors. (authors)
Efficient Low Rank Tensor Ring Completion
Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin
2017-01-01
Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation. This development is motivated in part by the success of matrix completion algorithms that alternate over the (low-rank) factors. In this paper, we propose a spectral initialization for the tensor ring completion algorithm and ana...
1991 Acceptance priority ranking
International Nuclear Information System (INIS)
1991-12-01
The Standard Contract for Disposal of Spent Nuclear Fuel and/or High- Level Radioactive Waste (10 CFR Part 961) that the Department of Energy (DOE) has executed with the owners and generators of civilian spent nuclear fuel requires annual publication of the Acceptance Priority Ranking (APR). The 1991 APR details the order in which DOE will allocate Federal waste acceptance capacity. As required by the Standard Contract, the ranking is based on the age of permanently discharged spent nuclear fuel (SNF), with the owners of the oldest SNF, on an industry-wide basis, given the highest priority. the 1991 APR will be the basis for the annual allocation of waste acceptance capacity to the Purchasers in the 1991 Annual Capacity Report (ACR), to be issued later this year. This document is based on SNF discharges as of December 31, 1990, and reflects Purchaser comments and corrections, as appropriate, to the draft APR issued on May 15, 1991
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; ...
Fair ranking of researchers and research teams.
Vavryčuk, Václav
2018-01-01
The main drawback of ranking of researchers by the number of papers, citations or by the Hirsch index is ignoring the problem of distributing authorship among authors in multi-author publications. So far, the single-author or multi-author publications contribute to the publication record of a researcher equally. This full counting scheme is apparently unfair and causes unjust disproportions, in particular, if ranked researchers have distinctly different collaboration profiles. These disproportions are removed by less common fractional or authorship-weighted counting schemes, which can distribute the authorship credit more properly and suppress a tendency to unjustified inflation of co-authors. The urgent need of widely adopting a fair ranking scheme in practise is exemplified by analysing citation profiles of several highly-cited astronomers and astrophysicists. While the full counting scheme often leads to completely incorrect and misleading ranking, the fractional or authorship-weighted schemes are more accurate and applicable to ranking of researchers as well as research teams. In addition, they suppress differences in ranking among scientific disciplines. These more appropriate schemes should urgently be adopted by scientific publication databases as the Web of Science (Thomson Reuters) or the Scopus (Elsevier).
Rank reduction of correlation matrices by majorization
R. Pietersz (Raoul); P.J.F. Groenen (Patrick)
2004-01-01
textabstractIn this paper a novel method is developed for the problem of finding a low-rank correlation matrix nearest to a given correlation matrix. The method is based on majorization and therefore it is globally convergent. The method is computationally efficient, is straightforward to implement,
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.
University Rankings and Social Science
Marginson, S.
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 outputs are of no common value. It is necessary that rankings be soundly based in scientific terms if a virtuous relationship between performance and...
Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K
2011-10-01
To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods
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…
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...
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.
Fractional cointegration rank estimation
DEFF Research Database (Denmark)
Lasak, Katarzyna; Velasco, Carlos
the parameters of the model under the null hypothesis of the cointegration rank r = 1, 2, ..., p-1. This step provides consistent estimates of the cointegration degree, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup......-likelihood ratio test of no-cointegration on the estimated p - r common trends that are not cointegrated under the null. The cointegration degree is re-estimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step...... 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...
Neural modelling of ranking data with an application to stated preference data
Directory of Open Access Journals (Sweden)
Catherine Krier
2013-05-01
Full Text Available Although neural networks are commonly encountered to solve classification problems, ranking data present specificities which require adapting the model. Based on a latent utility function defined on the characteristics of the objects to be ranked, the approach suggested in this paper leads to a perceptron-based algorithm for a highly non linear model. Data on stated preferences obtained through a survey by face-to-face interviews, in the field of freight transport, are used to illustrate the method. Numerical difficulties are pinpointed and a Pocket type algorithm is shown to provide an efficient heuristic to minimize the discrete error criterion. A substantial merit of this approach is to provide a workable estimation of contextually interpretable parameters along with a statistical evaluation of the goodness of fit.
Rankings, creatividad y urbanismo
Directory of Open Access Journals (Sweden)
JOAQUÍN SABATÉ
2008-08-01
Full Text Available La competencia entre ciudades constituye uno de los factores impulsores de procesos de renovación urbana y los rankings han devenido instrumentos de medida de la calidad de las ciudades. Nos detendremos en el caso de un antiguo barrio industrial hoy en vías de transformación en distrito "creativo" por medio de una intervención urbanística de gran escala. Su análisis nos descubre tres claves críticas. En primer lugar, nos obliga a plantearnos la definición de innovación urbana y cómo se integran el pasado, la identidad y la memoria en la construcción del futuro. Nos lleva a comprender que la innovación y el conocimiento no se "dan" casualmente, sino que son el fruto de una larga y compleja red en la que participan saberes, espacios, actores e instituciones diversas en naturaleza, escala y magnitud. Por último nos obliga a reflexionar sobre el valor que se le otorga a lo local en los procesos de renovación urbana.Competition among cities constitutes one ofthe main factors o furban renewal, and rankings have become instruments to indícate cities quality. Studying the transformation of an old industrial quarter into a "creative district" by the means ofa large scale urban project we highlight three main conclusions. First, itasks us to reconsider the notion ofurban innovation and hoto past, identity and memory should intégrate the future development. Second, it shows that innovation and knowledge doesn't yield per chance, but are the result ofa large and complex grid of diverse knowledges, spaces, agents and institutions. Finally itforces us to reflect about the valué attributed to the "local" in urban renewalprocesses.
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
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.
Tensor rank of the tripartite state |W>xn
International Nuclear Information System (INIS)
Yu Nengkun; Guo Cheng; Duan Runyao; Chitambar, Eric
2010-01-01
Tensor rank refers to the number of product states needed to express a given multipartite quantum state. Its nonadditivity as an entanglement measure has recently been observed. In this Brief Report, we estimate the tensor rank of multiple copies of the tripartite state |W>=(1/√(3))(|100>+|010>+|001>). Both an upper bound and a lower bound of this rank are derived. In particular, it is proven that the rank of |W> x 2 is 7, thus resolving a previously open problem. Some implications of this result are discussed in terms of transformation rates between |W> xn and multiple copies of the state |GHZ>=(1/√(2))(|000>+|111>).
Ranking accounting, banking and finance journals: A note
Halkos, George; Tzeremes, Nickolaos
2012-01-01
This paper by applying Data Envelopment Analysis (DEA) ranks Economics journals in the field of Accounting, Banking and Finance. By using one composite input and one composite output the paper ranks 57 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 the highest rankings in the field are Journal of Fi...
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.
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).
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.
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.
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.
Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning
Lai, Rongjie; Li, Jia
2017-01-01
Low-rank structures play important role in recent advances of many problems in image science and data science. As a natural extension of low-rank structures for data with nonlinear structures, the concept of the low-dimensional manifold structure has been considered in many data processing problems. Inspired by this concept, we consider a manifold based low-rank regularization as a linear approximation of manifold dimension. This regularization is less restricted than the global low-rank regu...
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.
Minimal quantization and confinement
International Nuclear Information System (INIS)
Ilieva, N.P.; Kalinowskij, Yu.L.; Nguyen Suan Han; Pervushin, V.N.
1987-01-01
A ''minimal'' version of the Hamiltonian quantization based on the explicit solution of the Gauss equation and on the gauge-invariance principle is considered. By the example of the one-particle Green function we show that the requirement for gauge invariance leads to relativistic covariance of the theory and to more proper definition of the Faddeev - Popov integral that does not depend on the gauge choice. The ''minimal'' quantization is applied to consider the gauge-ambiguity problem and a new topological mechanism of confinement
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
Tensor completion and low-n-rank tensor recovery via convex optimization
International Nuclear Information System (INIS)
Gandy, Silvia; Yamada, Isao; Recht, Benjamin
2011-01-01
In this paper we consider sparsity on a tensor level, as given by the n-rank of a tensor. In an important sparse-vector approximation problem (compressed sensing) and the low-rank matrix recovery problem, using a convex relaxation technique proved to be a valuable solution strategy. Here, we will adapt these techniques to the tensor setting. We use the n-rank of a tensor as a sparsity measure and consider the low-n-rank tensor recovery problem, i.e. the problem of finding the tensor of the lowest n-rank that fulfills some linear constraints. We introduce a tractable convex relaxation of the n-rank and propose efficient algorithms to solve the low-n-rank tensor recovery problem numerically. The algorithms are based on the Douglas–Rachford splitting technique and its dual variant, the alternating direction method of multipliers
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.…
Texture Repairing by Unified Low Rank Optimization
Institute of Scientific and Technical Information of China (English)
Xiao Liang; Xiang Ren; Zhengdong Zhang; Yi Ma
2016-01-01
In this paper, we show how to harness both low-rank and sparse structures in regular or near-regular textures for image completion. Our method is based on a unified formulation for both random and contiguous corruption. In addition to the low rank property of texture, the algorithm also uses the sparse assumption of the natural image: because the natural image is piecewise smooth, it is sparse in certain transformed domain (such as Fourier or wavelet transform). We combine low-rank and sparsity properties of the texture image together in the proposed algorithm. Our algorithm based on convex optimization can automatically and correctly repair the global structure of a corrupted texture, even without precise information about the regions to be completed. This algorithm integrates texture rectification and repairing into one optimization problem. Through extensive simulations, we show our method can complete and repair textures corrupted by errors with both random and contiguous supports better than existing low-rank matrix recovery methods. Our method demonstrates significant advantage over local patch based texture synthesis techniques in dealing with large corruption, non-uniform texture, and large perspective deformation.
Universal scaling in sports ranking
International Nuclear Information System (INIS)
Deng Weibing; Li Wei; Cai Xu; Bulou, Alain; Wang Qiuping A
2012-01-01
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. (paper)
Directory of Open Access Journals (Sweden)
Samah Ibrahim Abdel Aal
2018-03-01
Full Text Available The concept of neutrosophic can provide a generalization of fuzzy set and intuitionistic fuzzy set that make it is the best fit in representing indeterminacy and uncertainty. Single Valued Triangular Numbers (SVTrN-numbers is a special case of neutrosophic set that can handle ill-known quantity very difficult problems. This work intended to introduce a framework with two types of ranking methods. The results indicated that each ranking method has its own advantage. In this perspective, the weighted value and ambiguity based method gives more attention to uncertainty in ranking and evaluating ISQ as well as it takes into account cut sets of SVTrN numbers that can reflect the information on Truth-membership-membership degree, false membership-membership degree and Indeterminacy-membership degree. The value index and ambiguity index method can reflect the decision maker's subjectivity attitude to the SVTrN- numbers.
Application of third order stochastic dominance algorithm in investments ranking
Directory of Open Access Journals (Sweden)
Lončar Sanja
2012-01-01
Full Text Available The paper presents the use of third order stochastic dominance in ranking Investment alternatives, using TSD algorithms (Levy, 2006for testing third order stochastic dominance. The main goal of using TSD rule is minimization of efficient investment set for investor with risk aversion, who prefers more money and likes positive skew ness.
Classical strings and minimal surfaces
International Nuclear Information System (INIS)
Urbantke, H.
1986-01-01
Real Lorentzian forms of some complex or complexified Euclidean minimal surfaces are obtained as an application of H.A. Schwarz' solution to the initial value problem or a search for surfaces admitting a group of Poincare transformations. (Author)
UTV Expansion Pack: Special-Purpose Rank-Revealing Algorithms
DEFF Research Database (Denmark)
Fierro, Ricardo D.; Hansen, Per Christian
2005-01-01
This collection of Matlab 7.0 software supplements and complements the package UTV Tools from 1999, and includes implementations of special-purpose rank-revealing algorithms developed since the publication of the original package. We provide algorithms for computing and modifying symmetric rank-r...... values of a sparse or structured matrix. These new algorithms have applications in signal processing, optimization and LSI information retrieval.......This collection of Matlab 7.0 software supplements and complements the package UTV Tools from 1999, and includes implementations of special-purpose rank-revealing algorithms developed since the publication of the original package. We provide algorithms for computing and modifying symmetric rank......-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...
A Case-Based Reasoning Method with Rank Aggregation
Sun, Jinhua; Du, Jiao; Hu, Jian
2018-03-01
In order to improve the accuracy of case-based reasoning (CBR), this paper addresses a new CBR framework with the basic principle of rank aggregation. First, the ranking methods are put forward in each attribute subspace of case. The ordering relation between cases on each attribute is got between cases. Then, a sorting matrix is got. Second, the similar case retrieval process from ranking matrix is transformed into a rank aggregation optimal problem, which uses the Kemeny optimal. On the basis, a rank aggregation case-based reasoning algorithm, named RA-CBR, is designed. The experiment result on UCI data sets shows that case retrieval accuracy of RA-CBR algorithm is higher than euclidean distance CBR and mahalanobis distance CBR testing.So we can get the conclusion that RA-CBR method can increase the performance and efficiency of CBR.
International Nuclear Information System (INIS)
Frahm, K M; Shepelyansky, D L; Chepelianskii, A D
2012-01-01
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. (paper)
Freudenthal ranks: GHZ versus W
International Nuclear Information System (INIS)
Borsten, L
2013-01-01
The Hilbert space of three-qubit pure states may be identified with a Freudenthal triple system. Every state has an unique Freudenthal rank ranging from 1 to 4, which is determined by a set of automorphism group covariants. It is shown here that the optimal success rates for winning a three-player non-local game, varying over all local strategies, are strictly ordered by the Freudenthal rank of the shared three-qubit resource. (paper)
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.
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.
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.
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 species in mutualistic networks
Domínguez-García, Virginia; Muñoz, Miguel A.
2015-02-01
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.
Ranking Theory and Conditional Reasoning.
Skovgaard-Olsen, Niels
2016-05-01
Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's (2013) ranking theoretic approach to conditionals. Copyright © 2015 Cognitive Science Society, Inc.
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.
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination.
Zhao, Qibin; Zhang, Liqing; Cichocki, Andrzej
2015-09-01
CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank . In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.
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…
On Rank Driven Dynamical Systems
Veerman, J. J. P.; Prieto, F. J.
2014-08-01
We investigate a class of models related to the Bak-Sneppen (BS) model, initially proposed to study evolution. The BS model is extremely simple and yet captures some forms of "complex behavior" such as self-organized criticality that is often observed in physical and biological systems. In this model, random fitnesses in are associated to agents located at the vertices of a graph . Their fitnesses are ranked from worst (0) to best (1). At every time-step the agent with the worst fitness and some others with a priori given rank probabilities are replaced by new agents with random fitnesses. We consider two cases: The exogenous case where the new fitnesses are taken from an a priori fixed distribution, and the endogenous case where the new fitnesses are taken from the current distribution as it evolves. We approximate the dynamics by making a simplifying independence assumption. We use Order Statistics and Dynamical Systems to define a rank-driven dynamical system that approximates the evolution of the distribution of the fitnesses in these rank-driven models, as well as in the BS model. For this simplified model we can find the limiting marginal distribution as a function of the initial conditions. Agreement with experimental results of the BS model is excellent.
African Journals Online (AJOL)
maths/stats
... GAUSS SEIDEL'S. NUMERICAL ALGORITHMS IN PAGE RANK ANALYSIS. ... The convergence is guaranteed, if the absolute value of the largest eigen ... improved Gauss-Seidel iteration algorithm, based on the decomposition. U. L. D. M. +. +. = ..... This corresponds to determine the eigen vector of T with eigen value 1.
14 CFR 1214.1105 - Final ranking.
2010-01-01
... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Final ranking. 1214.1105 Section 1214.1105... Recruitment and Selection Program § 1214.1105 Final ranking. Final rankings will be based on a combination of... preference will be included in this final ranking in accordance with applicable regulations. ...
Weighted Discriminative Dictionary Learning based on Low-rank Representation
International Nuclear Information System (INIS)
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. (paper)
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
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.
Energy Technology Data Exchange (ETDEWEB)
Helmboldt, Alexander; Humbert, Pascal; Lindner, Manfred; Smirnov, Juri [Max-Planck-Institut fuer Kernphysik, Heidelberg (Germany)
2016-07-01
The gauge hierarchy problem is one of the crucial drawbacks of the standard model of particle physics (SM) and thus has triggered model building over the last decades. Its most famous solution is the introduction of low-scale supersymmetry. However, without any significant signs of supersymmetric particles at the LHC to date, it makes sense to devise alternative mechanisms to remedy the hierarchy problem. One such mechanism is based on classically scale-invariant extensions of the SM, in which both the electroweak symmetry and the (anomalous) scale symmetry are broken radiatively via the Coleman-Weinberg mechanism. Apart from giving an introduction to classically scale-invariant models, the talk presents our results on obtaining a theoretically consistent minimal extension of the SM, which reproduces the correct low-scale phenomenology.
A Survey on PageRank Computing
Berkhin, Pavel
2005-01-01
This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much mor...
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.
Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling.
Bo Zhao; Bilgic, Berkin; Adalsteinsson, Elfar; Griswold, Mark A; Wald, Lawrence L; Setsompop, Kawin
2017-07-01
Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T 1 , T 2 , and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.
The minimally tuned minimal supersymmetric standard model
International Nuclear Information System (INIS)
Essig, Rouven; Fortin, Jean-Francois
2008-01-01
The regions in the Minimal Supersymmetric Standard Model with the minimal amount of fine-tuning of electroweak symmetry breaking are presented for general messenger scale. No a priori relations among the soft supersymmetry breaking parameters are assumed and fine-tuning is minimized with respect to all the important parameters which affect electroweak symmetry breaking. The superpartner spectra in the minimally tuned region of parameter space are quite distinctive with large stop mixing at the low scale and negative squark soft masses at the high scale. The minimal amount of tuning increases enormously for a Higgs mass beyond roughly 120 GeV
Finite-rank potential that reproduces the Pade approximant
International Nuclear Information System (INIS)
Tani, S.
1979-01-01
If a scattering potential is of a finite rank, say N, the exact solution of the problem can be obtained from the Born series, if the potential strength is within the radius of convergence; the exact solution can be obtained from the analytical continuation of the formal Born series outside the radius of convergence. Beyond the first 2N terms of the Born series, an individual term of the Born series depends on the first 2N terms, and the [N/N] Pade approximant and the exact solution agree with each other. The above-mentioned features of a finite-rank problem are relevant to scattering theory in general, because most scattering problems may be handled as an extension of the rank-N problem, in which the rank N tends to infinity. The foregoing aspects of scattering theory will be studied in depth in the present paper, and in so doing we proceed in the opposite direction. Namely, given a potential, we calculate the first 2N terms of the Born series for the K matrix and the first N terms of the Born series for the wave function. Using these data, a special rank-N potential is constructed in such a way that it reproduces the [N/N] Pade approximant of the K matrix of the original scattering problem. One great advantage of obtaining such a rank-N potential is that the wave function of the system may be approximated in the same spirit as done for the K matrix; hence, we can introduce a new approximation method for dealing with an off-shell T matrix. A part of the mathematical work is incomplete, but the physical aspects are thoroughly discussed
Rank Dynamics of Word Usage at Multiple Scales
Directory of Open Access Journals (Sweden)
José A. Morales
2018-05-01
Full Text Available The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of N-grams in a given rank, the probability that an N-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that N-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.
A scoring mechanism for the rank aggregation of network robustness
Yazdani, Alireza; Dueñas-Osorio, Leonardo; Li, Qilin
2013-10-01
To date, a number of metrics have been proposed to quantify inherent robustness of network topology against failures. However, each single metric usually only offers a limited view of network vulnerability to different types of random failures and targeted attacks. When applied to certain network configurations, different metrics rank network topology robustness in different orders which is rather inconsistent, and no single metric fully characterizes network robustness against different modes of failure. To overcome such inconsistency, this work proposes a multi-metric approach as the basis of evaluating aggregate ranking of network topology robustness. This is based on simultaneous utilization of a minimal set of distinct robustness metrics that are standardized so to give way to a direct comparison of vulnerability across networks with different sizes and configurations, hence leading to an initial scoring of inherent topology robustness. Subsequently, based on the inputs of initial scoring a rank aggregation method is employed to allocate an overall ranking of robustness to each network topology. A discussion is presented in support of the presented multi-metric approach and its applications to more realistically assess and rank network topology robustness.
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.
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
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.
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.
Low rank magnetic resonance fingerprinting.
Mazor, Gal; Weizman, Lior; Tal, Assaf; Eldar, Yonina C
2016-08-01
Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.
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.
A new mutually reinforcing network node and link ranking algorithm
Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.
2015-01-01
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. PMID:26492958
The minimal non-minimal standard model
International Nuclear Information System (INIS)
Bij, J.J. van der
2006-01-01
In this Letter I discuss a class of extensions of the standard model that have a minimal number of possible parameters, but can in principle explain dark matter and inflation. It is pointed out that the so-called new minimal standard model contains a large number of parameters that can be put to zero, without affecting the renormalizability of the model. With the extra restrictions one might call it the minimal (new) non-minimal standard model (MNMSM). A few hidden discrete variables are present. It is argued that the inflaton should be higher-dimensional. Experimental consequences for the LHC and the ILC are discussed
The ranking of negative-cost emissions reduction measures
International Nuclear Information System (INIS)
Taylor, Simon
2012-01-01
A flaw has been identified in the calculation of the cost-effectiveness in marginal abatement cost curves (MACCs). The problem affects “negative-cost” emissions reduction measures—those that produce a return on investment. The resulting ranking sometimes favours measures that produce low emissions savings and is therefore unreliable. The issue is important because incorrect ranking means a potential failure to achieve the best-value outcome. A simple mathematical analysis shows that not only is the standard cost-effectiveness calculation inadequate for ranking negative-cost measures, but there is no possible replacement that satisfies reasonable requirements. Furthermore, the concept of negative cost-effectiveness is found to be unsound and its use should be avoided. Among other things, this means that MACCs are unsuitable for ranking negative-cost measures. As a result, MACCs produced by a range of organizations including UK government departments may need to be revised. An alternative partial ranking method has been devised by making use of Pareto optimization. The outcome can be presented as a stacked bar chart that indicates both the preferred ordering and the total emissions saving available for each measure without specifying a cost-effectiveness. - Highlights: ► Marginal abatement cost curves (MACCs) are used to rank emission reduction measures. ► There is a flaw in the standard ranking method for negative-cost measures. ► Negative values of cost-effectiveness (in £/tC or equivalent) are invalid. ► There may be errors in published MACCs. ► A method based on Pareto principles provides an alternative ranking method.
Ranking network of a captive rhesus macaque society: a sophisticated corporative kingdom.
Fushing, Hsieh; McAssey, Michael P; Beisner, Brianne; McCowan, Brenda
2011-03-15
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.
Ranking network of a captive rhesus macaque society: a sophisticated corporative kingdom.
Directory of Open Access Journals (Sweden)
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.
Low-Rank Sparse Coding for Image Classification
Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Xu, Changsheng; Ahuja, Narendra
2013-01-01
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.
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.
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
International Nuclear Information System (INIS)
Park, Juyong
2010-01-01
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
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.
SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking
Shams, Bita; Haratizadeh, Saman
2016-09-01
Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.
International Nuclear Information System (INIS)
Chala, Mikael; Grojean, Christophe; Humboldt-Univ. Berlin; Lima, Leonardo de; Univ. Estadual Paulista, Sao Paulo
2017-03-01
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.
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.
Rank Two Affine Manifolds in Genus 3
Aulicino, David; Nguyen, Duc-Manh
2016-01-01
We complete the classification of rank two affine manifolds in the moduli space of translation surfaces in genus three. Combined with a recent result of Mirzakhani and Wright, this completes the classification of higher rank affine manifolds in genus three.
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.
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.
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.
Ranked retrieval of Computational Biology models.
Henkel, Ron; Endler, Lukas; Peters, Andre; Le Novère, Nicolas; Waltemath, Dagmar
2010-08-11
The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind. Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models. The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.
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;…
A Comprehensive Analysis of Marketing Journal Rankings
Steward, Michelle D.; Lewis, Bruce R.
2010-01-01
The purpose of this study is to offer a comprehensive assessment of journal standings in Marketing from two perspectives. The discipline perspective of rankings is obtained from a collection of published journal ranking studies during the past 15 years. The studies in the published ranking stream are assessed for reliability by examining internal…
On the viability of rank-six superstring models
International Nuclear Information System (INIS)
Campbell, B.A.; Olive, K.A.; Reiss, D.B.
1988-01-01
We consider the possibility of breaking a rank-six superstring model to the rank-four standard model. In particular, we point out the difficulties in generating two vacuum expectation values for the two standard model singlets contained in the 27 of E 6 . Although one expectation value is compatible with low energy phenomenology, a vev for ν c is problematic because of the absence of large neutrino masses and/or flavor changing neutral currents. We show that even simple models containing extra fields from incomplete multiplets or E 6 singlets do not resolve these problems. (orig.)
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.
Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.
Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao
2016-06-14
In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.
RANKING ENTERPRISES IN TERMS OF COMPETENCES INSIDE REGIONAL PRODUCTION NETWORK
Directory of Open Access Journals (Sweden)
Marko Mladineo
2013-02-01
Full Text Available Today's economic crisis has led to bankruptcy of many successful, but usually large-sized enterprises. This brought into question the future of large-sized enterprises. However, the only alternative to largesized enterprises (LEs is networking of small and medium-sized enterprises (SMEs into Regional Production Networks (RPNet. RPNet is non-hierarchical organizational form in which every SME is autonomous. Hence, every SME of production network is capable and wiling to be part of special cooperation inside network called Virtual Enterprise (VE. For each new product a new virtual enterprise is formed from different SMEs. The question is: which SMEs will be part of new virtual enterprise? If it is possible to evaluate SME's competences, it is also possible to rank SMEs. Ranking of SMEs according to technical, organizational and human competences is multi-criteria decision analysis (MCDA problem. So, in this paper PROMETHEE method is selected to perform a ranking of SMEs.
Logic-based aggregation methods for ranking student applicants
Directory of Open Access Journals (Sweden)
Milošević Pavle
2017-01-01
Full Text Available In this paper, we present logic-based aggregation models used for ranking student applicants and we compare them with a number of existing aggregation methods, each more complex than the previous one. The proposed models aim to include depen- dencies in the data using Logical aggregation (LA. LA is a aggregation method based on interpolative Boolean algebra (IBA, a consistent multi-valued realization of Boolean algebra. This technique is used for a Boolean consistent aggregation of attributes that are logically dependent. The comparison is performed in the case of student applicants for master programs at the University of Belgrade. We have shown that LA has some advantages over other presented aggregation methods. The software realization of all applied aggregation methods is also provided. This paper may be of interest not only for student ranking, but also for similar problems of ranking people e.g. employees, team members, etc.
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...
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.
24 CFR 599.401 - Ranking of applications.
2010-04-01
... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false Ranking of applications. 599.401... Communities § 599.401 Ranking of applications. (a) Ranking order. Rural and urban applications will be ranked... applications ranked first. (b) Separate ranking categories. After initial ranking, both rural and urban...
Rank-based model selection for multiple ions quantum tomography
International Nuclear Information System (INIS)
Guţă, Mădălin; Kypraios, Theodore; Dryden, Ian
2012-01-01
The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the ‘sparsity’ properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods—the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)—two models consisting of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of four ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a four ions experiment aimed at creating a Smolin state of rank 4. By applying the two methods together with the Pearson χ 2 test we conclude that the data can be suitably described with a model whose rank is between 7 and 9. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements. (paper)
Multimodal biometric system using rank-level fusion approach.
Monwar, Md Maruf; Gavrilova, Marina L
2009-08-01
In many real-world applications, unimodal biometric systems often face significant limitations due to sensitivity to noise, intraclass variability, data quality, nonuniversality, and other factors. Attempting to improve the performance of individual matchers in such situations may not prove to be highly effective. Multibiometric systems seek to alleviate some of these problems by providing multiple pieces of evidence of the same identity. These systems help achieve an increase in performance that may not be possible using a single-biometric indicator. This paper presents an effective fusion scheme that combines information presented by multiple domain experts based on the rank-level fusion integration method. The developed multimodal biometric system possesses a number of unique qualities, starting from utilizing principal component analysis and Fisher's linear discriminant methods for individual matchers (face, ear, and signature) identity authentication and utilizing the novel rank-level fusion method in order to consolidate the results obtained from different biometric matchers. The ranks of individual matchers are combined using the highest rank, Borda count, and logistic regression approaches. The results indicate that fusion of individual modalities can improve the overall performance of the biometric system, even in the presence of low quality data. Insights on multibiometric design using rank-level fusion and its performance on a variety of biometric databases are discussed in the concluding section.
Cioca, L. I.; Giurea, R.; Precazzini, I.; Ragazzi, M.; Achim, M. I.; Schiavon, M.; Rada, E. C.
2018-05-01
Nowadays the global tourism growth has caused a significant interest in research focused on the impact of the tourism on environment and community. The purpose of this study is to introduce a new ranking for the classification of tourist accommodation establishments with the functions of agro-tourism boarding house type by examining the sector of agro-tourism based on a research aimed to improve the economic, socio-cultural and environmental performance of agrotourism structures. This paper links the criteria for the classification of agro-tourism boarding houses (ABHs) to the impact of agro-tourism activities on the environment, enhancing an eco-friendly approach on agro-tourism activities by increasing the quality reputation of the agro-tourism products and services. Taking into account the impact on the environment, agrotourism can play an important role by protecting and conserving it.
Colleges Question Data Used by "Yahoo!" To Rank the "Most Wired" Campus.
Young, Jeffrey R.
1997-01-01
College administrators are complaining that "Yahoo! Internet Life" magazine used a flawed surveying process and inaccurate data to select the institutions it named in a recent ranking of "American's 100 Most Wired Colleges." Even some institutions faring well in the ranking have concerns about the survey, citing problems with…
Elitism, Sharing and Ranking Choices in Evolutionary Multi-Criterion Optimisation
Pursehouse, R.C.; Fleming, P.J.
2002-01-01
Elitism and sharing are two mechanisms that are believed to improve the performance of an evolutionary multi-criterion optimiser. The relative performance of of the two most popular ranking strategies is largely unknown. Using a new empirical inquiry framework, this report studies the effect of elitism, sharing and ranking design choices using a benchmark suite of two-criterion problems.........
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...
Reweighted Low-Rank Tensor Completion and its Applications in Video Recovery
M., Baburaj; George, Sudhish N.
2016-01-01
This paper focus on recovering multi-dimensional data called tensor from randomly corrupted incomplete observation. Inspired by reweighted $l_1$ norm minimization for sparsity enhancement, this paper proposes a reweighted singular value enhancement scheme to improve tensor low tubular rank in the tensor completion process. An efficient iterative decomposition scheme based on t-SVD is proposed which improves low-rank signal recovery significantly. The effectiveness of the proposed method is es...
Rank One Strange Attractors in Periodically Kicked Predator-Prey System with Time-Delay
Yang, Wenjie; Lin, Yiping; Dai, Yunxian; Zhao, Huitao
2016-06-01
This paper is devoted to the study of the problem of rank one strange attractor in a periodically kicked predator-prey system with time-delay. Our discussion is based on the theory of rank one maps formulated by Wang and Young. Firstly, we develop the rank one chaotic theory to delayed systems. It is shown that strange attractors occur when the delayed system undergoes a Hopf bifurcation and encounters an external periodic force. Then we use the theory to the periodically kicked predator-prey system with delay, deriving the conditions for Hopf bifurcation and rank one chaos along with the results of numerical simulations.
Fusion algebras of logarithmic minimal models
International Nuclear Information System (INIS)
Rasmussen, Joergen; Pearce, Paul A
2007-01-01
We present explicit conjectures for the chiral fusion algebras of the logarithmic minimal models LM(p,p') considering Virasoro representations with no enlarged or extended symmetry algebra. The generators of fusion are countably infinite in number but the ensuing fusion rules are quasi-rational in the sense that the fusion of a finite number of representations decomposes into a finite direct sum of representations. The fusion rules are commutative, associative and exhibit an sl(2) structure but require so-called Kac representations which are typically reducible yet indecomposable representations of rank 1. In particular, the identity of the fundamental fusion algebra p ≠ 1 is a reducible yet indecomposable Kac representation of rank 1. We make detailed comparisons of our fusion rules with the results of Gaberdiel and Kausch for p = 1 and with Eberle and Flohr for (p, p') = (2, 5) corresponding to the logarithmic Yang-Lee model. In the latter case, we confirm the appearance of indecomposable representations of rank 3. We also find that closure of a fundamental fusion algebra is achieved without the introduction of indecomposable representations of rank higher than 3. The conjectured fusion rules are supported, within our lattice approach, by extensive numerical studies of the associated integrable lattice models. Details of our lattice findings and numerical results will be presented elsewhere. The agreement of our fusion rules with the previous fusion rules lends considerable support for the identification of the logarithmic minimal models LM(p,p') with the augmented c p,p' (minimal) models defined algebraically
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.
Ranking Reproductive Health Problems to Define Service Priorities ...
African Journals Online (AJOL)
Le classement des problèmes de la santé reproductive afin de définir les priorités des services. Le Rapport du Développement Mondial de 1993 a proposé un nouveau paradigme à l'établissement des priorités dans les services de santé. Les deux démarches importantes par rapport à cette approche sont les estimations ...
Robust Ranking of Multivariate GARCH Models by Problem Dimension
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2012-01-01
textabstractDuring the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models,
Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation
M. Caporin (Massimiliano); M.J. McAleer (Michael)
2011-01-01
textabstractIn the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. In this paper, we provide an empirical comparison of a set of models,
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.
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.
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.
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.
Methodology for ranking restoration options
International Nuclear Information System (INIS)
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)
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...
Ranking Entities in Networks via Lefschetz Duality
DEFF Research Database (Denmark)
Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Poulsen, Bjarne
2014-01-01
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......, in the network. In this paper it is shown that the topology of a given network induces a ranking of the entities in the network. Further, it is demonstrated how to calculate this ranking and thus how to identify weak sub-networks in any given network....
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.
Ranking multiple docking solutions based on the conservation of inter-residue contacts
Oliva, Romina M.; Vangone, Anna; Cavallo, Luigi
2013-01-01
) conformations in the top positions is still an open problem. Herein we present CONSRANK, a simple and effective tool to rank multiple docking solutions, which relies on the conservation of inter-residue contacts in the analyzed decoys ensemble. First
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.
LANL environmental restoration site ranking system: System description. Final report
International Nuclear Information System (INIS)
Merkhofer, L.; Kann, A.; Voth, M.
1992-01-01
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
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.
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.
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.
Hexavalent Chromium Minimization Strategy
2011-05-01
Logistics 4 Initiative - DoD Hexavalent Chromium Minimization Non- Chrome Primer IIEXAVAJ ENT CHRO:M I~UMI CHROMIUM (VII Oil CrfVli.J CANCEfl HAnRD CD...Management Office of the Secretary of Defense Hexavalent Chromium Minimization Strategy Report Documentation Page Form ApprovedOMB No. 0704-0188...00-2011 4. TITLE AND SUBTITLE Hexavalent Chromium Minimization Strategy 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6
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....
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...
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.
A Rational Method for Ranking Engineering Programs.
Glower, Donald D.
1980-01-01
Compares two methods for ranking academic programs, the opinion poll v examination of career successes of the program's alumni. For the latter, "Who's Who in Engineering" and levels of research funding provided data. Tables display resulting data and compare rankings by the two methods for chemical engineering and civil engineering. (CS)
Lerot: An Online Learning to Rank Framework
Schuth, A.; Hofmann, K.; Whiteson, S.; de Rijke, M.
2013-01-01
Online learning to rank methods for IR allow retrieval systems to optimize their own performance directly from interactions with users via click feedback. In the software package Lerot, presented in this paper, we have bundled all ingredients needed for experimenting with online learning to rank for
Adaptive distributional extensions to DFR ranking
DEFF Research Database (Denmark)
Petersen, Casper; Simonsen, Jakob Grue; Järvelin, Kalervo
2016-01-01
-fitting distribution. We call this model Adaptive Distributional Ranking (ADR) because it adapts the ranking to the statistics of the specific dataset being processed each time. Experiments on TREC data show ADR to outperform DFR models (and their extensions) and be comparable in performance to a query likelihood...
Contests with rank-order spillovers
M.R. Baye (Michael); D. Kovenock (Dan); C.G. de Vries (Casper)
2012-01-01
textabstractThis paper presents a unified framework for characterizing symmetric equilibrium in simultaneous move, two-player, rank-order contests with complete information, in which each player's strategy generates direct or indirect affine "spillover" effects that depend on the rank-order of her
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...
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
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…
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
Rank 2 fusion rings are complete intersections
DEFF Research Database (Denmark)
Andersen, Troels Bak
We give a non-constructive proof that fusion rings attached to a simple complex Lie algebra of rank 2 are complete intersections.......We give a non-constructive proof that fusion rings attached to a simple complex Lie algebra of rank 2 are complete intersections....
A Ranking Method for Evaluating Constructed Responses
Attali, Yigal
2014-01-01
This article presents a comparative judgment approach for holistically scored constructed response tasks. In this approach, the grader rank orders (rather than rate) the quality of a small set of responses. A prior automated evaluation of responses guides both set formation and scaling of rankings. Sets are formed to have similar prior scores and…
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...
Ranking of Unwarranted Variations in Healthcare Treatments
Moes, Herry; Brekelmans, Ruud; Hamers, Herbert; Hasaart, F.
2017-01-01
In this paper, we introduce a framework designed to identify and rank possible unwarranted variation of treatments in healthcare. The innovative aspect of this framework is a ranking procedure that aims to identify healthcare institutions where unwarranted variation is most severe, and diagnosis
The Rankings Game: Who's Playing Whom?
Burness, John F.
2008-01-01
This summer, Forbes magazine published its new rankings of "America's Best Colleges," implying that it had developed a methodology that would give the public the information that it needed to choose a college wisely. "U.S. News & World Report," which in 1983 published the first annual ranking, just announced its latest ratings last week--including…
Dynamic collective entity representations for entity ranking
Graus, D.; Tsagkias, M.; Weerkamp, W.; Meij, E.; de Rijke, M.
2016-01-01
Entity ranking, i.e., successfully positioning a relevant entity at the top of the ranking for a given query, is inherently difficult due to the potential mismatch between the entity's description in a knowledge base, and the way people refer to the entity when searching for it. To counter this
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.
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)
Universal emergence of PageRank
International Nuclear Information System (INIS)
Frahm, K M; Georgeot, B; Shepelyansky, D L
2011-01-01
The PageRank algorithm enables us to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter α ∈ ]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 α → 1. The whole network can be divided into a core part and a group of invariant subspaces. For α → 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 α → 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)
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.
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.
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....
Ruled Laguerre minimal surfaces
Skopenkov, Mikhail; Pottmann, Helmut; Grohs, Philipp
2011-01-01
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
An adaptive ES with a ranking based constraint handling strategy
Directory of Open Access Journals (Sweden)
Kusakci Ali Osman
2014-01-01
Full Text Available To solve a constrained optimization problem, equality constraints can be used to eliminate a problem variable. If it is not feasible, the relations imposed implicitly by the constraints can still be exploited. Most conventional constraint handling methods in Evolutionary Algorithms (EAs do not consider the correlations between problem variables imposed by the constraints. This paper relies on the idea that a proper search operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. To realize this, an Evolution Strategy (ES along with a simplified Covariance Matrix Adaptation (CMA based mutation operator is used with a ranking based constraint-handling method. The proposed algorithm is tested on 13 benchmark problems as well as on a real life design problem. The outperformance of the algorithm is significant when compared with conventional ES-based methods.
MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering
Energy Technology Data Exchange (ETDEWEB)
Adluru, Ganesh, E-mail: gadluru@gmail.com; Anderson, Jeffrey [UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108 (United States); Gur, Yaniv [IBM Almaden Research Center, San Jose, California 95120 (United States); Chen, Liyong; Feinberg, David [Advanced MRI Technologies, Sebastpool, California, 95472 (United States); DiBella, Edward V. R. [UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108 and Department of Bioengineering, University of Utah, Salt Lake City, Utah 84112 (United States)
2015-08-15
Purpose: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. Methods: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. Results: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. Conclusions: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.
MRI reconstruction of multi-image acquisitions using a rank regularizer with data reordering
International Nuclear Information System (INIS)
Adluru, Ganesh; Anderson, Jeffrey; Gur, Yaniv; Chen, Liyong; Feinberg, David; DiBella, Edward V. R.
2015-01-01
Purpose: To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions. Methods: Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data. Results: Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively. Conclusions: The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions
First rank symptoms for schizophrenia.
Soares-Weiser, Karla; Maayan, Nicola; Bergman, Hanna; Davenport, Clare; Kirkham, Amanda J; Grabowski, Sarah; Adams, Clive E
2015-01-25
Early and accurate diagnosis and treatment of schizophrenia may have long-term advantages for the patient; the longer psychosis goes untreated the more severe the repercussions for relapse and recovery. If the correct diagnosis is not schizophrenia, but another psychotic disorder with some symptoms similar to schizophrenia, appropriate treatment might be delayed, with possible severe repercussions for the person involved and their family. There is widespread uncertainty about the diagnostic accuracy of First Rank Symptoms (FRS); we examined whether they are a useful diagnostic tool to differentiate schizophrenia from other psychotic disorders. To determine the diagnostic accuracy of one or multiple FRS for diagnosing schizophrenia, verified by clinical history and examination by a qualified professional (e.g. psychiatrists, nurses, social workers), with or without the use of operational criteria and checklists, in people thought to have non-organic psychotic symptoms. We conducted searches in MEDLINE, EMBASE, and PsycInfo using OvidSP in April, June, July 2011 and December 2012. We also searched MEDION in December 2013. We selected studies that consecutively enrolled or randomly selected adults and adolescents with symptoms of psychosis, and assessed the diagnostic accuracy of FRS for schizophrenia compared to history and clinical examination performed by a qualified professional, which may or may not involve the use of symptom checklists or based on operational criteria such as ICD and DSM. Two review authors independently screened all references for inclusion. Risk of bias in included studies were assessed using the QUADAS-2 instrument. We recorded the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) for constructing a 2 x 2 table for each study or derived 2 x 2 data from reported summary statistics such as sensitivity, specificity, and/or likelihood ratios. We included 21 studies with a total of 6253 participants
Hierarchical low-rank approximation for high dimensional approximation
Nouy, Anthony
2016-01-01
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.
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.
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.
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.
RankExplorer: Visualization of Ranking Changes in Large Time Series Data.
Shi, Conglei; Cui, Weiwei; Liu, Shixia; Xu, Panpan; Chen, Wei; Qu, Huamin
2012-12-01
For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations.
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.
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. PMID:27812192
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.
Sugeno integral ranking of release scenarios in a low and intermediate waste repository
International Nuclear Information System (INIS)
Kim, S. Ho; Kim, Tae Woon; Ha, Jae Joo
2004-01-01
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 λ-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.
; Environment Human Health Animal Health Safe Use Practices Food Safety Environment Air Water Soil Wildlife Home Page Pesticide Health and Safety Information Safe Use Practices Minimizing Exposure at Work Pesticides - Pennsylvania State University Cooperative Extension Personal Protective Equipment for Working
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)
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.
Minimal and careful processing
Nielsen, Thorkild
2004-01-01
In several standards, guidelines and publications, organic food processing is strongly associated with "minimal processing" and "careful processing". The term "minimal processing" is nowadays often used in the general food processing industry and described in literature. The term "careful processing" is used more specifically within organic food processing but is not yet clearly defined. The concept of carefulness seems to fit very well with the processing of organic foods, especially if it i...
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.
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.
Sparse reduced-rank regression with covariance estimation
Chen, Lisha; Huang, Jianhua Z.
2014-01-01
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.
Analysis of some methods for reduced rank Gaussian process regression
DEFF Research Database (Denmark)
Quinonero-Candela, J.; Rasmussen, Carl Edward
2005-01-01
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...... 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...... 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...
Trajectory Stability in the Traveling Salesman Problem
Directory of Open Access Journals (Sweden)
Sergio Sánchez
2018-01-01
Full Text Available Two generalizations of the traveling salesman problem in which sites change their position in time are presented. The way the rank of different trajectory lengths changes in time is studied using the rank diversity. We analyze the statistical properties of rank distributions and rank dynamics and give evidence that the shortest and longest trajectories are more predictable and robust to change, that is, more stable.
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...... in a comparison with the new plot(s). Data from studies using this set-up were analysed by a Thurstonian random utility model, which assumed that the judge's rankings were obtained by comparing latent continuous utilities or treatment effects. For the latent utilities a variance component model was considered...
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
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.
González-Galván, María del Carmen; Mosqueda-Taylor, Adalberto; Bologna-Molina, Ronell; Setien-Olarra, Amaia; Marichalar-Mendia, Xabier; Aguirre-Urizar, José-Manuel
2018-01-01
Background Odontogenic myxoma (OM) is a benign intraosseous neoplasm that exhibits local aggressiveness and high recurrence rates. Osteoclastogenesis is an important phenomenon in the tumor growth of maxillary neoplasms. RANK (Receptor Activator of Nuclear Factor κappa B) is the signaling receptor of RANK-L (Receptor activator of nuclear factor kappa-Β ligand) that activates the osteoclasts. OPG (osteoprotegerin) is a decoy receptor for RANK-L that inhibits pro-osteoclastogenesis. The RANK / RANKL / OPG system participates in the regulation of osteolytic activity under normal conditions, and its alteration has been associated with greater bone destruction, and also with tumor growth. Objectives To analyze the immunohistochemical expression of OPG, RANK and RANK-L proteins in odontogenic myxomas (OMs) and their relationship with the tumor size. Material and Methods Eighteen OMs, 4 small ( 3cm) and 18 dental follicles (DF) that were included as control were studied by means of standard immunohistochemical procedure with RANK, RANKL and OPG antibodies. For the evaluation, 5 fields (40x) of representative areas of OM and DF were selected where the expression of each antibody was determined. Descriptive and comparative statistical analyses were performed with the obtained data. Results There are significant differences in the expression of RANK in OM samples as compared to DF (p = 0.022) and among the OMSs and OMLs (p = 0.032). Also a strong association is recognized in the expression of RANK-L and OPG in OM samples. Conclusions Activation of the RANK / RANK-L / OPG triad seems to be involved in the mechanisms of bone balance and destruction, as well as associated with tumor growth in odontogenic myxomas. Key words:Odontogenic myxoma, dental follicle, RANK, RANK-L, OPG, osteoclastogenesis. PMID:29680857
How Many Alternatives Can Be Ranked? A Comparison of the Paired Comparison and Ranking Methods.
Ock, Minsu; Yi, Nari; Ahn, Jeonghoon; Jo, Min-Woo
2016-01-01
To determine the feasibility of converting ranking data into paired comparison (PC) data and suggest the number of alternatives that can be ranked by comparing a PC and a ranking method. Using a total of 222 health states, a household survey was conducted in a sample of 300 individuals from the general population. Each respondent performed a PC 15 times and a ranking method 6 times (two attempts of ranking three, four, and five health states, respectively). The health states of the PC and the ranking method were constructed to overlap each other. We converted the ranked data into PC data and examined the consistency of the response rate. Applying probit regression, we obtained the predicted probability of each method. Pearson correlation coefficients were determined between the predicted probabilities of those methods. The mean absolute error was also assessed between the observed and the predicted values. The overall consistency of the response rate was 82.8%. The Pearson correlation coefficients were 0.789, 0.852, and 0.893 for ranking three, four, and five health states, respectively. The lowest mean absolute error was 0.082 (95% confidence interval [CI] 0.074-0.090) in ranking five health states, followed by 0.123 (95% CI 0.111-0.135) in ranking four health states and 0.126 (95% CI 0.113-0.138) in ranking three health states. After empirically examining the consistency of the response rate between a PC and a ranking method, we suggest that using five alternatives in the ranking method may be superior to using three or four alternatives. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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.
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.
Waste minimization assessment procedure
International Nuclear Information System (INIS)
Kellythorne, L.L.
1993-01-01
Perry Nuclear Power Plant began developing a waste minimization plan early in 1991. In March of 1991 the plan was documented following a similar format to that described in the EPA Waste Minimization Opportunity Assessment Manual. Initial implementation involved obtaining management's commitment to support a waste minimization effort. The primary assessment goal was to identify all hazardous waste streams and to evaluate those streams for minimization opportunities. As implementation of the plan proceeded, non-hazardous waste streams routinely generated in large volumes were also evaluated for minimization opportunities. The next step included collection of process and facility data which would be useful in helping the facility accomplish its assessment goals. This paper describes the resources that were used and which were most valuable in identifying both the hazardous and non-hazardous waste streams that existed on site. For each material identified as a waste stream, additional information regarding the materials use, manufacturer, EPA hazardous waste number and DOT hazard class was also gathered. Once waste streams were evaluated for potential source reduction, recycling, re-use, re-sale, or burning for heat recovery, with disposal as the last viable alternative
Problem situations in management activity
N.A. DUBINKO
2009-01-01
This article reviews contemporary methodological and theoretical approaches to the problem situations in management activity. Revealed and analyzed the types of problem situations managers dealing with in their activity. Rank correlation of problem situations shows distinctions depending on management work experience. Revealed gender distinctions in the managers' ideas of management problems.
IMPORTANCE, Minimal Cut Sets and System Availability from Fault Tree Analysis
International Nuclear Information System (INIS)
Lambert, H. W.
1987-01-01
1 - Description of problem or function: IMPORTANCE computes various measures of probabilistic importance of basic events and minimal cut sets to a fault tree or reliability network diagram. The minimal cut sets, the failure rates and the fault duration times (i.e., the repair times) of all basic events contained in the minimal cut sets are supplied as input data. The failure and repair distributions are assumed to be exponential. IMPORTANCE, a quantitative evaluation code, then determines the probability of the top event and computes the importance of minimal cut sets and basic events by a numerical ranking. Two measures are computed. The first describes system behavior at one point in time; the second describes sequences of failures that cause the system to fail in time. All measures are computed assuming statistical independence of basic events. In addition, system unavailability and expected number of system failures are computed by the code. 2 - Method of solution: Seven measures of basic event importance and two measures of cut set importance can be computed. Birnbaum's measure of importance (i.e., the partial derivative) and the probability of the top event are computed using the min cut upper bound. If there are no replicated events in the minimal cut sets, then the min cut upper bound is exact. If basic events are replicated in the minimal cut sets, then based on experience the min cut upper bound is accurate if the probability of the top event is less than 0.1. Simpson's rule is used in computing the time-integrated measures of importance. Newton's method for approximating the roots of an equation is employed in the options where the importance measures are computed as a function of the probability of the top event, and a shell sort puts the output in descending order of importance
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.
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.
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...
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.
International Nuclear Information System (INIS)
Hosomichi, Kazuo
2008-01-01
We study FZZT-branes and open string amplitudes in (p, q) minimal string theory. We focus on the simplest boundary changing operators in two-matrix models, and identify the corresponding operators in worldsheet theory through the comparison of amplitudes. Along the way, we find a novel linear relation among FZZT boundary states in minimal string theory. We also show that the boundary ground ring is realized on physical open string operators in a very simple manner, and discuss its use for perturbative computation of higher open string amplitudes.
Graphical approach for multiple values logic minimization
Awwal, Abdul Ahad S.; Iftekharuddin, Khan M.
1999-03-01
Multiple valued logic (MVL) is sought for designing high complexity, highly compact, parallel digital circuits. However, the practical realization of an MVL-based system is dependent on optimization of cost, which directly affects the optical setup. We propose a minimization technique for MVL logic optimization based on graphical visualization, such as a Karnaugh map. The proposed method is utilized to solve signed-digit binary and trinary logic minimization problems. The usefulness of the minimization technique is demonstrated for the optical implementation of MVL circuits.
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.
[Minimally invasive coronary artery surgery].
Zalaquett, R; Howard, M; Irarrázaval, M J; Morán, S; Maturana, G; Becker, P; Medel, J; Sacco, C; Lema, G; Canessa, R; Cruz, F
1999-01-01
There is a growing interest to perform a left internal mammary artery (LIMA) graft to the left anterior descending coronary artery (LAD) on a beating heart through a minimally invasive access to the chest cavity. To report the experience with minimally invasive coronary artery surgery. Analysis of 11 patients aged 48 to 79 years old with single vessel disease that, between 1996 and 1997, had a LIMA graft to the LAD performed through a minimally invasive left anterior mediastinotomy, without cardiopulmonary bypass. A 6 to 10 cm left parasternal incision was done. The LIMA to the LAD anastomosis was done after pharmacological heart rate and blood pressure control and a period of ischemic pre conditioning. Graft patency was confirmed intraoperatively by standard Doppler techniques. Patients were followed for a mean of 11.6 months (7-15 months). All patients were extubated in the operating room and transferred out of the intensive care unit on the next morning. Seven patients were discharged on the third postoperative day. Duplex scanning confirmed graft patency in all patients before discharge; in two patients, it was confirmed additionally by arteriography. There was no hospital mortality, no perioperative myocardial infarction and no bleeding problems. After follow up, ten patients were free of angina, in functional class I and pleased with the surgical and cosmetic results. One patient developed atypical angina on the seventh postoperative month and a selective arteriography confirmed stenosis of the anastomosis. A successful angioplasty of the original LAD lesion was carried out. A minimally invasive left anterior mediastinotomy is a good surgical access to perform a successful LIMA to LAD graft without cardiopulmonary bypass, allowing a shorter hospital stay and earlier postoperative recovery. However, a larger experience and a longer follow up is required to define its role in the treatment of coronary artery disease.
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.
Country-specific determinants of world university rankings
Pietrucha, Jacek
2017-01-01
This paper examines country-specific factors that affect the three most influential world university rankings (the Academic Ranking of World Universities, the QS World University Ranking, and the Times Higher Education World University Ranking). We run a cross sectional regression that covers 42–71 countries (depending on the ranking and data availability). We show that the position of universities from a country in the ranking is determined by the following country-specific variables: econom...
Clean utilization of low-rank coals for low-cost power generation
International Nuclear Information System (INIS)
Sondreal, E.A.
1992-01-01
Despite the unique utilization problems of low-rank coals, the ten US steam electric plants having the lowest operating cost in 1990 were all fueled on either lignite or subbituminous coal. Ash deposition problems, which have been a major barrier to sustaining high load on US boilers burning high-sodium low-rank coals, have been substantially reduced by improvements in coal selection, boiler design, on-line cleaning, operating conditions, and additives. Advantages of low-rank coals in advanced systems are their noncaking behavior when heated, their high reactivity allowing more complete reaction at lower temperatures, and the low sulfur content of selected deposits. The principal barrier issues are the high-temperature behavior of ash and volatile alkali derived from the coal-bound sodium found in some low-rank coals. Successful upgrading of low-rank coals requires that the product be both stable and suitable for end use in conventional and advanced systems. Coal-water fuel produced by hydrothermal processing of high-moisture low-rank coal meets these criteria, whereas most dry products from drying or carbonizing in hot gas tend to create dust and spontaneous ignition problems unless coated, agglomerated, briquetted, or afforded special handling
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.
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…
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
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.
Diversity rankings among bacterial lineages in soil.
Youssef, Noha H; Elshahed, Mostafa S
2009-03-01
We used rarefaction curve analysis and diversity ordering-based approaches to rank the 11 most frequently encountered bacterial lineages in soil according to diversity in 5 previously reported 16S rRNA gene clone libraries derived from agricultural, undisturbed tall grass prairie and forest soils (n=26,140, 28 328, 31 818, 13 001 and 53 533). The Planctomycetes, Firmicutes and the delta-Proteobacteria were consistently ranked among the most diverse lineages in all data sets, whereas the Verrucomicrobia, Gemmatimonadetes and beta-Proteobacteria were consistently ranked among the least diverse. On the other hand, the rankings of alpha-Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes and Chloroflexi varied widely in different soil clone libraries. In general, lineages exhibiting largest differences in diversity rankings also exhibited the largest difference in relative abundance in the data sets examined. Within these lineages, a positive correlation between relative abundance and diversity was observed within the Acidobacteria, Actinobacteria and Chloroflexi, and a negative diversity-abundance correlation was observed within the Bacteroidetes. The ecological and evolutionary implications of these results are discussed.
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.
RANK und RANKL - Vom Knochen zum Mammakarzinom
Directory of Open Access Journals (Sweden)
Sigl V
2012-01-01
Full Text Available RANK (Receptor Activator of NF-κB und sein Ligand RANKL sind Schlüsselmoleküle im Knochenmetabolismus und spielen eine essenzielle Rolle in der Entstehung von pathologischen Knochenveränderungen. Die Deregulation des RANK/RANKL-Systems ist zum Beispiel ein Hauptgrund für das Auftreten von postmenopausaler Osteoporose bei Frauen. Eine weitere wesentliche Funktion von RANK und RANKL liegt in der Entwicklung von milchsekretierenden Drüsen während der Schwangerschaft. Dabei regulieren Sexualhormone, wie zum Beispiel Progesteron, die Expression von RANKL und induzieren dadurch die Proliferation von epithelialen Zellen der Brust. Seit Längerem war schon bekannt, dass RANK und RANKL in der Metastasenbildung von Brustkrebszellen im Knochengewebe beteiligt sind. Wir konnten nun das RANK/RANKLSystem auch als essenziellen Mechanismus in der Entstehung von hormonellem Brustkrebs identifizieren. In diesem Beitrag werden wir daher den neuesten Erkenntnissen besondere Aufmerksamkeit schenken und diese kritisch in Bezug auf Brustkrebsentwicklung betrachten.
International Nuclear Information System (INIS)
Gaberdiel, Matthias R; Gopakumar, Rajesh
2013-01-01
We review the duality relating 2D W N minimal model conformal field theories, in a large-N ’t Hooft like limit, to higher spin gravitational theories on AdS 3 . This article is part of a special issue of Journal of Physics A: Mathematical and Theoretical devoted to ‘Higher spin theories and holography’. (review)
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.
International Nuclear Information System (INIS)
Freeman, H.
1990-01-01
This book presents an overview of waste minimization. Covers applications of technology to waste reduction, techniques for implementing programs, incorporation of programs into R and D, strategies for private industry and the public sector, and case studies of programs already in effect
Minimally invasive distal pancreatectomy
Røsok, Bård I.; de Rooij, Thijs; van Hilst, Jony; Diener, Markus K.; Allen, Peter J.; Vollmer, Charles M.; Kooby, David A.; Shrikhande, Shailesh V.; Asbun, Horacio J.; Barkun, Jeffrey; Besselink, Marc G.; Boggi, Ugo; Conlon, Kevin; Han, Ho Seong; Hansen, Paul; Kendrick, Michael L.; Kooby, David; Montagnini, Andre L.; Palanivelu, Chinnasamy; Wakabayashi, Go; Zeh, Herbert J.
2017-01-01
The first International conference on Minimally Invasive Pancreas Resection was arranged in conjunction with the annual meeting of the International Hepato-Pancreato-Biliary Association (IHPBA), in Sao Paulo, Brazil on April 19th 2016. The presented evidence and outcomes resulting from the session
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
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.
Minimal constrained supergravity
International Nuclear Information System (INIS)
Cribiori, N.; Dall'Agata, G.; Farakos, F.; Porrati, M.
2017-01-01
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.
Conservation threats and the phylogenetic utility of IUCN Red List rankings in Incilius toads.
Schachat, Sandra R; Mulcahy, Daniel G; Mendelson, Joseph R
2016-02-01
Phylogenetic analysis of extinction threat is an emerging tool in the field of conservation. However, there are problems with the methods and data as commonly used. Phylogenetic sampling usually extends to the level of family or genus, but International Union for Conservation of Nature (IUCN) rankings are available only for individual species, and, although different species within a taxonomic group may have the same IUCN rank, the species may have been ranked as such for different reasons. Therefore, IUCN rank may not reflect evolutionary history and thus may not be appropriate for use in a phylogenetic context. To be used appropriately, threat-risk data should reflect the cause of extinction threat rather than the IUCN threat ranking. In a case study of the toad genus Incilius, with phylogenetic sampling at the species level (so that the resolution of the phylogeny matches character data from the IUCN Red List), we analyzed causes of decline and IUCN threat rankings by calculating metrics of phylogenetic signal (such as Fritz and Purvis' D). We also analyzed the extent to which cause of decline and threat ranking overlap by calculating phylogenetic correlation between these 2 types of character data. Incilius species varied greatly in both threat ranking and cause of decline; this variability would be lost at a coarser taxonomic resolution. We found far more phylogenetic signal, likely correlated with evolutionary history, for causes of decline than for IUCN threat ranking. Individual causes of decline and IUCN threat rankings were largely uncorrelated on the phylogeny. Our results demonstrate the importance of character selection and taxonomic resolution when extinction threat is analyzed in a phylogenetic context. © 2015 Society for Conservation Biology.
About the use of rank transformation in sensitivity analysis of model output
International Nuclear Information System (INIS)
Saltelli, Andrea; Sobol', Ilya M
1995-01-01
Rank transformations are frequently employed in numerical experiments involving a computational model, especially in the context of sensitivity and uncertainty analyses. Response surface replacement and parameter screening are tasks which may benefit from a rank transformation. Ranks can cope with nonlinear (albeit monotonic) input-output distributions, allowing the use of linear regression techniques. Rank transformed statistics are more robust, and provide a useful solution in the presence of long tailed input and output distributions. As is known to practitioners, care must be employed when interpreting the results of such analyses, as any conclusion drawn using ranks does not translate easily to the original model. In the present note an heuristic approach is taken, to explore, by way of practical examples, the effect of a rank transformation on the outcome of a sensitivity analysis. An attempt is made to identify trends, and to correlate these effects to a model taxonomy. Employing sensitivity indices, whereby the total variance of the model output is decomposed into a sum of terms of increasing dimensionality, we show that the main effect of the rank transformation is to increase the relative weight of the first order terms (the 'main effects'), at the expense of the 'interactions' and 'higher order interactions'. As a result the influence of those parameters which influence the output mostly by way of interactions may be overlooked in an analysis based on the ranks. This difficulty increases with the dimensionality of the problem, and may lead to the failure of a rank based sensitivity analysis. We suggest that the models can be ranked, with respect to the complexity of their input-output relationship, by mean of an 'Association' index I y . I y may complement the usual model coefficient of determination R y 2 as a measure of model complexity for the purpose of uncertainty and sensitivity analysis
Ross, David A; Moore, Edward Z
2013-09-01
As part of the National Resident Matching Program, programs must submit a rank order list of desired applicants. Despite the importance of this process and the numerous manifest limitations with traditional approaches, minimal research has been conducted to examine the accuracy of different ranking strategies. The authors developed the Moore Optimized Ordinal Rank Estimator (MOORE), a novel algorithm for ranking applicants that is based on college sports ranking systems. Because it is not possible to study the Match in vivo, the authors then designed the Recruitment Outcomes Simulation System (ROSS). This program was used to simulate a series of interview seasons and to compare MOORE and traditional approaches under different conditions. The accuracy of traditional ranking and the MOORE approach are equally and adversely affected with higher levels of intrarater variability. However, compared with traditional ranking methods, MOORE produces a more accurate rank order list as interrater variability increases. The present data demonstrate three key findings. First, they provide proof of concept that it is possible to scientifically test the accuracy of different rank methods used in the Match. Second, they show that small amounts of variability can have a significant adverse impact on the accuracy of rank order lists. Finally, they demonstrate that an ordinal approach may lead to a more accurate rank order list in the presence of interviewer bias. The ROSS-MOORE approach offers programs a novel way to optimize the recruitment process and, potentially, to construct a more accurate rank order list.
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.
Fast nonconvex nonsmooth minimization methods for image restoration and reconstruction.
Nikolova, Mila; Ng, Michael K; Tam, Chi-Pan
2010-12-01
Nonconvex nonsmooth regularization has advantages over convex regularization for restoring images with neat edges. However, its practical interest used to be limited by the difficulty of the computational stage which requires a nonconvex nonsmooth minimization. In this paper, we deal with nonconvex nonsmooth minimization methods for image restoration and reconstruction. Our theoretical results show that the solution of the nonconvex nonsmooth minimization problem is composed of constant regions surrounded by closed contours and neat edges. The main goal of this paper is to develop fast minimization algorithms to solve the nonconvex nonsmooth minimization problem. Our experimental results show that the effectiveness and efficiency of the proposed algorithms.
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...
Data envelopment analysis of randomized ranks
Directory of Open Access Journals (Sweden)
Sant'Anna Annibal P.
2002-01-01
Full Text Available Probabilities and odds, derived from vectors of ranks, are here compared as measures of efficiency of decision-making units (DMUs. These measures are computed with the goal of providing preliminary information before starting a Data Envelopment Analysis (DEA or the application of any other evaluation or composition of preferences methodology. Preferences, quality and productivity evaluations are usually measured with errors or subject to influence of other random disturbances. Reducing evaluations to ranks and treating the ranks as estimates of location parameters of random variables, we are able to compute the probability of each DMU being classified as the best according to the consumption of each input and the production of each output. Employing the probabilities of being the best as efficiency measures, we stretch distances between the most efficient units. We combine these partial probabilities in a global efficiency score determined in terms of proximity to the efficiency frontier.
Ranking spreaders by decomposing complex networks
International Nuclear Information System (INIS)
Zeng, An; Zhang, Cheng-Jun
2013-01-01
Ranking the nodes' ability of spreading in networks is crucial for designing efficient strategies to hinder spreading in the case of diseases or accelerate spreading in the case of information dissemination. In the well-known k-shell method, nodes are ranked only according to the links between the remaining nodes (residual links) while the links connecting to the removed nodes (exhausted links) are entirely ignored. In this Letter, we propose a mixed degree decomposition (MDD) procedure in which both the residual degree and the exhausted degree are considered. By simulating the epidemic spreading process on real networks, we show that the MDD method can outperform the k-shell and degree methods in ranking spreaders.
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.
RankProdIt: A web-interactive Rank Products analysis tool
Directory of Open Access Journals (Sweden)
Laing Emma
2010-08-01
Full Text Available Abstract Background The first objective of a DNA microarray experiment is typically to generate a list of genes or probes that are found to be differentially expressed or represented (in the case of comparative genomic hybridizations and/or copy number variation between two conditions or strains. Rank Products analysis comprises a robust algorithm for deriving such lists from microarray experiments that comprise small numbers of replicates, for example, less than the number required for the commonly used t-test. Currently, users wishing to apply Rank Products analysis to their own microarray data sets have been restricted to the use of command line-based software which can limit its usage within the biological community. Findings Here we have developed a web interface to existing Rank Products analysis tools allowing users to quickly process their data in an intuitive and step-wise manner to obtain the respective Rank Product or Rank Sum, probability of false prediction and p-values in a downloadable file. Conclusions The online interactive Rank Products analysis tool RankProdIt, for analysis of any data set containing measurements for multiple replicated conditions, is available at: http://strep-microarray.sbs.surrey.ac.uk/RankProducts
Rank-based Tests of the Cointegrating Rank in Semiparametric Error Correction Models
Hallin, M.; van den Akker, R.; Werker, B.J.M.
2012-01-01
Abstract: This paper introduces rank-based tests for the cointegrating rank in an Error Correction Model with i.i.d. elliptical innovations. The tests are asymptotically distribution-free, and their validity does not depend on the actual distribution of the innovations. This result holds despite the
Learning to rank for information retrieval
Liu, Tie-Yan
2011-01-01
Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as coll
Cointegration rank testing under conditional heteroskedasticity
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, Robert M.
2010-01-01
We analyze the properties of the conventional Gaussian-based cointegrating rank tests of Johansen (1996, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models) in the case where the vector of series under test is driven by globally stationary, conditionally heteroskedastic......, relative to tests based on the asymptotic critical values or the i.i.d. bootstrap, the wild bootstrap rank tests perform very well in small samples under a variety of conditionally heteroskedastic innovation processes. An empirical application to the term structure of interest rates is given....
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 and docum......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...
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...
Ranking mutual funds using Sortino method
Directory of Open Access Journals (Sweden)
Khosro Faghani Makrani
2014-04-01
Full Text Available One of the primary concerns on most business activities is to determine an efficient method for ranking mutual funds. This paper performs an empirical investigation to rank 42 mutual funds listed on Tehran Stock Exchange using Sortino method over the period 2011-2012. The results of survey have been compared with market return and the results have confirmed that there were some positive and meaningful relationships between Sortino return and market return. In addition, there were some positive and meaningful relationship between two Sortino methods.
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.
International Nuclear Information System (INIS)
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
Directory of Open Access Journals (Sweden)
João Carlos Magi
2017-04-01
Full Text Available Minimally invasive procedures aim to resolve the disease with minimal trauma to the body, resulting in a rapid return to activities and in reductions of infection, complications, costs and pain. Minimally incised laparotomy, sometimes referred to as minilaparotomy, is an example of such minimally invasive procedures. The aim of this study is to demonstrate the feasibility and utility of laparotomy with minimal incision based on the literature and exemplifying with a case. The case in question describes reconstruction of the intestinal transit with the use of this incision. Male, young, HIV-positive patient in a late postoperative of ileotiflectomy, terminal ileostomy and closing of the ascending colon by an acute perforating abdomen, due to ileocolonic tuberculosis. The barium enema showed a proximal stump of the right colon near the ileostomy. The access to the cavity was made through the orifice resulting from the release of the stoma, with a lateral-lateral ileo-colonic anastomosis with a 25 mm circular stapler and manual closure of the ileal stump. These surgeries require their own tactics, such as rigor in the lysis of adhesions, tissue traction, and hemostasis, in addition to requiring surgeon dexterity – but without the need for investments in technology; moreover, the learning curve is reported as being lower than that for videolaparoscopy. Laparotomy with minimal incision should be considered as a valid and viable option in the treatment of surgical conditions. Resumo: Procedimentos minimamente invasivos visam resolver a doença com o mínimo de trauma ao organismo, resultando em retorno rápido às atividades, reduções nas infecções, complicações, custos e na dor. A laparotomia com incisão mínima, algumas vezes referida como minilaparotomia, é um exemplo desses procedimentos minimamente invasivos. O objetivo deste trabalho é demonstrar a viabilidade e utilidade das laparotomias com incisão mínima com base na literatura e
DEFF Research Database (Denmark)
Foadi, Roshan; Frandsen, Mads Toudal; A. Ryttov, T.
2007-01-01
Different theoretical and phenomenological aspects of the Minimal and Nonminimal Walking Technicolor theories have recently been studied. The goal here is to make the models ready for collider phenomenology. We do this by constructing the low energy effective theory containing scalars......, 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...... interesting relations for the spin-one spectrum. We derive the electroweak parameters using the newly constructed effective theory and compare the results with the underlying gauge theory. Our analysis is sufficiently general such that the resulting model can be used to represent a generic walking technicolor...
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.
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;
International Nuclear Information System (INIS)
Ferreira, P.L.; Alcaras, J.A.C.
1980-01-01
The group theoretical properties of the Dirac groups of rank n are discussed together with the properties and construction of their IR's. The cases n even and n odd show distinct features. Furthermore, for n odd, the cases n=4K+1 and n=4K+3 exhibit some different properties too. (Author) [pt
A tilting approach to ranking influence
Genton, Marc G.; Hall, Peter
2014-01-01
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
Semantic association ranking schemes for information retrieval ...
Indian Academy of Sciences (India)
retrieval applications using term association graph representation ... Department of Computer Science and Engineering, Government College of ... Introduction ... leads to poor precision, e.g., model, python, and chip. ...... The approaches proposed in this paper focuses on the query-centric re-ranking of search results.
Efficient Rank Reduction of Correlation Matrices
I. Grubisic (Igor); R. Pietersz (Raoul)
2005-01-01
textabstractGeometric optimisation algorithms are developed that efficiently find the nearest low-rank correlation matrix. We show, in numerical tests, that our methods compare favourably to the existing methods in the literature. The connection with the Lagrange multiplier method is established,
Language Games: University Responses to Ranking Metrics
Heffernan, Troy A.; Heffernan, Amanda
2018-01-01
League tables of universities that measure performance in various ways are now commonplace, with numerous bodies providing their own rankings of how institutions throughout the world are seen to be performing on a range of metrics. This paper uses Lyotard's notion of language games to theorise that universities are regaining some power over being…
Ranking Thinning Potential of Lodgepole Pine Stands
United States Department of Agriculture, Forest Service
1987-01-01
This paper presents models for predicting edge-response of dominant and codominant trees to clearing. Procedures are given for converting predictions to a thinning response index, for ranking stands for thinning priority. Data requirements, sampling suggestions, examples of application, and suggestions for management use are included to facilitate use as a field guide.
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
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…
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
A generalization of Friedman's rank statistic
Kroon, de J.; Laan, van der P.
1983-01-01
In this paper a very natural generalization of the two·way analysis of variance rank statistic of FRIEDMAN is given. The general distribution-free test procedure based on this statistic for the effect of J treatments in a random block design can be applied in general two-way layouts without
Sequential unconstrained minimization algorithms for constrained optimization
International Nuclear Information System (INIS)
Byrne, Charles
2008-01-01
The problem of minimizing a function f(x):R J → R, subject to constraints on the vector variable x, occurs frequently in inverse problems. Even without constraints, finding a minimizer of f(x) may require iterative methods. We consider here a general class of iterative algorithms that find a solution to the constrained minimization problem as the limit of a sequence of vectors, each solving an unconstrained minimization problem. Our sequential unconstrained minimization algorithm (SUMMA) is an iterative procedure for constrained minimization. At the kth step we minimize the function G k (x)=f(x)+g k (x), to obtain x k . The auxiliary functions g k (x):D subset of R J → R + are nonnegative on the set D, each x k is assumed to lie within D, and the objective is to minimize the continuous function f:R J → R over x in the set C = D-bar, the closure of D. We assume that such minimizers exist, and denote one such by x-circumflex. We assume that the functions g k (x) satisfy the inequalities 0≤g k (x)≤G k-1 (x)-G k-1 (x k-1 ), for k = 2, 3, .... Using this assumption, we show that the sequence {(x k )} is decreasing and converges to f(x-circumflex). If the restriction of f(x) to D has bounded level sets, which happens if x-circumflex is unique and f(x) is closed, proper and convex, then the sequence {x k } is bounded, and f(x*)=f(x-circumflex), for any cluster point x*. Therefore, if x-circumflex is unique, x* = x-circumflex and {x k } → x-circumflex. When x-circumflex is not unique, convergence can still be obtained, in particular cases. The SUMMA includes, as particular cases, the well-known barrier- and penalty-function methods, the simultaneous multiplicative algebraic reconstruction technique (SMART), the proximal minimization algorithm of Censor and Zenios, the entropic proximal methods of Teboulle, as well as certain cases of gradient descent and the Newton–Raphson method. The proof techniques used for SUMMA can be extended to obtain related results
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.
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
Generalized reduced rank tests using the singular value decomposition
Kleibergen, F.R.; Paap, R.
2002-01-01
We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables for the LDU
Nominal versus Attained Weights in Universitas 21 Ranking
Soh, Kaycheng
2014-01-01
Universitas 21 Ranking of National Higher Education Systems (U21 Ranking) is one of the three new ranking systems appearing in 2012. In contrast with the other systems, U21 Ranking uses countries as the unit of analysis. It has several features which lend it with greater trustworthiness, but it also shared some methodological issues with the other…
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
F.R. Kleibergen (Frank); R. Paap (Richard)
2003-01-01
textabstractWe propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables
On the growth of rank for subgroups of finitely generated groups
International Nuclear Information System (INIS)
Osin, D V
1999-01-01
In [1] and [2] the functions of rank growth were independently introduced and investigated for subgroups of a finitely generated free group. In the present paper the concept of growth of rank is extended to subgroups of an arbitrary finitely generated group G, and the dependence of the asymptotic behaviour of the above functions on the choice of a finite generating set in G is studied. For a broad class of groups (which includes, in particular, the free polynilpotent groups) estimates for the growth of rank for subgroups are obtained that generalize the wellknown Baumslag-Eidel'kind result on finitely generated normal subgroups. Some problems related to the realization of arbitrary functions as functions of rank growth for subgroups of soluble groups are treated
Prewhitening for Rank-Deficient Noise in Subspace Methods for Noise Reduction
DEFF Research Database (Denmark)
Hansen, Per Christian; Jensen, Søren Holdt
2005-01-01
A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, e.g., when the noise...... has narrow-band characteristics, or in the case of tonal noise. We extend the concept of prewhitening to include the case when the noise covariance matrix is rank deficient, using a weighted pseudoinverse and the quotient SVD, and we show how to formulate a general rank-reduction algorithm that works...... also for rank deficient noise. We also demonstrate how to formulate this algorithm by means of a quotient ULV decomposition, which allows for faster computation and updating. Finally we apply our algorithm to a problem involving a speech signal contaminated by narrow-band noise....
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/.
Nonnegative Matrix Factorization with Rank Regularization and Hard Constraint.
Shang, Ronghua; Liu, Chiyang; Meng, Yang; Jiao, Licheng; Stolkin, Rustam
2017-09-01
Nonnegative matrix factorization (NMF) is well known to be an effective tool for dimensionality reduction in problems involving big data. For this reason, it frequently appears in many areas of scientific and engineering literature. This letter proposes a novel semisupervised NMF algorithm for overcoming a variety of problems associated with NMF algorithms, including poor use of prior information, negative impact on manifold structure of the sparse constraint, and inaccurate graph construction. Our proposed algorithm, nonnegative matrix factorization with rank regularization and hard constraint (NMFRC), incorporates label information into data representation as a hard constraint, which makes full use of prior information. NMFRC also measures pairwise similarity according to geodesic distance rather than Euclidean distance. This results in more accurate measurement of pairwise relationships, resulting in more effective manifold information. Furthermore, NMFRC adopts rank constraint instead of norm constraints for regularization to balance the sparseness and smoothness of data. In this way, the new data representation is more representative and has better interpretability. Experiments on real data sets suggest that NMFRC outperforms four other state-of-the-art algorithms in terms of clustering accuracy.
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.
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.
International Nuclear Information System (INIS)
Naftchi-Ardebili, Kasra; Hau, Nathania W.; Mazziotti, David A.
2011-01-01
Variational minimization of the ground-state energy as a function of the two-electron reduced density matrix (2-RDM), constrained by necessary N-representability conditions, provides a polynomial-scaling approach to studying strongly correlated molecules without computing the many-electron wave function. Here we introduce a route to enhancing necessary conditions for N representability through rank restriction of the 2-RDM. Rather than adding computationally more expensive N-representability conditions, we directly enhance the accuracy of two-particle (2-positivity) conditions through rank restriction, which removes degrees of freedom in the 2-RDM that are not sufficiently constrained. We select the rank of the particle-hole 2-RDM by deriving the ranks associated with model wave functions, including both mean-field and antisymmetrized geminal power (AGP) wave functions. Because the 2-positivity conditions are exact for quantum systems with AGP ground states, the rank of the particle-hole 2-RDM from the AGP ansatz provides a minimum for its value in variational 2-RDM calculations of general quantum systems. To implement the rank-restricted conditions, we extend a first-order algorithm for large-scale semidefinite programming. The rank-restricted conditions significantly improve the accuracy of the energies; for example, the percentages of correlation energies recovered for HF, CO, and N 2 improve from 115.2%, 121.7%, and 121.5% without rank restriction to 97.8%, 101.1%, and 100.0% with rank restriction. Similar results are found at both equilibrium and nonequilibrium geometries. While more accurate, the rank-restricted N-representability conditions are less expensive computationally than the full-rank conditions.
Yun, Yong-Huan; Deng, Bai-Chuan; Cao, Dong-Sheng; Wang, Wei-Ting; Liang, Yi-Zeng
2016-03-10
Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable. Copyright © 2016 Elsevier B.V. All rights reserved.
Legal incentives for minimizing waste
International Nuclear Information System (INIS)
Clearwater, S.W.; Scanlon, J.M.
1991-01-01
Waste minimization, or pollution prevention, has become an integral component of federal and state environmental regulation. Minimizing waste offers many economic and public relations benefits. In addition, waste minimization efforts can also dramatically reduce potential criminal requirements. This paper addresses the legal incentives for minimizing waste under current and proposed environmental laws and regulations
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.
Differential invariants for higher-rank tensors. A progress report
International Nuclear Information System (INIS)
Tapial, V.
2004-07-01
We outline the construction of differential invariants for higher-rank tensors. In section 2 we outline the general method for the construction of differential invariants. A first result is that the simplest tensor differential invariant contains derivatives of the same order as the rank of the tensor. In section 3 we review the construction for the first-rank tensors (vectors) and second-rank tensors (metrics). In section 4 we outline the same construction for higher-rank tensors. (author)
International Nuclear Information System (INIS)
Fischler, Mark S.; Sachs, D.
2004-01-01
A new object-oriented Minimization package is available for distribution in the same manner as CLHEP. This package, designed for use in HEP applications, has all the capabilities of Minuit, but is a re-write from scratch, adhering to modern C++ design principles. A primary goal of this package is extensibility in several directions, so that its capabilities can be kept fresh with as little maintenance effort as possible. This package is distinguished by the priority that was assigned to C++ design issues, and the focus on producing an extensible system that will resist becoming obsolete
International Nuclear Information System (INIS)
Ritson, D.; Chou, W.
1997-10-01
The Pacman bunches will experience two deleterious effects: tune shift and orbit displacement. It is known that the tune shift can be compensated by arranging crossing planes 900 relative to each other at successive interaction points (lPs). This paper gives an analytical estimate of the Pacman orbit displacement for a single as well as for two crossings. For the latter, it can be minimized by using equal phase advances from one IP to another. In the LHC, this displacement is in any event small and can be neglected
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.
Relaxations to Sparse Optimization Problems and Applications
Skau, Erik West
Parsimony is a fundamental property that is applied to many characteristics in a variety of fields. Of particular interest are optimization problems that apply rank, dimensionality, or support in a parsimonious manner. In this thesis we study some optimization problems and their relaxations, and focus on properties and qualities of the solutions of these problems. The Gramian tensor decomposition problem attempts to decompose a symmetric tensor as a sum of rank one tensors.We approach the Gramian tensor decomposition problem with a relaxation to a semidefinite program. We study conditions which ensure that the solution of the relaxed semidefinite problem gives the minimal Gramian rank decomposition. Sparse representations with learned dictionaries are one of the leading image modeling techniques for image restoration. When learning these dictionaries from a set of training images, the sparsity parameter of the dictionary learning algorithm strongly influences the content of the dictionary atoms.We describe geometrically the content of trained dictionaries and how it changes with the sparsity parameter.We use statistical analysis to characterize how the different content is used in sparse representations. Finally, a method to control the structure of the dictionaries is demonstrated, allowing us to learn a dictionary which can later be tailored for specific applications. Variations of dictionary learning can be broadly applied to a variety of applications.We explore a pansharpening problem with a triple factorization variant of coupled dictionary learning. Another application of dictionary learning is computer vision. Computer vision relies heavily on object detection, which we explore with a hierarchical convolutional dictionary learning model. Data fusion of disparate modalities is a growing topic of interest.We do a case study to demonstrate the benefit of using social media data with satellite imagery to estimate hazard extents. In this case study analysis we
Fourth-rank gravity. A progress report
International Nuclear Information System (INIS)
Tapia, V.
1992-04-01
We consider the consequences of describing the metric properties of space-time through a quartic line element. The associated ''metric'' is a fourth-rank tensor. After developing some fundamentals for such geometry, we construct a field theory for the gravitational field. This theory coincides with General Relativity in the vacuum case. Departures from General Relativity are obtained only in the presence of matter. We develop a simple cosmological model which is not in contradiction with the observed value Ω approx. 0.2-0.3 for the energy density parameter. A further application concerns conformal field theory. We are able to prove that a conformal field theory possesses an infinite-dimensional symmetry group only if the dimension of space-time is equal to the rank of the metric. In this case we are able to construct an integrable conformal field theory in four dimensions. The model is renormalisable by power counting. (author). 9 refs
Ranking oil sands bitumen recovery techniques
Energy Technology Data Exchange (ETDEWEB)
Lam, A.; Nobes, D.S.; Lipsett, M.G. [Alberta Univ., Edmonton, AB (Canada). Dept. of Mechanical Engineering
2009-07-01
The preference ranking organization method (PROMETHEE) was used to assess and rank 3 techniques for in situ bitumen recovery: (1) steam assisted gravity drainage; (2) vapour extraction (VAPEX); and (3) toe-to-heel air injection (THAI). The study used a business scenario where management-type indicators included potential production rates; estimated overall operating costs; energy consumption; facilities requirement; recovery efficiency; and energy loss. Amounts of carbon dioxide (CO{sub 2}) emissions were also considered, as well as the production depth, formation thickness, and API gravity of the produced bitumen. The study showed that THAI recovery methods had the most beneficial criteria weighting of the 3 processes, while SAGD was the least favourable choice. However, SAGD processes are the most widely used of the 3 processes, while THAI has only been demonstrated on a limited scale. It was concluded that the maturity of a technology should be weighted more heavily when using the PROMETHEE method. 8 refs., 2 tabs.
Social Media Impact on Website Ranking
Vaghela, Dushyant
2014-01-01
Internet is fast becoming critically important to commerce, industry and individuals. Search Engine (SE) is the most vital component for communication network and also used for discover information for users or people. Search engine optimization (SEO) is the process that is mostly used to increasing traffic from free, organic or natural listings on search engines and also helps to increase website ranking. It includes techniques like link building, directory submission, classified submission ...
On Locally Most Powerful Sequential Rank Tests
Czech Academy of Sciences Publication Activity Database
Kalina, Jan
2017-01-01
Roč. 36, č. 1 (2017), s. 111-125 ISSN 0747-4946 R&D Projects: GA ČR GA17-07384S Grant - others:Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : nonparametric test s * sequential ranks * stopping variable Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.339, year: 2016
Probabilistic real-time contingency ranking method
International Nuclear Information System (INIS)
Mijuskovic, N.A.; Stojnic, D.
2000-01-01
This paper describes a real-time contingency method based on a probabilistic index-expected energy not supplied. This way it is possible to take into account the stochastic nature of the electric power system equipment outages. This approach enables more comprehensive ranking of contingencies and it is possible to form reliability cost values that can form the basis for hourly spot price calculations. The electric power system of Serbia is used as an example for the method proposed. (author)
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...... relative to their peer workers), as predicted by theories on unionized and insider-outsider markets....
Minimally conscious state or cortically mediated state?
Naccache, Lionel
2018-04-01
Durable impairments of consciousness are currently classified in three main neurological categories: comatose state, vegetative state (also recently coined unresponsive wakefulness syndrome) and minimally conscious state. While the introduction of minimally conscious state, in 2002, was a major progress to help clinicians recognize complex non-reflexive behaviours in the absence of functional communication, it raises several problems. The most important issue related to minimally conscious state lies in its criteria: while behavioural definition of minimally conscious state lacks any direct evidence of patient's conscious content or conscious state, it includes the adjective 'conscious'. I discuss this major problem in this review and propose a novel interpretation of minimally conscious state: its criteria do not inform us about the potential residual consciousness of patients, but they do inform us with certainty about the presence of a cortically mediated state. Based on this constructive criticism review, I suggest three proposals aiming at improving the way we describe the subjective and cognitive state of non-communicating patients. In particular, I present a tentative new classification of impairments of consciousness that combines behavioural evidence with functional brain imaging data, in order to probe directly and univocally residual conscious processes.
Milton, Graeme W.; Camar-Eddine, Mohamed
2018-05-01
For a composite containing one isotropic elastic material, with positive Lame moduli, and void, with the elastic material occupying a prescribed volume fraction f, and with the composite being subject to an average stress, σ0 , Gibiansky, Cherkaev, and Allaire provided a sharp lower bound Wf(σ0) on the minimum compliance energy σ0 :ɛ0 , in which ɛ0 is the average strain. Here we show these bounds also provide sharp bounds on the possible (σ0 ,ɛ0) -pairs that can coexist in such composites, and thus solve the weak G-closure problem for 3d-printed materials. The materials we use to achieve the extremal (σ0 ,ɛ0) -pairs are denoted as near optimal pentamodes. We also consider two-phase composites containing this isotropic elasticity material and a rigid phase with the elastic material occupying a prescribed volume fraction f, and with the composite being subject to an average strain, ɛ0. For such composites, Allaire and Kohn provided a sharp lower bound W˜f(ɛ0) on the minimum elastic energy σ0 :ɛ0 . We show that these bounds also provide sharp bounds on the possible (σ0 ,ɛ0) -pairs that can coexist in such composites of the elastic and rigid phases, and thus solve the weak G-closure problem in this case too. The materials we use to achieve these extremal (σ0 ,ɛ0) -pairs are denoted as near optimal unimodes.
Citation ranking versus peer evaluation of senior faculty research performance
DEFF Research Database (Denmark)
Meho, Lokman I.; Sonnenwald, Diane H.
2000-01-01
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...... 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...
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.
Rank-dependant factorization of entanglement evolution
International Nuclear Information System (INIS)
Siomau, Michael
2016-01-01
Highlights: • In some cases the complex entanglement evolution can be factorized on simple terms. • We suggest factorization equations for multiqubit entanglement evolution. • The factorization is solely defined by the rank of the final state density matrices. • The factorization is independent on the local noisy channels and initial pure states. - Abstract: The description of the entanglement evolution of a complex quantum system can be significantly simplified due to the symmetries of the initial state and the quantum channels, which simultaneously affect parts of the system. Using concurrence as the entanglement measure, we study the entanglement evolution of few qubit systems, when each of the qubits is affected by a local unital channel independently on the others. We found that for low-rank density matrices of the final quantum state, such complex entanglement dynamics can be completely described by a combination of independent factors representing the evolution of entanglement of the initial state, when just one of the qubits is affected by a local channel. We suggest necessary conditions for the rank of the density matrices to represent the entanglement evolution through the factors. Our finding is supported with analytical examples and numerical simulations.
Fourth-rank gravity and cosmology
International Nuclear Information System (INIS)
Marrakchi, A.L.; Tapia, V.
1992-07-01
We consider the consequences of describing the metric properties of space-time through a quartic line element. The associated ''metric'' is a fourth-rank tensor G μυλπ . In order to recover a Riemannian behaviour of the geometry it is necessary to have G μυλπ = g (μυ g λπ) . We construct a theory for the gravitational field based on the fourth-rank metric G μυλπ . In the absence of matter the fourth-rank metric becomes separable and the theory coincides with General Relativity. In the presence of matter we can maintain Riemmanianicity, but now gravitation couples, as compared to General Relativity, in a different way to matter. We develop a simple cosmological model based on a FRW metric with matter described by a perfect fluid. For the present time the field equations are compatible with k OBS = O and Ω OBS t CLAS approx. 10 20 t PLANCK approx. 10 -23 s. Our final and most important result is the fact that the entropy is an increasing function of time. When interpreted at the light of General Relativity the treatment is shown to be almost equivalent to that of the standard model of cosmology combined with the inflationary scenario. (author). 16 refs, 1 fig
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.
Estimation of rank correlation for clustered data.
Rosner, Bernard; Glynn, Robert J
2017-06-30
It is well known that the sample correlation coefficient (R xy ) is the maximum likelihood estimator of the Pearson correlation (ρ xy ) 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 ρ xy 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.
Ranking environmental liabilities at a petroleum refinery
International Nuclear Information System (INIS)
Lupo, M.
1995-01-01
A new computer model is available to allow the management of a petroleum refinery to prioritize environmental action and construct a holistic approach to remediation. A large refinery may have numerous solid waste management units regulated by the Resource Conservation and Recovery Act (RCRA), as well as process units that emit hazardous chemicals into the environment. These sources can impact several environmental media, potentially including the air, the soil, the groundwater, the unsaturated zone water, and surface water. The number of chemicals of concern may be large. The new model is able to rank the sources by considering the impact of each chemical in each medium from each source in terms of concentration, release rate, and a weighted index based on toxicity. In addition to environmental impact, the sources can be ranked in three other ways: (1) by cost to remediate, (2) by environmental risk reduction caused by the remediation in terms of the decreases in release rate, concentration, and weighted index, and (3) by cost-benefit, which is the environmental risk reduction for each source divided by the cost of the remedy. Ranking each unit in the refinery allows management to use its limited environmental resources in a pro-active strategic manner that produces long-term results, rather than in reactive, narrowly focused, costly, regulatory-driven campaigns that produce only short-term results
Iris Template Protection Based on Local Ranking
Directory of Open Access Journals (Sweden)
Dongdong Zhao
2018-01-01
Full Text Available Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1 show that the proposed method could maintain the recognition performance while protecting the privacy of iris data.
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.
A survey on classical minimal surface theory
Meeks, William H
2012-01-01
Meeks and Pérez present a survey of recent spectacular successes in classical minimal surface theory. The classification of minimal planar domains in three-dimensional Euclidean space provides the focus of the account. The proof of the classification depends on the work of many currently active leading mathematicians, thus making contact with much of the most important results in the field. Through the telling of the story of the classification of minimal planar domains, the general mathematician may catch a glimpse of the intrinsic beauty of this theory and the authors' perspective of what is happening at this historical moment in a very classical subject. This book includes an updated tour through some of the recent advances in the theory, such as Colding-Minicozzi theory, minimal laminations, the ordering theorem for the space of ends, conformal structure of minimal surfaces, minimal annular ends with infinite total curvature, the embedded Calabi-Yau problem, local pictures on the scale of curvature and t...
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
With the fast growth of Chinese economic,more and more capital will be invested in environmental projects.How to select the environmental investment projects(alternatives)for obtaining the best environmental quality and economic benefits is an important problem for the decision makers.The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria.A model for ranking the projects of municipal sewage treatment plants is proposed by using exports' information and the data of the real projects.And,the ranking result is given based on the PROMETHEE method. Furthermore,by means of the concept of the weight stability intervals(WSI),the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed.The result shows that some criteria,such as"proportion of benefit to projoct cost",will influence the ranking result of alternatives very strong while others not.The influence are not only from the value of criterion but also from the changing the weight of criterion.So,some criteria such as"proportion of benefit to projoct cost" are key critera for ranking the projects. Decision makers must be cautious to them.
On the convergence of nonconvex minimization methods for image recovery.
Xiao, Jin; Ng, Michael Kwok-Po; Yang, Yu-Fei
2015-05-01
Nonconvex nonsmooth regularization method has been shown to be effective for restoring images with neat edges. Fast alternating minimization schemes have also been proposed and developed to solve the nonconvex nonsmooth minimization problem. The main contribution of this paper is to show the convergence of these alternating minimization schemes, based on the Kurdyka-Łojasiewicz property. In particular, we show that the iterates generated by the alternating minimization scheme, converges to a critical point of this nonconvex nonsmooth objective function. We also extend the analysis to nonconvex nonsmooth regularization model with box constraints, and obtain similar convergence results of the related minimization algorithm. Numerical examples are given to illustrate our convergence analysis.
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....
Country-specific determinants of world university rankings.
Pietrucha, Jacek
2018-01-01
This paper examines country-specific factors that affect the three most influential world university rankings (the Academic Ranking of World Universities, the QS World University Ranking, and the Times Higher Education World University Ranking). We run a cross sectional regression that covers 42-71 countries (depending on the ranking and data availability). We show that the position of universities from a country in the ranking is determined by the following country-specific variables: economic potential of the country, research and development expenditure, long-term political stability (freedom from war, occupation, coups and major changes in the political system), and institutional variables, including government effectiveness.
Tensor Rank Preserving Discriminant Analysis for Facial Recognition.
Tao, Dapeng; Guo, Yanan; Li, Yaotang; Gao, Xinbo
2017-10-12
Facial recognition, one of the basic topics in computer vision and pattern recognition, has received substantial attention in recent years. However, for those traditional facial recognition algorithms, the facial images are reshaped to a long vector, thereby losing part of the original spatial constraints of each pixel. In this paper, a new tensor-based feature extraction algorithm termed tensor rank preserving discriminant analysis (TRPDA) for facial image recognition is proposed; the proposed method involves two stages: in the first stage, the low-dimensional tensor subspace of the original input tensor samples was obtained; in the second stage, discriminative locality alignment was utilized to obtain the ultimate vector feature representation for subsequent facial recognition. On the one hand, the proposed TRPDA algorithm fully utilizes the natural structure of the input samples, and it applies an optimization criterion that can directly handle the tensor spectral analysis problem, thereby decreasing the computation cost compared those traditional tensor-based feature selection algorithms. On the other hand, the proposed TRPDA algorithm extracts feature by finding a tensor subspace that preserves most of the rank order information of the intra-class input samples. Experiments on the three facial databases are performed here to determine the effectiveness of the proposed TRPDA algorithm.
Low-Rank Linear Dynamical Systems for Motor Imagery EEG.
Zhang, Wenchang; Sun, Fuchun; 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.
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.
Low-Rank Kalman Filtering in Subsurface Contaminant Transport Models
El Gharamti, Mohamad
2010-01-01
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.
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.
A framework for automatic information quality ranking of diabetes websites.
Belen Sağlam, Rahime; Taskaya Temizel, Tugba
2015-01-01
Objective: When searching for particular medical information on the internet the challenge lies in distinguishing the websites that are relevant to the topic, and contain accurate information. In this article, we propose a framework that automatically identifies and ranks diabetes websites according to their relevance and information quality based on the website content. Design: The proposed framework ranks diabetes websites according to their content quality, relevance and evidence based medicine. The framework combines information retrieval techniques with a lexical resource based on Sentiwordnet making it possible to work with biased and untrusted websites while, at the same time, ensuring the content relevance. Measurement: The evaluation measurements used were Pearson-correlation, true positives, false positives and accuracy. We tested the framework with a benchmark data set consisting of 55 websites with varying degrees of information quality problems. Results: The proposed framework gives good results that are comparable with the non-automated information quality measuring approaches in the literature. The correlation between the results of the proposed automated framework and ground-truth is 0.68 on an average with p < 0.001 which is greater than the other proposed automated methods in the literature (r score in average is 0.33).
A New Direction of Cancer Classification: Positive Effect of Low-Ranking MicroRNAs.
Li, Feifei; Piao, Minghao; Piao, Yongjun; Li, Meijing; Ryu, Keun Ho
2014-10-01
Many studies based on microRNA (miRNA) expression profiles showed a new aspect of cancer classification. Because one characteristic of miRNA expression data is the high dimensionality, feature selection methods have been used to facilitate dimensionality reduction. The feature selection methods have one shortcoming thus far: they just consider the problem of where feature to class is 1:1 or n:1. However, because one miRNA may influence more than one type of cancer, human miRNA is considered to be ranked low in traditional feature selection methods and are removed most of the time. In view of the limitation of the miRNA number, low-ranking miRNAs are also important to cancer classification. We considered both high- and low-ranking features to cover all problems (1:1, n:1, 1:n, and m:n) in cancer classification. First, we used the correlation-based feature selection method to select the high-ranking miRNAs, and chose the support vector machine, Bayes network, decision tree, k-nearest-neighbor, and logistic classifier to construct cancer classification. Then, we chose Chi-square test, information gain, gain ratio, and Pearson's correlation feature selection methods to build the m:n feature subset, and used the selected miRNAs to determine cancer classification. The low-ranking miRNA expression profiles achieved higher classification accuracy compared with just using high-ranking miRNAs in traditional feature selection methods. Our results demonstrate that the m:n feature subset made a positive impression of low-ranking miRNAs in cancer classification.
Akbudak, Kadir; Ltaief, Hatem; Mikhalev, Aleksandr; Keyes, David E.
2017-01-01
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.
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.
On generator systems for non-torsion Abelian groups of infinite free rank
International Nuclear Information System (INIS)
Lebedenko, V.M.
1977-01-01
The paper is further advance in solution of the Dlab problem related to the systems of generators of Abelian groups. Some existence criteria for hereditarily strongly reducible systems of generators of Abelian groups are presented. On this basis the distribution of non-torsion groups of infinite free rank on Dlab's classes is obtained
A Distributed Taxation Based Rank Adaptation Scheme for 5G Small Cells
DEFF Research Database (Denmark)
Catania, Davide; Cattoni, Andrea Fabio; Mahmood, Nurul Huda
2015-01-01
The further densification of small cells impose high and undesirable levels of inter-cell interference. Multiple Input Multiple Output (MIMO) systems along with advanced receiver techniques provide us with extra degrees of freedom to combat such a problem. With such tools, rank adaptation...
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
Class ranking of secondary schools in the North West province of ...
African Journals Online (AJOL)
2016-11-14
Nov 14, 2016 ... The country is facing a number of national problems in various socio-economic .... The class-ranking concept that is proposed in this paper stems from the ..... Solving the linear programming model (in equations (18) to (21)) by ...
Tuthill, Jonathan W.; Frechette, Darren L.
2002-01-01
This paper discusses some of the failings of expected utility including the Allais paradox and expected utility's inadequate one dimensional characterization of risk. Three alternatives to expected utility are discussed at length; weighted expected utility, rank dependent utility, and cumulative prospect theory. Each alternative is capable of explaining Allais paradox type problems and permits more sophisticated multi dimensional risk preferences.
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
Minimal Marking: A Success Story
McNeilly, Anne
2014-01-01
The minimal-marking project conducted in Ryerson's School of Journalism throughout 2012 and early 2013 resulted in significantly higher grammar scores in two first-year classes of minimally marked university students when compared to two traditionally marked classes. The "minimal-marking" concept (Haswell, 1983), which requires…
Sharkey, Amanda J. C.
2007-09-01
Swarm Robotics (SR) is closely related to Swarm Intelligence, and both were initially inspired by studies of social insects. Their guiding principles are based on their biological inspiration and take the form of an emphasis on decentralized local control and communication. Earlier studies went a step further in emphasizing the use of simple reactive robots that only communicate indirectly through the environment. More recently SR studies have moved beyond these constraints to explore the use of non-reactive robots that communicate directly, and that can learn and represent their environment. There is no clear agreement in the literature about how far such extensions of the original principles could go. Should there be any limitations on the individual abilities of the robots used in SR studies? Should knowledge of the capabilities of social insects lead to constraints on the capabilities of individual robots in SR studies? There is a lack of explicit discussion of such questions, and researchers have adopted a variety of constraints for a variety of reasons. A simple taxonomy of swarm robotics is presented here with the aim of addressing and clarifying these questions. The taxonomy distinguishes subareas of SR based on the emphases and justifications for minimalism and individual simplicity.