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.
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.
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)
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.
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.
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)
Paired comparisons analysis: an axiomatic approach to ranking methods
Gonzalez-Diaz, J.; Hendrickx, Ruud; Lohmann, E.R.M.A.
2014-01-01
In this paper we present an axiomatic analysis of several ranking methods for general tournaments. We find that the ranking method obtained by applying maximum likelihood to the (Zermelo-)Bradley-Terry model, the most common method in statistics and psychology, is one of the ranking methods that
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.
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…
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.
Monte Carlo methods of PageRank computation
Litvak, Nelli
2004-01-01
We describe and analyze an on-line Monte Carlo method of PageRank computation. The PageRank is being estimated basing on results of a large number of short independent simulation runs initiated from each page that contains outgoing hyperlinks. The method does not require any storage of the hyperlink
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.
Method ranks competing projects by priorities, risk
International Nuclear Information System (INIS)
Moeckel, D.R.
1993-01-01
A practical, objective guide for ranking projects based on risk-based priorities has been developed by Sun Pipe Line Co. The deliberately simple system guides decisions on how to allocate scarce company resources because all managers employ the same criteria in weighing potential risks to the company versus benefits. Managers at all levels are continuously having to comply with an ever growing amount of legislative and regulatory requirements while at the same time trying to run their businesses effectively. The system primarily is designed for use as a compliance oversight and tracking process to document, categorize, and follow-up on work concerning various issues or projects. That is, the system consists of an electronic database which is updated periodically, and is used by various levels of management to monitor progress of health, safety, environmental and compliance-related projects. Criteria used in determining a risk factor and assigning a priority also have been adapted and found useful for evaluating other types of projects. The process enables management to better define potential risks and/or loss of benefits that are being accepted when a project is rejected from an immediate work plan or budget. In times of financial austerity, it is extremely important that the right decisions are made at the right time
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.
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.
Toward optimal feature selection using ranking methods and classification algorithms
Directory of Open Access Journals (Sweden)
Novaković Jasmina
2011-01-01
Full Text Available We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.
Evaluating ranking methods on heterogeneous digital library collections
Canévet, Olivier; Marian, Ludmila; Chonavel, Thierry
In the frame of research in particle physics, CERN has been developing its own web-based software /Invenio/ to run the digital library of all the documents related to CERN and fundamental physics. The documents (articles, photos, news, thesis, ...) can be retrieved through a search engine. The results matching the query of the user can be displayed in several ways: sorted by latest first, author, title and also ranked by word similarity. The purpose of this project is to study and implement a new ranking method in Invenio: distributed-ranking (D-Rank). This method aims at aggregating several ranking scores coming from different ranking methods into a new score. In addition to query-related scores such as word similarity, the goal of the work is to take into account non-query-related scores such as citations, journal impact factor and in particular scores related to the document access frequency in the database. The idea is that for two equally query-relevant documents, if one has been more downloaded for inst...
Logic-based aggregation methods for ranking student applicants
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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...
THE USE OF RANKING SAMPLING METHOD WITHIN MARKETING RESEARCH
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CODRUŢA DURA
2011-01-01
Full Text Available Marketing and statistical literature available to practitioners provides a wide range of sampling methods that can be implemented in the context of marketing research. Ranking sampling method is based on taking apart the general population into several strata, namely into several subdivisions which are relatively homogenous regarding a certain characteristic. In fact, the sample will be composed by selecting, from each stratum, a certain number of components (which can be proportional or non-proportional to the size of the stratum until the pre-established volume of the sample is reached. Using ranking sampling within marketing research requires the determination of some relevant statistical indicators - average, dispersion, sampling error etc. To that end, the paper contains a case study which illustrates the actual approach used in order to apply the ranking sample method within a marketing research made by a company which provides Internet connection services, on a particular category of customers – small and medium enterprises.
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
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...
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...
A cross-benchmark comparison of 87 learning to rank methods
Tax, N.; Bockting, S.; Hiemstra, D.
2015-01-01
Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered
Monte Carlo methods in PageRank computation: When one iteration is sufficient
Avrachenkov, K.; Litvak, Nelli; Nemirovsky, D.; Osipova, N.
2005-01-01
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. Google computes the PageRank using the power iteration method which requires
Monte Carlo methods in PageRank computation: When one iteration is sufficient
Avrachenkov, K.; Litvak, Nelli; Nemirovsky, D.; Osipova, N.
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. Google computes the PageRank using the power iteration method, which requires
Population based ranking of frameless CT-MRI registration methods
Energy Technology Data Exchange (ETDEWEB)
Opposits, Gabor; Kis, Sandor A.; Tron, Lajos; Emri, Miklos [Debrecen Univ. (Hungary). Dept. of Nuclear Medicine; Berenyi, Ervin [Debrecen Univ. (Hungary). Dept. of Biomedical Laboratory and Imaging Science; Takacs, Endre [Rotating Gamma Ltd., Debrecen (Hungary); Dobai, Jozsef G.; Bognar, Laszlo [Debrecen Univ., Medical Center (Hungary). Dept. of Neurosurgery; Szuecs, Bernadett [ScanoMed Ltd., Debrecen (Hungary)
2015-07-01
Clinical practice often requires simultaneous information obtained by two different imaging modalities. Registration algorithms are commonly used for this purpose. Automated procedures are very helpful in cases when the same kind of registration has to be performed on images of a high number of subjects. Radiotherapists would prefer to use the best automated method to assist therapy planning, however there are not accepted procedures for ranking the different registration algorithms. We were interested in developing a method to measure the population level performance of CT-MRI registration algorithms by a parameter of values in the [0,1] interval. Pairs of CT and MRI images were collected from 1051 subjects. Results of an automated registration were corrected manually until a radiologist and a neurosurgeon expert both accepted the result as good. This way 1051 registered MRI images were produced by the same pair of experts to be used as gold standards for the evaluation of the performance of other registration algorithms. Pearson correlation coefficient, mutual information, normalized mutual information, Kullback-Leibler divergence, L{sub 1} norm and square L{sub 2} norm (dis)similarity measures were tested for sensitivity to indicate the extent of (dis)similarity of a pair of individual mismatched images. The square Hellinger distance proved suitable to grade the performance of registration algorithms at population level providing the developers with a valuable tool to rank algorithms. The developed procedure provides an objective method to find the registration algorithm performing the best on the population level out of newly constructed or available preselected ones.
Variants of the Borda count method for combining ranked classifier hypotheses
van Erp, Merijn; Schomaker, Lambert; Schomaker, Lambert; Vuurpijl, Louis
2000-01-01
The Borda count is a simple yet effective method of combining rankings. In pattern recognition, classifiers are often able to return a ranked set of results. Several experiments have been conducted to test the ability of the Borda count and two variant methods to combine these ranked classifier
Distant Supervision for Relation Extraction with Ranking-Based Methods
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Yang Xiang
2016-05-01
Full Text Available Relation extraction has benefited from distant supervision in recent years with the development of natural language processing techniques and data explosion. However, distant supervision is still greatly limited by the quality of training data, due to its natural motivation for greatly reducing the heavy cost of data annotation. In this paper, we construct an architecture called MIML-sort (Multi-instance Multi-label Learning with Sorting Strategies, which is built on the famous MIML framework. Based on MIML-sort, we propose three ranking-based methods for sample selection with which we identify relation extractors from a subset of the training data. Experiments are set up on the KBP (Knowledge Base Propagation corpus, one of the benchmark datasets for distant supervision, which is large and noisy. Compared with previous work, the proposed methods produce considerably better results. Furthermore, the three methods together achieve the best F1 on the official testing set, with an optimal enhancement of F1 from 27.3% to 29.98%.
Use of the dry-weight-rank method of botanical analysis in the ...
African Journals Online (AJOL)
The dry-weight-rank method of botanical analysis was tested in the highveld of the Eastern Transvaal and was found to be an efficient and accurate means of determining the botanical composition of veld herbage. Accuracy was increased by weighting ranks on the basis of quadrat yield, and by allocation of equal ranks to ...
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)
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.
A novel three-stage distance-based consensus ranking method
Aghayi, Nazila; Tavana, Madjid
2018-05-01
In this study, we propose a three-stage weighted sum method for identifying the group ranks of alternatives. In the first stage, a rank matrix, similar to the cross-efficiency matrix, is obtained by computing the individual rank position of each alternative based on importance weights. In the second stage, a secondary goal is defined to limit the vector of weights since the vector of weights obtained in the first stage is not unique. Finally, in the third stage, the group rank position of alternatives is obtained based on a distance of individual rank positions. The third stage determines a consensus solution for the group so that the ranks obtained have a minimum distance from the ranks acquired by each alternative in the previous stage. A numerical example is presented to demonstrate the applicability and exhibit the efficacy of the proposed method and algorithms.
Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.
Tian, Yuling; Zhang, Hongxian
2016-01-01
For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.
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...
The Extrapolation-Accelerated Multilevel Aggregation Method in PageRank Computation
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Bing-Yuan Pu
2013-01-01
Full Text Available An accelerated multilevel aggregation method is presented for calculating the stationary probability vector of an irreducible stochastic matrix in PageRank computation, where the vector extrapolation method is its accelerator. We show how to periodically combine the extrapolation method together with the multilevel aggregation method on the finest level for speeding up the PageRank computation. Detailed numerical results are given to illustrate the behavior of this method, and comparisons with the typical methods are also made.
Solutions of interval type-2 fuzzy polynomials using a new ranking method
Rahman, Nurhakimah Ab.; Abdullah, Lazim; Ghani, Ahmad Termimi Ab.; Ahmad, Noor'Ani
2015-10-01
A few years ago, a ranking method have been introduced in the fuzzy polynomial equations. Concept of the ranking method is proposed to find actual roots of fuzzy polynomials (if exists). Fuzzy polynomials are transformed to system of crisp polynomials, performed by using ranking method based on three parameters namely, Value, Ambiguity and Fuzziness. However, it was found that solutions based on these three parameters are quite inefficient to produce answers. Therefore in this study a new ranking method have been developed with the aim to overcome the inherent weakness. The new ranking method which have four parameters are then applied in the interval type-2 fuzzy polynomials, covering the interval type-2 of fuzzy polynomial equation, dual fuzzy polynomial equations and system of fuzzy polynomials. The efficiency of the new ranking method then numerically considered in the triangular fuzzy numbers and the trapezoidal fuzzy numbers. Finally, the approximate solutions produced from the numerical examples indicate that the new ranking method successfully produced actual roots for the interval type-2 fuzzy polynomials.
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.
Directory of Open Access Journals (Sweden)
P. Phani Bushan Rao
2011-01-01
Full Text Available Ranking fuzzy numbers are an important aspect of decision making in a fuzzy environment. Since their inception in 1965, many authors have proposed different methods for ranking fuzzy numbers. However, there is no method which gives a satisfactory result to all situations. Most of the methods proposed so far are nondiscriminating and counterintuitive. This paper proposes a new method for ranking fuzzy numbers based on the Circumcenter of Centroids and uses an index of optimism to reflect the decision maker's optimistic attitude and also an index of modality that represents the neutrality of the decision maker. This method ranks various types of fuzzy numbers which include normal, generalized trapezoidal, and triangular fuzzy numbers along with crisp numbers with the particularity that crisp numbers are to be considered particular cases of fuzzy numbers.
Solving the interval type-2 fuzzy polynomial equation using the ranking method
Rahman, Nurhakimah Ab.; Abdullah, Lazim
2014-07-01
Polynomial equations with trapezoidal and triangular fuzzy numbers have attracted some interest among researchers in mathematics, engineering and social sciences. There are some methods that have been developed in order to solve these equations. In this study we are interested in introducing the interval type-2 fuzzy polynomial equation and solving it using the ranking method of fuzzy numbers. The ranking method concept was firstly proposed to find real roots of fuzzy polynomial equation. Therefore, the ranking method is applied to find real roots of the interval type-2 fuzzy polynomial equation. We transform the interval type-2 fuzzy polynomial equation to a system of crisp interval type-2 fuzzy polynomial equation. This transformation is performed using the ranking method of fuzzy numbers based on three parameters, namely value, ambiguity and fuzziness. Finally, we illustrate our approach by numerical example.
The use of fuzzy real option valuation method to rank Giga ...
African Journals Online (AJOL)
The use of fuzzy real option valuation method to rank Giga Investment Projects on Iran's natural gas reserves. ... Journal of Fundamental and Applied Sciences ... methodology – discounted cash flow analysis – in valuation of Giga investments.
Ranking filter methods for concentrating pathogens in lake water
Accurately comparing filtration methods for concentrating waterborne pathogens is difficult because of two important water matrix effects on recovery measurements, the effect on PCR quantification and the effect on filter performance. Regarding the first effect, we show how to create a control water...
Yager’s ranking method for solving the trapezoidal fuzzy number linear programming
Karyati; Wutsqa, D. U.; Insani, N.
2018-03-01
In the previous research, the authors have studied the fuzzy simplex method for trapezoidal fuzzy number linear programming based on the Maleki’s ranking function. We have found some theories related to the term conditions for the optimum solution of fuzzy simplex method, the fuzzy Big-M method, the fuzzy two-phase method, and the sensitivity analysis. In this research, we study about the fuzzy simplex method based on the other ranking function. It is called Yager's ranking function. In this case, we investigate the optimum term conditions. Based on the result of research, it is found that Yager’s ranking function is not like Maleki’s ranking function. Using the Yager’s function, the simplex method cannot work as well as when using the Maleki’s function. By using the Yager’s function, the value of the subtraction of two equal fuzzy numbers is not equal to zero. This condition makes the optimum table of the fuzzy simplex table is undetected. As a result, the simplified fuzzy simplex table becomes stopped and does not reach the optimum solution.
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....
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
The optimized expansion based low-rank method for wavefield extrapolation
Wu, Zedong
2014-03-01
Spectral methods are fast becoming an indispensable tool for wavefield extrapolation, especially in anisotropic media because it tends to be dispersion and artifact free as well as highly accurate when solving the wave equation. However, for inhomogeneous media, we face difficulties in dealing with the mixed space-wavenumber domain extrapolation operator efficiently. To solve this problem, we evaluated an optimized expansion method that can approximate this operator with a low-rank variable separation representation. The rank defines the number of inverse Fourier transforms for each time extrapolation step, and thus, the lower the rank, the faster the extrapolation. The method uses optimization instead of matrix decomposition to find the optimal wavenumbers and velocities needed to approximate the full operator with its explicit low-rank representation. As a result, we obtain lower rank representations compared with the standard low-rank method within reasonable accuracy and thus cheaper extrapolations. Additional bounds set on the range of propagated wavenumbers to adhere to the physical wave limits yield unconditionally stable extrapolations regardless of the time step. An application on the BP model provided superior results compared to those obtained using the decomposition approach. For transversely isotopic media, because we used the pure P-wave dispersion relation, we obtained solutions that were free of the shear wave artifacts, and the algorithm does not require that n > 0. In addition, the required rank for the optimization approach to obtain high accuracy in anisotropic media was lower than that obtained by the decomposition approach, and thus, it was more efficient. A reverse time migration result for the BP tilted transverse isotropy model using this method as a wave propagator demonstrated the ability of the algorithm.
Accuracy Evaluation of C4.5 and Naive Bayes Classifiers Using Attribute Ranking Method
Directory of Open Access Journals (Sweden)
S. Sivakumari
2009-03-01
Full Text Available This paper intends to classify the Ljubljana Breast Cancer dataset using C4.5 Decision Tree and Nai?ve Bayes classifiers. In this work, classification is carriedout using two methods. In the first method, dataset is analysed using all the attributes in the dataset. In the second method, attributes are ranked using information gain ranking technique and only the high ranked attributes are used to build the classification model. We are evaluating the results of C4.5 Decision Tree and Nai?ve Bayes classifiers in terms of classifier accuracy for various folds of cross validation. Our results show that both the classifiers achieve good accuracy on the dataset.
Research Notes Use of the dry-weight-rank method of botanical ...
African Journals Online (AJOL)
When used in combination with the double sampling (or comparative yield) method of yield estimation, the dry-weight-rank method of botanical analysis provides a rapid non-destructive means of estimating botanical composition. The composition is expressed in terms of the contribution of individual species to total herbage ...
A Ranking Method for Neutral Pion and Eta Selection in Hadronic Events
International Nuclear Information System (INIS)
Bingoel, A.
2004-01-01
The selection of neutral pions and etas with a high purity while maintaining also a high efficiency can be important in the formation of statistically significant mass spectra in the reconstruction of short-lived particles such as the omega meson (ω→π + + π - + π 0 ). In this study a Ranking method has been optimized for data from the ALEPH Experiment, CERN. The results show that the Ranking method, when applied to high multiplicity events, yields significant improvements in the purity of selected pion candidates and facilitates the relaxation of standard cuts thereby avoiding some systematic uncertainties
A ranking method for the concurrent learning of compounds with various activity profiles.
Dörr, Alexander; Rosenbaum, Lars; Zell, Andreas
2015-01-01
In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods. SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.
Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia
2013-01-01
Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008
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
Fast and precise method of contingency ranking in modern power system
DEFF Research Database (Denmark)
Rather, Zakir Hussain; Chen, Zhe; Thøgersen, Paul
2011-01-01
Contingency Analysis is one of the most important aspect of Power System Security Analysis. This paper presents a fast and precise method of contingency ranking for effective power system security analysis. The method proposed in this research work takes due consideration of both apparent power o...... is based on realistic approach taking practical situations into account. Besides taking real situations into consideration the proposed method is fast enough to be considered for on-line security analysis.......Contingency Analysis is one of the most important aspect of Power System Security Analysis. This paper presents a fast and precise method of contingency ranking for effective power system security analysis. The method proposed in this research work takes due consideration of both apparent power...
A Multiobjective Programming Method for Ranking All Units Based on Compensatory DEA Model
Directory of Open Access Journals (Sweden)
Haifang Cheng
2014-01-01
Full Text Available In order to rank all decision making units (DMUs on the same basis, this paper proposes a multiobjective programming (MOP model based on a compensatory data envelopment analysis (DEA model to derive a common set of weights that can be used for the full ranking of all DMUs. We first revisit a compensatory DEA model for ranking all units, point out the existing problem for solving the model, and present an improved algorithm for which an approximate global optimal solution of the model can be obtained by solving a sequence of linear programming. Then, we applied the key idea of the compensatory DEA model to develop the MOP model in which the objectives are to simultaneously maximize all common weights under constraints that the sum of efficiency values of all DMUs is equal to unity and the sum of all common weights is also equal to unity. In order to solve the MOP model, we transform it into a single objective programming (SOP model using a fuzzy programming method and solve the SOP model using the proposed approximation algorithm. To illustrate the ranking method using the proposed method, two numerical examples are solved.
A result-driven minimum blocking method for PageRank parallel computing
Tao, Wan; Liu, Tao; Yu, Wei; Huang, Gan
2017-01-01
Matrix blocking is a common method for improving computational efficiency of PageRank, but the blocking rules are hard to be determined, and the following calculation is complicated. In tackling these problems, we propose a minimum blocking method driven by result needs to accomplish a parallel implementation of PageRank algorithm. The minimum blocking just stores the element which is necessary for the result matrix. In return, the following calculation becomes simple and the consumption of the I/O transmission is cut down. We do experiments on several matrixes of different data size and different sparsity degree. The results show that the proposed method has better computational efficiency than traditional blocking methods.
The application of low-rank and sparse decomposition method in the field of climatology
Gupta, Nitika; Bhaskaran, Prasad K.
2018-04-01
The present study reports a low-rank and sparse decomposition method that separates the mean and the variability of a climate data field. Until now, the application of this technique was limited only in areas such as image processing, web data ranking, and bioinformatics data analysis. In climate science, this method exactly separates the original data into a set of low-rank and sparse components, wherein the low-rank components depict the linearly correlated dataset (expected or mean behavior), and the sparse component represents the variation or perturbation in the dataset from its mean behavior. The study attempts to verify the efficacy of this proposed technique in the field of climatology with two examples of real world. The first example attempts this technique on the maximum wind-speed (MWS) data for the Indian Ocean (IO) region. The study brings to light a decadal reversal pattern in the MWS for the North Indian Ocean (NIO) during the months of June, July, and August (JJA). The second example deals with the sea surface temperature (SST) data for the Bay of Bengal region that exhibits a distinct pattern in the sparse component. The study highlights the importance of the proposed technique used for interpretation and visualization of climate data.
Weakly intrusive low-rank approximation method for nonlinear parameter-dependent equations
Giraldi, Loic; Nouy, Anthony
2017-01-01
This paper presents a weakly intrusive strategy for computing a low-rank approximation of the solution of a system of nonlinear parameter-dependent equations. The proposed strategy relies on a Newton-like iterative solver which only requires evaluations of the residual of the parameter-dependent equation and of a preconditioner (such as the differential of the residual) for instances of the parameters independently. The algorithm provides an approximation of the set of solutions associated with a possibly large number of instances of the parameters, with a computational complexity which can be orders of magnitude lower than when using the same Newton-like solver for all instances of the parameters. The reduction of complexity requires efficient strategies for obtaining low-rank approximations of the residual, of the preconditioner, and of the increment at each iteration of the algorithm. For the approximation of the residual and the preconditioner, weakly intrusive variants of the empirical interpolation method are introduced, which require evaluations of entries of the residual and the preconditioner. Then, an approximation of the increment is obtained by using a greedy algorithm for low-rank approximation, and a low-rank approximation of the iterate is finally obtained by using a truncated singular value decomposition. When the preconditioner is the differential of the residual, the proposed algorithm is interpreted as an inexact Newton solver for which a detailed convergence analysis is provided. Numerical examples illustrate the efficiency of the method.
Weakly intrusive low-rank approximation method for nonlinear parameter-dependent equations
Giraldi, Loic
2017-06-30
This paper presents a weakly intrusive strategy for computing a low-rank approximation of the solution of a system of nonlinear parameter-dependent equations. The proposed strategy relies on a Newton-like iterative solver which only requires evaluations of the residual of the parameter-dependent equation and of a preconditioner (such as the differential of the residual) for instances of the parameters independently. The algorithm provides an approximation of the set of solutions associated with a possibly large number of instances of the parameters, with a computational complexity which can be orders of magnitude lower than when using the same Newton-like solver for all instances of the parameters. The reduction of complexity requires efficient strategies for obtaining low-rank approximations of the residual, of the preconditioner, and of the increment at each iteration of the algorithm. For the approximation of the residual and the preconditioner, weakly intrusive variants of the empirical interpolation method are introduced, which require evaluations of entries of the residual and the preconditioner. Then, an approximation of the increment is obtained by using a greedy algorithm for low-rank approximation, and a low-rank approximation of the iterate is finally obtained by using a truncated singular value decomposition. When the preconditioner is the differential of the residual, the proposed algorithm is interpreted as an inexact Newton solver for which a detailed convergence analysis is provided. Numerical examples illustrate the efficiency of the method.
Prioritizing sewer rehabilitation projects using AHP-PROMETHEE II ranking method.
Kessili, Abdelhak; Benmamar, Saadia
2016-01-01
The aim of this paper is to develop a methodology for the prioritization of sewer rehabilitation projects for Algiers (Algeria) sewer networks to support the National Sanitation Office in its challenge to make decisions on prioritization of sewer rehabilitation projects. The methodology applies multiple-criteria decision making. The study includes 47 projects (collectors) and 12 criteria to evaluate them. These criteria represent the different issues considered in the prioritization of the projects, which are structural, hydraulic, environmental, financial, social and technical. The analytic hierarchy process (AHP) is used to determine weights of the criteria and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE II) method is used to obtain the final ranking of the projects. The model was verified using the sewer data of Algiers. The results have shown that the method can be used for prioritizing sewer rehabilitation projects.
Computational Methods for Large Spatio-temporal Datasets and Functional Data Ranking
Huang, Huang
2017-07-16
This thesis focuses on two topics, computational methods for large spatial datasets and functional data ranking. Both are tackling the challenges of big and high-dimensional data. The first topic is motivated by the prohibitive computational burden in fitting Gaussian process models to large and irregularly spaced spatial datasets. Various approximation methods have been introduced to reduce the computational cost, but many rely on unrealistic assumptions about the process and retaining statistical efficiency remains an issue. We propose a new scheme to approximate the maximum likelihood estimator and the kriging predictor when the exact computation is infeasible. The proposed method provides different types of hierarchical low-rank approximations that are both computationally and statistically efficient. We explore the improvement of the approximation theoretically and investigate the performance by simulations. For real applications, we analyze a soil moisture dataset with 2 million measurements with the hierarchical low-rank approximation and apply the proposed fast kriging to fill gaps for satellite images. The second topic is motivated by rank-based outlier detection methods for functional data. Compared to magnitude outliers, it is more challenging to detect shape outliers as they are often masked among samples. We develop a new notion of functional data depth by taking the integration of a univariate depth function. Having a form of the integrated depth, it shares many desirable features. Furthermore, the novel formation leads to a useful decomposition for detecting both shape and magnitude outliers. Our simulation studies show the proposed outlier detection procedure outperforms competitors in various outlier models. We also illustrate our methodology using real datasets of curves, images, and video frames. Finally, we introduce the functional data ranking technique to spatio-temporal statistics for visualizing and assessing covariance properties, such as
Network-based ranking methods for prediction of novel disease associated microRNAs.
Le, Duc-Hau
2015-10-01
Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a "disease module" principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the "disease module" principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In
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.
Low rank approximation methods for MR fingerprinting with large scale dictionaries.
Yang, Mingrui; Ma, Dan; Jiang, Yun; Hamilton, Jesse; Seiberlich, Nicole; Griswold, Mark A; McGivney, Debra
2018-04-01
This work proposes new low rank approximation approaches with significant memory savings for large scale MR fingerprinting (MRF) problems. We introduce a compressed MRF with randomized singular value decomposition method to significantly reduce the memory requirement for calculating a low rank approximation of large sized MRF dictionaries. We further relax this requirement by exploiting the structures of MRF dictionaries in the randomized singular value decomposition space and fitting them to low-degree polynomials to generate high resolution MRF parameter maps. In vivo 1.5T and 3T brain scan data are used to validate the approaches. T 1 , T 2 , and off-resonance maps are in good agreement with that of the standard MRF approach. Moreover, the memory savings is up to 1000 times for the MRF-fast imaging with steady-state precession sequence and more than 15 times for the MRF-balanced, steady-state free precession sequence. The proposed compressed MRF with randomized singular value decomposition and dictionary fitting methods are memory efficient low rank approximation methods, which can benefit the usage of MRF in clinical settings. They also have great potentials in large scale MRF problems, such as problems considering multi-component MRF parameters or high resolution in the parameter space. Magn Reson Med 79:2392-2400, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
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.
Directory of Open Access Journals (Sweden)
Lei Guo
2017-02-01
Full Text Available Point-of-interest (POI recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method.
Energy Technology Data Exchange (ETDEWEB)
Pourgol-Mohammad, Mohammad, E-mail: pourgolmohammad@sut.ac.ir [Department of Mechanical Engineering, Sahand University of Technology, Tabriz (Iran, Islamic Republic of); Hoseyni, Seyed Mohsen [Department of Basic Sciences, East Tehran Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of); Hoseyni, Seyed Mojtaba [Building & Housing Research Center, Tehran (Iran, Islamic Republic of); Sepanloo, Kamran [Nuclear Science and Technology Research Institute, Tehran (Iran, Islamic Republic of)
2016-08-15
Highlights: • Existing uncertainty ranking methods prove inconsistent for TH applications. • Introduction of a new method for ranking sources of uncertainty in TH codes. • Modified PIRT qualitatively identifies and ranks uncertainty sources more precisely. • The importance of parameters is calculated by a limited number of TH code executions. • Methodology is applied successfully on LOFT-LB1 test facility. - Abstract: In application to thermal–hydraulic calculations by system codes, sensitivity analysis plays an important role for managing the uncertainties of code output and risk analysis. Sensitivity analysis is also used to confirm the results of qualitative Phenomena Identification and Ranking Table (PIRT). Several methodologies have been developed to address uncertainty importance assessment. Generally, uncertainty importance measures, mainly devised for the Probabilistic Risk Assessment (PRA) applications, are not affordable for computationally demanding calculations of the complex thermal–hydraulics (TH) system codes. In other words, for effective quantification of the degree of the contribution of each phenomenon to the total uncertainty of the output, a practical approach is needed by considering high computational burden of TH calculations. This study aims primarily to show the inefficiency of the existing approaches and then introduces a solution to cope with the challenges in this area by modification of variance-based uncertainty importance method. Important parameters are identified by the modified PIRT approach qualitatively then their uncertainty importance is quantified by a local derivative index. The proposed index is attractive from its practicality point of view on TH applications. It is capable of calculating the importance of parameters by a limited number of TH code executions. Application of the proposed methodology is demonstrated on LOFT-LB1 test facility.
International Nuclear Information System (INIS)
Pourgol-Mohammad, Mohammad; Hoseyni, Seyed Mohsen; Hoseyni, Seyed Mojtaba; Sepanloo, Kamran
2016-01-01
Highlights: • Existing uncertainty ranking methods prove inconsistent for TH applications. • Introduction of a new method for ranking sources of uncertainty in TH codes. • Modified PIRT qualitatively identifies and ranks uncertainty sources more precisely. • The importance of parameters is calculated by a limited number of TH code executions. • Methodology is applied successfully on LOFT-LB1 test facility. - Abstract: In application to thermal–hydraulic calculations by system codes, sensitivity analysis plays an important role for managing the uncertainties of code output and risk analysis. Sensitivity analysis is also used to confirm the results of qualitative Phenomena Identification and Ranking Table (PIRT). Several methodologies have been developed to address uncertainty importance assessment. Generally, uncertainty importance measures, mainly devised for the Probabilistic Risk Assessment (PRA) applications, are not affordable for computationally demanding calculations of the complex thermal–hydraulics (TH) system codes. In other words, for effective quantification of the degree of the contribution of each phenomenon to the total uncertainty of the output, a practical approach is needed by considering high computational burden of TH calculations. This study aims primarily to show the inefficiency of the existing approaches and then introduces a solution to cope with the challenges in this area by modification of variance-based uncertainty importance method. Important parameters are identified by the modified PIRT approach qualitatively then their uncertainty importance is quantified by a local derivative index. The proposed index is attractive from its practicality point of view on TH applications. It is capable of calculating the importance of parameters by a limited number of TH code executions. Application of the proposed methodology is demonstrated on LOFT-LB1 test facility.
Many-Objective Optimization Using Adaptive Differential Evolution with a New Ranking Method
Directory of Open Access Journals (Sweden)
Xiaoguang He
2014-01-01
Full Text Available Pareto dominance is an important concept and is usually used in multiobjective evolutionary algorithms (MOEAs to determine the nondominated solutions. However, for many-objective problems, using Pareto dominance to rank the solutions even in the early generation, most obtained solutions are often the nondominated solutions, which results in a little selection pressure of MOEAs toward the optimal solutions. In this paper, a new ranking method is proposed for many-objective optimization problems to verify a relatively smaller number of representative nondominated solutions with a uniform and wide distribution and improve the selection pressure of MOEAs. After that, a many-objective differential evolution with the new ranking method (MODER for handling many-objective optimization problems is designed. At last, the experiments are conducted and the proposed algorithm is compared with several well-known algorithms. The experimental results show that the proposed algorithm can guide the search to converge to the true PF and maintain the diversity of solutions for many-objective problems.
A DYNAMIC FEATURE SELECTION METHOD FOR DOCUMENT RANKING WITH RELEVANCE FEEDBACK APPROACH
Directory of Open Access Journals (Sweden)
K. Latha
2010-07-01
Full Text Available Ranking search results is essential for information retrieval and Web search. Search engines need to not only return highly relevant results, but also be fast to satisfy users. As a result, not all available features can be used for ranking, and in fact only a small percentage of these features can be used. Thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. In this paper we describe a 0/1 knapsack procedure for automatically selecting features to use within Generalization model for Document Ranking. We propose an approach for Relevance Feedback using Expectation Maximization method and evaluate the algorithm on the TREC Collection for describing classes of feedback textual information retrieval features. Experimental results, evaluated on standard TREC-9 part of the OHSUMED collections, show that our feature selection algorithm produces models that are either significantly more effective than, or equally effective as, models such as Markov Random Field model, Correlation Co-efficient and Count Difference method
Critical review of methods for risk ranking of food related hazards, based on risks for human health
DEFF Research Database (Denmark)
van der Fels-Klerx, H. J.; van Asselt, E. D.; Raley, M.
2018-01-01
This study aimed to critically review methods for ranking risks related to food safety and dietary hazards on the basis of their anticipated human health impacts. A literature review was performed to identify and characterize methods for risk ranking from the fields of food, environmental science......, and the risk ranking method characterized. The methods were then clustered - based on their characteristics - into eleven method categories. These categories included: risk assessment, comparative risk assessment, risk ratio method, scoring method, cost of illness, health adjusted life years, multi......-criteria decision analysis, risk matrix, flow charts/decision trees, stated preference techniques and expert synthesis. Method categories were described by their characteristics, weaknesses and strengths, data resources, and fields of applications. It was concluded there is no single best method for risk ranking...
Low-rank Quasi-Newton updates for Robust Jacobian lagging in Newton methods
International Nuclear Information System (INIS)
Brown, J.; Brune, P.
2013-01-01
Newton-Krylov methods are standard tools for solving nonlinear problems. A common approach is to 'lag' the Jacobian when assembly or preconditioner setup is computationally expensive, in exchange for some degradation in the convergence rate and robustness. We show that this degradation may be partially mitigated by using the lagged Jacobian as an initial operator in a quasi-Newton method, which applies unassembled low-rank updates to the Jacobian until the next full reassembly. We demonstrate the effectiveness of this technique on problems in glaciology and elasticity. (authors)
Feature selection for splice site prediction: A new method using EDA-based feature ranking
Directory of Open Access Journals (Sweden)
Rouzé Pierre
2004-05-01
Full Text Available Abstract Background The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. Results In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing. Conclusion We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.
The Typicality Ranking Task: A New Method to Derive Typicality Judgments from Children
Ameel, Eef; Storms, Gert
2016-01-01
An alternative method for deriving typicality judgments, applicable in young children that are not familiar with numerical values yet, is introduced, allowing researchers to study gradedness at younger ages in concept development. Contrary to the long tradition of using rating-based procedures to derive typicality judgments, we propose a method that is based on typicality ranking rather than rating, in which items are gradually sorted according to their typicality, and that requires a minimum of linguistic knowledge. The validity of the method is investigated and the method is compared to the traditional typicality rating measurement in a large empirical study with eight different semantic concepts. The results show that the typicality ranking task can be used to assess children’s category knowledge and to evaluate how this knowledge evolves over time. Contrary to earlier held assumptions in studies on typicality in young children, our results also show that preference is not so much a confounding variable to be avoided, but that both variables are often significantly correlated in older children and even in adults. PMID:27322371
Directory of Open Access Journals (Sweden)
M. Mousavi
2014-06-01
Full Text Available Evaluating and prioritizing appropriate renewable energy sources is inevitably a complex decision process. Various information and conflicting attributes should be taken into account. For this purpose, multi-attribute decision making (MADM methods can assist managers or decision makers in formulating renewable energy sources priorities by considering important objective and attributes. In this paper, a new extension of compromise ranking method with interval numbers is presented for the prioritization of renewable energy sources that is based on the performance similarity of alternatives to ideal solutions. To demonstrate the applicability of the proposed decision method, an application example is provided and the computational results are analyzed. Results illustrate that the presented method is viable in solving the evaluation and prioritization problem of renewable energy sources.
Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation
Yokota, Rio
2018-01-03
There has been a large increase in the amount of work on hierarchical low-rank approximation methods, where the interest is shared by multiple communities that previously did not intersect. This objective of this article is two-fold; to provide a thorough review of the recent advancements in this field from both analytical and algebraic perspectives, and to present a comparative benchmark of two highly optimized implementations of contrasting methods for some simple yet representative test cases. The first half of this paper has the form of a survey paper, to achieve the former objective. We categorize the recent advances in this field from the perspective of compute-memory tradeoff, which has not been considered in much detail in this area. Benchmark tests reveal that there is a large difference in the memory consumption and performance between the different methods.
Zeng, Lang; He, Yu; Povolotskyi, Michael; Liu, XiaoYan; Klimeck, Gerhard; Kubis, Tillmann
2013-06-01
In this work, the low rank approximation concept is extended to the non-equilibrium Green's function (NEGF) method to achieve a very efficient approximated algorithm for coherent and incoherent electron transport. This new method is applied to inelastic transport in various semiconductor nanodevices. Detailed benchmarks with exact NEGF solutions show (1) a very good agreement between approximated and exact NEGF results, (2) a significant reduction of the required memory, and (3) a large reduction of the computational time (a factor of speed up as high as 150 times is observed). A non-recursive solution of the inelastic NEGF transport equations of a 1000 nm long resistor on standard hardware illustrates nicely the capability of this new method.
Fast Multipole Method as a Matrix-Free Hierarchical Low-Rank Approximation
Yokota, Rio; Ibeid, Huda; Keyes, David E.
2018-01-01
There has been a large increase in the amount of work on hierarchical low-rank approximation methods, where the interest is shared by multiple communities that previously did not intersect. This objective of this article is two-fold; to provide a thorough review of the recent advancements in this field from both analytical and algebraic perspectives, and to present a comparative benchmark of two highly optimized implementations of contrasting methods for some simple yet representative test cases. The first half of this paper has the form of a survey paper, to achieve the former objective. We categorize the recent advances in this field from the perspective of compute-memory tradeoff, which has not been considered in much detail in this area. Benchmark tests reveal that there is a large difference in the memory consumption and performance between the different methods.
Directory of Open Access Journals (Sweden)
Prasenjit Chatterjee
2012-04-01
Full Text Available Evaluation of proper supplier for manufacturing organizations is one of the most challenging problems in real time manufacturing environment due to a wide variety of customer demands. It has become more and more complicated to meet the challenges of international competitiveness and as the decision makers need to assess a wide range of alternative suppliers based on a set of conflicting criteria. Thus, the main objective of supplier selection is to select highly potential supplier through which all the set goals regarding the purchasing and manufacturing activity can be achieved. Because of these reasons, supplier selection has got considerable attention by the academicians and researchers. This paper presents a combined multi-criteria decision making methodology for supplier evaluation for given industrial applications. The proposed methodology is based on a compromise ranking method combined with Grey Interval Numbers considering different cardinal and ordinal criteria and their relative importance. A ‘supplier selection index’ is also proposed to help evaluation and ranking the alternative suppliers. Two examples are illustrated to demonstrate the potentiality and applicability of the proposed method.
Noma, Hisashi; Matsui, Shigeyuki
2013-05-20
The main purpose of microarray studies is screening of differentially expressed genes as candidates for further investigation. Because of limited resources in this stage, prioritizing genes are relevant statistical tasks in microarray studies. For effective gene selections, parametric empirical Bayes methods for ranking and selection of genes with largest effect sizes have been proposed (Noma et al., 2010; Biostatistics 11: 281-289). The hierarchical mixture model incorporates the differential and non-differential components and allows information borrowing across differential genes with separation from nuisance, non-differential genes. In this article, we develop empirical Bayes ranking methods via a semiparametric hierarchical mixture model. A nonparametric prior distribution, rather than parametric prior distributions, for effect sizes is specified and estimated using the "smoothing by roughening" approach of Laird and Louis (1991; Computational statistics and data analysis 12: 27-37). We present applications to childhood and infant leukemia clinical studies with microarrays for exploring genes related to prognosis or disease progression. Copyright © 2012 John Wiley & Sons, Ltd.
MCDM based evaluation and ranking of commercial off-the-shelf using fuzzy based matrix method
Directory of Open Access Journals (Sweden)
Rakesh Garg
2017-04-01
Full Text Available In today’s scenario, software has become an essential component in all kinds of systems. The size and the complexity of the software increases with a corresponding increase in its functionality, hence leads to the development of the modular software systems. Software developers emphasize on the concept of component based software engineering (CBSE for the development of modular software systems. The CBSE concept consists of dividing the software into a number of modules; selecting Commercial Off-the-Shelf (COTS for each module; and finally integrating the modules to develop the final software system. The selection of COTS for any module plays a vital role in software development. To address the problem of selection of COTS, a framework for ranking and selection of various COTS components for any software system based on expert opinion elicitation and fuzzy-based matrix methodology is proposed in this research paper. The selection problem is modeled as a multi-criteria decision making (MCDM problem. The evaluation criteria are identified through extensive literature study and the COTS components are ranked based on these identified and selected evaluation criteria using the proposed methods according to the value of a permanent function of their criteria matrices. The methodology is explained through an example and is validated by comparing with an existing method.
Improving predicted protein loop structure ranking using a Pareto-optimality consensus method.
Li, Yaohang; Rata, Ionel; Chiu, See-wing; Jakobsson, Eric
2010-07-20
Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.
Use of rank sum method in identifying high occupational dose jobs for ALARA implementation
International Nuclear Information System (INIS)
Cho, Yeong Ho; Kang, Chang Sun
1998-01-01
The cost-effective reduction of occupational radiation exposure (ORE) dose at a nuclear power plant could not be achieved without going through an extensive analysis of accumulated ORE dose data of existing plants. It is necessary to identify what are high ORE jobs for ALARA implementation. In this study, the Rank Sum Method (RSM) is used in identifying high ORE jobs. As a case study, the database of ORE-related maintenance and repair jobs for Kori Units 3 and 4 is used for assessment, and top twenty high ORE jobs are identified. The results are also verified and validated using the Friedman test, and RSM is found to be a very efficient way of analyzing the data. (author)
Evaluation of image reconstruction methods for 123I-MIBG-SPECT. A rank-order study
International Nuclear Information System (INIS)
Soederberg, Marcus; Mattsson, Soeren; Oddstig, Jenny; Uusijaervi-Lizana, Helena; Leide-Svegborn, Sigrid; Valind, Sven; Thorsson, Ola; Garpered, Sabine; Prautzsch, Tilmann; Tischenko, Oleg
2012-01-01
Background: There is an opportunity to improve the image quality and lesion detectability in single photon emission computed tomography (SPECT) by choosing an appropriate reconstruction method and optimal parameters for the reconstruction. Purpose: To optimize the use of the Flash 3D reconstruction algorithm in terms of equivalent iteration (EI) number (number of subsets times the number of iterations) and to compare with two recently developed reconstruction algorithms ReSPECT and orthogonal polynomial expansion on disc (OPED) for application on 123 I-metaiodobenzylguanidine (MIBG)-SPECT. Material and Methods: Eleven adult patients underwent SPECT 4 h and 14 patients 24 h after injection of approximately 200 MBq 123 I-MIBG using a Siemens Symbia T6 SPECT/CT. Images were reconstructed from raw data using the Flash 3D algorithm at eight different EI numbers. The images were ranked by three experienced nuclear medicine physicians according to their overall impression of the image quality. The obtained optimal images were then compared in one further visual comparison with images reconstructed using the ReSPECT and OPED algorithms. Results: The optimal EI number for Flash 3D was determined to be 32 for acquisition 4 h and 24 h after injection. The average rank order (best first) for the different reconstructions for acquisition after 4 h was: Flash 3D 32 > ReSPECT > Flash 3D 64 > OPED, and after 24 h: Flash 3D 16 > ReSPECT > Flash 3D 32 > OPED. A fair level of inter-observer agreement concerning optimal EI number and reconstruction algorithm was obtained, which may be explained by the different individual preferences of what is appropriate image quality. Conclusion: Using Siemens Symbia T6 SPECT/CT and specified acquisition parameters, Flash 3D 32 (4 h) and Flash 3D 16 (24 h), followed by ReSPECT, were assessed to be the preferable reconstruction algorithms in visual assessment of 123 I-MIBG images
APPLYING ROBUST RANKING METHOD IN TWO PHASE FUZZY OPTIMIZATION LINEAR PROGRAMMING PROBLEMS (FOLPP
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Monalisha Pattnaik
2014-12-01
Full Text Available Background: This paper explores the solutions to the fuzzy optimization linear program problems (FOLPP where some parameters are fuzzy numbers. In practice, there are many problems in which all decision parameters are fuzzy numbers, and such problems are usually solved by either probabilistic programming or multi-objective programming methods. Methods: In this paper, using the concept of comparison of fuzzy numbers, a very effective method is introduced for solving these problems. This paper extends linear programming based problem in fuzzy environment. With the problem assumptions, the optimal solution can still be theoretically solved using the two phase simplex based method in fuzzy environment. To handle the fuzzy decision variables can be initially generated and then solved and improved sequentially using the fuzzy decision approach by introducing robust ranking technique. Results and conclusions: The model is illustrated with an application and a post optimal analysis approach is obtained. The proposed procedure was programmed with MATLAB (R2009a version software for plotting the four dimensional slice diagram to the application. Finally, numerical example is presented to illustrate the effectiveness of the theoretical results, and to gain additional managerial insights.
Crabbe, Helen; Fletcher, Tony; Close, Rebecca; Watts, Michael J; Ander, E Louise; Smedley, Pauline L; Verlander, Neville Q; Gregory, Martin; Middleton, Daniel R S; Polya, David A; Studden, Mike; Leonardi, Giovanni S
2017-12-01
Approximately one million people in the UK are served by private water supplies (PWS) where main municipal water supply system connection is not practical or where PWS is the preferred option. Chronic exposure to contaminants in PWS may have adverse effects on health. South West England is an area with elevated arsenic concentrations in groundwater and over 9000 domestic dwellings here are supplied by PWS. There remains uncertainty as to the extent of the population exposed to arsenic (As), and the factors predicting such exposure. We describe a hazard assessment model based on simplified geology with the potential to predict exposure to As in PWS. Households with a recorded PWS in Cornwall were recruited to take part in a water sampling programme from 2011 to 2013. Bedrock geologies were aggregated and classified into nine Simplified Bedrock Geological Categories (SBGC), plus a cross-cutting "mineralized" area. PWS were sampled by random selection within SBGCs and some 508 households volunteered for the study. Transformations of the data were explored to estimate the distribution of As concentrations for PWS by SBGC. Using the distribution per SBGC, we predict the proportion of dwellings that would be affected by high concentrations and rank the geologies according to hazard. Within most SBGCs, As concentrations were found to have log-normal distributions. Across these areas, the proportion of dwellings predicted to have drinking water over the prescribed concentration value (PCV) for As ranged from 0% to 20%. From these results, a pilot predictive model was developed calculating the proportion of PWS above the PCV for As and hazard ranking supports local decision making and prioritization. With further development and testing, this can help local authorities predict the number of dwellings that might fail the PCV for As, based on bedrock geology. The model presented here for Cornwall could be applied in areas with similar geologies. Application of the method
Van der Fels-Klerx, H J; Van Asselt, E D; Raley, M; Poulsen, M; Korsgaard, H; Bredsdorff, L; Nauta, M; D'agostino, M; Coles, D; Marvin, H J P; Frewer, L J
2018-01-22
This study aimed to critically review methods for ranking risks related to food safety and dietary hazards on the basis of their anticipated human health impacts. A literature review was performed to identify and characterize methods for risk ranking from the fields of food, environmental science and socio-economic sciences. The review used a predefined search protocol, and covered the bibliographic databases Scopus, CAB Abstracts, Web of Sciences, and PubMed over the period 1993-2013. All references deemed relevant, on the basis of predefined evaluation criteria, were included in the review, and the risk ranking method characterized. The methods were then clustered-based on their characteristics-into eleven method categories. These categories included: risk assessment, comparative risk assessment, risk ratio method, scoring method, cost of illness, health adjusted life years (HALY), multi-criteria decision analysis, risk matrix, flow charts/decision trees, stated preference techniques and expert synthesis. Method categories were described by their characteristics, weaknesses and strengths, data resources, and fields of applications. It was concluded there is no single best method for risk ranking. The method to be used should be selected on the basis of risk manager/assessor requirements, data availability, and the characteristics of the method. Recommendations for future use and application are provided.
Low rank alternating direction method of multipliers reconstruction for MR fingerprinting.
Assländer, Jakob; Cloos, Martijn A; Knoll, Florian; Sodickson, Daniel K; Hennig, Jürgen; Lattanzi, Riccardo
2018-01-01
The proposed reconstruction framework addresses the reconstruction accuracy, noise propagation and computation time for magnetic resonance fingerprinting. Based on a singular value decomposition of the signal evolution, magnetic resonance fingerprinting is formulated as a low rank (LR) inverse problem in which one image is reconstructed for each singular value under consideration. This LR approximation of the signal evolution reduces the computational burden by reducing the number of Fourier transformations. Also, the LR approximation improves the conditioning of the problem, which is further improved by extending the LR inverse problem to an augmented Lagrangian that is solved by the alternating direction method of multipliers. The root mean square error and the noise propagation are analyzed in simulations. For verification, in vivo examples are provided. The proposed LR alternating direction method of multipliers approach shows a reduced root mean square error compared to the original fingerprinting reconstruction, to a LR approximation alone and to an alternating direction method of multipliers approach without a LR approximation. Incorporating sensitivity encoding allows for further artifact reduction. The proposed reconstruction provides robust convergence, reduced computational burden and improved image quality compared to other magnetic resonance fingerprinting reconstruction approaches evaluated in this study. Magn Reson Med 79:83-96, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.
Karlitasari, L.; Suhartini, D.; Nurrosikawati, L.
2018-03-01
Selection of Student Achievement is conducted every year, starting from the level of Study Program, Faculty, to University, which then rank one will be sent to Kopertis level. The criteria made for the selection are Academic and Rich Scientific, Organizational, Personality, and English. In order for the selection of Student Achievement is Objective, then in addition to the presence of the jury is expected to use methods that support the decision to be more optimal in determining the Student Achievement. One method used is the Promethee Method. Preference Ranking Organization Method for Enrichment Evaluation (Promethee) is a method of ranking in Multi Criteria Decision Making (MCDM). PROMETHEE has the advantage that there is a preference type against the criteria that can take into account alternatives with other alternatives on the same criteria. The conjecture of alternate dominance over a criterion used in PROMETHEE is the use of values in the relationships between alternative ranking values. Based on the calculation result, from 7 applicants between Manual and Promethee Matrices, rank 1, 2, and 3, did not change, only 4 to 7 positions were changed. However, after the sensitivity test, almost all criteria experience a high level of sensitivity. Although it does not affect the students who will be sent to the next level, but can bring psychological impact on prospective student’s achievement
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Angelis, Luca De; Rahbek, Anders
2015-01-01
In this article, we investigate the behaviour of a number of methods for estimating the co-integration rank in VAR systems characterized by heteroskedastic innovation processes. In particular, we compare the efficacy of the most widely used information criteria, such as Akaike Information Criterion....... The relative finite-sample properties of the different methods are investigated by means of a Monte Carlo simulation study. For the simulation DGPs considered in the analysis, we find that the BIC-based procedure and the bootstrap sequential test procedure deliver the best overall performance in terms......-based method to over-estimate the co-integration rank in relatively small sample sizes....
Lake and Reservoir Evaporation Estimation: Sensitivity Analysis and Ranking Existing Methods
Directory of Open Access Journals (Sweden)
maysam majidi
2016-02-01
were acquired from the Doosti Dam weather station. Relative humidity, wind speed, atmospheric pressure and precipitation were acquired from the Pol−Khatoon weather station. Dew point temperature and sunshine data were collected from the Sarakhs weather station. Lake area was estimated from hypsometric curve in relation to lake level data. Temperature measurements were often performed in 16−day periods or biweekly from September 2011 to September 2012. Temperature profile of the lake (required for lake evaporation estimation was measured at different points of the reservoir using a portable multi−meter. The eighteen existing methods were compared and ranked based on Bowen ratio energy balance method (BREB. Results and Discussion: The estimated annual evaporation values by all of the applied methods in this study, ranged from 21 to 113mcm (million cubic meters. BREB annual evaporation obtained value was equal to 69.86mcm and evaporation rate averaged 5.47mm d-1 during the study period. According to the results, there is a relatively large difference between the obtained evaporation values from the adopted methods. The sensitivity analysis of evaporation methods for some input parameters indicated that the Hamon method (Eq. 16 was the most sensitive to the input parameters followed by the Brutsaert−Stricker and BREB, and radiation−temperature methods (Makkink, Jensen−Haise and Stephen−Stewart had the least sensitivity to input data. Besides, the air temperature, solar radiation (sunshine data, water surface temperature and wind speed data had the most effect on lake evaporation estimations, respectively. Finally, all evaporation estimation methods in this study have been ranked based on RMSD values. On a daily basis, the Jensen−Haise and the Makkink (solar radiation, temperature group, Penman (Combination group and Hamon (temperature, day length group methods had a relatively reasonable performance. As the results on a monthly scale, the Jensen−Haise and
Reduced-rank approximations to the far-field transform in the gridded fast multipole method
Hesford, Andrew J.; Waag, Robert C.
2011-05-01
The fast multipole method (FMM) has been shown to have a reduced computational dependence on the size of finest-level groups of elements when the elements are positioned on a regular grid and FFT convolution is used to represent neighboring interactions. However, transformations between plane-wave expansions used for FMM interactions and pressure distributions used for neighboring interactions remain significant contributors to the cost of FMM computations when finest-level groups are large. The transformation operators, which are forward and inverse Fourier transforms with the wave space confined to the unit sphere, are smooth and well approximated using reduced-rank decompositions that further reduce the computational dependence of the FMM on finest-level group size. The adaptive cross approximation (ACA) is selected to represent the forward and adjoint far-field transformation operators required by the FMM. However, the actual error of the ACA is found to be greater than that predicted using traditional estimates, and the ACA generally performs worse than the approximation resulting from a truncated singular-value decomposition (SVD). To overcome these issues while avoiding the cost of a full-scale SVD, the ACA is employed with more stringent accuracy demands and recompressed using a reduced, truncated SVD. The results show a greatly reduced approximation error that performs comparably to the full-scale truncated SVD without degrading the asymptotic computational efficiency associated with ACA matrix assembly.
Population models and simulation methods: The case of the Spearman rank correlation.
Astivia, Oscar L Olvera; Zumbo, Bruno D
2017-11-01
The purpose of this paper is to highlight the importance of a population model in guiding the design and interpretation of simulation studies used to investigate the Spearman rank correlation. The Spearman rank correlation has been known for over a hundred years to applied researchers and methodologists alike and is one of the most widely used non-parametric statistics. Still, certain misconceptions can be found, either explicitly or implicitly, in the published literature because a population definition for this statistic is rarely discussed within the social and behavioural sciences. By relying on copula distribution theory, a population model is presented for the Spearman rank correlation, and its properties are explored both theoretically and in a simulation study. Through the use of the Iman-Conover algorithm (which allows the user to specify the rank correlation as a population parameter), simulation studies from previously published articles are explored, and it is found that many of the conclusions purported in them regarding the nature of the Spearman correlation would change if the data-generation mechanism better matched the simulation design. More specifically, issues such as small sample bias and lack of power of the t-test and r-to-z Fisher transformation disappear when the rank correlation is calculated from data sampled where the rank correlation is the population parameter. A proof for the consistency of the sample estimate of the rank correlation is shown as well as the flexibility of the copula model to encompass results previously published in the mathematical literature. © 2017 The British Psychological Society.
Directory of Open Access Journals (Sweden)
Darjan Karabasevic
2016-05-01
Full Text Available Corporate sector and companies have recognized the importance of implementation of strategy of corporate social responsibility in order to increase the company's image and responsibility towards society and the communities where they operate. Multinational companies in their everyday activities and operations pay more attention to sustainable models of corporate social responsibility. The focus of this paper is to identify the indicators of corporate social responsibility and to rank companies according to the indicators. Proposed framework for evaluation and ranking is based on the SWARA and the ARAS methods. The usability and efficiency of the proposed framework is shown on an illustrative example.
Church, Lewis
2010-01-01
This dissertation answers three research questions: (1) What are the characteristics of a combinatoric measure, based on the Average Search Length (ASL), that performs the same as a probabilistic version of the ASL?; (2) Does the combinatoric ASL measure produce the same performance result as the one that is obtained by ranking a collection 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
A method for generating permutation distribution of ranks in a k ...
African Journals Online (AJOL)
... in a combinatorial sense the distribution of the ranks is obtained via its generating function. The formulas are defined recursively to speed up computations using the computer algebra system Mathematica. Key words: Partitions, generating functions, combinatorics, permutation test, exact tests, computer algebra, k-sample, ...
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
Directory of Open Access Journals (Sweden)
Chaoxing Li
2018-04-01
Full Text Available Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway’s topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher’s exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several
Li, Chaoxing; Liu, Li; Dinu, Valentin
2018-01-01
Complex diseases such as cancer are usually the result of a combination of environmental factors and one or several biological pathways consisting of sets of genes. Each biological pathway exerts its function by delivering signaling through the gene network. Theoretically, a pathway is supposed to have a robust topological structure under normal physiological conditions. However, the pathway's topological structure could be altered under some pathological condition. It is well known that a normal biological network includes a small number of well-connected hub nodes and a large number of nodes that are non-hubs. In addition, it is reported that the loss of connectivity is a common topological trait of cancer networks, which is an assumption of our method. Hence, from normal to cancer, the process of the network losing connectivity might be the process of disrupting the structure of the network, namely, the number of hub genes might be altered in cancer compared to that in normal or the distribution of topological ranks of genes might be altered. Based on this, we propose a new PageRank-based method called Pathways of Topological Rank Analysis (PoTRA) to detect pathways involved in cancer. We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fisher's exact test to test if the number of hub genes in each pathway is altered from normal to cancer. Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer. Hence, we use the Kolmogorov-Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes. We apply PoTRA to study hepatocellular carcinoma (HCC) and several subtypes of HCC. Very interestingly, we discover that all significant pathways in HCC are cancer-associated generally, while several significant pathways in subtypes
Directory of Open Access Journals (Sweden)
Matthew eMaestri
2014-03-01
Full Text Available For scientific, clinical, and machine learning purposes alike, it is desirable to quantify the verbal reports of high-level visual percepts. Methods to do this simply do not exist at present. Here we propose a novel methodological principle to help fill this gap, and provide empirical evidence designed to serve as the initial ‘proof’ of this principle. In the proposed method, subjects view images real-world scenes and describe, in their own words, what they saw. The verbal description is independently evaluated by several evaluators. Each evaluator assigns a rank score to the subject’s description of each visual object in each image using a novel ranking principle, which takes advantage of the well-known fact that semantic descriptions of real-life objects and scenes can usually be rank-ordered. Thus, for instance, ‘animal’, ‘dog’, and ‘retriever’ can be regarded as increasingly finer-level, and therefore higher-ranking, descriptions of a given object. These numeric scores can preserve the richness of the original verbal description, and can be subsequently evaluated using conventional statistical procedures. We describe an exemplar implementation of this method and empirical data that show its feasibility. With appropriate future standardization and validation, this novel method can serve as an important tool to help quantify the subjective experience of the visual world. In addition to being a novel, potentially powerful testing tool, our method also represents, to our knowledge, the only available method for numerically representing verbal accounts of real-world experience. Given that its minimal requirements, i.e., a verbal description and the ground truth that elicited the description, our method has a wide variety of potential real-world applications.
International Nuclear Information System (INIS)
Chou, Jui-Sheng; Ongkowijoyo, Citra Satria
2015-01-01
Corporate competitiveness is heavily influenced by the information acquired, processed, utilized and transferred by professional staff involved in the supply chain. This paper develops a decision aid for selecting on-site ready-mix concrete (RMC) unloading type in decision making situations involving multiple stakeholders and evaluation criteria. The uncertainty of criteria weights set by expert judgment can be transformed in random ways based on the probabilistic virtual-scale method within a prioritization matrix. The ranking is performed by grey relational grade systems considering stochastic criteria weight based on individual preference. Application of the decision aiding model in actual RMC case confirms that the method provides a robust and effective tool for facilitating decision making under uncertainty. - Highlights: • This study models decision aiding method to assess ready-mix concrete unloading type. • Applying Monte Carlo simulation to virtual-scale method achieves a reliable process. • Individual preference ranking method enhances the quality of global decision making. • Robust stochastic superiority and inferiority ranking obtains reasonable results
Hadyan, Fadhlil; Shaufiah; Arif Bijaksana, Moch.
2017-01-01
Automatic summarization is a system that can help someone to take the core information of a long text instantly. The system can help by summarizing text automatically. there’s Already many summarization systems that have been developed at this time but there are still many problems in those system. In this final task proposed summarization method using document index graph. This method utilizes the PageRank and HITS formula used to assess the web page, adapted to make an assessment of words in the sentences in a text document. The expected outcome of this final task is a system that can do summarization of a single document, by utilizing document index graph with TextRank and HITS to improve the quality of the summary results automatically.
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...
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.
Designing fuzzy expert system for creating and ranking of tourism scenarios using fuzzy AHP method
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Zohre Nikkhah
2011-01-01
Full Text Available One of the most important activities of tour and travel agencies is to select the appropriate tour configuration. There are normally two primary objectives of season and time period to set a group of cities called designing tour scenarios. The success of tour scenarios is deeply related to the experiments and wisdom of the experts and planners in travel agencies. This paper presents a fuzzy rule decision making to find the suitable set of cities where different possible criteria are ranked using analytical hierarchy procedure. The proposed model of this paper is applied for a real-world case study of Iranian tour agency and the results are analyzed under different circumstances.
Ranking critical success factor in chaos management using BSC and AHP method
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Ehsan Khosravi Asil
2013-06-01
Full Text Available Managing an organization under chaos and uncertainty is often a concern of academic society. These days, we may face unpleasant natural, economical or even political incidents where mangers need to handle them, properly. This paper presents an empirical survey to investigate on an electromotor maker when it faces different chaos. The proposed study uses balanced scorecard in terms of four different perspectives including internal process, learning and growth, customer and financial performances. For each perspective, the proposed study uses analytical hierarchy process to rank different sub-criteria. Based on the results of our survey profit margin is the most important item followed by profit capability and brand name while productivity and sales force performance were the least important items.
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Motoki Yokoyama
2017-07-01
Full Text Available The prevalence of smartphones and wireless broadband networks have been progressing as a new Railway infomration environment. According to the spread of such devices and information technology, various types of information can be obtained from databases connected to the Internet. One scenario of obtaining such a wide variety of information resources is in the phase of user’s transportation. This paper proposes an information provision system, named the Station Concierge System that matches the situation and intention of passengers. The purpose of this system is to estimate the needs of passengers like station staff or hotel concierge and to provide information resources that satisfy user’s expectations dynamically. The most important module of the system is constructed based on a new information ranking method for passenger intention prediction and service recommendation. This method has three main features, which are (1 projecting a user to semantic vector space by using her current context, (2 predicting the intention of a user based on selecting a semantic vector subspace, and (3 ranking the services by a descending order of relevant scores to the user’ intention. By comparing the predicted results of our method with those of two straightforward computation methods, the experimental studies show the effectiveness and efficiency of the proposed method. Using this system, users can obtain transit information and service map that dynamically matches their context.
An empirical investigation on ranking financial risk factors using AHP method
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Hassan Ghodrati
2014-05-01
Full Text Available This paper determines and ranks financial risk factors in Iranian corporations, using analytical hierarchy process (AHP. The present research includes one main question and four sub- questions. Its universe population includes managers, production and financial personnel of great corporations activating in Tehran Stock Exchange, who were selected to explain importance and weight of economic risks indices. The source of great corporations recognition is the Companies Registration Organization in Tehran Province, and according to this, there are 120 corporations. The results have indicated that financing risk maintains the highest priority followed by credit risk, liquidity risk, inflation risk and exchange risk. In terms of different risks associated with financing risk, risk of profit per share has been the number one priority followed by the risk of divisional profit per share, the risk of recessionary or boom and the risk of increasing partial pay profit rate. In terms of credit risk, the risk of loan has been number one priority followed by the risk of inability of loan payment and interest payment. Liquidity risk is another risk factor where demand has been the most important factor followed by rules and regulations and inflation risk. In terms of inflation, producers price risk has been the most important factor followed by consumer price risk, gross domestic product and producers price risk. Finally, in terms of different factors influencing exchange risk, export related issues are considered as the most important factors.
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.
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Renata Maciel de Melo
2015-03-01
Full Text Available The quality of the construction production process may be improved using several different methods such as Lean Construction, ISO 9001, ISO 14001 or ISO 18001. Construction companies need a preliminary study and systematic implementation of changes to become more competitive and efficient. This paper presents a multicriteria decision model for the selection and ranking of such alternatives for improvement approaches regarding the aspects of quality, sustainability and safety, based on the PROMETHEE II method. The adoption of this model provides more confidence and visibility for decision makers. One of the differentiators of this model is the use of a fragmented set of improvement alternatives. These alternatives were combined with some restrictions to create a global set of alternatives. An application to three scenarios, considering realistic data, was developed. The results of the application show that the model should be incorporated into the strategic planning process of organizations.
Ranking Method for Peak-Load Shifting Considering Different Types of Data
DEFF Research Database (Denmark)
Wang, Peng; Wen, Fushuan; Pinson, Pierre
2016-01-01
, an evaluation system for the purpose of peak-load shifting is established from three aspects: economic, social, and environmental impacts. Then a mixed-data dominance method is employed in this work to determine the comprehensive closeness degree of each user under each index, and an optimal comprehensive...
Lei Guo; Haoran Jiang; Xinhua Wang; Fangai Liu
2017-01-01
Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem ...
Ranking the types of intersections for assessing the safety of pedestrians using TOPSIS method
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Călin ŞERBU
2014-11-01
Full Text Available Every year, more than 1500 accidents with pedestrian occur in the intersections in Romania. The number of accidents involving pedestrians in roundabouts intersections type increased approximately three times in 2013 compared to 2009 in Romania. This alarming increase led to the need of assessing the safety of pedestrians in intersections with or without safety systems. The safety systems for pedestrians and drivers include: the road marking, the pedestrian crossings marking, signal intersections with road signs, traffic lights or pedestrian safety barriers. We propose to assess the types of intersections with TOPSIS method.
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…
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Zeeshan Ali Siddiqui
2016-01-01
Full Text Available Component-based software system (CBSS development technique is an emerging discipline that promises to take software development into a new era. As hardware systems are presently being constructed from kits of parts, software systems may also be assembled from components. It is more reliable to reuse software than to create. It is the glue code and individual components reliability that contribute to the reliability of the overall system. Every component contributes to overall system reliability according to the number of times it is being used, some components are of critical usage, known as usage frequency of component. The usage frequency decides the weight of each component. According to their weights, each component contributes to the overall reliability of the system. Therefore, ranking of components may be obtained by analyzing their reliability impacts on overall application. In this paper, we propose the application of fuzzy multi-objective optimization on the basis of ratio analysis, Fuzzy-MOORA. The method helps us find the best suitable alternative, software component, from a set of available feasible alternatives named software components. It is an accurate and easy to understand tool for solving multi-criteria decision making problems that have imprecise and vague evaluation data. By the use of ratio analysis, the proposed method determines the most suitable alternative among all possible alternatives, and dimensionless measurement will realize the job of ranking of components for estimating CBSS reliability in a non-subjective way. Finally, three case studies are shown to illustrate the use of the proposed technique.
An Integrated MCDM Method in Ranking BSC Perspectives and key Performance Indicators (KPIs
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Mohsen Alvandi
2012-04-01
Full Text Available The balanced scorecard (BSC approach is an effective technique for performance evaluation. BSC can better reflect the dependence and feedback problems of each factor in real world situations. This study aims at developing a set of appropriate key performance indicators according to (BSC approach for SAPCO using multiple criteria decision making(MCDM method. We provide key performance indicators through literature reviews and experts' idea in SAPCO, which is one of the biggest vehicle spare suppliers in Iran. The proposed study uses decision making trial and evaluation laboratory (DEMATEL and analytic network process (ANP, respectively to measure the casual relationship between the perspectives as well as the relative weights. The results based on ANP method shows that ‘‘Customer’’ is the most influential factor. In addition, internal process, financial and learning and growth are in two to four positions. Three important key performance indicators are as bellow: Total price of parts, Customer satisfaction and Lack of parts in production.
A Bayesian method to rank different model forecasts of the same volcanic ash cloud: Chapter 24
Denlinger, Roger P.; Webley, P.; Mastin, Larry G.; Schwaiger, Hans F.
2012-01-01
Volcanic eruptions often spew fine ash high into the atmosphere, where it is carried downwind, forming long ash clouds that disrupt air traffic and pose a hazard to air travel. To mitigate such hazards, the community studying ash hazards must assess risk of ash ingestion for any flight path and provide robust and accurate forecasts of volcanic ash dispersal. We provide a quantitative and objective method to evaluate the efficacy of ash dispersal estimates from different models, using Bayes theorem to assess the predictions that each model makes about ash dispersal. We incorporate model and measurement uncertainty and produce a posterior probability for model input parameters. The integral of the posterior over all possible combinations of model inputs determines the evidence for each model and is used to compare models. We compare two different types of transport models, an Eulerian model (Ash3d) and a Langrangian model (PUFF), as applied to the 2010 eruptions of Eyjafjallajökull volcano in Iceland. The evidence for each model benefits from common physical characteristics of ash dispersal from an eruption column and provides a measure of how well each model forecasts cloud transport. Given the complexity of the wind fields, we find that the differences between these models depend upon the differences in the way the models disperse ash into the wind from the source plume. With continued observation, the accuracy of the estimates made by each model increases, increasing the efficacy of each model’s ability to simulate ash dispersal.
Shin, Saemi; Moon, Hyung-Il; Lee, Kwon Seob; Hong, Mun Ki; Byeon, Sang-Hoon
2014-01-01
This study aimed to devise a method for prioritizing hazardous chemicals for further regulatory action. To accomplish this objective, we chose appropriate indicators and algorithms. Nine indicators from the Globally Harmonized System of Classification and Labeling of Chemicals were used to identify categories to which the authors assigned numerical scores. Exposure indicators included handling volume, distribution, and exposure level. To test the method devised by this study, sixty-two harmful substances controlled by the Occupational Safety and Health Act in Korea, including acrylamide, acrylonitrile, and styrene were ranked using this proposed method. The correlation coefficients between total score and each indicator ranged from 0.160 to 0.641, and those between total score and hazard indicators ranged from 0.603 to 0.641. The latter were higher than the correlation coefficients between total score and exposure indicators, which ranged from 0.160 to 0.421. Correlations between individual indicators were low (−0.240 to 0.376), except for those between handling volume and distribution (0.613), suggesting that each indicator was not strongly correlated. The low correlations between each indicator mean that the indicators and independent and were well chosen for prioritizing harmful chemicals. This method proposed by this study can improve the cost efficiency of chemical management as utilized in occupational regulatory systems. PMID:25419874
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Saemi Shin
2014-11-01
Full Text Available This study aimed to devise a method for prioritizing hazardous chemicals for further regulatory action. To accomplish this objective, we chose appropriate indicators and algorithms. Nine indicators from the Globally Harmonized System of Classification and Labeling of Chemicals were used to identify categories to which the authors assigned numerical scores. Exposure indicators included handling volume, distribution, and exposure level. To test the method devised by this study, sixty-two harmful substances controlled by the Occupational Safety and Health Act in Korea, including acrylamide, acrylonitrile, and styrene were ranked using this proposed method. The correlation coefficients between total score and each indicator ranged from 0.160 to 0.641, and those between total score and hazard indicators ranged from 0.603 to 0.641. The latter were higher than the correlation coefficients between total score and exposure indicators, which ranged from 0.160 to 0.421. Correlations between individual indicators were low (−0.240 to 0.376, except for those between handling volume and distribution (0.613, suggesting that each indicator was not strongly correlated. The low correlations between each indicator mean that the indicators and independent and were well chosen for prioritizing harmful chemicals. This method proposed by this study can improve the cost efficiency of chemical management as utilized in occupational regulatory systems.
International Nuclear Information System (INIS)
Cho, Y.H.; Ko, H.S.; Kim, S.H.; Kang, C.S.; Moon, J.H.; Kim, K.D.
2004-01-01
The cost-effective reduction of occupational radiation dose (ORD) at a nuclear power plant could not be achieved without going through an extensive analysis of accumulated ORD data of existing plants. Through the data analysis, it is required to identify what are the jobs of repetitive high ORD at the nuclear power plant. In general the point value method commonly used, over-estimates the role of mean and median values to identify the high ORD jobs which can lead to misjudgment. In this study, Percentile Rank Sum Method (PRSM) is proposed to identify repetitive high ORD jobs, which is based on non-parametric statistical theory. As a case study, the method is applied to ORD data of maintenance and repair jobs at Kori units 3 and 4 that are pressurized water reactors with 950 MWe capacity and have been operated since 1986 and 1987, respectively in Korea. The results were verified and validated, and PRSM has been demonstrated to be an efficient method of analyzing the data. (authors)
Criado, Regino; García, Esther; Pedroche, Francisco; Romance, Miguel
2013-12-01
In this paper, we show a new technique to analyze families of rankings. In particular, we focus on sports rankings and, more precisely, on soccer leagues. We consider that two teams compete when they change their relative positions in consecutive rankings. This allows to define a graph by linking teams that compete. We show how to use some structural properties of this competitivity graph to measure to what extend the teams in a league compete. These structural properties are the mean degree, the mean strength, and the clustering coefficient. We give a generalization of the Kendall's correlation coefficient to more than two rankings. We also show how to make a dynamic analysis of a league and how to compare different leagues. We apply this technique to analyze the four major European soccer leagues: Bundesliga, Italian Lega, Spanish Liga, and Premier League. We compare our results with the classical analysis of sport ranking based on measures of competitive balance.
Evaluation of image reconstruction methods for {sup 123}I-MIBG-SPECT. A rank-order study
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Soederberg, Marcus; Mattsson, Soeren; Oddstig, Jenny; Uusijaervi-Lizana, Helena; Leide-Svegborn, Sigrid [Medical Radiation Physics, Dept. of Clinical Sciences Malmoe, Lund Univ., Skaane Univ. Hospital, Malmoe (Sweden)], e-mail: marcus.soderberg@med.lu.se; Valind, Sven; Thorsson, Ola; Garpered, Sabine [Dept. of Clinical Physiology, Skaane Univ. Hospital, Malmoe (Sweden); Prautzsch, Tilmann [Scivis wissenschaftlice Bildverarbeitung GmbH, Goettingen (Germany); Tischenko, Oleg [Research Unit Medical Radiation Physics and Diagnostics (AMSD), Helmholtz Zentrum Muenchen (Germany); German Research Center for Environmental Health, Neuherberg (Germany)
2012-09-15
Background: There is an opportunity to improve the image quality and lesion detectability in single photon emission computed tomography (SPECT) by choosing an appropriate reconstruction method and optimal parameters for the reconstruction. Purpose: To optimize the use of the Flash 3D reconstruction algorithm in terms of equivalent iteration (EI) number (number of subsets times the number of iterations) and to compare with two recently developed reconstruction algorithms ReSPECT and orthogonal polynomial expansion on disc (OPED) for application on {sup 123}I-metaiodobenzylguanidine (MIBG)-SPECT. Material and Methods: Eleven adult patients underwent SPECT 4 h and 14 patients 24 h after injection of approximately 200 MBq {sup 123}I-MIBG using a Siemens Symbia T6 SPECT/CT. Images were reconstructed from raw data using the Flash 3D algorithm at eight different EI numbers. The images were ranked by three experienced nuclear medicine physicians according to their overall impression of the image quality. The obtained optimal images were then compared in one further visual comparison with images reconstructed using the ReSPECT and OPED algorithms. Results: The optimal EI number for Flash 3D was determined to be 32 for acquisition 4 h and 24 h after injection. The average rank order (best first) for the different reconstructions for acquisition after 4 h was: Flash 3D{sub 32} > ReSPECT > Flash 3D{sub 64} > OPED, and after 24 h: Flash 3D{sub 16} > ReSPECT > Flash 3D{sub 32} > OPED. A fair level of inter-observer agreement concerning optimal EI number and reconstruction algorithm was obtained, which may be explained by the different individual preferences of what is appropriate image quality. Conclusion: Using Siemens Symbia T6 SPECT/CT and specified acquisition parameters, Flash 3D{sub 32} (4 h) and Flash 3D{sub 16} (24 h), followed by ReSPECT, were assessed to be the preferable reconstruction algorithms in visual assessment of {sup 123}I-MIBG images.
Schrader, Ulf; Hennig-Thurau, Thorsten
2009-01-01
VHB-JOURQUAL represents the official journal ranking of the German Academic Association for Business Research. Since its introduction in 2003, the ranking has become the most influential journal evaluation approach in German-speaking countries, impacting several key managerial decisions of German, Austrian, and Swiss business schools. This article reports the methodological approach of the ranking’s second edition. It also presents the main results and additional analyses on the validity of t...
American Society for Testing and Materials. Philadelphia
2005-01-01
1.1 This test method covers laboratory procedures for determining the resistance of materials to sliding wear. The test utilizes a block-on-ring friction and wear testing machine to rank pairs of materials according to their sliding wear characteristics under various conditions. 1.2 An important attribute of this test is that it is very flexible. Any material that can be fabricated into, or applied to, blocks and rings can be tested. Thus, the potential materials combinations are endless. However, the interlaboratory testing has been limited to metals. In addition, the test can be run with various lubricants, liquids, or gaseous atmospheres, as desired, to simulate service conditions. Rotational speed and load can also be varied to better correspond to service requirements. 1.3 The values stated in SI units are to be regarded as the standard. The values given in parentheses are for information only. Wear test results are reported as the volume loss in cubic millimetres for both the block and ring. Materials...
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Diana Purwitasari
2008-01-01
Full Text Available Ranking module is an important component of search process which sorts through relevant pages. Since collection of Web pages has additional information inherent in the hyperlink structure of the Web, it can be represented as link score and then combined with the usual information retrieval techniques of content score. In this paper we report our studies about ranking score of Web pages combined from link analysis, PageRank Scoring, and content analysis, Fourier Domain Scoring. Our experiments use collection of Web pages relate to Statistic subject from Wikipedia with objectives to check correctness and performance evaluation of combination ranking method. Evaluation of PageRank Scoring show that the highest score does not always relate to Statistic. Since the links within Wikipedia articles exists so that users are always one click away from more information on any point that has a link attached, it it possible that unrelated topics to Statistic are most likely frequently mentioned in the collection. While the combination method show link score which is given proportional weight to content score of Web pages does effect the retrieval results.
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Vassal Aurélien
2008-01-01
Full Text Available Abstract Background The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix® technology provides both a quantitative fluorescence signal and a decision (detection call: absent or present based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM patients. Results After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM. Conclusion This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with
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
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Wang Jim
2012-11-01
Full Text Available Abstract Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.
Directory of Open Access Journals (Sweden)
Dániel Bánky
Full Text Available Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks that compensates for the low degree (non-hub vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well, but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus, and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures
Bánky, Dániel; Iván, Gábor; Grolmusz, Vince
2013-01-01
Biological network data, such as metabolic-, signaling- or physical interaction graphs of proteins are increasingly available in public repositories for important species. Tools for the quantitative analysis of these networks are being developed today. Protein network-based drug target identification methods usually return protein hubs with large degrees in the networks as potentially important targets. Some known, important protein targets, however, are not hubs at all, and perturbing protein hubs in these networks may have several unwanted physiological effects, due to their interaction with numerous partners. Here, we show a novel method applicable in networks with directed edges (such as metabolic networks) that compensates for the low degree (non-hub) vertices in the network, and identifies important nodes, regardless of their hub properties. Our method computes the PageRank for the nodes of the network, and divides the PageRank by the in-degree (i.e., the number of incoming edges) of the node. This quotient is the same in all nodes in an undirected graph (even for large- and low-degree nodes, that is, for hubs and non-hubs as well), but may differ significantly from node to node in directed graphs. We suggest to assign importance to non-hub nodes with large PageRank/in-degree quotient. Consequently, our method gives high scores to nodes with large PageRank, relative to their degrees: therefore non-hub important nodes can easily be identified in large networks. We demonstrate that these relatively high PageRank scores have biological relevance: the method correctly finds numerous already validated drug targets in distinct organisms (Mycobacterium tuberculosis, Plasmodium falciparum and MRSA Staphylococcus aureus), and consequently, it may suggest new possible protein targets as well. Additionally, our scoring method was not chosen arbitrarily: its value for all nodes of all undirected graphs is constant; therefore its high value captures importance in the
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...
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.
Directory of Open Access Journals (Sweden)
Zutao Zhang
2016-06-01
Full Text Available Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety.
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.
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
American Society for Testing and Materials. Philadelphia
2003-01-01
1.1 This test method covers laboratory procedures for determining the resistance of plastics to sliding wear. The test utilizes a block-on-ring friction and wear testing machine to rank plastics according to their sliding wear characteristics against metals or other solids. 1.2 An important attribute of this test is that it is very flexible. Any material that can be fabricated into, or applied to, blocks and rings can be tested. Thus, the potential materials combinations are endless. In addition, the test can be run with different gaseous atmospheres and elevated temperatures, as desired, to simulate service conditions. 1.3 Wear test results are reported as the volume loss in cubic millimetres for the block and ring. Materials of higher wear resistance will have lower volume loss. 1.4 The values stated in SI units are to be regarded as the standard. The values given in parentheses are for information only. 1.5 This standard does not purport to address all of the safety concerns, if any, associated with it...
Directory of Open Access Journals (Sweden)
Mohsen Sayyah Markabi
2014-10-01
Full Text Available Purpose: Evaluation and selection of efficient suppliers is one of the key issues in supply chain management which depends on wide range of qualitative and quantitative criteria. The aim of this research is to develop a mathematical model for evaluating and selecting efficient suppliers when faced with supply and demand uncertainties.Design/methodology/approach: In this research Grey Relational Analysis (GRA and Data Envelopment Analysis (DEA are used to evaluate and select efficient suppliers under uncertainties. Furthermore, a novel ranking method is introduced for the units that their efficiencies are obtained in the form of interval grey numbers.Findings: The study indicates that the proposed model in addition to providing satisfactory and acceptable results avoids time-consuming computations and consequently reduces the solution time. To name another advantage of the proposed model, we can point out that it enables us to make decision based on different levels of risk.Originality/value: The paper presents a mathematical model for evaluating and selecting efficient suppliers in a stochastic environment so that companies can use in order to make better decisions.
Directory of Open Access Journals (Sweden)
Wei Dong
2017-02-01
Full Text Available This paper researches the problem of decline in translation accuracy caused by language “vagueness” in literary translation, and proposes to use the catastrophe model for importance ranking of various factors affecting the fuzzy language translation accuracy in literary works, and finally gives out the order of factors to be considered before translation. The multi-level evaluation system can be used to construct the relevant catastrophe progression model, and the normalization formula can be used to calculate the relative membership degree of each system and evaluation index, and make evaluation combined with the evaluation criteria table. The results show that, in the fuzzy language translation, in order to improve the translation accuracy, there is a need to consider the indicators ranking: A2 fuzzy language context → A1 words attribute → A3 specific meaning of digital words; B2 fuzzy semantics, B3 blur color words → B1 multiple meanings of words → B4 fuzzy digital words; C3 combination with context and cultural background, C4 specific connotation of color words → C1 combination with words emotion, C2 selection of words meaning → C5 combination with digits and language background.
Ma, Xu; Cheng, Yongmei; Hao, Shuai
2016-12-10
Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.
Bigus, Paulina; Tsakovski, Stefan; Simeonov, Vasil; Namieśnik, Jacek; Tobiszewski, Marek
2016-05-01
This study presents an application of the Hasse diagram technique (HDT) as the assessment tool to select the most appropriate analytical procedures according to their greenness or the best analytical performance. The dataset consists of analytical procedures for benzo[a]pyrene determination in sediment samples, which were described by 11 variables concerning their greenness and analytical performance. Two analyses with the HDT were performed-the first one with metrological variables and the second one with "green" variables as input data. Both HDT analyses ranked different analytical procedures as the most valuable, suggesting that green analytical chemistry is not in accordance with metrology when benzo[a]pyrene in sediment samples is determined. The HDT can be used as a good decision support tool to choose the proper analytical procedure concerning green analytical chemistry principles and analytical performance merits.
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...
Sanders, Anthony P; Brannon, Rebecca M
2014-02-01
This research has developed a novel test method for evaluating the wear resistance of ceramic materials under severe contact stresses simulating edge loading in prosthetic hip bearings. Simply shaped test specimens - a cylinder and a spheroid - were designed as surrogates for an edge-loaded, head/liner implant pair. Equivalency of the simpler specimens was assured in the sense that their theoretical contact dimensions and pressures were identical, according to Hertzian contact theory, to those of the head/liner pair. The surrogates were fabricated in three ceramic materials: Al2 O3 , zirconia-toughened alumina (ZTA), and ZrO2 . They were mated in three different material pairs and reciprocated under a 200 N normal contact force for 1000-2000 cycles, which created small (material pairs were ranked by their wear resistance, quantified by the volume of abraded material measured using an interferometer. Similar tests were performed on edge-loaded hip implants in the same material pairs. The surrogates replicated the wear rankings of their full-scale implant counterparts and mimicked their friction force trends. The results show that a proxy test using simple test specimens can validly rank the wear performance of ceramic materials under severe, edge-loading contact stresses, while replicating the beginning stage of edge-loading wear. This simple wear test is therefore potentially useful for screening and ranking new, prospective materials early in their development, to produce optimized candidates for more complicated full-scale hip simulator wear tests. Copyright © 2013 Wiley Periodicals, Inc.
Directory of Open Access Journals (Sweden)
Hofstede Robert G.M.
1993-12-01
Full Text Available The use of the combination of the semi-destructive comparative yield method for overall biomass estimation and the non- destructive dry-weight-rank method for studying botanical composition on a dry weight basis in an undisturbed páramo vegetation in the Los Nevados national park (Colombian Central Cordillera was evaluated. These methods, developed for Australian production grasslands, were adapted for use in the páramo ecosystem. The average above ground biomass in the area was estimated as 2864 g dryweight. m-2 (sd.48, of which the bunchgrass Calamagrostis effusa contributed with ca 70%. When used with some adaptations, the comparative yield method seems suitable for biomass estimations in the páramo ecosystem. The here presented estimation of botanical eomposition with this method gave better results than dry-weight-rank method, which had too many shortcomings for use in the complex páramo grassland ecosystem.Se evaluó la aplicabilidad de una combinación de dos étodos para estimar la biomasa general y la composición botánica, en una vegetación natural paramuna en el Parque Nacional Natural los Nevados (Cordillera Central de Colombia. El primer método (ecomparative yield determina la biomasa general, destruyendo parcialmente la vegetación de los cuadrantes de muestreo y el segundo (dryweight rank determina la composición botánica con base en el peso seco, sin destruir la vegetación. Estos métodos, inicialmente desarrollados para pajonales forrajeros en Australia, se adaptaron para ser utilizados en el ecosistema paramuno. Como resultado se obtuvo una estimación de la biomasa aérea de 2864 g peso seco m2 (desviación stándard 48 en la cual, la gramínea Calamagrostis effusa contribuyó con el 70%. Puede concluirse que el método de producción comparativa es útil para estimar la biomasa en el ecosistema paramuno, siempre y cuando se utilicen las adaptaciones mencionadas. Por otra parte la estimación de la composición bot
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.
Alharthi, Hana; Sultana, Nahid; Al-Amoudi, Amjaad; Basudan, Afrah
2015-01-01
Pharmacy barcode scanning is used to reduce errors during the medication dispensing process. However, this technology has rarely been used in hospital pharmacies in Saudi Arabia. This article describes the barriers to successful implementation of a barcode scanning system in Saudi Arabia. A literature review was conducted to identify the relevant critical success factors (CSFs) for a successful dispensing barcode system implementation. Twenty-eight pharmacists from a local hospital in Saudi Arabia were interviewed to obtain their perception of these CSFs. In this study, planning (process flow issues and training requirements), resistance (fear of change, communication issues, and negative perceptions about technology), and technology (software, hardware, and vendor support) were identified as the main barriers. The analytic hierarchy process (AHP), one of the most widely used tools for decision making in the presence of multiple criteria, was used to compare and rank these identified CSFs. The results of this study suggest that resistance barriers have a greater impact than planning and technology barriers. In particular, fear of change is the most critical factor, and training is the least critical factor.
Dunne, Suzanne; Cummins, Niamh Maria; Hannigan, Ailish; Shannon, Bill; Dunne, Colum; Cullen, Walter
2013-08-27
The Internet is a widely used source of information for patients searching for medical/health care information. While many studies have assessed existing medical/health care information on the Internet, relatively few have examined methods for design and delivery of such websites, particularly those aimed at the general public. This study describes a method of evaluating material for new medical/health care websites, or for assessing those already in existence, which is correlated with higher rankings on Google's Search Engine Results Pages (SERPs). A website quality assessment (WQA) tool was developed using criteria related to the quality of the information to be contained in the website in addition to an assessment of the readability of the text. This was retrospectively applied to assess existing websites that provide information about generic medicines. The reproducibility of the WQA tool and its predictive validity were assessed in this study. The WQA tool demonstrated very high reproducibility (intraclass correlation coefficient=0.95) between 2 independent users. A moderate to strong correlation was found between WQA scores and rankings on Google SERPs. Analogous correlations were seen between rankings and readability of websites as determined by Flesch Reading Ease and Flesch-Kincaid Grade Level scores. The use of the WQA tool developed in this study is recommended as part of the design phase of a medical or health care information provision website, along with assessment of readability of the material to be used. This may ensure that the website performs better on Google searches. The tool can also be used retrospectively to make improvements to existing websites, thus, potentially enabling better Google search result positions without incurring the costs associated with Search Engine Optimization (SEO) professionals or paid promotion.
Ajmera, Puneeta
2017-10-09
Purpose Organizations have to evaluate their internal and external environments in this highly competitive world. Strengths, weaknesses, opportunities and threats (SWOT) analysis is a very useful technique which analyzes the strengths, weaknesses, opportunities and threats of an organization for taking strategic decisions and it also provides a foundation for the formulation of strategies. But the drawback of SWOT analysis is that it does not quantify the importance of individual factors affecting the organization and the individual factors are described in brief without weighing them. Because of this reason, SWOT analysis can be integrated with any multiple attribute decision-making (MADM) technique like the technique for order preference by similarity to ideal solution (TOPSIS), analytical hierarchy process, etc., to evaluate the best alternative among the available strategic alternatives. The paper aims to discuss these issues. Design/methodology/approach In this study, SWOT analysis is integrated with a multicriteria decision-making technique called TOPSIS to rank different strategies for Indian medical tourism in order of priority. Findings SO strategy (providing best facilitation and care to the medical tourists at par to developed countries) is the best strategy which matches with the four elements of S, W, O and T of SWOT matrix and 35 strategic indicators. Practical implications This paper proposes a solution based on a combined SWOT analysis and TOPSIS approach to help the organizations to evaluate and select strategies. Originality/value Creating a new technology or administering a new strategy always has some degree of resistance by employees. To minimize resistance, the author has used TOPSIS as it involves group thinking, requiring every manager of the organization to analyze and evaluate different alternatives and average measure of each parameter in final decision matrix.
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
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.
Lerche, Dorte; Brüggemann, Rainer; Sørensen, Peter; Carlsen, Lars; Nielsen, Ole John
2002-01-01
An alternative to the often cumbersome and time-consuming risk assessments of chemical substances could be more reliable and advanced priority setting methods. An elaboration of the simple scoring methods is provided by Hasse Diagram Technique (HDT) and/or Multi-Criteria Analysis (MCA). The present study provides an in depth evaluation of HDT relative to three MCA techniques. The new and main methodological step in the comparison is the use of probability concepts based on mathematical tools such as linear extensions of partially ordered sets and Monte Carlo simulations. A data set consisting of 12 High Production Volume Chemicals (HPVCs) is used for illustration. It is a paradigm in this investigation to claim that the need of external input (often subjective weightings of criteria) should be minimized and that the transparency should be maximized in any multicriteria prioritisation. The study illustrates that the Hasse diagram technique (HDT) needs least external input, is most transparent and is least subjective. However, HDT has some weaknesses if there are criteria which exclude each other. Then weighting is needed. Multi-Criteria Analysis (i.e. Utility Function approach, PROMETHEE and concordance analysis) can deal with such mutual exclusions because their formalisms to quantify preferences allow participation e.g. weighting of criteria. Consequently MCA include more subjectivity and loose transparency. The recommendation which arises from this study is that the first step in decision making is to run HDT and as the second step possibly is to run one of the MCA algorithms.
Directory of Open Access Journals (Sweden)
Mojtaba Soltannezhad Dizaji
2017-07-01
Full Text Available In an environment where markets go through a volatile process, and rapid fundamental changes occur due to technological advances, it is important to ensure and maintain a good performance measurement. Organizations, in their performance evaluation, should consider different types of financial and non-financial indicators. In systems like direct sales stores in which decision units have multiple inputs and outputs, all criteria influencing on performance must be combined and examined in a system, simultaneously. The purpose of this study is to evaluate the performance of different products through direct sales of a firm named Shirin Asal with a combination of Balanced Scorecard, fuzzy AHP and TOPSIS so that the weaknesses of subjectivity and selective consideration of evaluators in evaluating the performance indicators are reduced and evaluation integration is provided by considering the contribution of each indicator and each indicator group of balanced scorecard. The research method of this case study is applied. The data collection method is a questionnaire from the previous studies, the use of experts' opinions and the study of documents in the organization. MATLAB and SPSS were used to analyze the data. During this study, the customer and financial perspectives are of the utmost importance to assess the company branches. Among the sub-criteria, the rate of new customer acquisition in the customer dimension and the net income to sales ratio in financial dimension are of the utmost importance.
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.
Directory of Open Access Journals (Sweden)
Megi Adhiyani
2016-04-01
Full Text Available The members election process of Student Executive Board of FMIPA UNLAM Banjarbaru is still done manually, so that the election of members require a long time and sometimes subjective. Thus, it needs a decision support system that could help ease the decision-making process using Promethee. Promethee is a method determining the sequence (priority to analyze the problems with the point is the simplicity, clarity, and stability (Arsita, 2013. From the result of the research and observation of the system has been made, can be known that Decision Support System for Member Election of Student Executive Board using Promethee get compliance 89.80 percent and the remaining 10.20 percent not in accordance with the decision of Student Executive Board of FMIPA UNLAM. Keywords : Decision Support System, Member Election of Student Executive Board, Promethee. Badan Eksekutif Mahasiswa (BEM FMIPA UNLAM Banjarbaru memiliki proses pemilihan anggota yang masih dilakukan secara manual sehingga pemilihan anggota BEM memerlukan waktu yang lama dan terkadang subjektif. Oleh karena itu, diperlukan sebuah sistem pendukung keputusan yang dapat mempermudah proses pengambilan keputusan menggunakan metode Promethee. Metode Promethee adalah suatu metode penentuan urutan (prioritas dalam analisa yang masalah pokoknya adalah kesederhanaan, kejelasan, dan kestabilan (2. Dari hasil penelitian dan pengamatan dari sistem yang telah dibuat, dapat diketahui bahwa Sistem Pendukung Keputusan Pemilihan Anggota BEM menggunakan metode Promethee ini mendapatkan nilai kesesuaian 89,80% dan sisanya 10,20% tidak sesuai dengan keputusan dari BEM FMIPA UNLAM. Kata kunci : Sistem Pendukung Keputusan, Pemilihan Anggota BEM, Metode Promethee.
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).
Tashobya, Christine K; Dubourg, Dominique; Ssengooba, Freddie; Speybroeck, Niko; Macq, Jean; Criel, Bart
2016-03-01
In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance data and compares this approach with the use of the league table in Uganda. Ministry of Health and district plans and reports, and published documents were used to provide information on the development and utilization of the Uganda district league table. Quantitative data were accessed from the Ministry of Health databases. Statistical analysis using SPSS version 20 and hierarchical cluster analysis, utilizing Wards' method was used. The hierarchical cluster analysis was conducted on the basis of seven clusters determined for each year from 2003 to 2010, ranging from a cluster of good through moderate-to-poor performers. The characteristics and membership of clusters varied from year to year and were determined by the identity and magnitude of performance of the individual variables. Criticisms of the league table include: perceived unfairness, as it did not take into consideration district peculiarities; and being oversummarized and not adequately informative. Clustering organizes the many data points into clusters of similar entities according to an agreed set of indicators and can provide the beginning point for identifying factors behind the observed performance of districts. Although league table ranking emphasize summation and external control, clustering has the potential to encourage a formative, learning approach. More research is required to shed more light on factors behind observed performance of the different clusters. Other countries especially low-income countries that share many similarities with Uganda can learn from these experiences. © The Author 2015
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.
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...
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…
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)
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.
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
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...
Gregor, Ivan; Dröge, Johannes; Schirmer, Melanie; Quince, Christopher; McHardy, Alice C
2016-01-01
Background. Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into 'bins' representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in the model and identifies 'training' sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have. Results. We have developed PhyloPythiaS+, a successor to our PhyloPythia(S) software. The new (+) component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated the simultaneous counting of 4-6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion. PhyloPythiaS+ was compared to MEGAN, taxator-tk, Kraken and the generic PhyloPythiaS model. The results showed that PhyloPythiaS+ performs especially well for samples originating from novel environments in comparison to the other methods. Availability. PhyloPythiaS+ in a virtual machine is available for installation under Windows, Unix systems or OS X on: https://github.com/algbioi/ppsp/wiki.
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
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
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.
Huybrechts, Inge; Lioret, Sandrine; Mouratidou, Theodora; Gunter, Marc J; Manios, Yannis; Kersting, Mathilde; Gottrand, Frederic; Kafatos, Anthony; De Henauw, Stefaan; Cuenca-García, Magdalena; Widhalm, Kurt; Gonzales-Gross, Marcela; Molnar, Denes; Moreno, Luis A; McNaughton, Sarah A
2017-01-01
This study aims to examine repeatability of reduced rank regression (RRR) methods in calculating dietary patterns (DP) and cross-sectional associations with overweight (OW)/obesity across European and Australian samples of adolescents. Data from two cross-sectional surveys in Europe (2006/2007 Healthy Lifestyle in Europe by Nutrition in Adolescence study, including 1954 adolescents, 12-17 years) and Australia (2007 National Children's Nutrition and Physical Activity Survey, including 1498 adolescents, 12-16 years) were used. Dietary intake was measured using two non-consecutive, 24-h recalls. RRR was used to identify DP using dietary energy density, fibre density and percentage of energy intake from fat as the intermediate variables. Associations between DP scores and body mass/fat were examined using multivariable linear and logistic regression as appropriate, stratified by sex. The first DP extracted (labelled 'energy dense, high fat, low fibre') explained 47 and 31 % of the response variation in Australian and European adolescents, respectively. It was similar for European and Australian adolescents and characterised by higher consumption of biscuits/cakes, chocolate/confectionery, crisps/savoury snacks, sugar-sweetened beverages, and lower consumption of yogurt, high-fibre bread, vegetables and fresh fruit. DP scores were inversely associated with BMI z-scores in Australian adolescent boys and borderline inverse in European adolescent boys (so as with %BF). Similarly, a lower likelihood for OW in boys was observed with higher DP scores in both surveys. No such relationships were observed in adolescent girls. In conclusion, the DP identified in this cross-country study was comparable for European and Australian adolescents, demonstrating robustness of the RRR method in calculating DP among populations. However, longitudinal designs are more relevant when studying diet-obesity associations, to prevent reverse causality.
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.
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...
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
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.
Directory of Open Access Journals (Sweden)
Ivan Gregor
2016-02-01
Full Text Available Background. Metagenomics is an approach for characterizing environmental microbial communities in situ, it allows their functional and taxonomic characterization and to recover sequences from uncultured taxa. This is often achieved by a combination of sequence assembly and binning, where sequences are grouped into ‘bins’ representing taxa of the underlying microbial community. Assignment to low-ranking taxonomic bins is an important challenge for binning methods as is scalability to Gb-sized datasets generated with deep sequencing techniques. One of the best available methods for species bins recovery from deep-branching phyla is the expert-trained PhyloPythiaS package, where a human expert decides on the taxa to incorporate in the model and identifies ‘training’ sequences based on marker genes directly from the sample. Due to the manual effort involved, this approach does not scale to multiple metagenome samples and requires substantial expertise, which researchers who are new to the area do not have. Results. We have developed PhyloPythiaS+, a successor to our PhyloPythia(S software. The new (+ component performs the work previously done by the human expert. PhyloPythiaS+ also includes a new k-mer counting algorithm, which accelerated the simultaneous counting of 4–6-mers used for taxonomic binning 100-fold and reduced the overall execution time of the software by a factor of three. Our software allows to analyze Gb-sized metagenomes with inexpensive hardware, and to recover species or genera-level bins with low error rates in a fully automated fashion. PhyloPythiaS+ was compared to MEGAN, taxator-tk, Kraken and the generic PhyloPythiaS model. The results showed that PhyloPythiaS+ performs especially well for samples originating from novel environments in comparison to the other methods. Availability. PhyloPythiaS+ in a virtual machine is available for installation under Windows, Unix systems or OS X on: https://github.com/algbioi/ppsp/wiki.
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....
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....
Blyth, Kathryn
2014-01-01
This article considers the Australian entry score system, the Australian Tertiary Admissions Rank (ATAR), and its usage as a selection mechanism for undergraduate places in Australian higher education institutions and asks whether its role as the main selection criterion will continue with the introduction of demand driven funding in 2012.…
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.
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...
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…
Ranking the Online Documents Based on Relative Credibility Measures
Directory of Open Access Journals (Sweden)
Ahmad Dahlan
2013-09-01
Full Text Available Information searching is the most popular activity in Internet. Usually the search engine provides the search results ranked by the relevance. However, for a certain purpose that concerns with information credibility, particularly citing information for scientific works, another approach of ranking the search engine results is required. This paper presents a study on developing a new ranking method based on the credibility of information. The method is built up upon two well-known algorithms, PageRank and Citation Analysis. The result of the experiment that used Spearman Rank Correlation Coefficient to compare the proposed rank (generated by the method with the standard rank (generated manually by a group of experts showed that the average Spearman 0 < rS < critical value. It means that the correlation was proven but it was not significant. Hence the proposed rank does not satisfy the standard but the performance could be improved.
Ranking the Online Documents Based on Relative Credibility Measures
Directory of Open Access Journals (Sweden)
Ahmad Dahlan
2009-05-01
Full Text Available Information searching is the most popular activity in Internet. Usually the search engine provides the search results ranked by the relevance. However, for a certain purpose that concerns with information credibility, particularly citing information for scientific works, another approach of ranking the search engine results is required. This paper presents a study on developing a new ranking method based on the credibility of information. The method is built up upon two well-known algorithms, PageRank and Citation Analysis. The result of the experiment that used Spearman Rank Correlation Coefficient to compare the proposed rank (generated by the method with the standard rank (generated manually by a group of experts showed that the average Spearman 0 < rS < critical value. It means that the correlation was proven but it was not significant. Hence the proposed rank does not satisfy the standard but the performance could be improved.
Algebraic and computational aspects of real tensor ranks
Sakata, Toshio; Miyazaki, Mitsuhiro
2016-01-01
This book provides comprehensive summaries of theoretical (algebraic) and computational aspects of tensor ranks, maximal ranks, and typical ranks, over the real number field. Although tensor ranks have been often argued in the complex number field, it should be emphasized that this book treats real tensor ranks, which have direct applications in statistics. The book provides several interesting ideas, including determinant polynomials, determinantal ideals, absolutely nonsingular tensors, absolutely full column rank tensors, and their connection to bilinear maps and Hurwitz-Radon numbers. In addition to reviews of methods to determine real tensor ranks in details, global theories such as the Jacobian method are also reviewed in details. The book includes as well an accessible and comprehensive introduction of mathematical backgrounds, with basics of positive polynomials and calculations by using the Groebner basis. Furthermore, this book provides insights into numerical methods of finding tensor ranks through...
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
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 ...
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.
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
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.
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.
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...
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.
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,
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,
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.
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
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...
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…
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.
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
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.
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.
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...
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.
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.
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.
International Nuclear Information System (INIS)
Stenner, R.D.; Cramer, K.H.; Higley, K.A.; Jette, S.J.; Lamar, D.A.; McLaughlin, T.J.; Sherwood, D.R.; Van Houten, N.C.
1988-10-01
The purpose of this report is to formally document the individual site Hazard Ranking System (HRS) evaluations conducted as part of the preliminary assessment/site inspection (PA/SI) activities at the US Department of Energy (DOE) Hanford Site. These activities were carried out pursuant to the DOE orders that describe the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) Program addressing the cleanup of inactive waste sites. These orders incorporate the US Environmental Protection Agency methodology, which is based on the Superfund Amendments and Reauthorization Act of 1986 (SARA). The methodology includes six parts: PA/SI, remedial investigation/feasibility study, record of decision, design and implementation of remedial action, operation and monitoring, and verification monitoring. Volume 1 of this report discusses the CERCLA inactive waste-site evaluation process, assumptions, and results of the HRS methodology employed. Volume 2 presents the data on the individual CERCLA engineered-facility sites at Hanford, as contained in the Hanford Inactive Site Surveillance (HISS) Data Base. Volume 3 presents the data on the individual CERCLA unplanned-release sites at Hanford, as contained in the HISS Data Base. 34 refs., 43 figs., 47 tabs
Energy Technology Data Exchange (ETDEWEB)
Stenner, R.D.; Cramer, K.H.; Higley, K.A.; Jette, S.J.; Lamar, D.A.; McLaughlin, T.J.; Sherwood, D.R.; Van Houten, N.C.
1988-10-01
The purpose of this report is to formally document the individual site Hazard Ranking System (HRS) evaluations conducted as part of the preliminary assessment/site inspection (PA/SI) activities at the US Department of Energy (DOE) Hanford Site. These activities were carried out pursuant to the DOE orders that describe the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) Program addressing the cleanup of inactive waste sites. These orders incorporate the US Environmental Protection Agency methodology, which is based on the Superfund Amendments and Reauthorization Act of 1986 (SARA). The methodology includes six parts: PA/SI, remedial investigation/feasibility study, record of decision, design and implementation of remedial action, operation and monitoring, and verification monitoring. Volume 1 of this report discusses the CERCLA inactive waste-site evaluation process, assumptions, and results of the HRS methodology employed. Volume 2 presents the data on the individual CERCLA engineered-facility sites at Hanford, as contained in the Hanford Inactive Site Surveillance (HISS) Data Base. Volume 3 presents the data on the individual CERCLA unplanned-release sites at Hanford, as contained in the HISS Data Base. 34 refs., 43 figs., 47 tabs.
Directory of Open Access Journals (Sweden)
Gholam Reza Bordbar
2014-10-01
Full Text Available ILO, adopted and communicated several types of document which include Conventions, recommendations, resolutions and statements etc.among all of them, convention has a special place because the members states accept duties and obligations. Among these conventions, those conventions that are related to the fundamental rights of labor had special importance and prestige. Member countries of the Organization like Iran are required to implement the relevant commitments. this study aimed to analyze administrative obstacles of the Fundamental Principles and Rights at Work by getting weight to problems and ranking them. The result of study show that Prohibition of child labor is more important than other aspects of Fundamental Principles and Rights at Work. in aspect of child labor most important obstacle was "red tape" ,in others categories result was as follow: in lack of collective bargaining most important obstacle was made emotional decisions, in forced labor most important obstacle was distrust of other workers, in gender discrimination most important obstacle was Working environment, in people inequality most important obstacle was Subjective judgments manager.
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...
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).
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.
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.
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.
Low-Rank Matrix Factorization With Adaptive Graph Regularizer.
Lu, Gui-Fu; Wang, Yong; Zou, Jian
2016-05-01
In this paper, we present a novel low-rank matrix factorization algorithm with adaptive graph regularizer (LMFAGR). We extend the recently proposed low-rank matrix with manifold regularization (MMF) method with an adaptive regularizer. Different from MMF, which constructs an affinity graph in advance, LMFAGR can simultaneously seek graph weight matrix and low-dimensional representations of data. That is, graph construction and low-rank matrix factorization are incorporated into a unified framework, which results in an automatically updated graph rather than a predefined one. The experimental results on some data sets demonstrate that the proposed algorithm outperforms the state-of-the-art low-rank matrix factorization methods.
Inhibition of osteoclastogenesis by RNA interference targeting RANK
Directory of Open Access Journals (Sweden)
Ma Ruofan
2012-08-01
Full Text Available Abstract Background Osteoclasts and osteoblasts regulate bone resorption and formation to allow bone remodeling and homeostasis. The balance between bone resorption and formation is disturbed by abnormal recruitment of osteoclasts. Osteoclast differentiation is dependent on the receptor activator of nuclear factor NF-kappa B (RANK ligand (RANKL as well as the macrophage colony-stimulating factor (M-CSF. The RANKL/RANK system and RANK signaling induce osteoclast formation mediated by various cytokines. The RANK/RANKL pathway has been primarily implicated in metabolic, degenerative and neoplastic bone disorders or osteolysis. The central role of RANK/RANKL interaction in osteoclastogenesis makes RANK an attractive target for potential therapies in treatment of osteolysis. The purpose of this study was to assess the effect of inhibition of RANK expression in mouse bone marrow macrophages on osteoclast differentiation and bone resorption. Methods Three pairs of short hairpin RNAs (shRNA targeting RANK were designed and synthesized. The optimal shRNA was selected among three pairs of shRNAs by RANK expression analyzed by Western blot and Real-time PCR. We investigated suppression of osteoclastogenesis of mouse bone marrow macrophages (BMMs using the optimal shRNA by targeting RANK. Results Among the three shRANKs examined, shRANK-3 significantly suppressed [88.3%] the RANK expression (p Conclusions These findings suggest that retrovirus-mediated shRNA targeting RANK inhibits osteoclast differentiation and osteolysis. It may appear an attractive target for preventing osteolysis in humans with a potential clinical application.
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.
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; ...
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
38 CFR 61.32 - Ranking non-capital grant recipients for per diem.
2010-07-01
... 38 Pensions, Bonuses, and Veterans' Relief 2 2010-07-01 2010-07-01 false Ranking non-capital grant... AFFAIRS (CONTINUED) VA HOMELESS PROVIDERS GRANT AND PER DIEM PROGRAM § 61.32 Ranking non-capital grant... ranking. (c) All applicants responding to a NOFA for “Per Diem Only” will be subject to the ranking method...
Are university rankings useful to improve research? A systematic review.
Vernon, Marlo M; Balas, E Andrew; Momani, Shaher
2018-01-01
Concerns about reproducibility and impact of research urge improvement initiatives. Current university ranking systems evaluate and compare universities on measures of academic and research performance. Although often useful for marketing purposes, the value of ranking systems when examining quality and outcomes is unclear. The purpose of this study was to evaluate usefulness of ranking systems and identify opportunities to support research quality and performance improvement. A systematic review of university ranking systems was conducted to investigate research performance and academic quality measures. Eligibility requirements included: inclusion of at least 100 doctoral granting institutions, be currently produced on an ongoing basis and include both global and US universities, publish rank calculation methodology in English and independently calculate ranks. Ranking systems must also include some measures of research outcomes. Indicators were abstracted and contrasted with basic quality improvement requirements. Exploration of aggregation methods, validity of research and academic quality indicators, and suitability for quality improvement within ranking systems were also conducted. A total of 24 ranking systems were identified and 13 eligible ranking systems were evaluated. Six of the 13 rankings are 100% focused on research performance. For those reporting weighting, 76% of the total ranks are attributed to research indicators, with 24% attributed to academic or teaching quality. Seven systems rely on reputation surveys and/or faculty and alumni awards. Rankings influence academic choice yet research performance measures are the most weighted indicators. There are no generally accepted academic quality indicators in ranking systems. No single ranking system provides a comprehensive evaluation of research and academic quality. Utilizing a combined approach of the Leiden, Thomson Reuters Most Innovative Universities, and the SCImago ranking systems may provide
Sailaukhanuly, Yerbolat; Zhakupbekova, Arai; Amutova, Farida; Carlsen, Lars
2013-01-01
Knowledge of the environmental behavior of chemicals is a fundamental part of the risk assessment process. The present paper discusses various methods of ranking of a series of persistent organic pollutants (POPs) according to the persistence, bioaccumulation and toxicity (PBT) characteristics. Traditionally ranking has been done as an absolute (total) ranking applying various multicriteria data analysis methods like simple additive ranking (SAR) or various utility functions (UFs) based rankings. An attractive alternative to these ranking methodologies appears to be partial order ranking (POR). The present paper compares different ranking methods like SAR, UF and POR. Significant discrepancies between the rankings are noted and it is concluded that partial order ranking, as a method without any pre-assumptions concerning possible relation between the single parameters, appears as the most attractive ranking methodology. In addition to the initial ranking partial order methodology offers a wide variety of analytical tools to elucidate the interplay between the objects to be ranked and the ranking parameters. In the present study is included an analysis of the relative importance of the single P, B and T parameters. Copyright © 2012 Elsevier Ltd. All rights reserved.
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.
Development and first application of an operating events ranking tool
International Nuclear Information System (INIS)
Šimić, Zdenko; Zerger, Benoit; Banov, Reni
2015-01-01
Highlights: • A method using analitycal hierarchy process for ranking operating events is developed and tested. • The method is applied for 5 years of U.S. NRC Licensee Event Reports (1453 events). • Uncertainty and sensitivity of the ranking results are evaluated. • Real events assessment shows potential of the method for operating experience feedback. - Abstract: The operating experience feedback is important for maintaining and improving safety and availability in nuclear power plants. Detailed investigation of all events is challenging since it requires excessive resources, especially in case of large event databases. This paper presents an event groups ranking method to complement the analysis of individual operating events. The basis for the method is the use of an internationally accepted events characterization scheme that allows different ways of events grouping and ranking. The ranking method itself consists of implementing the analytical hierarchy process (AHP) by means of a custom developed tool which allows events ranking based on ranking indexes pre-determined by expert judgment. Following the development phase, the tool was applied to analyze a complete set of 5 years of real nuclear power plants operating events (1453 events). The paper presents the potential of this ranking method to identify possible patterns throughout the event database and therefore to give additional insights into the events as well as to give quantitative input for the prioritization of further more detailed investigation of selected event groups
A model-based approach to operational event groups ranking
Energy Technology Data Exchange (ETDEWEB)
Simic, Zdenko [European Commission Joint Research Centre, Petten (Netherlands). Inst. for Energy and Transport; Maqua, Michael [Gesellschaft fuer Anlagen- und Reaktorsicherheit mbH (GRS), Koeln (Germany); Wattrelos, Didier [Institut de Radioprotection et de Surete Nucleaire (IRSN), Fontenay-aux-Roses (France)
2014-04-15
The operational experience (OE) feedback provides improvements in all industrial activities. Identification of the most important and valuable groups of events within accumulated experience is important in order to focus on a detailed investigation of events. The paper describes the new ranking method and compares it with three others. Methods have been described and applied to OE events utilised by nuclear power plants in France and Germany for twenty years. The results show that different ranking methods only roughly agree on which of the event groups are the most important ones. In the new ranking method the analytical hierarchy process is applied in order to assure consistent and comprehensive weighting determination for ranking indexes. The proposed method allows a transparent and flexible event groups ranking and identification of the most important OE for further more detailed investigation in order to complete the feedback. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Viforr, Silvia
2010-11-15
The production of thermomechanical pulp (TMP) demands high levels of energy. This, together with current expensive energy prices of nowadays results in significant costs, which is the reason why there is a demand for processes that require less energy. One way of reducing energy consumption in TMP refining could be to pretreat the wood chips with enzymes before the subsequent refining step. However, enzymes molecules are relatively large, which limits the impregnation process, and so the pores in the fibre walls are not large enough to fit the size of the enzymes. By mechanically pretreating wood chips in a screw feeder and press equipment, this opens the wood structure significantly which increases enzyme penetration. If enzymes are used for reducing energy consumption in TMP processes, it is necessary to optimise the enzymatic effect during the pretreatment of wood chips. It is very expensive to evaluate completely the effect of enzymes in large scale refining. Thus there is a need for other relevant methods for rapidly and effectively evaluating the energy saving effects when it comes to refining enzymatic pretreated wood chips. The aim of this project was to find a method for ranking of enzymes for pretreatment of chips for energy savings at TMP production. This method was to be independent of the type of enzyme used and of the type of pretreated wood chips involved. In order to asses the method for ranking enzymes being used in the pretreatment of chips to reduce energy input during refining, a comparison between the method and a mill trial was carried out in the mill trial. A known chemical pretreatment was used; here it was sulphonation of the wood chips before refining with low sulphite levels. Further, a laboratory wing refiner was used as an evaluation equipment. The trial started with the running conditions for a wing refiner that the best correspond with industrial refining. An evaluation was made on the effect of enzymatic pretreatment on energy
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)
Extreme learning machine for ranking: generalization analysis and applications.
Chen, Hong; Peng, Jiangtao; Zhou, Yicong; Li, Luoqing; Pan, Zhibin
2014-05-01
The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM. The generalization analysis is established for the ELM-based ranking (ELMRank) in terms of the covering numbers of hypothesis space. Empirical results on the benchmark datasets show the competitive performance of the ELMRank over the state-of-the-art ranking methods. Copyright © 2014 Elsevier Ltd. All rights reserved.
Quantum probability ranking principle for ligand-based virtual screening
Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal
2017-04-01
Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.
Quantum probability ranking principle for ligand-based virtual screening.
Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Himmat, Mubarak; Ahmed, Ali; Saeed, Faisal
2017-04-01
Chemical libraries contain thousands of compounds that need screening, which increases the need for computational methods that can rank or prioritize compounds. The tools of virtual screening are widely exploited to enhance the cost effectiveness of lead drug discovery programs by ranking chemical compounds databases in decreasing probability of biological activity based upon probability ranking principle (PRP). In this paper, we developed a novel ranking approach for molecular compounds inspired by quantum mechanics, called quantum probability ranking principle (QPRP). The QPRP ranking criteria would make an attempt to draw an analogy between the physical experiment and molecular structure ranking process for 2D fingerprints in ligand based virtual screening (LBVS). The development of QPRP criteria in LBVS has employed the concepts of quantum at three different levels, firstly at representation level, this model makes an effort to develop a new framework of molecular representation by connecting the molecular compounds with mathematical quantum space. Secondly, estimate the similarity between chemical libraries and references based on quantum-based similarity searching method. Finally, rank the molecules using QPRP approach. Simulated virtual screening experiments with MDL drug data report (MDDR) data sets showed that QPRP outperformed the classical ranking principle (PRP) for molecular chemical compounds.
Institute of Scientific and Technical Information of China (English)
陈侠; 陈岩
2011-01-01
It is a new important research topic to discuss the problem of ranking in group decision making based on ordinal interval preference information. In this paper, an analytic method is proposed to solve the problem of ranking based on the ordinal interval preference information in decision making. Firstly, some concepts and characters of the ordinal interval preference information are introduced. Then, based on introducing the concepts of possibility and possibility matrix, the conclusion is obtained that the matrices of possibility of all experts are fuzzy reciprocal matrices and they are weak consistent. Furthermore, an optimization model of group consensus is constructed to calculate the optimization weigh vector, and an analysis method of ranking in group decision making based on the ordinal interval preference information is proposed. Finally, a numerical example is given to illustrate the use of the proposed analysis method.%在群决策分析中,基于序区间偏好信息的排序方法的研究是一个新的重要研究课题.针对决策分析中基于序区间偏好信息的群决策方法问题,提出了一种新的分析方法.首先,提出了序区间的有关定义及性质；其次,通过定义序区间的可能度及可能度矩阵的概念,得出了每个专家的可能度矩阵均具有满意一致性的互补判断矩阵结论.进而构建了基于群体一致性的最优化模型,依据计算的最优权重向量给出了一种关于序区间偏好信息的群决策方案排序方法.最后,通过一个算例说明了提出的分析方法.
LENUS (Irish Health Repository)
Dunne, Suzanne
2013-01-01
The Internet is a widely used source of information for patients searching for medical\\/health care information. While many studies have assessed existing medical\\/health care information on the Internet, relatively few have examined methods for design and delivery of such websites, particularly those aimed at the general public.
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.
Ranking production units according to marginal efficiency contribution
DEFF Research Database (Denmark)
Ghiyasi, Mojtaba; Hougaard, Jens Leth
League tables associated with various forms of service activities from schools to hospitals illustrate the public need for ranking institutions by their productive performance. We present a new method for ranking production units which is based on each units marginal contribution to the technical...
Optimal ranking regime analysis of TreeFlow dendrohydrological reconstructions
The Optimal Ranking Regime (ORR) method was used to identify 6-100 year time windows containing significant ranking sequences in 55 western U.S. streamflow reconstructions, and reconstructions of the level of the Great Salt Lake and San Francisco Bay salinity during 1500-2007. The method’s ability t...
The Ranking Phenomenon and the Experience of Academics in Taiwan
Lo, William Yat Wai
2014-01-01
The primary aim of the paper is to examine how global university rankings have influenced the higher education sector in Taiwan from the perspective of academics. A qualitative case study method was used to examine how university ranking influenced the Taiwanese higher education at institutional and individual levels, respectively, thereby…
Ranking Exponential Trapezoidal Fuzzy Numbers by Median Value
Directory of Open Access Journals (Sweden)
S. Rezvani
2013-12-01
Full Text Available In this paper, we want represented a method for ranking of two exponential trapezoidal fuzzy numbers. A median value is proposed for the ranking of exponential trapezoidal fuzzy numbers. For the validation the results of the proposed approach are compared with different existing approaches.
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)
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.
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)
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
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.
AptRank: an adaptive PageRank model for protein function prediction on bi-relational graphs.
Jiang, Biaobin; Kloster, Kyle; Gleich, David F; Gribskov, Michael
2017-06-15
Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . gribskov@purdue.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Centrality based Document Ranking
2014-11-01
support task. As it turned out, the results were very poor, which suggests that using a general purpose IR sytem in this way is not a good idea... Management on the Semantic Web, 2005. 15. P. Mika, T. Tudorache, A. Bernstein, C. Welty, C. A. Knoblock, D. Vrandecic, P. T. Groth, N. F. Noy, K. Janowicz...question- focused sentence retrieval. In HLT ’05: Proceedings of the conference on Hu- man Language Technology and Empirical Methods in Natural
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.
DEFF Research Database (Denmark)
Govindan, Kannan; Kadziński, Miłosz; Sivakumar, R.
2017-01-01
green supply chain management (GSCM) elements is essential for utilizing the food supply chain in an environmentally benign way. As a solution to the above challenge, the economic and green characteristics for supplier selection in green purchasing are studied in this paper. For an organization......, the evaluation and selection of the green supplier is a vital issue due to several tangible and intangible criteria involved. Accordingly, we apply multiple criteria decision aiding techniques.We propose a hybrid approach that combines the revised Simos procedure, PROMETHEE methods, algorithms for constructing......The food sector has a prodigious focus and is constantly gaining in importance in today's global economic marketplace. Due to an increasing global population, society faces a greater challenge for sustainable food production, quality, distribution, and food safety in the food supply chain. Adopting...
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.
Sensitivity ranking for freshwater invertebrates towards hydrocarbon contaminants.
Gerner, Nadine V; Cailleaud, Kevin; Bassères, Anne; Liess, Matthias; Beketov, Mikhail A
2017-11-01
Hydrocarbons have an utmost economical importance but may also cause substantial ecological impacts due to accidents or inadequate transportation and use. Currently, freshwater biomonitoring methods lack an indicator that can unequivocally reflect the impacts caused by hydrocarbons while being independent from effects of other stressors. The aim of the present study was to develop a sensitivity ranking for freshwater invertebrates towards hydrocarbon contaminants, which can be used in hydrocarbon-specific bioindicators. We employed the Relative Sensitivity method and developed the sensitivity ranking S hydrocarbons based on literature ecotoxicological data supplemented with rapid and mesocosm test results. A first validation of the sensitivity ranking based on an earlier field study has been conducted and revealed the S hydrocarbons ranking to be promising for application in sensitivity based indicators. Thus, the first results indicate that the ranking can serve as the core component of future hydrocarbon-specific and sensitivity trait based bioindicators.
Feasibility study of component risk ranking for plant maintenance
International Nuclear Information System (INIS)
Ushijima, Koji; Yonebayashi, Kenji; Narumiya, Yoshiyuki; Sakata, Kaoru; Kumano, Tetsuji
1999-01-01
Nuclear power is the base load electricity source in Japan, and reduction of operation and maintenance cost maintaining or improving plant safety is one of the major issues. Recently, Risk Informed Management (RIM) is focused as a solution. In this paper, the outline regarding feasibility study of component risk ranking for plant maintenance for a typical Japanese PWR plant is described. A feasibility study of component risk raking for plant maintenance optimization is performed on check valves and motor-operated valves. Risk ranking is performed in two steps using probabilistic analysis (quantitative method) for risk ranking of components, and deterministic examination (qualitative method) for component review. In this study, plant components are ranked from the viewpoint of plant safety / reliability, and the applicability for maintenance is assessed. As a result, distribution of maintenance resources using risk ranking is considered effective. (author)
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.
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.
Cao, Yiping; Sivaganesan, Mano; Kelty, Catherine A; Wang, Dan; Boehm, Alexandria B; Griffith, John F; Weisberg, Stephen B; Shanks, Orin C
2018-01-01
Human fecal pollution of recreational waters remains a public health concern worldwide. As a result, there is a growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for water quality research and management. However, there are currently no standardized approaches for field implementation and interpretation of qPCR data. In this study, a standardized HF183/BacR287 qPCR method was combined with a water sampling strategy and a novel Bayesian weighted average approach to establish a human fecal contamination score (HFS) that can be used to prioritize sampling sites for remediation based on measured human waste levels. The HFS was then used to investigate 975 study design scenarios utilizing different combinations of sites with varying sampling intensities (daily to once per week) and number of qPCR replicates per sample (2-14 replicates). Findings demonstrate that site prioritization with HFS is feasible and that both sampling intensity and number of qPCR replicates influence reliability of HFS estimates. The novel data analysis strategy presented here provides a prescribed approach for the implementation and interpretation of human-associated HF183/BacR287 qPCR data with the goal of site prioritization based on human fecal pollution levels. In addition, information is provided for future users to customize study designs for optimal HFS performance. Published by Elsevier Ltd.
Reduced-Rank Adaptive Filtering Using Krylov Subspace
Directory of Open Access Journals (Sweden)
Sergueï Burykh
2003-01-01
Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.
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...
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
A deductive approach to select or rank journals in multifaceted subject, Oceanography
Digital Repository Service at National Institute of Oceanography (India)
Sahu, S.R.; Panda, K.C.
journal) whereas Bradford’s differential approach (articles in the bibliographies of specific subject field) to account/rank the core journals. Both these methods make sense in the journal selection/ranking process to a specific subject field...
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....
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.
A cautionary note on the rank product statistic.
Koziol, James A
2016-06-01
The rank product method introduced by Breitling R et al. [2004, FEBS Letters 573, 83-92] has rapidly generated popularity in practical settings, in particular, detecting differential expression of genes in microarray experiments. The purpose of this note is to point out a particular property of the rank product method, namely, its differential sensitivity to over- and underexpression. It turns out that overexpression is less likely to be detected than underexpression with the rank product statistic. We have conducted both empirical and exact power studies that demonstrate this phenomenon, and summarize these findings in this note. © 2016 Federation of European Biochemical Societies.
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.
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.
Enhancing the Ranking of a Web Page in the Ocean of Data
Directory of Open Access Journals (Sweden)
Hitesh KUMAR SHARMA
2013-10-01
Full Text Available In today's world, web is considered as ocean of data and information (like text, videos, multimedia etc. consisting of millions and millions of web pages in which web pages are linked with each other like a tree. It is often argued that, especially considering the dynamic of the internet, too much time has passed since the scientific work on PageRank, as that it still could be the basis for the ranking methods of the Google search engine. There is no doubt that within the past years most likely many changes, adjustments and modifications regarding the ranking methods of Google have taken place, but PageRank was absolutely crucial for Google's success, so that at least the fundamental concept behind PageRank should still be constitutive. This paper describes the components which affects the ranking of the web pages and helps in increasing the popularity of web site. By adapting these factors website developers can increase their site's page rank and within the PageRank concept, considering the rank of a document is given by the rank of those documents which link to it. Their rank again is given by the rank of documents which link to them. The PageRank of a document is always determined recursively by the PageRank of other documents.
Around power law for PageRank components in Buckley-Osthus model of web graph
Gasnikov, Alexander; Zhukovskii, Maxim; Kim, Sergey; Noskov, Fedor; Plaunov, Stepan; Smirnov, Daniil
2017-01-01
In the paper we investigate power law for PageRank components for the Buckley-Osthus model for web graph. We compare different numerical methods for PageRank calculation. With the best method we do a lot of numerical experiments. These experiments confirm the hypothesis about power law. At the end we discuss real model of web-ranking based on the classical PageRank approach.
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. ...
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.
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)
Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.
Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel
2017-08-18
Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among
Who's #1? The Science of Rating and Ranking
Langville, Amy N
2012-01-01
A website's ranking on Google can spell the difference between success and failure for a new business. NCAA football ratings determine which schools get to play for the big money in postseason bowl games. Product ratings influence everything from the clothes we wear to the movies we select on Netflix. Ratings and rankings are everywhere, but how exactly do they work? Who's #1? offers an engaging and accessible account of how scientific rating and ranking methods are created and applied to a variety of uses. Amy Langville and Carl Meyer provide the first comprehensive overview of the mathemat
Identification of significant features by the Global Mean Rank test.
Klammer, Martin; Dybowski, J Nikolaj; Hoffmann, Daniel; Schaab, Christoph
2014-01-01
With the introduction of omics-technologies such as transcriptomics and proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as small numbers of replicates, missing values, non-normally distributed expression levels, and non-identical distributions of features. With the MeanRank test we aimed at developing a test that performs robustly under these conditions, while favorably scaling with the number of replicates. The test proposed here is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation. In extensive simulations comparing MeanRank to other frequently used methods, we found that it performs well with small and large numbers of replicates, feature dependent variance between replicates, and variable regulation across features on simulation data and a recent two-color microarray spike-in dataset. The tests were then used to identify significant changes in the phosphoproteomes of cancer cells induced by the kinase inhibitors erlotinib and 3-MB-PP1 in two independently published mass spectrometry-based studies. MeanRank outperformed the other global rank-based methods applied in this study. Compared to the popular Significance Analysis of Microarrays and Linear Models for Microarray methods, MeanRank performed similar or better. Furthermore, MeanRank exhibits more consistent behavior regarding the degree of regulation and is robust against the choice of preprocessing methods. MeanRank does not require any imputation of missing values, is easy to understand, and yields results that are easy to interpret. The software implementing the algorithm is freely available for academic and commercial use.
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.
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
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.
Demographic Ranking of the Baltic Sea States
Directory of Open Access Journals (Sweden)
Sluka N.
2014-06-01
Full Text Available The relevance of the study lies in the acute need to modernise the tools for a more accurate and comparable reflection of the demographic reality of spatial objects of different scales. This article aims to test the methods of “demographic rankings” developed by Yermakov and Shmakov. The method is based on the principles of indirect standardisation of the major demographic coefficients relative to the age structure.The article describes the first attempt to apply the method to the analysis of birth and mortality rates in 1995 and 2010 for 140 countries against the global average, and for the Baltic Sea states against the European average. The grouping of countries and the analysis of changes over the given period confirmed a number of demographic development trends and the persistence of wide territorial disparities in major indicators. The authors identify opposite trends in ranking based on the standardised birth (country consolidation at the level of averaged values and mortality (polarisation rates. The features of demographic process development in the Baltic regions states are described against the global and European background. The study confirmed the validity of the demographic ranking method, which can be instrumental in solving not only scientific but also practical tasks, including those in the field of demographic and social policy.
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.
Third-rank chromatic aberrations of electron lenses.
Liu, Zhixiong
2018-02-01
In this paper the third-rank chromatic aberration coefficients of round electron lenses are analytically derived and numerically calculated by Mathematica. Furthermore, the numerical results are cross-checked by the differential algebraic (DA) method, which verifies that all the formulas for the third-rank chromatic aberration coefficients are completely correct. It is hoped that this work would be helpful for further chromatic aberration correction in electron microscopy. Copyright © 2017 Elsevier B.V. All rights reserved.
Deriving consensus rankings via multicriteria decision making methodology
Amy Poh Ai Ling; Mohamad Nasir Saludin; Masao Mukaidono
2012-01-01
Purpose - This paper seeks to take a cautionary stance to the impact of the marketing mix on customer satisfaction, via a case study deriving consensus rankings for benchmarking on selected retail stores in Malaysia. Design/methodology/approach - The ELECTRE I model is used in deriving consensus rankings via multicriteria decision making method for benchmarking base on the marketing mix model 4P's. Descriptive analysis is used to analyze best practice among the four marketing tactics. Finding...
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.
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
(5515 were included in the analysis). Studies were conducted from 1974 to 2011, with 80% of the studies conducted in the 1970's, 1980's or 1990's. Most studies did not report study methods sufficiently and many had high applicability concerns. In 20 studies, FRS differentiated schizophrenia from all other diagnoses with a sensitivity of 57% (50.4% to 63.3%), and a specificity of 81.4% (74% to 87.1%) In seven studies, FRS differentiated schizophrenia from non-psychotic mental health disorders with a sensitivity of 61.8% (51.7% to 71%) and a specificity of 94.1% (88% to 97.2%). In sixteen studies, FRS differentiated schizophrenia from other types of psychosis with a sensitivity of 58% (50.3% to 65.3%) and a specificity of 74.7% (65.2% to 82.3%). The synthesis of old studies of limited quality in this review indicates that FRS correctly identifies people with schizophrenia 75% to 95% of the time. The use of FRS to diagnose schizophrenia in triage will incorrectly diagnose around five to 19 people in every 100 who have FRS as having schizophrenia and specialists will not agree with this diagnosis. These people will still merit specialist assessment and help due to the severity of disturbance in their behaviour and mental state. Again, with a sensitivity of FRS of 60%, reliance on FRS to diagnose schizophrenia in triage will not correctly diagnose around 40% of people that specialists will consider to have schizophrenia. Some of these people may experience a delay in getting appropriate treatment. Others, whom specialists will consider to have schizophrenia, could be prematurely discharged from care, if triage relies on the presence of FRS to diagnose schizophrenia. Empathetic, considerate use of FRS as a diagnostic aid - with known limitations - should avoid a good proportion of these errors.We hope that newer tests - to be included in future Cochrane reviews - will show better results. However, symptoms of first rank can still be helpful where newer tests are not available
Motif discovery in ranked lists of sequences
DEFF Research Database (Denmark)
Nielsen, Morten Muhlig; Tataru, Paula; Madsen, Tobias
2016-01-01
Motif analysis has long been an important method to characterize biological functionality and the current growth of sequencing-based genomics experiments further extends its potential. These diverse experiments often generate sequence lists ranked by some functional property. There is therefore...... advantage of the regular expression feature, including enrichments for combinations of different microRNA seed sites. The method is implemented and made publicly available as an R package and supports high parallelization on multi-core machinery....... a growing need for motif analysis methods that can exploit this coupled data structure and be tailored for specific biological questions. Here, we present an exploratory motif analysis tool, Regmex (REGular expression Motif EXplorer), which offers several methods to evaluate the correlation of motifs...
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.
Ranking stability and super-stable nodes in complex networks.
Ghoshal, Gourab; Barabási, Albert-László
2011-07-19
Pagerank, a network-based diffusion algorithm, has emerged as the leading method to rank web content, ecological species and even scientists. Despite its wide use, it remains unknown how the structure of the network on which it operates affects its performance. Here we show that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems. In contrast, in scale-free networks we predict analytically the emergence of super-stable nodes whose ranking is exceptionally stable to perturbations. We calculate the dependence of the number of super-stable nodes on network characteristics and demonstrate their presence in real networks, in agreement with the analytical predictions. These results not only deepen our understanding of the interplay between network topology and dynamical processes but also have implications in all areas where ranking has a role, from science to marketing.
Diffusion of scientific credits and the ranking of scientists
Radicchi, Filippo; Fortunato, Santo; Markines, Benjamin; Vespignani, Alessandro
2009-11-01
Recently, the abundance of digital data is enabling the implementation of graph-based ranking algorithms that provide system level analysis for ranking publications and authors. Here, we take advantage of the entire Physical Review publication archive (1893-2006) to construct authors’ networks where weighted edges, as measured from opportunely normalized citation counts, define a proxy for the mechanism of scientific credit transfer. On this network, we define a ranking method based on a diffusion algorithm that mimics the spreading of scientific credits on the network. We compare the results obtained with our algorithm with those obtained by local measures such as the citation count and provide a statistical analysis of the assignment of major career awards in the area of physics. A website where the algorithm is made available to perform customized rank analysis can be found at the address http://www.physauthorsrank.org.
Ranking online quality and reputation via the user activity
Liu, Xiao-Lu; Guo, Qiang; Hou, Lei; Cheng, Can; Liu, Jian-Guo
2015-10-01
How to design an accurate algorithm for ranking the object quality and user reputation is of importance for online rating systems. In this paper we present an improved iterative algorithm for online ranking object quality and user reputation in terms of the user degree (IRUA), where the user's reputation is measured by his/her rating vector, the corresponding objects' quality vector and the user degree. The experimental results for the empirical networks show that the AUC values of the IRUA algorithm can reach 0.9065 and 0.8705 in Movielens and Netflix data sets, respectively, which is better than the results generated by the traditional iterative ranking methods. Meanwhile, the results for the synthetic networks indicate that user degree should be considered in real rating systems due to users' rating behaviors. Moreover, we find that enhancing or reducing the influences of the large-degree users could produce more accurate reputation ranking lists.
Intergenerational Educational Rank Mobility in 20th Century United States
DEFF Research Database (Denmark)
Karlson, Kristian Bernt
2015-01-01
in the overall schooling distribution both over time and among population groups defined by race and gender.METHODS & DATA: To analyze educational rank mobility, I use quantile transition matrices known from studies on intergenerational income mobility. However, because schooling distributions are quite lumpy......, particularly around 12 and 16 years of schooling, percentile ranks of interest may not always be defined among parents or offspring (e.g., the lower or upper quartile may not be given by the data). To deal with this issue, I use a cohort-adjustment that deflates the schooling distribution in proportion...... performance of historically disadvantaged groups. To reconcile these diverging trends, I propose examining educational mobility in terms of percentile ranks in the respective schooling distributions of parents and offspring. Using a novel estimator of educational rank, I compare patterns of mobility...
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.
Bayesian Plackett-Luce Mixture Models for Partially Ranked Data.
Mollica, Cristina; Tardella, Luca
2017-06-01
The elicitation of an ordinal judgment on multiple alternatives is often required in many psychological and behavioral experiments to investigate preference/choice orientation of a specific population. The Plackett-Luce model is one of the most popular and frequently applied parametric distributions to analyze rankings of a finite set of items. The present work introduces a Bayesian finite mixture of Plackett-Luce models to account for unobserved sample heterogeneity of partially ranked data. We describe an efficient way to incorporate the latent group structure in the data augmentation approach and the derivation of existing maximum likelihood procedures as special instances of the proposed Bayesian method. Inference can be conducted with the combination of the Expectation-Maximization algorithm for maximum a posteriori estimation and the Gibbs sampling iterative procedure. We additionally investigate several Bayesian criteria for selecting the optimal mixture configuration and describe diagnostic tools for assessing the fitness of ranking distributions conditionally and unconditionally on the number of ranked items. The utility of the novel Bayesian parametric Plackett-Luce mixture for characterizing sample heterogeneity is illustrated with several applications to simulated and real preference ranked data. We compare our method with the frequentist approach and a Bayesian nonparametric mixture model both assuming the Plackett-Luce model as a mixture component. Our analysis on real datasets reveals the importance of an accurate diagnostic check for an appropriate in-depth understanding of the heterogenous nature of the partial ranking data.
Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.
Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad
2014-11-01
Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.
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…
A stable systemic risk ranking in China's banking sector: Based on principal component analysis
Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing
2018-02-01
In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.
Complete hazard ranking to analyze right-censored data: An ALS survival study.
Directory of Open Access Journals (Sweden)
Zhengnan Huang
2017-12-01
Full Text Available Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.
Complete hazard ranking to analyze right-censored data: An ALS survival study.
Huang, Zhengnan; Zhang, Hongjiu; Boss, Jonathan; Goutman, Stephen A; Mukherjee, Bhramar; Dinov, Ivo D; Guan, Yuanfang
2017-12-01
Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.
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.
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.
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.
Speech Denoising in White Noise Based on Signal Subspace Low-rank Plus Sparse Decomposition
Directory of Open Access Journals (Sweden)
yuan Shuai
2017-01-01
Full Text Available In this paper, a new subspace speech enhancement method using low-rank and sparse decomposition is presented. In the proposed method, we firstly structure the corrupted data as a Toeplitz matrix and estimate its effective rank for the underlying human speech signal. Then the low-rank and sparse decomposition is performed with the guidance of speech rank value to remove the noise. Extensive experiments have been carried out in white Gaussian noise condition, and experimental results show the proposed method performs better than conventional speech enhancement methods, in terms of yielding less residual noise and lower speech distortion.
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...
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.
Generating pseudo test collections for learning to rank scientific articles
Berendsen, R.; Tsagkias, M.; de Rijke, M.; Meij, E.
2012-01-01
Pseudo test collections are automatically generated to provide training material for learning to rank methods. We propose a method for generating pseudo test collections in the domain of digital libraries, where data is relatively sparse, but comes with rich annotations. Our intuition is that
Multicriterial ranking approach for evaluating bank branch performance
Aleskerov, F; Ersel, H; Yolalan, R
14 ranking methods based on multiple criteria are suggested for evaluating the performance of the bank branches. The methods are explained via an illustrative example, and some of them are applied to a real-life data for 23 retail bank branches in a large-scale private Turkish commercial bank.
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.
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
Robust rankings of socioeconomic health inequality using a categorical variable.
Makdissi, Paul; Yazbeck, Myra
2017-09-01
When assessing socioeconomic health inequalities, researchers often draw upon measures of income inequality that were developed for ratio scale variables. As a result, the use of categorical data (such as self-reported health status) produces rankings that may be arbitrary and contingent to the numerical scale adopted. In this paper, we develop a method that overcomes this issue by providing conditions for which these rankings are invariant to the numerical scale chosen by the researcher. In doing so, we draw on the insight provided by Allison and Foster (2004) and extend their method to the dimension of socioeconomic inequality by exploiting the properties of rank-dependent indices such as Wagstaff (2002) achievement and extended concentration indices. We also provide an empirical illustration using the National Institute of Health Survey 2012. Copyright © 2017 John Wiley & Sons, Ltd.
Using Bibliographic Knowledge for Ranking in Scientific Publication Databases
Vesely, Martin; Le Meur, Jean-Yves
2008-01-01
Document ranking for scientific publications involves a variety of specialized resources (e.g. author or citation indexes) that are usually difficult to use within standard general purpose search engines that usually operate on large-scale heterogeneous document collections for which the required specialized resources are not always available for all the documents present in the collections. Integrating such resources into specialized information retrieval engines is therefore important to cope with community-specific user expectations that strongly influence the perception of relevance within the considered community. In this perspective, this paper extends the notion of ranking with various methods exploiting different types of bibliographic knowledge that represent a crucial resource for measuring the relevance of scientific publications. In our work, we experimentally evaluated the adequacy of two such ranking methods (one based on freshness, i.e. the publication date, and the other on a novel index, the ...
Sparse Contextual Activation for Efficient Visual Re-Ranking.
Bai, Song; Bai, Xiang
2016-03-01
In this paper, we propose an extremely efficient algorithm for visual re-ranking. By considering the original pairwise distance in the contextual space, we develop a feature vector called sparse contextual activation (SCA) that encodes the local distribution of an image. Hence, re-ranking task can be simply accomplished by vector comparison under the generalized Jaccard metric, which has its theoretical meaning in the fuzzy set theory. In order to improve the time efficiency of re-ranking procedure, inverted index is successfully introduced to speed up the computation of generalized Jaccard metric. As a result, the average time cost of re-ranking for a certain query can be controlled within 1 ms. Furthermore, inspired by query expansion, we also develop an additional method called local consistency enhancement on the proposed SCA to improve the retrieval performance in an unsupervised manner. On the other hand, the retrieval performance using a single feature may not be satisfactory enough, which inspires us to fuse multiple complementary features for accurate retrieval. Based on SCA, a robust feature fusion algorithm is exploited that also preserves the characteristic of high time efficiency. We assess our proposed method in various visual re-ranking tasks. Experimental results on Princeton shape benchmark (3D object), WM-SRHEC07 (3D competition), YAEL data set B (face), MPEG-7 data set (shape), and Ukbench data set (image) manifest the effectiveness and efficiency of SCA.
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)
Image Re-Ranking Based on Topic Diversity.
Qian, Xueming; Lu, Dan; Wang, Yaxiong; Zhu, Li; Tang, Yuan Yan; Wang, Meng
2017-08-01
Social media sharing Websites allow users to annotate images with free tags, which significantly contribute to the development of the web image retrieval. Tag-based image search is an important method to find images shared by users in social networks. However, how to make the top ranked result relevant and with diversity is challenging. In this paper, we propose a topic diverse ranking approach for tag-based image retrieval with the consideration of promoting the topic coverage performance. First, we construct a tag graph based on the similarity between each tag. Then, the community detection method is conducted to mine the topic community of each tag. After that, inter-community and intra-community ranking are introduced to obtain the final retrieved results. In the inter-community ranking process, an adaptive random walk model is employed to rank the community based on the multi-information of each topic community. Besides, we build an inverted index structure for images to accelerate the searching process. Experimental results on Flickr data set and NUS-Wide data sets show the effectiveness of the proposed approach.
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)
Reducing the rank of gauge groups in orbifold compactification
International Nuclear Information System (INIS)
Sato, Hikaru
1989-01-01
The report introduces general twisted boundary conditions on fermionic string variables and shows that a non-Abelian embedding is possible when background gauge field is introduced on orbifold. This leads to reduction of the rank of the gauge group. The report presents a procedure to obtain the lower-rank gauge groups by the use of non-Abelian Wilson lines. The unbroken gauge group is essentially determined by the eigen vector which should obey the level-matching conditions. The gauge symmetry is determined by certain conditions. In a particular application, it is not necessary to introduce explicit form of the non-Abelian Wilson lines. The procedure starts with introduction of desired eigen vectors which are supposed to be obtained by diagonalization of the boundary conditions with the appropriate transformation matrix. The rank is reduced by one by using the Wilson lines which transform as 3 of SU(2) R or SU(2) in SU(4). A possible way of reducing the rank by two is to use the Wilson lines from SU(2) R x SU(2) or SU(3) in SU(4). The rank is reduced by three by means of the Wilson lines which transform as SU(4) or SU(2) R SU(3). Finally the rank is reduced by four when the Wilson lines with full symmetry of SU(2) R x SU(4) are used. The report tabulates the possible lower-rank gauge groups obtained by the proposed method. Massless fermions corresponding to the eigen vectors are also listed. (N.K.)
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....
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....
Energy Technology Data Exchange (ETDEWEB)
Yoon, Sae Rom [Dept of Quantum Energy Chemical Engineering, Korea University of Science and Technology (KUST), Daejeon (Korea, Republic of); Choi, Sung Yeol [Ulsan National Institute of Science and Technology, Ulju (Korea, Republic of); Ko, Wonil [Nonproliferation System Development Division, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2017-02-15
The focus on the issues surrounding spent nuclear fuel and lifetime extension of old nuclear power plants continues to grow nowadays. A transparent decision-making process to identify the best suitable nuclear fuel cycle (NFC) is considered to be the key task in the current situation. Through this study, an attempt is made to develop an equilibrium model for the NFC to calculate the material flows based on 1 TWh of electricity production, and to perform integrated multicriteria decision-making method analyses via the analytic hierarchy process technique for order of preference by similarity to ideal solution, preference ranking organization method for enrichment evaluation, and multiattribute utility theory methods. This comparative study is aimed at screening and ranking the three selected NFC options against five aspects: sustainability, environmental friendliness, economics, proliferation resistance, and technical feasibility. The selected fuel cycle options include pressurized water reactor (PWR) once-through cycle, PWR mixed oxide cycle, or pyroprocessing sodium-cooled fast reactor cycle. A sensitivity analysis was performed to prove the robustness of the results and explore the influence of criteria on the obtained ranking. As a result of the comparative analysis, the pyroprocessing sodium-cooled fast reactor cycle is determined to be the most competitive option among the NFC scenarios.
International Nuclear Information System (INIS)
Yoon, Sae Rom; Choi, Sung Yeol; Ko, Wonil
2017-01-01
The focus on the issues surrounding spent nuclear fuel and lifetime extension of old nuclear power plants continues to grow nowadays. A transparent decision-making process to identify the best suitable nuclear fuel cycle (NFC) is considered to be the key task in the current situation. Through this study, an attempt is made to develop an equilibrium model for the NFC to calculate the material flows based on 1 TWh of electricity production, and to perform integrated multicriteria decision-making method analyses via the analytic hierarchy process technique for order of preference by similarity to ideal solution, preference ranking organization method for enrichment evaluation, and multiattribute utility theory methods. This comparative study is aimed at screening and ranking the three selected NFC options against five aspects: sustainability, environmental friendliness, economics, proliferation resistance, and technical feasibility. The selected fuel cycle options include pressurized water reactor (PWR) once-through cycle, PWR mixed oxide cycle, or pyroprocessing sodium-cooled fast reactor cycle. A sensitivity analysis was performed to prove the robustness of the results and explore the influence of criteria on the obtained ranking. As a result of the comparative analysis, the pyroprocessing sodium-cooled fast reactor cycle is determined to be the most competitive option among the NFC scenarios
Directory of Open Access Journals (Sweden)
Saerom Yoon
2017-02-01
Full Text Available The focus on the issues surrounding spent nuclear fuel and lifetime extension of old nuclear power plants continues to grow nowadays. A transparent decision-making process to identify the best suitable nuclear fuel cycle (NFC is considered to be the key task in the current situation. Through this study, an attempt is made to develop an equilibrium model for the NFC to calculate the material flows based on 1 TWh of electricity production, and to perform integrated multicriteria decision-making method analyses via the analytic hierarchy process technique for order of preference by similarity to ideal solution, preference ranking organization method for enrichment evaluation, and multiattribute utility theory methods. This comparative study is aimed at screening and ranking the three selected NFC options against five aspects: sustainability, environmental friendliness, economics, proliferation resistance, and technical feasibility. The selected fuel cycle options include pressurized water reactor (PWR once-through cycle, PWR mixed oxide cycle, or pyroprocessing sodium-cooled fast reactor cycle. A sensitivity analysis was performed to prove the robustness of the results and explore the influence of criteria on the obtained ranking. As a result of the comparative analysis, the pyroprocessing sodium-cooled fast reactor cycle is determined to be the most competitive option among the NFC scenarios.
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.
Comparative Case Studies on Indonesian Higher Education Rankings
Kurniasih, Nuning; Hasyim, C.; Wulandari, A.; Setiawan, M. I.; Ahmar, A. S.
2018-01-01
The quality of the higher education is the result of a continuous process. There are many indicators that can be used to assess the quality of a higher education. The existence of different indicators makes the different result of university rankings. This research aims to find variables that can connect ranking indicators that are used by Indonesian Ministry of Research, Technology, and Higher Education with indicators that are used by international rankings by taking two kind of ranking systems i.e. Webometrics and 4icu. This research uses qualitative research method with comparative case studies approach. The result of the research shows that to bridge the indicators that are used by Indonesian Ministry or Research, Technology, and Higher Education with web-based ranking system like Webometrics and 4icu so that the Indonesian higher education institutions need to open access towards either scientific or non-scientific that are publicly used into web-based environment. One of the strategies that can be used to improve the openness and access towards scientific work of a university is by involving in open science and collaboration.
A study of metrics of distance and correlation between ranked lists for compositionality detection
DEFF Research Database (Denmark)
Lioma, Christina; Hansen, Niels Dalum
2017-01-01
affects the measurement of semantic similarity. We propose a new compositionality detection method that represents phrases as ranked lists of term weights. Our method approximates the semantic similarity between two ranked list representations using a range of well-known distance and correlation metrics...... of compositionality using any of the distance and correlation metrics considered....
Adaptive Game Level Creation through Rank-based Interactive Evolution
DEFF Research Database (Denmark)
Liapis, Antonios; Martínez, Héctor Pérez; Togelius, Julian
2013-01-01
as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using...
Measuring Vocational Preferences: Ranking versus Categorical Rating Procedures.
Carifio, James
1978-01-01
Describes a study to compare the relative validities of ranking v categorical rating procedures for obtaining student vocational preference data in exploratory program assignment situations. Students indicated their vocational program preferences from career clusters, and the frequency of wrong assignments made by each method was analyzed. (MF)
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
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.
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.
Ranking of lignocellulosic biomass pellets through multicriteria modeling
Energy Technology Data Exchange (ETDEWEB)
Sultana, A.; Kumar, A. [Alberta Univ., Edmonton, AB (Canada). Dept. of Mechanical Engineering
2009-07-01
A study was conducted in which pellets from different lignocellulosic biomass sources were ranked using a multicriteria assessment model. Five different pellet alternatives were compared based on 10 criteria. The pair-wise comparison was done in order to develop preference indices for various alternatives. The methodology used in this study was the Preference Ranking Organization Method for Enrichment and Evaluation (PROMETHEE). The biomass included wood pellets, straw pellets, switchgrass pellets, alfalfa pellets and poultry pellets. The study considered both quantitative and qualitative criteria such as energy consumption to produce the pellets, production cost, bulk density, NOx emissions, SOx emissions, deposit formation, net calorific value, moisture content, maturity of technology, and quality of material. A sensitivity analysis was performed by changing weights of criteria and threshold values of the criteria. Different scenarios were developed for ranking cost and environmental impacts. According to preliminary results, the wood pellet is the best energy source, followed by switchgrass pellets, straw pellets, alfalfa pellets and poultry pellets.
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.
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
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....
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
Predicting disease risk using bootstrap ranking and classification algorithms.
Directory of Open Access Journals (Sweden)
Ohad Manor
Full Text Available Genome-wide association studies (GWAS are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a "black box" in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF, suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.
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)
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}.
Ranking Scientific Publications Based on Their Citation Graph
Marian, L; Rajman, M
2009-01-01
CDS Invenio is the web-based integrated digital library system developed at CERN. It is a suite of applications which provides the framework and tools for building and managing an autonomous digital library server. Within this framework, the goal of this project is to implement new ranking methods based on the bibliographic citation graph extracted from the CDS Invenio database. As a first step, we implemented the Citation Count as a baseline ranking method. The major disadvantage of this method is that all citations are treated equally, disregarding their importance and their publication date. To overcome this drawback, we consider two different approaches: a link-based approach which extends the PageRank model to the bibliographic citation graph and a time-dependent approach which takes into account time in the citation counts. In addition, we also combined these two approaches in a hybrid model based on a time-dependent PageRank. In the present document, we describe the conceptual background behind our new...
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.
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 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.
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
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
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.
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; 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.
Ranking important nodes in complex networks by simulated annealing
International Nuclear Information System (INIS)
Sun Yu; Yao Pei-Yang; Shen Jian; Zhong Yun; Wan Lu-Jun
2017-01-01
In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented. First, the concept of an importance sequence (IS) to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks. (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.
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.
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....
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
TAKING VARIOUS AGRO-TECHNICAL MEASURES FOR THE PRESERVATION AND ACCUMULATION OF MOISTURE
Directory of Open Access Journals (Sweden)
M. B. Khalilov
2016-01-01
Full Text Available ARTICLE RETRACTEDAim. The aim is to conduct a theoretical study of the conditions of occurrence of surface runoff and take technical measures for its prevention.Discussion. The occurrence of surface runoff is possible on condition that the intensity of moisture entry per time unit is greater than the intensity of its absorption and passage through the topsoil. Conditions of surface runoff occur at high intensity of moisture entering the soil surface which can be in the case of heavy rainfall, low water permeability of topsoil as a result of the increased density. The upper topsoil not affected by loosening passes moisture worse than loosened. Low water conductivity of the arable layer may be due to the fact that it is saturated with moisture up to the limit, and the underlying subsoil layers do not absorb or absorb not enough water, pass water less than enters through the upper topsoil. This phenomenon leads to oversaturation of the top plowed layer by water, which can lead to water erosion and landslide.Conclusion. We obtained analytical expressions describing the process of accumulation of moisture in the soil and the formation of surface runoff, which also allow to theoretically justify the need for different agronomic techniques impact on soil. We should select tools and soil impact techniques in order to preserve and accumulate moisture with account of the certain agrolandscape conditions specific to these fields.
Tupker, RA; Bunte, EE; Fidler, [No Value; Wiechers, JW; Coenraads, PJ
Discrepancies between the one-time patch test and the wash test regarding the ranking of irritancy of detergents have been found in the literature. The aim of the present study was to investigate the concordance of irritancy rank order of 4 anionic detergents tested by 3 different exposure methods,
World University Ranking Systems: An Alternative Approach Using Partial Least Squares Path Modelling
Jajo, Nethal K.; Harrison, Jen
2014-01-01
University rankings are key drivers in national and institutional strategic planning. The increase in the number of university ranking systems and the diversity of methods and indicators used by these systems necessitate the development of an index that can measure a university's performance in all these systems at once. This article presents…
Eisinga, R.N.; Heskes, T.M.; Pelzer, B.J.; Grotenhuis, H.F. te
2017-01-01
Background: The Friedman rank sum test is a widely-used nonparametric method in computational biology. In addition to examining the overall null hypothesis of no significant difference among any of the rank sums, it is typically of interest to conduct pairwise comparison tests. Current approaches to
The Optimal Ranking Regime (ORR) method was used to identify intra- to multi-decadal (IMD) time windows containing significant ranking sequences in U.S. climate division temperature data. The simplicity of the ORR procedure’s output – a time series’ most significant non-overlapping periods of high o...
Holosko, Michael J.; Barner, John R.
2016-01-01
Objectives: We sought the answer to one major research question--Does psychology have a more defined culture of research than social work? Methods: Using "U.S. News and World Report" 2012 and 2013 rankings, we compared psychology faculty (N = 969) from their 25 top ranked programs with a controlled sample of social work faculty (N = 970)…
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.
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
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.
Ranking beta sheet topologies with applications to protein structure prediction
DEFF Research Database (Denmark)
Fonseca, Rasmus; Helles, Glennie; Winter, Pawel
2011-01-01
One reason why ab initio protein structure predictors do not perform very well is their inability to reliably identify long-range interactions between amino acids. To achieve reliable long-range interactions, all potential pairings of ß-strands (ß-topologies) of a given protein are enumerated......, including the native ß-topology. Two very different ß-topology scoring methods from the literature are then used to rank all potential ß-topologies. This has not previously been attempted for any scoring method. The main result of this paper is a justification that one of the scoring methods, in particular......, consistently top-ranks native ß-topologies. Since the number of potential ß-topologies grows exponentially with the number of ß-strands, it is unrealistic to expect that all potential ß-topologies can be enumerated for large proteins. The second result of this paper is an enumeration scheme of a subset of ß-topologies...
Tensor surgery and tensor rank
M. Christandl (Matthias); J. Zuiddam (Jeroen)
2018-01-01
textabstractWe introduce a method for transforming low-order tensors into higher-order tensors and apply it to tensors defined by graphs and hypergraphs. The transformation proceeds according to a surgery-like procedure that splits vertices, creates and absorbs virtual edges and inserts new vertices
DEFF Research Database (Denmark)
Martinez Peñas, Umberto; Pellikaan, Ruud
2017-01-01
Error-correcting pairs were introduced as a general method of decoding linear codes with respect to the Hamming metric using coordinatewise products of vectors, and are used for many well-known families of codes. In this paper, we define new types of vector products, extending the coordinatewise ...
Tensor surgery and tensor rank
M. Christandl (Matthias); J. Zuiddam (Jeroen)
2016-01-01
textabstractWe introduce a method for transforming low-order tensors into higher-order tensors and apply it to tensors defined by graphs and hypergraphs. The transformation proceeds according to a surgery-like procedure that splits vertices, creates and absorbs virtual edges and inserts new
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.
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).
Connectivity ranking of heterogeneous random conductivity models
Rizzo, C. B.; de Barros, F.
2017-12-01
To overcome the challenges associated with hydrogeological data scarcity, the hydraulic conductivity (K) field is often represented by a spatial random process. The state-of-the-art provides several methods to generate 2D or 3D random K-fields, such as the classic multi-Gaussian fields or non-Gaussian fields, training image-based fields and object-based fields. We provide a systematic comparison of these models based on their connectivity. We use the minimum hydraulic resistance as a connectivity measure, which it has been found to be strictly correlated with early time arrival of dissolved contaminants. A computationally efficient graph-based algorithm is employed, allowing a stochastic treatment of the minimum hydraulic resistance through a Monte-Carlo approach and therefore enabling the computation of its uncertainty. The results show the impact of geostatistical parameters on the connectivity for each group of random fields, being able to rank the fields according to their minimum hydraulic resistance.
Improving CBIR Systems Using Automated Ranking
Directory of Open Access Journals (Sweden)
B. D. Reljin
2012-11-01
Full Text Available The most common way of searching images on the Internet and in private collections is based on a similarity measuring of a series of text words that are assigned to each image with users query series. This method imposes strong constraints (the number of words to describe the image, the time necessary to thoroughly describe the subjective experience of images, the level of details in the picture, language barrier, etc., and is therefore very inefficient. Modern researches in this area are focused on the contentbased searching images (CBIR. In this way, all described disadvantages are overcome and the quality of searching results is improved. This paper presents a solution for CBIR systems where the search procedure is enhanced using sophisticated extraction and ranking of extracted images. The searching procedure is based on extraction and preprocessing of a large number of low level image features. Thus, when the user defines a query image, the proposed algorithm based on artificial intelligence, shows to the user a group of images which are most similar to a query image by content. The proposed algorithm is iterative, so the user can direct the searching procedure to an expected outcome and get a set of images that are more similar to the query one.
Phenomena identification and ranking tables (PIRT) for LBLOCA
International Nuclear Information System (INIS)
Shaw, R.A.; Dimenna, R.A.; Larson, T.K.; Wilson, G.E.
1988-01-01
The US Nuclear Regulatory Commission is sponsoring a program to provide validated reactor safety computer codes with quantified uncertainties. The intent is to quantify the accuracy of the codes for use in best estimate licensing applications. One of the tasks required to complete this program involves the identification and ranking of thermal-hydraulic phenomena that occur during particular accidents. This paper provides detailed tables of phenomena and importance ranks for a PWR LBLOCA. The phenomena were identified and ranked according to perceived impact on peak cladding temperature. Two approaches were used to complete this task. First, a panel of experts identified the physical processes considered to be most important during LBLOCA. A second team of experienced analysts then, in parallel, assembled complete tables of all plausible LBLOCA phenomena, regardless of perceived importance. Each phenomenon was then ranked in importance against every other phenomenon associated with a given component. The results were placed in matrix format and solved for the principal eigenvector. The results as determined by each method are presented in this report
A folk-psychological ranking of personality facets
Directory of Open Access Journals (Sweden)
Eka Roivainen
2016-10-01
Full Text Available Background Which personality facets should a general personality test measure? No consensus exists on the facet structure of personality, the nature of facets, or the correct method of identifying the most significant facets. However, it can be hypothesized (the lexical hypothesis that high frequency personality describing words more likely represent important personality facets and rarely used words refer to less significant aspects of personality. Participants and procedure A ranking of personality facets was performed by studying the frequency of the use of popular personality adjectives in causal clauses (because he is a kind person on the Internet and in books as attributes of the word person (kind person. Results In Study 1, the 40 most frequently used adjectives had a cumulative usage frequency equal to that of the rest of the 295 terms studied. When terms with a higher-ranking dictionary synonym or antonym were eliminated, 23 terms remained, which represent 23 different facets. In Study 2, clusters of synonymous terms were examined. Within the top 30 clusters, personality terms were used 855 times compared to 240 for the 70 lower-ranking clusters. Conclusions It is hypothesized that personality facets represented by the top-ranking terms and clusters of terms are important and impactful independent of their correlation with abstract underlying personality factors (five/six factor models. Compared to hierarchical personality models, lists of important facets probably better cover those aspects of personality that are situated between the five or six major domains.
Zipf rank approach and cross-country convergence of incomes
Shao, Jia; Ivanov, Plamen Ch.; Urošević, Branko; Stanley, H. Eugene; Podobnik, Boris
2011-05-01
We employ a concept popular in physics —the Zipf rank approach— in order to estimate the number of years that EU members would need in order to achieve "convergence" of their per capita incomes. Assuming that trends in the past twenty years continue to hold in the future, we find that after t≈30 years both developing and developed EU countries indexed by i will have comparable values of their per capita gross domestic product {\\cal G}_{i,t} . Besides the traditional Zipf rank approach we also propose a weighted Zipf rank method. In contrast to the EU block, on the world level the Zipf rank approach shows that, between 1960 and 2009, cross-country income differences increased over time. For a brief period during the 2007-2008 global economic crisis, at world level the {\\cal G}_{i,t} of richer countries declined more rapidly than the {\\cal G}_{i,t} of poorer countries, in contrast to EU where the {\\cal G}_{i,t} of developing EU countries declined faster than the {\\cal G}_{i,t} of developed EU countries, indicating that the recession interrupted the convergence between EU members. We propose a simple model of GDP evolution that accounts for the scaling we observe in the data.
A full ranking for decision making units using ideal and anti-ideal points in DEA.
Barzegarinegad, A; Jahanshahloo, G; Rostamy-Malkhalifeh, M
2014-01-01
We propose a procedure for ranking decision making units in data envelopment analysis, based on ideal and anti-ideal points in the production possibility set. Moreover, a model has been introduced to compute the performance of a decision making unit for these two points through using common set of weights. One of the best privileges of this method is that we can make ranking for all decision making units by solving only three programs, and also solving these programs is not related to numbers of decision making units. One of the other advantages of this procedure is to rank all the extreme and nonextreme efficient decision making units. In other words, the suggested ranking method tends to seek a set of common weights for all units to make them fully ranked. Finally, it was applied for different sets holding real data, and then it can be compared with other procedures.
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.
RANK/RANK-Ligand/OPG: Ein neuer Therapieansatz in der Osteoporosebehandlung
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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.
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.
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...
Fuzzy Logic and Its Application in Football Team Ranking
Directory of Open Access Journals (Sweden)
Wenyi Zeng
2014-01-01
some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T7, T3, T1, T9, T10, T8, T11, T12, T2, T6, T5, T4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range.
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.
Ranking metrics in gene set enrichment analysis: do they matter?
Zyla, Joanna; Marczyk, Michal; Weiner, January; Polanska, Joanna
2017-05-12
There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment Analysis seems to be one of the most commonly used (over 10,000 citations). An important parameter, which could affect the final result, is the choice of a metric for the ranking of genes. Applying a default ranking metric may lead to poor results. In this work 28 benchmark data sets were used to evaluate the sensitivity and false positive rate of gene set analysis for 16 different ranking metrics including new proposals. Furthermore, the robustness of the chosen methods to sample size was tested. Using k-means clustering algorithm a group of four metrics with the highest performance in terms of overall sensitivity, overall false positive rate and computational load was established i.e. absolute value of Moderated Welch Test statistic, Minimum Significant Difference, absolute value of Signal-To-Noise ratio and Baumgartner-Weiss-Schindler test statistic. In case of false positive rate estimation, all selected ranking metrics were robust with respect to sample size. In case of sensitivity, the absolute value of Moderated Welch Test statistic and absolute value of Signal-To-Noise ratio gave stable results, while Baumgartner-Weiss-Schindler and Minimum Significant Difference showed better results for larger sample size. Finally, the Gene Set Enrichment Analysis method with all tested ranking metrics was parallelised and implemented in MATLAB, and is available at https://github.com/ZAEDPolSl/MrGSEA . Choosing a ranking metric in Gene Set Enrichment Analysis has critical impact on results of pathway enrichment analysis. The absolute value of Moderated Welch Test has the best overall sensitivity and Minimum Significant Difference has the best overall specificity of gene set analysis. When the number of non-normally distributed genes is high, using Baumgartner
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
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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.
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.
Health systems around the world - a comparison of existing health system rankings.
Schütte, Stefanie; Acevedo, Paula N Marin; Flahault, Antoine
2018-06-01
Existing health systems all over the world are different due to the different combinations of components that can be considered for their establishment. The ranking of health systems has been a focal points for many years especially the issue of performance. In 2000 the World Health Organization (WHO) performed a ranking to compare the Performance of the health system of the member countries. Since then other health system rankings have been performed and it became an issue of public discussion. A point of contention regarding these rankings is the methodology employed by each of them, since no gold standard exists. Therefore, this review focuses on evaluating the methodologies of each existing health system performance ranking to assess their reproducibility and transparency. A search was conducted to identify existing health system rankings, and a questionnaire was developed for the comparison of the methodologies based on the following indicators: (1) General information, (2) Statistical methods, (3) Data (4) Indicators. Overall nine rankings were identified whereas six of them focused rather on the measurement of population health without any financial component and were therefore excluded. Finally, three health system rankings were selected for this review: "Health Systems: Improving Performance" by the WHO, "Mirror, Mirror on the wall: How the Performance of the US Health Care System Compares Internationally" by the Commonwealth Fund and "the Most efficient Health Care" by Bloomberg. After the completion of the comparison of the rankings by giving them scores according to the indicators, the ranking performed the WHO was considered the most complete regarding the ability of reproducibility and transparency of the methodology. This review and comparison could help in establishing consensus in the field of health system research. This may also help giving recommendations for future health rankings and evaluating the current gap in the literature.
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.
Development of the Operational Events Groups Ranking Tool
International Nuclear Information System (INIS)
Simic, Zdenko; Banov, Reni
2014-01-01
Both because of complexity and ageing, facilities like nuclear power plants require feedback from the operating experience in order to further improve safety and operation performance. That is the reason why significant effort is dedicated to operating experience feedback. This paper contains description of the specification and development of the application for the operating events ranking software tool. Robust and consistent way of selecting most important events for detail investigation is important because it is not feasible or even useful to investigate all of them. Development of the tool is based on the comprehensive events characterisation and methodical prioritization. This includes rich set of events parameters which allow their top level preliminary analysis, different ways of groupings and even to evaluate uncertainty propagation to the ranking results. One distinct feature of the implemented method is that user (i.e., expert) could determine how important is particular ranking parameter based on their pairwise comparison. For tools demonstration and usability it is crucial that sample database is also created. For useful analysis the whole set of events for 5 years is selected and characterised. Based on the preliminary results this tool seems valuable for new preliminary prospective on data as whole, and especially for the identification of events groups which should have priority in the more detailed assessment. The results are consisting of different informative views on the events groups importance and related sensitivity and uncertainty results. This presents valuable tool for improving overall picture about specific operating experience and also for helping identify the most important events groups for further assessment. It is clear that completeness and consistency of the input data characterisation is very important to get full and valuable importance ranking. Method and tool development described in this paper is part of continuous effort of
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...
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.
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...
Poisson statistics of PageRank probabilities of Twitter and Wikipedia networks
Frahm, Klaus M.; Shepelyansky, Dima L.
2014-04-01
We use the methods of quantum chaos and Random Matrix Theory for analysis of statistical fluctuations of PageRank probabilities in directed networks. In this approach the effective energy levels are given by a logarithm of PageRank probability at a given node. After the standard energy level unfolding procedure we establish that the nearest spacing distribution of PageRank probabilities is described by the Poisson law typical for integrable quantum systems. Our studies are done for the Twitter network and three networks of Wikipedia editions in English, French and German. We argue that due to absence of level repulsion the PageRank order of nearby nodes can be easily interchanged. The obtained Poisson law implies that the nearby PageRank probabilities fluctuate as random independent variables.
Ranking of input parameters importance for BWR stability based on Ringhals-1
International Nuclear Information System (INIS)
Gajev, Ivan; Kozlowski, Tomasz; Xu, Yunlin; Downar, Thomas
2011-01-01
Unstable behavior of Boiling Water Reactors (BWRs) is known to occur during operation at certain power and flow conditions. Uncertainty calculations for BWR stability, based on the Wilks' formula, have been already done for the Ringhals-1 benchmark. In this work, these calculations have been used to identify and rank the most important parameters affecting the stability of the Ringhals-1 plant. The ranking has been done in two different ways and a comparison of these two methods has been demonstrated. Results show that the methods provide different, but meaningful evaluations of the ranking. (author)
Ranking alternatives based on imprecise multi-criteria data and pairwise overlap dominance relations
DEFF Research Database (Denmark)
Franco de los Rios, Camilo Andres; Hougaard, Jens Leth; Nielsen, Kurt
illustrative example is given for comparison with standard methods like PROMETHEE. The proposed methodology takes into account the risk attitudes of decision makers, organizing the alternatives and ranking them according to their relevance. The whole interactive decision support allows understanding...
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.
Ranking Canadian oil and gas projects using TOPSIS
Directory of Open Access Journals (Sweden)
Seyed Jafar Sadjadi
2017-08-01
Full Text Available One of the primary concerns for investment in oil and gas projects is to have a comprehensive understanding on different issues associated with this industry. The industry is mainly influ-enced by the price of oil and gas and in some events, many production units have been forced to shut down solely because of low price of oil and gas. Environmental issues are other important factors, which may put pressure on Canada’s political affairs since the country has strong com-mitment to reduce green gas effect. In this paper, we introduce a multi-criteria decision making method, which helps us rank different projects in terms of investment. The proposed study con-siders different investment factors including net present value, rate of return, benefit-cost analy-sis and payback period along with the intensity of green gas effects for ranking the present oil and gas projects in Canada.
QV modal distance displacement - a criterion for contingency ranking
Energy Technology Data Exchange (ETDEWEB)
Rios, M.A.; Sanchez, J.L.; Zapata, C.J. [Universidad de Los Andes (Colombia). Dept. of Electrical Engineering], Emails: mrios@uniandes.edu.co, josesan@uniandes.edu.co, cjzapata@utp.edu.co
2009-07-01
This paper proposes a new methodology using concepts of fast decoupled load flow, modal analysis and ranking of contingencies, where the impact of each contingency is measured hourly taking into account the influence of each contingency over the mathematical model of the system, i.e. the Jacobian Matrix. This method computes the displacement of the reduced Jacobian Matrix eigenvalues used in voltage stability analysis, as a criterion of contingency ranking, considering the fact that the lowest eigenvalue in the normal operation condition is not the same lowest eigenvalue in N-1 contingency condition. It is made using all branches in the system and specific branches according to the IBPF index. The test system used is the IEEE 118 nodes. (author)
Low-rank matrix approximation with manifold regularization.
Zhang, Zhenyue; Zhao, Keke
2013-07-01
This paper proposes a new model of low-rank matrix factorization that incorporates manifold regularization to the matrix factorization. Superior to the graph-regularized nonnegative matrix factorization, this new regularization model has globally optimal and closed-form solutions. A direct algorithm (for data with small number of points) and an alternate iterative algorithm with inexact inner iteration (for large scale data) are proposed to solve the new model. A convergence analysis establishes the global convergence of the iterative algorithm. The efficiency and precision of the algorithm are demonstrated numerically through applications to six real-world datasets on clustering and classification. Performance comparison with existing algorithms shows the effectiveness of the proposed method for low-rank factorization in general.
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.
Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM)
International Nuclear Information System (INIS)
Gao, Hao; Osher, Stanley; Yu, Hengyong; Wang, Ge
2011-01-01
We propose a compressive sensing approach for multi-energy computed tomography (CT), namely the prior rank, intensity and sparsity model (PRISM). To further compress the multi-energy image for allowing the reconstruction with fewer CT data and less radiation dose, the PRISM models a multi-energy image as the superposition of a low-rank matrix and a sparse matrix (with row dimension in space and column dimension in energy), where the low-rank matrix corresponds to the stationary background over energy that has a low matrix rank, and the sparse matrix represents the rest of distinct spectral features that are often sparse. Distinct from previous methods, the PRISM utilizes the generalized rank, e.g., the matrix rank of tight-frame transform of a multi-energy image, which offers a way to characterize the multi-level and multi-filtered image coherence across the energy spectrum. Besides, the energy-dependent intensity information can be incorporated into the PRISM in terms of the spectral curves for base materials, with which the restoration of the multi-energy image becomes the reconstruction of the energy-independent material composition matrix. In other words, the PRISM utilizes prior knowledge on the generalized rank and sparsity of a multi-energy image, and intensity/spectral characteristics of base materials. Furthermore, we develop an accurate and fast split Bregman method for the PRISM and demonstrate the superior performance of the PRISM relative to several competing methods in simulations. (papers)
Multi-dimensional Rankings, Program Termination, and Complexity Bounds of Flowchart Programs
Alias, Christophe; Darte, Alain; Feautrier, Paul; Gonnord, Laure
Proving the termination of a flowchart program can be done by exhibiting a ranking function, i.e., a function from the program states to a well-founded set, which strictly decreases at each program step. A standard method to automatically generate such a function is to compute invariants for each program point and to search for a ranking in a restricted class of functions that can be handled with linear programming techniques. Previous algorithms based on affine rankings either are applicable only to simple loops (i.e., single-node flowcharts) and rely on enumeration, or are not complete in the sense that they are not guaranteed to find a ranking in the class of functions they consider, if one exists. Our first contribution is to propose an efficient algorithm to compute ranking functions: It can handle flowcharts of arbitrary structure, the class of candidate rankings it explores is larger, and our method, although greedy, is provably complete. Our second contribution is to show how to use the ranking functions we generate to get upper bounds for the computational complexity (number of transitions) of the source program. This estimate is a polynomial, which means that we can handle programs with more than linear complexity. We applied the method on a collection of test cases from the literature. We also show the links and differences with previous techniques based on the insertion of counters.
Ranking Decision Making Units with Stochastic Data by Using Coefficient of Variation
Lotfi, F.; Nematollahi, N.; Behzadi, M.H.; Mirbolouki, M.
2010-01-01
Data Envelopment Analysis (DEA) is a non-parametric technique which is based on mathematical programming for evaluating the efficiency of a set of Decision Making Units (DMUs). Throughout applications, managers encounter with stochastic data and the necessity of having a method that is able to evaluate efficiency and rank efficient units has been under consideration. In this paper considering the concept of coefficient of variation among efficient DMUs, two ranking methods has been proposed. ...
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;
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
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
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)
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.
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
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...
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.
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...
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
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
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/.
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.
Retif, Paul; Reinhard, Aurélie; Paquot, Héna; Jouan-Hureaux, Valérie; Chateau, Alicia; Sancey, Lucie; Barberi-Heyob, Muriel; Pinel, Sophie; Bastogne, Thierry
This article addresses the in silico-in vitro prediction issue of organometallic nanoparticles (NPs)-based radiosensitization enhancement. The goal was to carry out computational experiments to quickly identify efficient nanostructures and then to preferentially select the most promising ones for the subsequent in vivo studies. To this aim, this interdisciplinary article introduces a new theoretical Monte Carlo computational ranking method and tests it using 3 different organometallic NPs in terms of size and composition. While the ranking predicted in a classical theoretical scenario did not fit the reference results at all, in contrast, we showed for the first time how our accelerated in silico virtual screening method, based on basic in vitro experimental data (which takes into account the NPs cell biodistribution), was able to predict a relevant ranking in accordance with in vitro clonogenic efficiency. This corroborates the pertinence of such a prior ranking method that could speed up the preclinical development of NPs in radiation therapy.
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.
D-Iteration: diffusion approach for solving PageRank
Hong, Dohy; Huynh, The Dang; Mathieu, Fabien
2015-01-01
In this paper we present a new method that can accelerate the computation of the PageRank importance vector. Our method, called D-Iteration (DI), is based on the decomposition of the matrix-vector product that can be seen as a fluid diffusion model and is potentially adapted to asynchronous implementation. We give theoretical results about the convergence of our algorithm and we show through experimentations on a real Web graph that DI can improve the computation efficiency compared to other ...
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.
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.
A human fecal contamination index for ranking impaired ...
Human fecal pollution of surface water remains a public health concern worldwide. As a result, there is a growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for recreational water quality risk management. The transition from a research subject to a management tool requires the integration of standardized water sampling, laboratory, and data analysis procedures. In this study, a standardized HF183/BacR287 qPCR method was combined with a water sampling strategy and Bayesian data algorithm to establish a human fecal contamination index that can be used to rank impaired recreational water sites polluted with human waste. Stability and bias of index predictions were investigated under various parameters including siteswith different pollution levels, sampling period time range (1-15 weeks), and number of qPCR replicates per sample (2-14 replicates). Sensitivity analyses were conducted with simulated data sets (100 iterations) seeded with HF183/BacR287 qPCR laboratory measurements from water samples collected from three Southern California sites (588 qPCR measurements). Findings suggest that site ranking is feasible and that all parameters tested influence stability and bias in human fecal contamination indexscoring. Trends identified by sensitivity analyses will provide managers with the information needed to design and conduct field studies to rank impaired recreational water sites based
Adaptive linear rank tests for eQTL studies.
Szymczak, Silke; Scheinhardt, Markus O; Zeller, Tanja; Wild, Philipp S; Blankenberg, Stefan; Ziegler, Andreas
2013-02-10
Expression quantitative trait loci (eQTL) studies are performed to identify single-nucleotide polymorphisms that modify average expression values of genes, proteins, or metabolites, depending on the genotype. As expression values are often not normally distributed, statistical methods for eQTL studies should be valid and powerful in these situations. Adaptive tests are promising alternatives to standard approaches, such as the analysis of variance or the Kruskal-Wallis test. In a two-stage procedure, skewness and tail length of the distributions are estimated and used to select one of several linear rank tests. In this study, we compare two adaptive tests that were proposed in the literature using extensive Monte Carlo simulations of a wide range of different symmetric and skewed distributions. We derive a new adaptive test that combines the advantages of both literature-based approaches. The new test does not require the user to specify a distribution. It is slightly less powerful than the locally most powerful rank test for the correct distribution and at least as powerful as the maximin efficiency robust rank test. We illustrate the application of all tests using two examples from different eQTL studies. Copyright © 2012 John Wiley & Sons, Ltd.
GeneRank: Using search engine technology for the analysis of microarray experiments
Directory of Open Access Journals (Sweden)
Breitling Rainer
2005-09-01
Full Text Available Abstract Background Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method – based on the PageRank algorithm employed by the popular search engine Google – that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. Results GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. Conclusion Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments.
GeneRank: using search engine technology for the analysis of microarray experiments.
Morrison, Julie L; Breitling, Rainer; Higham, Desmond J; Gilbert, David R
2005-09-21
Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method--based on the PageRank algorithm employed by the popular search engine Google--that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies) or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments.
Global sensitivity analysis using low-rank tensor approximations
International Nuclear Information System (INIS)
Konakli, Katerina; Sudret, Bruno
2016-01-01
In the context of global sensitivity analysis, the Sobol' indices constitute a powerful tool for assessing the relative significance of the uncertain input parameters of a model. We herein introduce a novel approach for evaluating these indices at low computational cost, by post-processing the coefficients of polynomial meta-models belonging to the class of low-rank tensor approximations. Meta-models of this class can be particularly efficient in representing responses of high-dimensional models, because the number of unknowns in their general functional form grows only linearly with the input dimension. The proposed approach is validated in example applications, where the Sobol' indices derived from the meta-model coefficients are compared to reference indices, the latter obtained by exact analytical solutions or Monte-Carlo simulation with extremely large samples. Moreover, low-rank tensor approximations are confronted to the popular polynomial chaos expansion meta-models in case studies that involve analytical rank-one functions and finite-element models pertinent to structural mechanics and heat conduction. In the examined applications, indices based on the novel approach tend to converge faster to the reference solution with increasing size of the experimental design used to build the meta-model. - Highlights: • A new method is proposed for global sensitivity analysis of high-dimensional models. • Low-rank tensor approximations (LRA) are used as a meta-modeling technique. • Analytical formulas for the Sobol' indices in terms of LRA coefficients are derived. • The accuracy and efficiency of the approach is illustrated in application examples. • LRA-based indices are compared to indices based on polynomial chaos expansions.
Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.
Jia, Zhilong; Zhang, Xiang; Guan, Naiyang; Bo, Xiaochen; Barnes, Michael R; Luo, Zhigang
2015-01-01
RNA-sequencing is rapidly becoming the method of choice for studying the full complexity of transcriptomes, however with increasing dimensionality, accurate gene ranking is becoming increasingly challenging. This paper proposes an accurate and sensitive gene ranking method that implements discriminant non-negative matrix factorization (DNMF) for RNA-seq data. To the best of our knowledge, this is the first work to explore the utility of DNMF for gene ranking. When incorporating Fisher's discriminant criteria and setting the reduced dimension as two, DNMF learns two factors to approximate the original gene expression data, abstracting the up-regulated or down-regulated metagene by using the sample label information. The first factor denotes all the genes' weights of two metagenes as the additive combination of all genes, while the second learned factor represents the expression values of two metagenes. In the gene ranking stage, all the genes are ranked as a descending sequence according to the differential values of the metagene weights. Leveraging the nature of NMF and Fisher's criterion, DNMF can robustly boost the gene ranking performance. The Area Under the Curve analysis of differential expression analysis on two benchmarking tests of four RNA-seq data sets with similar phenotypes showed that our proposed DNMF-based gene ranking method outperforms other widely used methods. Moreover, the Gene Set Enrichment Analysis also showed DNMF outweighs others. DNMF is also computationally efficient, substantially outperforming all other benchmarked methods. Consequently, we suggest DNMF is an effective method for the analysis of differential gene expression and gene ranking for RNA-seq data.
The exact probability distribution of the rank product statistics for replicated experiments.
Eisinga, Rob; Breitling, Rainer; Heskes, Tom
2013-03-18
The rank product method is a widely accepted technique for detecting differentially regulated genes in replicated microarray experiments. To approximate the sampling distribution of the rank product statistic, the original publication proposed a permutation approach, whereas recently an alternative approximation based on the continuous gamma distribution was suggested. However, both approximations are imperfect for estimating small tail probabilities. In this paper we relate the rank product statistic to number theory and provide a derivation of its exact probability distribution and the true tail probabilities. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.
Detecting genuine multipartite correlations in terms of the rank of coefficient matrix
International Nuclear Information System (INIS)
Li Bo; Kwek, Leong Chuan; Fan Heng
2012-01-01
We propose a method to detect genuine quantum correlation for arbitrary quantum states in terms of the rank of coefficient matrices associated with the pure state. We then derive a necessary and sufficient condition for a quantum state to possess genuine correlation, namely that all corresponding coefficient matrices have rank larger than 1. We demonstrate an approach to decompose the genuine quantum correlated state with high rank coefficient matrix into the form of product states with no genuine quantum correlation for a pure state. (paper)
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
Ranking of biomass pellets by integration of economic, environmental and technical factors
International Nuclear Information System (INIS)
Sultana, Arifa; Kumar, Amit
2012-01-01
Interest in biomass as a renewable energy source has increased recently in response to a need to reduce greenhouse gas (GHG) emissions. The objective of this study is to develop a multi-criteria assessment model and rank different biomass feedstock-based pellets, in terms of their suitability for use in large heat and power generation plants and show the importance of environmental, economical and technical factors in making decision about different pellets. Five pellet alternatives, each produced from a different sustainable biomass feedstock i.e., wood, straw, switchgrass, alfalfa and poultry litter, are ranked according to eleven criteria, using the Preference Ranking Organization Method for Enrichment and Evaluation (PROMETHEE). Both quantitative and qualitative criteria are considered, including environmental, technical and economic factors. Three scenarios, namely base case, environmental and economic, are developed by changing the weight assigned to different criteria. In the base case scenario, equal weights are assigned to each criterion. In the economic and environmental scenarios, more weight is given to the economic and environmental factors, respectively. Based on the PROMETHEE rankings, wood pellets are the best source of energy for all scenarios followed by switchgrass, straw, poultry litter and alfalfa pellets except economic scenario, where straw pellets held higher position than switchgrass pellets. Sensitivity analysis on weights, threshold values, preference function and production cost indicate that the ranking was stable. The ranking in all scenarios remained same when qualitative criteria were omitted from the model; this indicates the stronger influence of quantitative criteria. -- Highlights: ► This study ranks the pellets produced from different biomass feedstocks. ► The ranking of the pellets is based on technical, economical and environmental factors. ► This study uses PROMETHEE method for ranking pellets based on a range of
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
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
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....
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...
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 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
Group social rank is associated with performance on a spatial learning task.
Langley, Ellis J G; van Horik, Jayden O; Whiteside, Mark A; Madden, Joah R
2018-02-01
Dominant individuals differ from subordinates in their performances on cognitive tasks across a suite of taxa. Previous studies often only consider dyadic relationships, rather than the more ecologically relevant social hierarchies or networks, hence failing to account for how dyadic relationships may be adjusted within larger social groups. We used a novel statistical method: randomized Elo-ratings, to infer the social hierarchy of 18 male pheasants, Phasianus colchicus , while in a captive, mixed-sex group with a linear hierarchy. We assayed individual learning performance of these males on a binary spatial discrimination task to investigate whether inter-individual variation in performance is associated with group social rank. Task performance improved with increasing trial number and was positively related to social rank, with higher ranking males showing greater levels of success. Motivation to participate in the task was not related to social rank or task performance, thus indicating that these rank-related differences are not a consequence of differences in motivation to complete the task. Our results provide important information about how variation in cognitive performance relates to an individual's social rank within a group. Whether the social environment causes differences in learning performance or instead, inherent differences in learning ability predetermine rank remains to be tested.
Management a marketing sportovní akce: ICF Slalom World Ranking Race Prague 2009
Kubričan, Lukáš
2009-01-01
Title: Management and marketing of sport's event: ICF Slalom World Ranking Race Prague 2009 Objectives: Present strengths and weaknesses of ICF Slalom World Ranking Race Prague 2009 based on analyse of recent years and present ideas for its improvement. Methods: Descriptive analysis, SWOT analysis and interview with expert. Results: Conclusion and advices for organizers of sport's events. Key words: Management, marketing, SWOT analysis, descriptive analysis, sport's event, canoe slalom compet...
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...
Multi-dimensional Rankings, Program Termination, and Complexity Bounds of Flowchart Programs
Alias , Christophe; Darte , Alain; Feautrier , Paul; Gonnord , Laure
2010-01-01
International audience; Proving the termination of a flowchart program can be done by exhibiting a ranking function, i.e., a function from the program states to a well-founded set, which strictly decreases at each program step. A standard method to automatically generate such a function is to compute invariants for each program point and to search for a ranking in a restricted class of functions that can be handled with linear programming techniques. Previous algorithms based on affine rankin...
Assessing the Readability of Medical Documents: A Ranking Approach.
Zheng, Jiaping; Yu, Hong
2018-03-23
The use of electronic health record (EHR) systems with patient engagement capabilities, including viewing, downloading, and transmitting health information, has recently grown tremendously. However, using these resources to engage patients in managing their own health remains challenging due to the complex and technical nature of the EHR narratives. Our objective was to develop a machine learning-based system to assess readability levels of complex documents such as EHR notes. We collected difficulty ratings of EHR notes and Wikipedia articles using crowdsourcing from 90 readers. We built a supervised model to assess readability based on relative orders of text difficulty using both surface text features and word embeddings. We evaluated system performance using the Kendall coefficient of concordance against human ratings. Our system achieved significantly higher concordance (.734) with human annotators than did a baseline using the Flesch-Kincaid Grade Level, a widely adopted readability formula (.531). The improvement was also consistent across different disease topics. This method's concordance with an individual human user's ratings was also higher than the concordance between different human annotators (.658). We explored methods to automatically assess the readability levels of clinical narratives. Our ranking-based system using simple textual features and easy-to-learn word embeddings outperformed a widely used readability formula. Our ranking-based method can predict relative difficulties of medical documents. It is not constrained to a predefined set of readability levels, a common design in many machine learning-based systems. Furthermore, the feature set does not rely on complex processing of the documents. One potential application of our readability ranking is personalization, allowing patients to better accommodate their own background knowledge. ©Jiaping Zheng, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.03.2018.
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.
Top Incomes, Heavy Tails, and Rank-Size Regressions
Directory of Open Access Journals (Sweden)
Christian Schluter
2018-03-01
Full Text Available In economics, rank-size regressions provide popular estimators of tail exponents of heavy-tailed distributions. We discuss the properties of this approach when the tail of the distribution is regularly varying rather than strictly Pareto. The estimator then over-estimates the true value in the leading parametric income models (so the upper income tail is less heavy than estimated, which leads to test size distortions and undermines inference. For practical work, we propose a sensitivity analysis based on regression diagnostics in order to assess the likely impact of the distortion. The methods are illustrated using data on top incomes in the UK.
[Rank distributions in community ecology from the statistical viewpoint].
Maksimov, V N
2004-01-01
Traditional statistical methods for definition of empirical functions of abundance distribution (population, biomass, production, etc.) of species in a community are applicable for processing of multivariate data contained in the above quantitative indices of the communities. In particular, evaluation of moments of distribution suffices for convolution of the data contained in a list of species and their abundance. At the same time, the species should be ranked in the list in ascending rather than descending population and the distribution models should be analyzed on the basis of the data on abundant species only.
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.
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
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.
Educational Background and Academic Rank of Faculty Members within US Schools of Pharmacy.
Assemi, Mitra; Hudmon, Karen Suchanek; Sowinski, Kevin M; Corelli, Robin L
2016-05-25
Objective. To characterize the educational background and academic rank of faculty members in US schools of pharmacy, estimate the extent to which they are employed by institutions where they received previous training, and determine whether differences in degree origin and rank exist between faculty members in established (≤1995) vs newer programs. Methods. A cross-sectional study was conducted using the American Association of Colleges of Pharmacy (AACP) faculty database and demographic information from the public domain. Results. Among 5516 faculty members, 50.3% held two or more types of degrees. Established schools had a higher median number of faculty members and a higher mean faculty rank than did newer schools. Conclusion. The difference in mean faculty rank highlights the shortage of experienced faculty members in newer schools. Future research efforts should investigate educational attainment in correlation to other faculty and school characteristics and prospectively track and report trends related to pharmacy faculty members composition.
Ranking of bank branches with undesirable and fuzzy data: A DEA-based approach
Directory of Open Access Journals (Sweden)
Sohrab Kordrostami
2016-07-01
Full Text Available Banks are one of the most important financial sectors in order to the economic development of each country. Certainly, efficiency scores and ranks of banks are significant and effective aspects towards future planning. Sometimes the performance of banks must be measured in the presence of undesirable and vague factors. For these reasons in the current paper a procedure based on data envelopment analysis (DEA is introduced for evaluating the efficiency and complete ranking of decision making units (DMUs where undesirable and fuzzy measures exist. To illustrate, in the presence of undesirable and fuzzy measures, DMUs are evaluated by using a fuzzy expected value approach and DMUs with similar efficiency scores are ranked by using constraints and the Maximal Balance Index based on the optimal shadow prices. Afterwards, the efficiency scores of 25 branches of an Iranian commercial bank are evaluated using the proposed method. Also, a complete ranking of bank branches is presented to discriminate branches.
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.
Hou, Mingjun; Fan, Peihua; Liu, Heng
2014-01-01
The authors rank the management schools in Greater China (including Mainland China, Hong Kong, Taiwan, and Macau) based on their academic publications in the Social Sciences Citation Index management and business journals from 2000 to 2010. Following K. Ritzberger's (2008) and X. Yu and Z. Gao's (2010) ranking method, the authors develop six…
Nagasinghe, Iranga
2010-01-01
This thesis investigates and develops a few acceleration techniques for the search engine algorithms used in PageRank and HITS computations. PageRank and HITS methods are two highly successful applications of modern Linear Algebra in computer science and engineering. They constitute the essential technologies accounted for the immense growth and…