Memory Efficient Ranking.

ERIC Educational Resources Information Center

Moffat, Alistair; And Others

1994-01-01

Describes an approximate document ranking process that uses a compact array of in-memory, low-precision approximations for document length. Combined with another rule for reducing the memory required by partial similarity accumulators, the approximation heuristic allows the ranking of large document collections using less than one byte of memory…

Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition

PubMed Central

Ong, Frank; Lustig, Michael

2016-01-01

We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales. Such decomposition is well motivated in practice as data matrices often exhibit local correlations in multiple scales. Concretely, we propose a multi-scale low rank modeling that represents a data matrix as a sum of block-wise low rank matrices with increasing scales of block sizes. We then consider the inverse problem of decomposing the data matrix into its multi-scale low rank components and approach the problem via a convex formulation. Theoretically, we show that under various incoherence conditions, the convex program recovers the multi-scale low rank components either exactly or approximately. Practically, we provide guidance on selecting the regularization parameters and incorporate cycle spinning to reduce blocking artifacts. Experimentally, we show that the multi-scale low rank decomposition provides a more intuitive decomposition than conventional low rank methods and demonstrate its effectiveness in four applications, including illumination normalization for face images, motion separation for surveillance videos, multi-scale modeling of the dynamic contrast enhanced magnetic resonance imaging and collaborative filtering exploiting age information. PMID:28450978

Rank restriction for the variational calculation of two-electron reduced density matrices of many-electron atoms and molecules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Naftchi-Ardebili, Kasra; Hau, Nathania W.; Mazziotti, David A.

2011-11-15

Variational minimization of the ground-state energy as a function of the two-electron reduced density matrix (2-RDM), constrained by necessary N-representability conditions, provides a polynomial-scaling approach to studying strongly correlated molecules without computing the many-electron wave function. Here we introduce a route to enhancing necessary conditions for N representability through rank restriction of the 2-RDM. Rather than adding computationally more expensive N-representability conditions, we directly enhance the accuracy of two-particle (2-positivity) conditions through rank restriction, which removes degrees of freedom in the 2-RDM that are not sufficiently constrained. We select the rank of the particle-hole 2-RDM by deriving the ranks associatedmore » with model wave functions, including both mean-field and antisymmetrized geminal power (AGP) wave functions. Because the 2-positivity conditions are exact for quantum systems with AGP ground states, the rank of the particle-hole 2-RDM from the AGP ansatz provides a minimum for its value in variational 2-RDM calculations of general quantum systems. To implement the rank-restricted conditions, we extend a first-order algorithm for large-scale semidefinite programming. The rank-restricted conditions significantly improve the accuracy of the energies; for example, the percentages of correlation energies recovered for HF, CO, and N{sub 2} improve from 115.2%, 121.7%, and 121.5% without rank restriction to 97.8%, 101.1%, and 100.0% with rank restriction. Similar results are found at both equilibrium and nonequilibrium geometries. While more accurate, the rank-restricted N-representability conditions are less expensive computationally than the full-rank conditions.« less

HIV quality report cards: impact of case-mix adjustment and statistical methods.

PubMed

Ohl, Michael E; Richardson, Kelly K; Goto, Michihiko; Vaughan-Sarrazin, Mary; Schweizer, Marin L; Perencevich, Eli N

2014-10-15

There will be increasing pressure to publicly report and rank the performance of healthcare systems on human immunodeficiency virus (HIV) quality measures. To inform discussion of public reporting, we evaluated the influence of case-mix adjustment when ranking individual care systems on the viral control quality measure. We used data from the Veterans Health Administration (VHA) HIV Clinical Case Registry and administrative databases to estimate case-mix adjusted viral control for 91 local systems caring for 12 368 patients. We compared results using 2 adjustment methods, the observed-to-expected estimator and the risk-standardized ratio. Overall, 10 913 patients (88.2%) achieved viral control (viral load ≤400 copies/mL). Prior to case-mix adjustment, system-level viral control ranged from 51% to 100%. Seventeen (19%) systems were labeled as low outliers (performance significantly below the overall mean) and 11 (12%) as high outliers. Adjustment for case mix (patient demographics, comorbidity, CD4 nadir, time on therapy, and income from VHA administrative databases) reduced the number of low outliers by approximately one-third, but results differed by method. The adjustment model had moderate discrimination (c statistic = 0.66), suggesting potential for unadjusted risk when using administrative data to measure case mix. Case-mix adjustment affects rankings of care systems on the viral control quality measure. Given the sensitivity of rankings to selection of case-mix adjustment methods-and potential for unadjusted risk when using variables limited to current administrative databases-the HIV care community should explore optimal methods for case-mix adjustment before moving forward with public reporting. Published by Oxford University Press on behalf of the Infectious Diseases Society of America 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Model's sparse representation based on reduced mixed GMsFE basis methods

NASA Astrophysics Data System (ADS)

Jiang, Lijian; Li, Qiuqi

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a large number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random porous

Model's sparse representation based on reduced mixed GMsFE basis methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Lijian, E-mail: ljjiang@hnu.edu.cn; Li, Qiuqi, E-mail: qiuqili@hnu.edu.cn

2017-06-01

In this paper, we propose a model's sparse representation based on reduced mixed generalized multiscale finite element (GMsFE) basis methods for elliptic PDEs with random inputs. A typical application for the elliptic PDEs is the flow in heterogeneous random porous media. Mixed generalized multiscale finite element method (GMsFEM) is one of the accurate and efficient approaches to solve the flow problem in a coarse grid and obtain the velocity with local mass conservation. When the inputs of the PDEs are parameterized by the random variables, the GMsFE basis functions usually depend on the random parameters. This leads to a largemore » number degree of freedoms for the mixed GMsFEM and substantially impacts on the computation efficiency. In order to overcome the difficulty, we develop reduced mixed GMsFE basis methods such that the multiscale basis functions are independent of the random parameters and span a low-dimensional space. To this end, a greedy algorithm is used to find a set of optimal samples from a training set scattered in the parameter space. Reduced mixed GMsFE basis functions are constructed based on the optimal samples using two optimal sampling strategies: basis-oriented cross-validation and proper orthogonal decomposition. Although the dimension of the space spanned by the reduced mixed GMsFE basis functions is much smaller than the dimension of the original full order model, the online computation still depends on the number of coarse degree of freedoms. To significantly improve the online computation, we integrate the reduced mixed GMsFE basis methods with sparse tensor approximation and obtain a sparse representation for the model's outputs. The sparse representation is very efficient for evaluating the model's outputs for many instances of parameters. To illustrate the efficacy of the proposed methods, we present a few numerical examples for elliptic PDEs with multiscale and random inputs. In particular, a two-phase flow model in random

Substantial shifts in ranking of California hospitals by hospital-associated methicillin-resistant Staphylococcus aureus infection following adjustment for hospital characteristics and case mix.

PubMed

Tehrani, David M; Phelan, Michael J; Cao, Chenghua; Billimek, John; Datta, Rupak; Nguyen, Hoanglong; Kwark, Homin; Huang, Susan S

2014-10-01

States have established public reporting of hospital-associated (HA) infections-including those of methicillin-resistant Staphylococcus aureus (MRSA)-but do not account for hospital case mix or postdischarge events. Identify facility-level characteristics associated with HA-MRSA infection admissions and create adjusted hospital rankings. A retrospective cohort study of 2009-2010 California acute care hospitals. We defined HA-MRSA admissions as involving MRSA pneumonia or septicemia events arising during hospitalization or within 30 days after discharge. We used mandatory hospitalization and US Census data sets to generate hospital population characteristics by summarizing across admissions. Facility-level factors associated with hospitals' proportions of HA-MRSA infection admissions were identified using generalized linear models. Using state methodology, hospitals were categorized into 3 tiers of HA-MRSA infection prevention performance, using raw and adjusted values. Among 323 hospitals, a median of 16 HA-MRSA infections (range, 0-102) per 10,000 admissions was found. Hospitals serving a greater proportion of patients who had serious comorbidities, were from low-education zip codes, and were discharged to locations other than home were associated with higher HA-MRSA infection risk. Total concordance between all raw and adjusted hospital rankings was 0.45 (95% confidence interval, 0.40-0.51). Among 53 community hospitals in the poor-performance category, more than 20% moved into the average-performance category after adjustment. Similarly, among 71 hospitals in the superior-performance category, half moved into the average-performance category after adjustment. When adjusting for nonmodifiable facility characteristics and case mix, hospital rankings based on HA-MRSA infections substantially changed. Quality indicators for hospitals require adequate adjustment for patient population characteristics for valid interhospital performance comparisons.

A CFD Study of Jet Mixing in Reduced Flow Areas for Lower Combustor Emissions

NASA Technical Reports Server (NTRS)

Smith, C. E.; Talpallikar, M. V.; Holdeman, J. D.

1991-01-01

The Rich-burn/Quick-mix/Lean-burn (RQL) combustor has the potential of significantly reducing NO(x) emissions in combustion chambers of High Speed Civil Transport aircraft. Previous work on RQL combustors for industrial applications suggested the benefit of necking down the mixing section. A 3-D numerical investigation was performed to study the effects of neckdown on NO(x) emissions and to develop a correlation for optimum mixing designs in terms of neckdown area ratio. The results of the study showed that jet mixing in reduced flow areas does not enhance mixing, but does decrease residence time at high flame temperatures, thus reducing NO(x) formation. By necking down the mixing flow area by 4, a potential NO(x) reduction of 16:1 is possible for annual combustors. However, there is a penalty that accompanies the mixing neckdown: reduced pressure drop across the combustor swirler. At conventional combustor loading parameters, the pressure drop penalty does not appear to be excessive.

Rank Dynamics

NASA Astrophysics Data System (ADS)

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

Does the Introduction of the Ranking Task in Valuation Studies Improve Data Quality and Reduce Inconsistencies? The Case of the EQ-5D-5L.

PubMed

Ramos-Goñi, Juan M; Rand-Hendriksen, Kim; Pinto-Prades, Jose Luis

2016-06-01

Time trade-off (TTO)-based valuation studies for the three-level version of the EuroQol five-dimensional questionnaire (EQ-5D) typically started off with a ranking task (ordering the health states by preference). This was not included in the protocol for the five-level EQ-5D (EQ-5D-5L) valuation study. To test whether reintroducing a ranking task before the composite TTO (C-TTO) could help to reduce inconsistencies in C-TTO responses and improve the data quality. Respondents were randomly assigned to three study arms. The control arm was the present EQ-5D-5L study protocol, without ranking. The second arm (ranking without sorting) preceded the present protocol by asking respondents to rank the target health states using physical cards. The states were then valued in random order using C-TTO. In the third arm (ranking and sorting), the ranked states remained visible through the C-TTO tasks and the order of valuation was determined by the ranking. The study used only 10 EQ-5D-5L health states. We compared the C-TTO-based inconsistent pairs of health states and ties. The final sample size was 196 in the control arm, 205 in the ranking without sorting arm, and 199 in the ranking and sorting arm. The percentages of ties by respondents were 15.1%, 12.5%, and 12.6% for the control arm, the ranking without sorting arm, and the ranking and sorting arm, respectively. The extra cost for adding the ranking task was about 15%. The benefit does not justify the effort involved in the ranking task. For this reason, the addition of the ranking task to the present EQ-5D-5L valuation protocol is not an attractive option. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Reduced-rank approximations to the far-field transform in the gridded fast multipole method

NASA Astrophysics Data System (ADS)

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.

Reduced-Rank Approximations to the Far-Field Transform in the Gridded Fast Multipole Method.

PubMed

Hesford, Andrew J; Waag, Robert C

2011-05-10

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.

Reduced-Rank Approximations to the Far-Field Transform in the Gridded Fast Multipole Method

PubMed Central

Hesford, Andrew J.; Waag, Robert C.

2011-01-01

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. PMID:21552350

Reduced rank models for travel time estimation of low order mode pulses.

PubMed

Chandrayadula, Tarun K; Wage, Kathleen E; Worcester, Peter F; Dzieciuch, Matthew A; Mercer, James A; Andrew, Rex K; Howe, Bruce M

2013-10-01

Mode travel time estimation in the presence of internal waves (IWs) is a challenging problem. IWs perturb the sound speed, which results in travel time wander and mode scattering. A standard approach to travel time estimation is to pulse compress the broadband signal, pick the peak of the compressed time series, and average the peak time over multiple receptions to reduce variance. The peak-picking approach implicitly assumes there is a single strong arrival and does not perform well when there are multiple arrivals due to scattering. This article presents a statistical model for the scattered mode arrivals and uses the model to design improved travel time estimators. The model is based on an Empirical Orthogonal Function (EOF) analysis of the mode time series. Range-dependent simulations and data from the Long-range Ocean Acoustic Propagation Experiment (LOAPEX) indicate that the modes are represented by a small number of EOFs. The reduced-rank EOF model is used to construct a travel time estimator based on the Matched Subspace Detector (MSD). Analysis of simulation and experimental data show that the MSDs are more robust to IW scattering than peak picking. The simulation analysis also highlights how IWs affect the mode excitation by the source.

On the degrees of freedom of reduced-rank estimators in multivariate regression

PubMed Central

Mukherjee, A.; Chen, K.; Wang, N.; Zhu, J.

2015-01-01

Summary We study the effective degrees of freedom of a general class of reduced-rank estimators for multivariate regression in the framework of Stein's unbiased risk estimation. A finite-sample exact unbiased estimator is derived that admits a closed-form expression in terms of the thresholded singular values of the least-squares solution and hence is readily computable. The results continue to hold in the high-dimensional setting where both the predictor and the response dimensions may be larger than the sample size. The derived analytical form facilitates the investigation of theoretical properties and provides new insights into the empirical behaviour of the degrees of freedom. In particular, we examine the differences and connections between the proposed estimator and a commonly-used naive estimator. The use of the proposed estimator leads to efficient and accurate prediction risk estimation and model selection, as demonstrated by simulation studies and a data example. PMID:26702155

Mixed reality virtual pets to reduce childhood obesity.

PubMed

Johnsen, Kyle; Ahn, Sun Joo; Moore, James; Brown, Scott; Robertson, Thomas P; Marable, Amanda; Basu, Aryabrata

2014-04-01

Novel approaches are needed to reduce the high rates of childhood obesity in the developed world. While multifactorial in cause, a major factor is an increasingly sedentary lifestyle of children. Our research shows that a mixed reality system that is of interest to children can be a powerful motivator of healthy activity. We designed and constructed a mixed reality system that allowed children to exercise, play with, and train a virtual pet using their own physical activity as input. The health, happiness, and intelligence of each virtual pet grew as its associated child owner exercised more, reached goals, and interacted with their pet. We report results of a research study involving 61 children from a local summer camp that shows a large increase in recorded and observed activity, alongside observational evidence that the virtual pet was responsible for that change. These results, and the ease at which the system integrated into the camp environment, demonstrate the practical potential to impact the exercise behaviors of children with mixed reality.

Improving the Incoherence of a Learned Dictionary via Rank Shrinkage.

PubMed

Ubaru, Shashanka; Seghouane, Abd-Krim; Saad, Yousef

2017-01-01

This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first update the dictionary using the method of optimal directions (MOD) and then apply a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of the column-normalized least squares estimate of the dictionary obtained from the MOD step. We then shrink the rank of this learned dictionary by transforming the problem of reducing the rank to a nonnegative garrotte estimation problem and solving it using a path-wise coordinate descent approach. We establish theoretical results that show that the rank shrinkage step included will reduce the coherence of the dictionary, which is further validated by experimental results. Numerical experiments illustrating the performance of the proposed algorithm in comparison to various other well-known dictionary learning algorithms are also presented.

The Effects of Sex and Size on Status Ranking.

ERIC Educational Resources Information Center

Crosbie, Paul V.

1979-01-01

Sex, body size, several sociocultural variables, and status rank were measured in mixed-sex small groups. The intervening sociocultural variables explained some but not all of the interrelationships. Results underscore the empirical vulnerability of social-psychological explanations of sex-role behavior and signal the need for further research.…

Comparison of factor-analytic and reduced rank models for test-day milk yield in Gyr dairy cattle (Bos indicus).

PubMed

Pereira, R J; Ayres, D R; El Faro, L; Verneque, R S; Vercesi Filho, A E; Albuquerque, L G

2013-09-27

We analyzed 46,161 monthly test-day records of milk production from 7453 first lactations of crossbred dairy Gyr (Bos indicus) x Holstein cows. The following seven models were compared: standard multivariate model (M10), three reduced rank models fitting the first 2, 3, or 4 genetic principal components, and three models considering a 2-, 3-, or 4-factor structure for the genetic covariance matrix. Full rank residual covariance matrices were considered for all models. The model fitting the first two principal components (PC2) was the best according to the model selection criteria. Similar phenotypic, genetic, and residual variances were obtained with models M10 and PC2. The heritability estimates ranged from 0.14 to 0.21 and from 0.13 to 0.21 for models M10 and PC2, respectively. The genetic correlations obtained with model PC2 were slightly higher than those estimated with model M10. PC2 markedly reduced the number of parameters estimated and the time spent to reach convergence. We concluded that two principal components are sufficient to model the structure of genetic covariances between test-day milk yields.

Frequency-Rank Distributions

ERIC Educational Resources Information Center

Brookes, Bertram C.; Griffiths, Jose M.

1978-01-01

Frequency, rank, and frequency rank distributions are defined. Extensive discussion on several aspects of frequency rank distributions includes the Poisson process as a means of exploring the stability of ranks; the correlation of frequency rank distributions; and the transfer coefficient, a new measure in frequency rank distribution. (MBR)

To Rank or to Be Ranked: The Impact of Global Rankings in Higher Education

ERIC Educational Resources Information Center

Marginson, Simon; van der Wende, Marijk

2007-01-01

Global university rankings have cemented the notion of a world university market arranged in a single "league table" for comparative purposes and have given a powerful impetus to intranational and international competitive pressures in the sector. Both the research rankings by Shanghai Jiao Tong University and the composite rankings by…

PageRank and rank-reversal dependence on the damping factor

NASA Astrophysics Data System (ADS)

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.

Rank Order Entropy: why one metric is not enough

PubMed Central

McLellan, Margaret R.; Ryan, M. Dominic; Breneman, Curt M.

2011-01-01

The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the mis-application of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r2, PRESS r2, F-tests, etc) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted endpoint values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon Entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also

Mixed-Mode Surveys: A Strategy to Reduce Costs and Enhance Response Rates

ERIC Educational Resources Information Center

Tobin, Daniel; Thomson, Joan; Radhakrishna, Rama; LaBorde, Luke

2012-01-01

Mixed-mode surveys present one opportunity for Extension to determine program outcomes at lower costs. In order to conduct a follow-up evaluation, we implemented a mixed-mode survey that relied on communication using the Web, postal mailings, and telephone calls. Using multiple modes conserved costs by reducing the number of postal mailings yet…

Validation of SmartRank: A likelihood ratio software for searching national DNA databases with complex DNA profiles.

PubMed

Benschop, Corina C G; van de Merwe, Linda; de Jong, Jeroen; Vanvooren, Vanessa; Kempenaers, Morgane; Kees van der Beek, C P; Barni, Filippo; Reyes, Eusebio López; Moulin, Léa; Pene, Laurent; Haned, Hinda; Sijen, Titia

2017-07-01

Searching a national DNA database with complex and incomplete profiles usually yields very large numbers of possible matches that can present many candidate suspects to be further investigated by the forensic scientist and/or police. Current practice in most forensic laboratories consists of ordering these 'hits' based on the number of matching alleles with the searched profile. Thus, candidate profiles that share the same number of matching alleles are not differentiated and due to the lack of other ranking criteria for the candidate list it may be difficult to discern a true match from the false positives or notice that all candidates are in fact false positives. SmartRank was developed to put forward only relevant candidates and rank them accordingly. The SmartRank software computes a likelihood ratio (LR) for the searched profile and each profile in the DNA database and ranks database entries above a defined LR threshold according to the calculated LR. In this study, we examined for mixed DNA profiles of variable complexity whether the true donors are retrieved, what the number of false positives above an LR threshold is and the ranking position of the true donors. Using 343 mixed DNA profiles over 750 SmartRank searches were performed. In addition, the performance of SmartRank and CODIS were compared regarding DNA database searches and SmartRank was found complementary to CODIS. We also describe the applicable domain of SmartRank and provide guidelines. The SmartRank software is open-source and freely available. Using the best practice guidelines, SmartRank enables obtaining investigative leads in criminal cases lacking a suspect. Copyright © 2017 Elsevier B.V. All rights reserved.

Impact of Doximity Residency Rankings on Emergency Medicine Applicant Rank Lists.

PubMed

Peterson, William J; Hopson, Laura R; Khandelwal, Sorabh; White, Melissa; Gallahue, Fiona E; Burkhardt, John; Rolston, Aimee M; Santen, Sally A

2016-05-01

This study investigates the impact of the Doximity rankings on the rank list choices made by residency applicants in emergency medicine (EM). We sent an 11-item survey by email to all students who applied to EM residency programs at four different institutions representing diverse geographical regions. Students were asked questions about their perception of Doximity rankings and how it may have impacted their rank list decisions. Response rate was 58% of 1,372 opened electronic surveys. This study found that a majority of medical students applying to residency in EM were aware of the Doximity rankings prior to submitting rank lists (67%). One-quarter of these applicants changed the number of programs and ranks of those programs when completing their rank list based on the Doximity rankings (26%). Though the absolute number of programs changed on the rank lists was small, the results demonstrate that the EM Doximity rankings impact applicant decision-making in ranking residency programs. While applicants do not find the Doximity rankings to be important compared to other factors in the application process, the Doximity rankings result in a small change in residency applicant ranking behavior. This unvalidated ranking, based principally on reputational data rather than objective outcome criteria, thus has the potential to be detrimental to students, programs, and the public. We feel it important for specialties to develop consensus around measurable training outcomes and provide freely accessible metrics for candidate education.

Ranking structures and rank-rank correlations of countries: The FIFA and UEFA cases

NASA Astrophysics Data System (ADS)

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.

Pre-packed vacuum bone cement mixing systems. A further step in reducing methylmethacrylate exposure in surgery.

PubMed

Schlegel, Ulf J; Sturm, Michael; Eysel, Peer; Breusch, Steffen J

2010-11-01

Polymethylmethacrylate bone cements are widely used in orthopaedic and trauma surgery as well as in dentistry. The toxic side effects of inhaled methylmethacrylate (MMA) fumes generated during mixing have been well studied. Vacuum cement mixing systems have been shown to reduce the risk of airborne MMA significantly compared to handmixing. In an effort to further reduce MMA exposure, the latest generation of mixing devices are pre-packed with the ingredients and thus allow preparation in nearly closed circuits. Until now, there has been no study proofing the efficacy of those systems in protecting theatre staff from MMA vapours. A pre-packed vacuum mixing system (Optipac®) was compared with two standard systems (Palamix® and Easymix®) regarding MMA emission. The latter systems require loading with the bone cement compounds prior to mixing. Following a standardized procedure, 10 mixes were performed with each system and the emission of MMA vapours in the breathing zone was recorded using photoionization detection over a period of 3 min. The mean MMA exposure was reduced when using the pre-packed system compared to the devices that require filling with the components. The highest emission peaks were recorded during the mixing and preparation steps in all systems. Modern pre-packed vacuum mixing systems further help to reduce the occupational hazards created by bone cement preparation. However, MMA fumes can still be detected using this technique. Although this is an important step in reducing MMA exposure in the operating theatre, further technical effort has to be taken to eliminate the continuous leakage of monomer from the devices while mixing and to minimize necessary manipulation for final delivery.

Method and apparatus for reducing mixed waste

DOEpatents

Elliott, Michael L.; Perez, Jr., Joseph M.; Chapman, Chris C.; Peters, Richard D.

1995-01-01

The present invention is a method and apparatus for in-can waste reduction. The method is mixing waste with combustible material prior to placing the waste into a waste reduction vessel. The combustible portion is ignited, thereby reducing combustible material to ash and non-combustible material to a slag. Further combustion or heating may be used to sinter or melt the ash. The apparatus is a waste reduction vessel having receiving canister connection means on a first end, and a waste/combustible mixture inlet on a second end. An oxygen supply is provided to support combustion of the combustible mixture.

Item Response Modeling of Paired Comparison and Ranking Data

ERIC Educational Resources Information Center

Maydeu-Olivares, Alberto; Brown, Anna

2010-01-01

The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally…

Sync-rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and SDP Synchronization

DTIC Science & Technology

2015-04-28

the players . In addition, we compare the algorithms on three real data sets: the outcome of soccer games in the English Premier League, a Microsoft...Premier League soccer games, a Halo 2 game tournament and NCAA College Basketball games), which show that our proposed method compares favorably to...information on the ground truth rank of a subset of players , and propose an algorithm based on SDP which is able to recover the ranking of the remaining

Bursting patterns and mixed-mode oscillations in reduced Purkinje model

NASA Astrophysics Data System (ADS)

Zhan, Feibiao; Liu, Shenquan; Wang, Jing; Lu, Bo

2018-02-01

Bursting discharge is a ubiquitous behavior in neurons, and abundant bursting patterns imply many physiological information. There exists a closely potential link between bifurcation phenomenon and the number of spikes per burst as well as mixed-mode oscillations (MMOs). In this paper, we have mainly explored the dynamical behavior of the reduced Purkinje cell and the existence of MMOs. First, we adopted the codimension-one bifurcation to illustrate the generation mechanism of bursting in the reduced Purkinje cell model via slow-fast dynamics analysis and demonstrate the process of spike-adding. Furthermore, we have computed the first Lyapunov coefficient of Hopf bifurcation to determine whether it is subcritical or supercritical and depicted the diagrams of inter-spike intervals (ISIs) to examine the chaos. Moreover, the bifurcation diagram near the cusp point is obtained by making the codimension-two bifurcation analysis for the fast subsystem. Finally, we have a discussion on mixed-mode oscillations and it is further investigated using the characteristic index that is Devil’s staircase.

SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking

NASA Astrophysics Data System (ADS)

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.

Green Power Partnership Top Partner Rankings

EPA Pesticide Factsheets

EPA's Green Power Partnership is a voluntary program designed to reduce the environmental impact of electricity generation by promoting renewable energy. Top Partner Rankings highlight the annual green power use of leading Green Power Partners.

Quantum anonymous ranking

NASA Astrophysics Data System (ADS)

Huang, Wei; Wen, Qiao-Yan; Liu, Bin; Su, Qi; Qin, Su-Juan; Gao, Fei

2014-03-01

Anonymous ranking is a kind of privacy-preserving ranking whereby each of the involved participants can correctly and anonymously get the rankings of his data. It can be utilized to solve many practical problems, such as anonymously ranking the students' exam scores. We investigate the issue of how quantum mechanics can be of use in maintaining the anonymity of the participants in multiparty ranking and present a series of quantum anonymous multiparty, multidata ranking protocols. In each of these protocols, a participant can get the correct rankings of his data and nobody else can match the identity to his data. Furthermore, the security of these protocols with respect to different kinds of attacks is proved.

[Cognitive enrichment in farm animals--the impact of social rank and social environment on learning behaviour of dwarf goats].

PubMed

Baymann, Ulrike; Langbein, Jan; Siebert, Katrin; Nürnberg, Gerd; Manteuffel, Gerhard; Mohr, Elmar

2007-01-01

The influence of social rank and social environment on visual discrimination learning of small groups of Nigerian dwarf goats (Capra hircus, n = 79) was studied using a computer-controlled learning device integrated in the animals' home pen. The experiment was divided into three sections (LE1, LE1 u, LE2; each 14d). In LE1 the goats learned a discrimination task in a socially stable environment. In LE1u animals were mixed and relocated to another pen and given the same task as in LE1. In LE2 the animals were mixed and relocated again and given a new discrimination task. We used drinking water as a primary reinforcer. The rank category of the goats were analysed as alpha, omega or middle ranking for each section of the experiment. The rank category had an influence on daily learning success (percentage of successful trials per day) only in LE1 u. Daily learning success decreased after mixing and relocation of the animals in LE1 u and LE2 compared to LE1. That resulted in an undersupply of drinking water on the first day of both these tasks. We discuss social stress induced by agonistic interactions after mixing as a reason for that decline. The absolute learning performance (trials to reach the learning criterion) of the omega animals was lower in LE2 compared to the other rank categories. Furthermore, their absolute learning performance was lower in LE2 compared to LE1. For future application of similar automated learning devices in animal husbandry, we recommend against the combination of management routines like mixing and relocation with changes in the learning task because of the negative effects on learning performance, particularly of the omega animals.

Group social rank is associated with performance on a spatial learning task.

PubMed

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.

RankExplorer: Visualization of Ranking Changes in Large Time Series Data.

PubMed

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.

Money and happiness: rank of income, not income, affects life satisfaction.

PubMed

Boyce, Christopher J; Brown, Gordon D A; Moore, Simon C

2010-04-01

Does money buy happiness, or does happiness come indirectly from the higher rank in society that money brings? We tested a rank-income hypothesis, according to which people gain utility from the ranked position of their income within a comparison group. The rank hypothesis contrasts with traditional reference-income hypotheses, which suggest that utility from income depends on comparison to a social reference-group norm. We found that the ranked position of an individual's income predicts general life satisfaction, whereas absolute income and reference income have no effect. Furthermore, individuals weight upward comparisons more heavily than downward comparisons. According to the rank hypothesis, income and utility are not directly linked: Increasing an individual's income will increase his or her utility only if ranked position also increases and will necessarily reduce the utility of others who will lose rank.

Estimation of Rank Correlation for Clustered Data

PubMed Central

Rosner, Bernard; Glynn, Robert

2017-01-01

It is well known that the sample correlation coefficient (Rxy) is the maximum likelihood estimator (MLE) of the Pearson correlation (ρxy) for 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 MLE of ρxy for clustered data, which can be implemented using standard mixed effects model software. This method is also extended to allow for estimation of partial correlation by controlling both X and Y for a vector U of other covariates. In addition, these methods can be extended to allow for estimation of rank correlation for clustered data by (a) converting ranks of both X and Y to the probit scale, (b) estimating the Pearson correlation between probit scores for X and Y, and (c) 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. PMID:28399615

Adjoints and Low-rank Covariance Representation

NASA Technical Reports Server (NTRS)

Tippett, Michael K.; Cohn, Stephen E.

2000-01-01

Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system.

Temporal Restricted Visual Tracking Via Reverse-Low-Rank Sparse Learning.

PubMed

Yang, Yehui; Hu, Wenrui; Xie, Yuan; Zhang, Wensheng; Zhang, Tianzhu

2017-02-01

An effective representation model, which aims to mine the most meaningful information in the data, plays an important role in visual tracking. Some recent particle-filter-based trackers achieve promising results by introducing the low-rank assumption into the representation model. However, their assumed low-rank structure of candidates limits the robustness when facing severe challenges such as abrupt motion. To avoid the above limitation, we propose a temporal restricted reverse-low-rank learning algorithm for visual tracking with the following advantages: 1) the reverse-low-rank model jointly represents target and background templates via candidates, which exploits the low-rank structure among consecutive target observations and enforces the temporal consistency of target in a global level; 2) the appearance consistency may be broken when target suffers from sudden changes. To overcome this issue, we propose a local constraint via l 1,2 mixed-norm, which can not only ensures the local consistency of target appearance, but also tolerates the sudden changes between two adjacent frames; and 3) to alleviate the inference of unreasonable representation values due to outlier candidates, an adaptive weighted scheme is designed to improve the robustness of the tracker. By evaluating on 26 challenge video sequences, the experiments show the effectiveness and favorable performance of the proposed algorithm against 12 state-of-the-art visual trackers.

Comparison of Mixing Calculations for Reacting and Non-Reacting Flows in a Cylindrical Duct

NASA Technical Reports Server (NTRS)

Oechsle, V. L.; Mongia, H. C.; Holdeman, J. D.

1994-01-01

A production 3-D elliptic flow code has been used to calculate non-reacting and reacting flow fields in an experimental mixing section relevant to a rich burn/quick mix/lean burn (RQL) combustion system. A number of test cases have been run to assess the effects of the variation in the number of orifices, mass flow ratio, and rich-zone equivalence ratio on the flow field and mixing rates. The calculated normalized temperature profiles for the non-reacting flow field agree qualitatively well with the normalized conserved variable isopleths for the reacting flow field indicating that non-reacting mixing experiments are appropriate for screening and ranking potential rapid mixing concepts. For a given set of jet momentum-flux ratio, mass flow ratio, and density ratio (J, MR, and DR), the reacting flow calculations show a reduced level of mixing compared to the non-reacting cases. In addition, the rich-zone equivalence ratio has noticeable effect on the mixing flow characteristics for reacting flows.

PageRank as a method to rank biomedical literature by importance.

PubMed

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 < 0.01) and we thus validate the former as a surrogate of literature importance. Furthermore, the algorithm can be run in trivial time on cheap, commodity cluster hardware, lowering the barrier of entry for resource-limited open access organisations. 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.

Dietary patterns derived by reduced rank regression (RRR) and depressive symptoms in Japanese employees: The Furukawa nutrition and health study.

PubMed

Miki, Takako; Kochi, Takeshi; Kuwahara, Keisuke; Eguchi, Masafumi; Kurotani, Kayo; Tsuruoka, Hiroko; Ito, Rie; Kabe, Isamu; Kawakami, Norito; Mizoue, Tetsuya; Nanri, Akiko

2015-09-30

Depression has been linked to the overall diet using both exploratory and pre-defined methods. However, neither of these methods incorporates specific knowledge on nutrient-disease associations. The aim of the present study was to empirically identify dietary patterns using reduced rank regression and to examine their relations to depressive symptoms. Participants were 2006 Japanese employees aged 19-69 years. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale. Diet was assessed using a validated, self-administered diet history questionnaire. Dietary patterns were extracted by reduced rank regression with 6 depression-related nutrients as response variables. Logistic regression was used to estimate odds ratios of depressive symptoms adjusted for potential confounders. A dietary pattern characterized by a high intake of vegetables, mushrooms, seaweeds, soybean products, green tea, potatoes, fruits, and small fish with bones and a low intake of rice was associated with fewer depressive symptoms. The multivariable-adjusted odds ratios of having depressive symptoms were 0.62 (95% confidence interval, 0.48-0.81) in the highest versus lowest tertiles of dietary score. Results suggest that adherence to a diet rich in vegetables, fruits, and typical Japanese foods, including mushrooms, seaweeds, soybean products, and green tea, is associated with a lower probability of having depressive symptoms. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Level/rank duality and Chern-Simons-matter theories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hsin, Po-Shen; Seiberg, Nathan

We discuss in detail level/rank duality in three-dimensional Chern-Simons theories and various related dualities in three-dimensional Chern-Simons-matter theories. We couple the dual Lagrangians to appropriate background fields (including gauge fields, spin c connections and the metric). The non-trivial maps between the currents and the line operators in the dual theories is accounted for by mixing of these fields. In order for the duality to be valid we must add finite counterterms depending on these background fields. This analysis allows us to resolve a number of puzzles with these dualities, to provide derivations of some of them, and to find newmore » consistency conditions and relations between them. In addition, we find new level/rank dualities of topological Chern-Simons theories and new dualities of Chern-Simons-matter theories, including new boson/boson and fermion/fermion dualities.« less

Level/rank duality and Chern-Simons-matter theories

DOE PAGES

Hsin, Po-Shen; Seiberg, Nathan

2016-09-16

We discuss in detail level/rank duality in three-dimensional Chern-Simons theories and various related dualities in three-dimensional Chern-Simons-matter theories. We couple the dual Lagrangians to appropriate background fields (including gauge fields, spin c connections and the metric). The non-trivial maps between the currents and the line operators in the dual theories is accounted for by mixing of these fields. In order for the duality to be valid we must add finite counterterms depending on these background fields. This analysis allows us to resolve a number of puzzles with these dualities, to provide derivations of some of them, and to find newmore » consistency conditions and relations between them. In addition, we find new level/rank dualities of topological Chern-Simons theories and new dualities of Chern-Simons-matter theories, including new boson/boson and fermion/fermion dualities.« less

Comparison of different eigensolvers for calculating vibrational spectra using low-rank, sum-of-product basis functions

NASA Astrophysics Data System (ADS)

Leclerc, Arnaud; Thomas, Phillip S.; Carrington, Tucker

2017-08-01

Vibrational spectra and wavefunctions of polyatomic molecules can be calculated at low memory cost using low-rank sum-of-product (SOP) decompositions to represent basis functions generated using an iterative eigensolver. Using a SOP tensor format does not determine the iterative eigensolver. The choice of the interative eigensolver is limited by the need to restrict the rank of the SOP basis functions at every stage of the calculation. We have adapted, implemented and compared different reduced-rank algorithms based on standard iterative methods (block-Davidson algorithm, Chebyshev iteration) to calculate vibrational energy levels and wavefunctions of the 12-dimensional acetonitrile molecule. The effect of using low-rank SOP basis functions on the different methods is analysed and the numerical results are compared with those obtained with the reduced rank block power method. Relative merits of the different algorithms are presented, showing that the advantage of using a more sophisticated method, although mitigated by the use of reduced-rank SOP functions, is noticeable in terms of CPU time.

Estimation of rank correlation for clustered data.

PubMed

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.

Assessing introduction risk using species’ rank-abundance distributions

PubMed Central

Chan, Farrah T.; Bradie, Johanna; Briski, Elizabeta; Bailey, Sarah A.; Simard, Nathalie; MacIsaac, Hugh J.

2015-01-01

Mixed-species assemblages are often unintentionally introduced into new ecosystems. Analysing how assemblage structure varies during transport may provide insights into how introduction risk changes before propagules are released. Characterization of introduction risk is typically based on assessments of colonization pressure (CP, the number of species transported) and total propagule pressure (total PP, the total abundance of propagules released) associated with an invasion vector. Generally, invasion potential following introduction increases with greater CP or total PP. Here, we extend these assessments using rank-abundance distributions to examine how CP : total PP relationships change temporally in ballast water of ocean-going ships. Rank-abundance distributions and CP : total PP patterns varied widely between trans-Atlantic and trans-Pacific voyages, with the latter appearing to pose a much lower risk than the former. Responses also differed by taxonomic group, with invertebrates experiencing losses mainly in total PP, while diatoms and dinoflagellates sustained losses mainly in CP. In certain cases, open-ocean ballast water exchange appeared to increase introduction risk by uptake of new species or supplementation of existing ones. Our study demonstrates that rank-abundance distributions provide new insights into the utility of CP and PP in characterizing introduction risk. PMID:25473007

Online ranking by projecting.

PubMed

Crammer, Koby; Singer, Yoram

2005-01-01

We discuss the problem of ranking instances. In our framework, each instance is associated with a rank or a rating, which is an integer in 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank that is as close as possible to the instance's true rank. We discuss a group of closely related online algorithms, analyze their performance in the mistake-bound model, and prove their correctness. We describe two sets of experiments, with synthetic data and with the EachMovie data set for collaborative filtering. In the experiments we performed, our algorithms outperform online algorithms for regression and classification applied to ranking.

The "sweet science" of reducing periorbital lacerations in mixed martial arts.

PubMed

Bastidas, Nicholas; Levine, Jamie P; Stile, Frank L

2012-01-01

The popularity of mixed martial arts competitions and televised events has grown exponentially since its inception, and with the growth of the sport, unique facial injury patterns have surfaced. In particular, upper eyelid and brow lacerations are common and are especially troublesome given the effect of hemorrhage from these areas on the fighter's vision and thus ability to continue. We propose that the convexity of the underlying supraorbital rim is responsible for the high frequency of lacerations in this region after blunt trauma and offer a method of reducing subsequent injury by reducing its prominence.

SRS: Site ranking system for hazardous chemical and radioactive waste

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rechard, R.P.; Chu, M.S.Y.; Brown, S.L.

1988-05-01

This report describes the rationale and presents instructions for a site ranking system (SRS). SRS ranks hazardous chemical and radioactive waste sites by scoring important and readily available factors that influence risk to human health. Using SRS, sites can be ranked for purposes of detailed site investigations. SRS evaluates the relative risk as a combination of potentially exposed population, chemical toxicity, and potential exposure of release from a waste site; hence, SRS uses the same concepts found in a detailed assessment of health risk. Basing SRS on the concepts of risk assessment tends to reduce the distortion of results foundmore » in other ranking schemes. More importantly, a clear logic helps ensure the successful application of the ranking procedure and increases its versatility when modifications are necessary for unique situations. Although one can rank sites using a detailed risk assessment, it is potentially costly because of data and resources required. SRS is an efficient approach to provide an order-of-magnitude ranking, requiring only readily available data (often only descriptive) and hand calculations. Worksheets are included to make the system easier to understand and use. 88 refs., 19 figs., 58 tabs.« less

Properties of experimental titanium cast investment mixing with water reducing agent solution.

PubMed

Zhang, Zutai; Ding, Ning; Tamaki, Yukimichi; Hotta, Yasuhiro; Han-Cheol, Cho; Miyazaki, Takashi

2012-01-01

This study aimed to develop a dental investment for titanium casting. ZrO(2) and Al(2)O(3) were selected as refractory materials to prepare three investments (Codes: A-C) according to the quantity of Zr. Al(2)O(3) cement was used as a binder at a ratio of 15%, they were mixed with special mixing liquid. B1 was used as a control mixed with water. Fundamental examinations were statistically evaluated. A casting test was performed with investment B. Fluidities, setting times, and green strengths showed no remarkable differences; however, they were significantly different from those of B1. Expansion values for A, B, C, and B1 at 850°C were 1.03%±0.08%, 1.96%±0.17%, 4.35%±0.23%, and 1.50%±0.28%, respectively. Castings were covered by only small amounts of mold materials. The hardness test showed no significant differences between castings from B and the ones from commercial investments. The experimental special mixing liquid effectively reduced the water/powder ratio and improved the strength and thermal expansion.

Does the patient's inherent rating tendency influence reported satisfaction scores and affect division ranking?

PubMed

Francis, Patricia; Agoritsas, Thomas; Chopard, Pierre; Perneger, Thomas

2016-04-01

To determine the impact of adjusting for rating tendency (RT) on patient satisfaction scores in a large teaching hospital and to assess the impact of adjustment on the ranking of divisions. Cross-sectional survey. Large 2200-bed university teaching hospital. All adult patients hospitalized during a 1-month period in one of 20 medical divisions. None. Patient experience of care measured by the Picker Patient Experience questionnaire and RT scores. Problem scores were weakly but significantly associated with RT. Division ranking was slightly modified in RT adjusted models. Division ranking changed substantially in case-mix adjusted models. Adjusting patient self-reported problem scores for RT did impact ranking of divisions, although marginally. Further studies are needed to determine the impact of RT when comparing different institutions, particularly across inter-cultural settings, where the difference in RT may be more substantial. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

Rank Dynamics of Word Usage at Multiple Scales

NASA Astrophysics Data System (ADS)

Morales, José A.; Colman, Ewan; Sánchez, Sergio; Sánchez-Puig, Fernanda; Pineda, Carlos; Iñiguez, Gerardo; Cocho, Germinal; Flores, Jorge; Gershenson, Carlos

2018-05-01

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.

How to Rank Journals

PubMed Central

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. PMID:26930052

How to Rank Journals.

PubMed

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.

Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization.

PubMed

Lu, Canyi; Lin, Zhouchen; Yan, Shuicheng

2015-02-01

This paper presents a general framework for solving the low-rank and/or sparse matrix minimization problems, which may involve multiple nonsmooth terms. The iteratively reweighted least squares (IRLSs) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables and their weights. However, the traditional IRLS can only solve a sparse only or low rank only minimization problem with squared loss or an affine constraint. This paper generalizes IRLS to solve joint/mixed low-rank and sparse minimization problems, which are essential formulations for many tasks. As a concrete example, we solve the Schatten-p norm and l2,q-norm regularized low-rank representation problem by IRLS, and theoretically prove that the derived solution is a stationary point (globally optimal if p,q ≥ 1). Our convergence proof of IRLS is more general than previous one that depends on the special properties of the Schatten-p norm and l2,q-norm. Extensive experiments on both synthetic and real data sets demonstrate that our IRLS is much more efficient.

Faculty Challenges across Rank in Liberal Arts Colleges: A Human Resources Perspective

ERIC Educational Resources Information Center

Baker, Vicki L.; Pifer, Meghan J.; Lunsford, Laura G.

2016-01-01

This article focuses on the challenges faced by faculty members in a consortium of 13 Liberal Arts Colleges (LACs). We present findings, by academic rank, from a mixed-methods study of faculty development needs and experiences within the consortium. Relying on human resource principles, we advocate a greater focus on the development of the person,…

Quantum Entanglement and Reduced Density Matrices

NASA Astrophysics Data System (ADS)

Purwanto, Agus; Sukamto, Heru; Yuwana, Lila

2018-05-01

We investigate entanglement and separability criteria of multipartite (n-partite) state by examining ranks of its reduced density matrices. Firstly, we construct the general formula to determine the criterion. A rank of origin density matrix always equals one, meanwhile ranks of reduced matrices have various ranks. Next, separability and entanglement criterion of multipartite is determined by calculating ranks of reduced density matrices. In this article we diversify multipartite state criteria into completely entangled state, completely separable state, and compound state, i.e. sub-entangled state and sub-entangledseparable state. Furthermore, we also shorten the calculation proposed by the previous research to determine separability of multipartite state and expand the methods to be able to differ multipartite state based on criteria above.

Ranking Reputation and Quality in Online Rating Systems

PubMed Central

Liao, Hao; Zeng, An; Xiao, Rui; Ren, Zhuo-Ming; Chen, Duan-Bing; Zhang, Yi-Cheng

2014-01-01

How to design an accurate and robust ranking algorithm is a fundamental problem with wide applications in many real systems. It is especially significant in online rating systems due to the existence of some spammers. In the literature, many well-performed iterative ranking methods have been proposed. These methods can effectively recognize the unreliable users and reduce their weight in judging the quality of objects, and finally lead to a more accurate evaluation of the online products. In this paper, we design an iterative ranking method with high performance in both accuracy and robustness. More specifically, a reputation redistribution process is introduced to enhance the influence of highly reputed users and two penalty factors enable the algorithm resistance to malicious behaviors. Validation of our method is performed in both artificial and real user-object bipartite networks. PMID:24819119

DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

PubMed Central

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307615

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

PubMed

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-06-15

Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. http://datamining-iip.fudan.edu.cn/service/DrugE-Rank zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

A special case of reduced rank models for identification and modelling of time varying effects in survival analysis.

PubMed

Perperoglou, Aris

2016-12-10

Flexible survival models are in need when modelling data from long term follow-up studies. In many cases, the assumption of proportionality imposed by a Cox model will not be valid. Instead, a model that can identify time varying effects of fixed covariates can be used. Although there are several approaches that deal with this problem, it is not always straightforward how to choose which covariates should be modelled having time varying effects and which not. At the same time, it is up to the researcher to define appropriate time functions that describe the dynamic pattern of the effects. In this work, we suggest a model that can deal with both fixed and time varying effects and uses simple hypotheses tests to distinguish which covariates do have dynamic effects. The model is an extension of the parsimonious reduced rank model of rank 1. As such, the number of parameters is kept low, and thus, a flexible set of time functions, such as b-splines, can be used. The basic theory is illustrated along with an efficient fitting algorithm. The proposed method is applied to a dataset of breast cancer patients and compared with a multivariate fractional polynomials approach for modelling time-varying effects. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Ranking online quality and reputation via the user activity

NASA Astrophysics Data System (ADS)

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.

Evaluation of the osteoclastogenic process associated with RANK / RANK-L / OPG in odontogenic myxomas

PubMed Central

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 (<3 cm) and 14 large (> 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

Mixed models and reduced/selective integration displacement models for nonlinear analysis of curved beams

NASA Technical Reports Server (NTRS)

Noor, A. K.; Peters, J. M.

1981-01-01

Simple mixed models are developed for use in the geometrically nonlinear analysis of deep arches. A total Lagrangian description of the arch deformation is used, the analytical formulation being based on a form of the nonlinear deep arch theory with the effects of transverse shear deformation included. The fundamental unknowns comprise the six internal forces and generalized displacements of the arch, and the element characteristic arrays are obtained by using Hellinger-Reissner mixed variational principle. The polynomial interpolation functions employed in approximating the forces are one degree lower than those used in approximating the displacements, and the forces are discontinuous at the interelement boundaries. Attention is given to the equivalence between the mixed models developed herein and displacement models based on reduced integration of both the transverse shear and extensional energy terms. The advantages of mixed models over equivalent displacement models are summarized. Numerical results are presented to demonstrate the high accuracy and effectiveness of the mixed models developed and to permit a comparison of their performance with that of other mixed models reported in the literature.

Suppression pheromone and cockroach rank formation

NASA Astrophysics Data System (ADS)

Kou, Rong; Chang, Huan-Wen; Chen, Shu-Chun; Ho, Hsiao-Yung

2009-06-01

Although agonistic behaviors in the male lobster cockroach ( Nauphoeta cinerea) are well known, the formation of an unstable hierarchy has long been a puzzle. In this study, we investigate how the unstable dominance hierarchy in N. cinerea is maintained via a pheromone signaling system. In agonistic interactions, aggressive posture (AP) is an important behavioral index of aggression. This study showed that, during the formation of a governing hierarchy, thousands of nanograms of 3-hydroxy-2-butanone (3H-2B) were released by the AP-adopting dominant in the first encounter fight, then during the early domination period and that this release of 3H-2B was related to rank maintenance, but not to rank establishment. For rank maintenance, 3H-2B functioned as a suppression pheromone, which suppressed the fighting capability of rivals and kept them in a submissive state. During the period of rank maintenance, as the dominant male gradually decreased his 3H-2B release, the fighting ability of the subordinate gradually developed, as shown by the increasing odds of a subordinate adopting an AP (OSAP). The OSAP was negatively correlated with the amount of 3H-2B released by the dominant and positively correlated with the number of domination days. The same OSAP could be achieved earlier by reducing the amount of 3H-2B released by the dominant indicates that whether the subordinate adopts an offensive strategy depends on what the dominant is doing.

Time evolution of Wikipedia network ranking

NASA Astrophysics Data System (ADS)

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.

Learning Low-Rank Decomposition for Pan-Sharpening With Spatial-Spectral Offsets.

PubMed

Yang, Shuyuan; Zhang, Kai; Wang, Min

2017-08-25

Finding accurate injection components is the key issue in pan-sharpening methods. In this paper, a low-rank pan-sharpening (LRP) model is developed from a new perspective of offset learning. Two offsets are defined to represent the spatial and spectral differences between low-resolution multispectral and high-resolution multispectral (HRMS) images, respectively. In order to reduce spatial and spectral distortions, spatial equalization and spectral proportion constraints are designed and cast on the offsets, to develop a spatial and spectral constrained stable low-rank decomposition algorithm via augmented Lagrange multiplier. By fine modeling and heuristic learning, our method can simultaneously reduce spatial and spectral distortions in the fused HRMS images. Moreover, our method can efficiently deal with noises and outliers in source images, for exploring low-rank and sparse characteristics of data. Extensive experiments are taken on several image data sets, and the results demonstrate the efficiency of the proposed LRP.

Classification of hyperbolic singularities of rank zero of integrable Hamiltonian systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oshemkov, Andrey A

2010-10-06

A complete invariant is constructed that is a solution of the problem of semilocal classification of saddle singularities of integrable Hamiltonian systems. Namely, a certain combinatorial object (an f{sub n}-graph) is associated with every nondegenerate saddle singularity of rank zero; as a result, the problem of semilocal classification of saddle singularities of rank zero is reduced to the problem of enumeration of the f{sub n}-graphs. This enables us to describe a simple algorithm for obtaining the lists of saddle singularities of rank zero for a given number of degrees of freedom and a given complexity. Bibliography: 24 titles.

Multiple graph regularized protein domain ranking.

PubMed

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.

What's wrong with hazard-ranking systems? An expository note.

PubMed

Cox, Louis Anthony Tony

2009-07-01

Two commonly recommended principles for allocating risk management resources to remediate uncertain hazards are: (1) select a subset to maximize risk-reduction benefits (e.g., maximize the von Neumann-Morgenstern expected utility of the selected risk-reducing activities), and (2) assign priorities to risk-reducing opportunities and then select activities from the top of the priority list down until no more can be afforded. When different activities create uncertain but correlated risk reductions, as is often the case in practice, then these principles are inconsistent: priority scoring and ranking fails to maximize risk-reduction benefits. Real-world risk priority scoring systems used in homeland security and terrorism risk assessment, environmental risk management, information system vulnerability rating, business risk matrices, and many other important applications do not exploit correlations among risk-reducing opportunities or optimally diversify risk-reducing investments. As a result, they generally make suboptimal risk management recommendations. Applying portfolio optimization methods instead of risk prioritization ranking, rating, or scoring methods can achieve greater risk-reduction value for resources spent.

Rheological properties of reduced fat ice cream mix containing octenyl succinylated pearl millet starch.

PubMed

Sharma, Monika; Singh, Ashish K; Yadav, Deep N

2017-05-01

The octenyl succinyl anhydride (OSA) esterified pearl millet ( Pennisetum typhoides ) starch was evaluated as fat replacer in soft serve ice cream in comparison to other fat replacers viz. inulin, whey protein concentrate-70 and commercial starch. During temperature sweep test, the yield stress and flow behaviour index of un-pasteurized ice cream mixes increased as the temperature increased from 40 to 80 °C, while the consistency index decreased. Consistency index of aged ice cream mixes containing 2% fat replacer was higher as compared to mixes with 1% level. The aged ice cream mixes exhibited non-Newtonian behaviour as flow behaviour index values were less than one. Apparent viscosity (at 50 s -1 shear rate) of control as well as ice cream mix containing 1% OSA-esterified pearl millet starch samples was 417 and 415 mPas, respectively and did not differ significantly. The overrun of the ice cream (with 5 and 7.5% fat) containing 1 and 2% of above fat replacers ranged between 29.7 and 34.3% and was significantly lower than control (40.3%). The percent melted ice cream was also low for the ice creams containing 2% of above fat replacers at 5% fat content as compared to control. However, sensory acceptability and rheological characteristics of reduced fat ice creams containing 1.0 and 2.0% OSA-esterified pearl millet starch were at par with other fat replacers under the study. Thus, OSA-esterified pearl millet starch has potential to be used as fat replacer in reduced fat ice cream.

College and School of Pharmacy Characteristics Associated With US News and World Report Rankings

PubMed Central

Coleman, Craig I.

2013-01-01

Objective. To determine the association between characteristics of colleges and schools of pharmacy and their rankings according to US News and World Report. Methods. The 2008 US News and World Report, mean ranking scores (ranging from 2.0 to 5.0) for 78 US colleges and schools of pharmacy were compared with college and school characteristics, including academic program, students, faculty, and scholarship. The adjusted difference in mean ranking score associated with each characteristic was determined using a multivariate mixed linear regression model. Results. The most powerful identified predictors of mean ranking score included the amount of grant funding (National Institutes of Health [NIH] and non-NIH funding) a college or school of pharmacy received and the yearly publication rates of its department of pharmacy (p≤0.001 for both). The adjusted mean ranking scores for colleges and schools receiving >$5 million and $1 million to $5 million in scholarly grant funding were respectively 0.77 and 0.26 points higher than those receiving none. Adjusted mean ranking scores for colleges and schools whose departments of pharmacy practice had publishing rates of >20 papers and 11 to 20 papers were respectively 0.40 and 0.17 points higher than those publishing ≤10 (p<0.05 for both). Conclusion. The characteristic of colleges and schools of pharmacy most associated with US News and World Report rankings appears to be their scholarly productivity. PMID:23610473

College and school of pharmacy characteristics associated with US News and World Report rankings.

PubMed

Schlesselman, Lauren; Coleman, Craig I

2013-04-12

OBJECTIVE. To determine the association between characteristics of colleges and schools of pharmacy and their rankings according to US News and World Report. METHODS. The 2008 US News and World Report, mean ranking scores (ranging from 2.0 to 5.0) for 78 US colleges and schools of pharmacy were compared with college and school characteristics, including academic program, students, faculty, and scholarship. The adjusted difference in mean ranking score associated with each characteristic was determined using a multivariate mixed linear regression model. RESULTS. The most powerful identified predictors of mean ranking score included the amount of grant funding (National Institutes of Health [NIH] and non-NIH funding) a college or school of pharmacy received and the yearly publication rates of its department of pharmacy (p≤0.001 for both). The adjusted mean ranking scores for colleges and schools receiving >$5 million and $1 million to $5 million in scholarly grant funding were respectively 0.77 and 0.26 points higher than those receiving none. Adjusted mean ranking scores for colleges and schools whose departments of pharmacy practice had publishing rates of >20 papers and 11 to 20 papers were respectively 0.40 and 0.17 points higher than those publishing ≤10 (p<0.05 for both). CONCLUSION. The characteristic of colleges and schools of pharmacy most associated with US News and World Report rankings appears to be their scholarly productivity.

DebtRank-transparency: Controlling systemic risk in financial networks

PubMed Central

Thurner, Stefan; Poledna, Sebastian

2013-01-01

Nodes in a financial network, such as banks, cannot assess the true risks associated with lending to other nodes in the network, unless they have full information on the riskiness of all other nodes. These risks can be estimated by using network metrics (as DebtRank) of the interbank liability network. With a simple agent based model we show that systemic risk in financial networks can be drastically reduced by increasing transparency, i.e. making the DebtRank of individual banks visible to others, and by imposing a rule, that reduces interbank borrowing from systemically risky nodes. This scheme does not reduce the efficiency of the financial network, but fosters a more homogeneous risk-distribution within the system in a self-organized critical way. The reduction of systemic risk is due to a massive reduction of cascading failures in the transparent system. A regulation-policy implementation of the proposed scheme is discussed. PMID:23712454

Rank 4 Premodular Categories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruillard, Paul J.; Galindo, Cesar; Ng, Siu Hung

2016-09-01

We consider the classification problem for rank 4 premodular categories. We uncover a formula for the 2nd Frobenius-Schur indicator of a premodular category is determined and the classification of rank 4 premodular categories (up to Grothendieck equivalence) is completed. In the appendix we show rank finiteness for premodular categories.

Multiplex PageRank.

PubMed

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.

Grand Junction projects office mixed-waste treatment program, VAC*TRAX mobile treatment unit process hazards analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bloom, R.R.

1996-04-01

The objective of this report is to demonstrate that a thorough assessment of the risks associated with the operation of the Rust Geotech patented VAC*TRAX mobile treatment unit (MTU) has been performed and documented. The MTU was developed to treat mixed wastes at the US Department of Energy (DOE) Albuquerque Operations Office sites. The MTU uses an indirectly heated, batch vacuum dryer to thermally desorb organic compounds from mixed wastes. This process hazards analysis evaluated 102 potential hazards. The three significant hazards identified involved the inclusion of oxygen in a process that also included an ignition source and fuel. Changesmore » to the design of the MTU were made concurrent with the hazard identification and analysis; all hazards with initial risk rankings of 1 or 2 were reduced to acceptable risk rankings of 3 or 4. The overall risk to any population group from operation of the MTU was determined to be very low; the MTU is classified as a Radiological Facility with low hazards.« less

Large-scale linear rankSVM.

PubMed

Lee, Ching-Pei; Lin, Chih-Jen

2014-04-01

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.

Multiple graph regularized protein domain ranking

PubMed Central

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. PMID:23157331

Low rank approach to computing first and higher order derivatives using automatic differentiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reed, J. A.; Abdel-Khalik, H. S.; Utke, J.

2012-07-01

This manuscript outlines a new approach for increasing the efficiency of applying automatic differentiation (AD) to large scale computational models. By using the principles of the Efficient Subspace Method (ESM), low rank approximations of the derivatives for first and higher orders can be calculated using minimized computational resources. The output obtained from nuclear reactor calculations typically has a much smaller numerical rank compared to the number of inputs and outputs. This rank deficiency can be exploited to reduce the number of derivatives that need to be calculated using AD. The effective rank can be determined according to ESM by computingmore » derivatives with AD at random inputs. Reduced or pseudo variables are then defined and new derivatives are calculated with respect to the pseudo variables. Two different AD packages are used: OpenAD and Rapsodia. OpenAD is used to determine the effective rank and the subspace that contains the derivatives. Rapsodia is then used to calculate derivatives with respect to the pseudo variables for the desired order. The overall approach is applied to two simple problems and to MATWS, a safety code for sodium cooled reactors. (authors)« less

Benchmarking antibiotic use in Finnish acute care hospitals using patient case-mix adjustment.

PubMed

Kanerva, Mari; Ollgren, Jukka; Lyytikäinen, Outi

2011-11-01

It is difficult to draw conclusions about the prudence of antibiotic use in different hospitals by directly comparing usage figures. We present a patient case-mix adjustment model of antibiotic use to rank hospitals while taking patient characteristics into account. Data on antibiotic use were collected during the national healthcare-associated infection (HAI) prevalence survey in 2005 in Finland in all 5 tertiary care, all 15 secondary care and 10 (25% of 40) other acute care hospitals. The use of antibiotics was measured using use-days/100 patient-days during a 7day period and the prevalence of patients receiving at least two antimicrobials during the study day. Case-mix-adjusted antibiotic use was calculated by using multivariate models and an indirect standardization method. Parameters in the model included age, sex, severity of underlying diseases, intensive care, haematology, preceding surgery, respirator, central venous and urinary catheters, community-associated infection, HAI and contact isolation due to methicillin-resistant Staphylococcus aureus. The ranking order changed one position in 12 (40%) hospitals and more than two positions in 13 (43%) hospitals when the case-mix-adjusted figures were compared with those observed. In 24 hospitals (80%), the antibiotic use density observed was lower than expected by the case-mix-adjusted use density. The patient case-mix adjustment of antibiotic use ranked the hospitals differently from the ranking according to observed use, and may be a useful tool for benchmarking hospital antibiotic use. However, the best set of easily and widely available parameters that would describe both patient material and hospital activities remains to be determined.

How Many Alternatives Can Be Ranked? A Comparison of the Paired Comparison and Ranking Methods.

PubMed

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.

Neophilia Ranking of Scientific Journals.

PubMed

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.

Neophilia Ranking of Scientific Journals

PubMed Central

Packalen, Mikko; Bhattacharya, Jay

2017-01-01

The ranking of scientific journals is important because of the signal it sends to scientists about what is considered most vital for scientific progress. Existing ranking systems focus on measuring the influence of a scientific paper (citations)—these rankings do not reward journals for publishing innovative work that builds on new ideas. We propose an alternative ranking based on the proclivity of journals to publish papers that build on new ideas, and we implement this ranking via a text-based analysis of all published biomedical papers dating back to 1946. In addition, we compare our neophilia ranking to citation-based (impact factor) rankings; this comparison shows that the two ranking approaches are distinct. Prior theoretical work suggests an active role for our neophilia index in science policy. Absent an explicit incentive to pursue novel science, scientists underinvest in innovative work because of a coordination problem: for work on a new idea to flourish, many scientists must decide to adopt it in their work. Rankings that are based purely on influence thus do not provide sufficient incentives for publishing innovative work. By contrast, adoption of the neophilia index as part of journal-ranking procedures by funding agencies and university administrators would provide an explicit incentive for journals to publish innovative work and thus help solve the coordination problem by increasing scientists' incentives to pursue innovative work. PMID:28713181

Pulling Rank: Military Rank Affects Hormone Levels and Fairness in an Allocation Experiment.

PubMed

Siart, Benjamin; Pflüger, Lena S; Wallner, Bernard

2016-01-01

Status within social hierarchies has great effects on the lives of socially organized mammals. Its effects on human behavior and related physiology, however, is relatively little studied. The present study investigated the impact of military rank on fairness and behavior in relation to salivary cortisol (C) and testosterone (T) levels in male soldiers. For this purpose 180 members of the Austrian Armed Forces belonging to two distinct rank groups participated in two variations of a computer-based guard duty allocation experiment. The rank groups were (1) warrant officers (high rank, HR) and (2) enlisted men (low rank, LR). One soldier from each rank group participated in every experiment. At the beginning of the experiment, one participant was assigned to start standing guard and the other participant at rest. The participant who started at rest could choose if and when to relieve his fellow soldier and therefore had control over the experiment. In order to trigger perception of unfair behavior, an additional experiment was conducted which was manipulated by the experimenter. In the manipulated version both soldiers started in the standing guard position and were never relieved, believing that their opponent was at rest , not relieving them. Our aim was to test whether unfair behavior causes a physiological reaction. Saliva samples for hormone analysis were collected at regular intervals throughout the experiment. We found that in the un-manipulated setup high-ranking soldiers spent less time standing guard than lower ranking individuals. Rank was a significant predictor for C but not for T levels during the experiment. C levels in the HR group were higher than in the LR group. C levels were also elevated in the manipulated experiment compared to the un-manipulated experiment, especially in LR. We assume that the elevated C levels in HR were caused by HR feeling their status challenged by the situation of having to negotiate with an individual of lower military

Pulling Rank: Military Rank Affects Hormone Levels and Fairness in an Allocation Experiment

PubMed Central

Siart, Benjamin; Pflüger, Lena S.; Wallner, Bernard

2016-01-01

Status within social hierarchies has great effects on the lives of socially organized mammals. Its effects on human behavior and related physiology, however, is relatively little studied. The present study investigated the impact of military rank on fairness and behavior in relation to salivary cortisol (C) and testosterone (T) levels in male soldiers. For this purpose 180 members of the Austrian Armed Forces belonging to two distinct rank groups participated in two variations of a computer-based guard duty allocation experiment. The rank groups were (1) warrant officers (high rank, HR) and (2) enlisted men (low rank, LR). One soldier from each rank group participated in every experiment. At the beginning of the experiment, one participant was assigned to start standing guard and the other participant at rest. The participant who started at rest could choose if and when to relieve his fellow soldier and therefore had control over the experiment. In order to trigger perception of unfair behavior, an additional experiment was conducted which was manipulated by the experimenter. In the manipulated version both soldiers started in the standing guard position and were never relieved, believing that their opponent was at rest, not relieving them. Our aim was to test whether unfair behavior causes a physiological reaction. Saliva samples for hormone analysis were collected at regular intervals throughout the experiment. We found that in the un-manipulated setup high-ranking soldiers spent less time standing guard than lower ranking individuals. Rank was a significant predictor for C but not for T levels during the experiment. C levels in the HR group were higher than in the LR group. C levels were also elevated in the manipulated experiment compared to the un-manipulated experiment, especially in LR. We assume that the elevated C levels in HR were caused by HR feeling their status challenged by the situation of having to negotiate with an individual of lower military rank

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

PubMed Central

Cao, Houwei; Verma, Ragini; Nenkova, Ani

2014-01-01

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion. PMID:25422534

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

PubMed

Cao, Houwei; Verma, Ragini; Nenkova, Ani

2015-01-01

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion.

Progress Report on SAM Reduced-Order Model Development for Thermal Stratification and Mixing during Reactor Transients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, R.

This report documents the initial progress on the reduced-order flow model developments in SAM for thermal stratification and mixing modeling. Two different modeling approaches are pursued. The first one is based on one-dimensional fluid equations with additional terms accounting for the thermal mixing from both flow circulations and turbulent mixing. The second approach is based on three-dimensional coarse-grid CFD approach, in which the full three-dimensional fluid conservation equations are modeled with closure models to account for the effects of turbulence.

Wikipedia ranking of world universities

NASA Astrophysics Data System (ADS)

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.

DockRank: Ranking docked conformations using partner-specific sequence homology-based protein interface prediction

PubMed Central

Xue, Li C.; Jordan, Rafael A.; EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2015-01-01

Selecting near-native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. Dock-Rank uses interface residues predicted by partner-specific sequence homology-based protein–protein interface predictor (PS-HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state-of-the-art docking scoring functions using Success Rate (the percentage of cases that have at least one near-native conformation among the top m conformations) and Hit Rate (the percentage of near-native conformations that are included among the top m conformations). In cases where it is possible to obtain partner-specific (PS) interface predictions from PS-HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state-of-the-art energy-based scoring functions (improving Success Rate by up to 4-fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39-fold). The latter result underscores the importance of using partner-specific interface residues in scoring docked conformations. We show that DockRank, when used to re-rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/. PMID:23873600

DockRank: ranking docked conformations using partner-specific sequence homology-based protein interface prediction.

PubMed

Xue, Li C; Jordan, Rafael A; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2014-02-01

Selecting near-native conformations from the immense number of conformations generated by docking programs remains a major challenge in molecular docking. We introduce DockRank, a novel approach to scoring docked conformations based on the degree to which the interface residues of the docked conformation match a set of predicted interface residues. DockRank uses interface residues predicted by partner-specific sequence homology-based protein-protein interface predictor (PS-HomPPI), which predicts the interface residues of a query protein with a specific interaction partner. We compared the performance of DockRank with several state-of-the-art docking scoring functions using Success Rate (the percentage of cases that have at least one near-native conformation among the top m conformations) and Hit Rate (the percentage of near-native conformations that are included among the top m conformations). In cases where it is possible to obtain partner-specific (PS) interface predictions from PS-HomPPI, DockRank consistently outperforms both (i) ZRank and IRAD, two state-of-the-art energy-based scoring functions (improving Success Rate by up to 4-fold); and (ii) Variants of DockRank that use predicted interface residues obtained from several protein interface predictors that do not take into account the binding partner in making interface predictions (improving success rate by up to 39-fold). The latter result underscores the importance of using partner-specific interface residues in scoring docked conformations. We show that DockRank, when used to re-rank the conformations returned by ClusPro, improves upon the original ClusPro rankings in terms of both Success Rate and Hit Rate. DockRank is available as a server at http://einstein.cs.iastate.edu/DockRank/. Copyright © 2013 Wiley Periodicals, Inc.

Case-Mix Adjusting Performance Measures in a Veteran Population: Pharmacy- and Diagnosis-Based Approaches

PubMed Central

Liu, Chuan-Fen; Sales, Anne E; Sharp, Nancy D; Fishman, Paul; Sloan, Kevin L; Todd-Stenberg, Jeff; Nichol, W Paul; Rosen, Amy K; Loveland, Susan

2003-01-01

Objective To compare the rankings for health care utilization performance measures at the facility level in a Veterans Health Administration (VHA) health care delivery network using pharmacy- and diagnosis-based case-mix adjustment measures. Data Sources/Study Setting The study included veterans who used inpatient or outpatient services in Veterans Integrated Service Network (VISN) 20 during fiscal year 1998 (October 1997 to September 1998; N=126,076). Utilization and pharmacy data were extracted from VHA national databases and the VISN 20 data warehouse. Study Design We estimated concurrent regression models using pharmacy or diagnosis information in the base year (FY1998) to predict health service utilization in the same year. Utilization measures included bed days of care for inpatient care and provider visits for outpatient care. Principal Findings Rankings of predicted utilization measures across facilities vary by case-mix adjustment measure. There is greater consistency within the diagnosis-based models than between the diagnosis- and pharmacy-based models. The eight facilities were ranked differently by the diagnosis- and pharmacy-based models. Conclusions Choice of case-mix adjustment measure affects rankings of facilities on performance measures, raising concerns about the validity of profiling practices. Differences in rankings may reflect differences in comparability of data capture across facilities between pharmacy and diagnosis data sources, and unstable estimates due to small numbers of patients in a facility. PMID:14596393

24 CFR 599.401 - Ranking of applications.

Code of Federal Regulations, 2010 CFR

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...

Active subspace: toward scalable low-rank learning.

PubMed

Liu, Guangcan; Yan, Shuicheng

2012-12-01

We address the scalability issues in low-rank matrix learning problems. Usually these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially in large-scale settings. Based on the fact that the optimal solution matrix to an NNROP is often low rank, we revisit the classic mechanism of low-rank matrix factorization, based on which we present an active subspace algorithm for efficiently solving NNROPs by transforming large-scale NNROPs into small-scale problems. The transformation is achieved by factorizing the large solution matrix into the product of a small orthonormal matrix (active subspace) and another small matrix. Although such a transformation generally leads to nonconvex problems, we show that a suboptimal solution can be found by the augmented Lagrange alternating direction method. For the robust PCA (RPCA) (Candès, Li, Ma, & Wright, 2009 ) problem, a typical example of NNROPs, theoretical results verify the suboptimality of the solution produced by our algorithm. For the general NNROPs, we empirically show that our algorithm significantly reduces the computational complexity without loss of optimality.

The highest-ranking rooster has priority to announce the break of dawn.

PubMed

Shimmura, Tsuyoshi; Ohashi, Shosei; Yoshimura, Takashi

2015-07-23

The "cock-a-doodle-doo" crowing of roosters, which symbolizes the break of dawn in many cultures, is controlled by the circadian clock. When one rooster announces the break of dawn, others in the vicinity immediately follow. Chickens are highly social animals, and they develop a linear and fixed hierarchy in small groups. We found that when chickens were housed in small groups, the top-ranking rooster determined the timing of predawn crowing. Specifically, the top-ranking rooster always started to crow first, followed by its subordinates, in descending order of social rank. When the top-ranking rooster was physically removed from a group, the second-ranking rooster initiated crowing. The presence of a dominant rooster significantly reduced the number of predawn crows in subordinates. However, the number of crows induced by external stimuli was independent of social rank, confirming that subordinates have the ability to crow. Although the timing of subordinates' predawn crowing was strongly dependent on that of the top-ranking rooster, free-running periods of body temperature rhythms differed among individuals, and crowing rhythm did not entrain to a crowing sound stimulus. These results indicate that in a group situation, the top-ranking rooster has priority to announce the break of dawn, and that subordinate roosters are patient enough to wait for the top-ranking rooster's first crow every morning and thus compromise their circadian clock for social reasons.

The highest-ranking rooster has priority to announce the break of dawn

PubMed Central

Shimmura, Tsuyoshi; Ohashi, Shosei; Yoshimura, Takashi

2015-01-01

The “cock-a-doodle-doo” crowing of roosters, which symbolizes the break of dawn in many cultures, is controlled by the circadian clock. When one rooster announces the break of dawn, others in the vicinity immediately follow. Chickens are highly social animals, and they develop a linear and fixed hierarchy in small groups. We found that when chickens were housed in small groups, the top-ranking rooster determined the timing of predawn crowing. Specifically, the top-ranking rooster always started to crow first, followed by its subordinates, in descending order of social rank. When the top-ranking rooster was physically removed from a group, the second-ranking rooster initiated crowing. The presence of a dominant rooster significantly reduced the number of predawn crows in subordinates. However, the number of crows induced by external stimuli was independent of social rank, confirming that subordinates have the ability to crow. Although the timing of subordinates’ predawn crowing was strongly dependent on that of the top-ranking rooster, free-running periods of body temperature rhythms differed among individuals, and crowing rhythm did not entrain to a crowing sound stimulus. These results indicate that in a group situation, the top-ranking rooster has priority to announce the break of dawn, and that subordinate roosters are patient enough to wait for the top-ranking rooster’s first crow every morning and thus compromise their circadian clock for social reasons. PMID:26203594

Learning to rank diversified results for biomedical information retrieval from multiple features.

PubMed

Wu, Jiajin; Huang, Jimmy; Ye, Zheng

2014-01-01

Different from traditional information retrieval (IR), promoting diversity in IR takes consideration of relationship between documents in order to promote novelty and reduce redundancy thus to provide diversified results to satisfy various user intents. Diversity IR in biomedical domain is especially important as biologists sometimes want diversified results pertinent to their query. A combined learning-to-rank (LTR) framework is learned through a general ranking model (gLTR) and a diversity-biased model. The former is learned from general ranking features by a conventional learning-to-rank approach; the latter is constructed with diversity-indicating features added, which are extracted based on the retrieved passages' topics detected using Wikipedia and ranking order produced by the general learning-to-rank model; final ranking results are given by combination of both models. Compared with baselines BM25 and DirKL on 2006 and 2007 collections, the gLTR has 0.2292 (+16.23% and +44.1% improvement over BM25 and DirKL respectively) and 0.1873 (+15.78% and +39.0% improvement over BM25 and DirKL respectively) in terms of aspect level of mean average precision (Aspect MAP). The LTR method outperforms gLTR on 2006 and 2007 collections with 4.7% and 2.4% improvement in terms of Aspect MAP. The learning-to-rank method is an efficient way for biomedical information retrieval and the diversity-biased features are beneficial for promoting diversity in ranking results.

University Rankings: The Web Ranking

ERIC Educational Resources Information Center

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…

Limited Rank Matrix Learning, discriminative dimension reduction and visualization.

PubMed

Bunte, Kerstin; Schneider, Petra; Hammer, Barbara; Schleif, Frank-Michael; Villmann, Thomas; Biehl, Michael

2012-02-01

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-dimensional representations of the data. This allows to incorporate prior knowledge of the intrinsic dimension and to reduce the number of adaptive parameters efficiently. In particular, for very large dimensional data, the limitation of the rank can reduce computation time and memory requirements significantly. Furthermore, two- or three-dimensional representations constitute an efficient visualization method for labeled data sets. The identification of a suitable projection is not treated as a pre-processing step but as an integral part of the supervised training. Several real world data sets serve as an illustration and demonstrate the usefulness of the suggested method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Sex Differences in Academic Rank and Publication Rate at Top-Ranked US Neurology Programs.

PubMed

McDermott, Mollie; Gelb, Douglas J; Wilson, Kelsey; Pawloski, Megan; Burke, James F; Shelgikar, Anita V; London, Zachary N

2018-04-02

Women are underrepresented in academic neurology, and the reasons for the underrepresentation are unclear. To explore potential sex differences in top-ranked academic neurology programs by comparing the number of men and women at each academic faculty rank and how many articles each group has published. Twenty-nine top-ranked neurology programs were identified by combining the top 20 programs listed on either the 2016 or 2017 Doximity Residency Navigator tool with the top 20 programs listed in the US News and World Report ranking of Best Graduate Schools. An internet search of the departmental websites was performed between December 1, 2015, and April 30, 2016. For each faculty member on a program site, the following biographical information was obtained: first name, last name, academic institution, sex, academic faculty rank, educational leadership (clerkship, fellowship, or residency director/assistant director), and year of medical school graduation. To compare the distribution of men vs women and the number of publications for men vs women at each academic faculty rank. Secondary analyses included Scopus h-index, book authorship, educational leadership (clerkship, residency, or fellowship director/assistant director), and clinical activity as inferred through Medicare claims data in men vs women after controlling for years since medical school graduation. Of 1712 academic neurologists in our sample, 528 (30.8%) were women and 1184 (69.2%) were men (P < .001). Men outnumbered women at all academic faculty ranks, and the difference increased with advancing rank (instructor/lecturer, 59.4% vs 40.5%; assistant professor, 56.7% vs 43.3%; associate professor, 69.8% vs 30.2%; and professor, 86.2% vs 13.8%). After controlling for clustering and years since medical school graduation, men were twice as likely as women to be full professors (odds ratio [OR], 2.06; 95% CI, 1.40-3.01), whereas men and women had the same odds of being associate professors (OR, 1.04; 95

Ranking Specific Sets of Objects.

PubMed

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.

Ranking Support Vector Machine with Kernel Approximation

PubMed Central

Dou, Yong

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. PMID:28293256

Ranking Support Vector Machine with Kernel Approximation.

PubMed

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.

University Rankings and Social Science

ERIC Educational Resources Information Center

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…

Two-dimensional ranking of Wikipedia articles

NASA Astrophysics Data System (ADS)

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.

Should measures of patient experience in primary care be adjusted for case mix? Evidence from the English General Practice Patient Survey.

PubMed

Paddison, Charlotte; Elliott, Marc; Parker, Richard; Staetsky, Laura; Lyratzopoulos, Georgios; Campbell, John L; Roland, Martin

2012-08-01

Uncertainties exist about when and how best to adjust performance measures for case mix. Our aims are to quantify the impact of case-mix adjustment on practice-level scores in a national survey of patient experience, to identify why and when it may be useful to adjust for case mix, and to discuss unresolved policy issues regarding the use of case-mix adjustment in performance measurement in health care. Secondary analysis of the 2009 English General Practice Patient Survey. Responses from 2 163 456 patients registered with 8267 primary care practices. Linear mixed effects models were used with practice included as a random effect and five case-mix variables (gender, age, race/ethnicity, deprivation, and self-reported health) as fixed effects. Primary outcome was the impact of case-mix adjustment on practice-level means (adjusted minus unadjusted) and changes in practice percentile ranks for questions measuring patient experience in three domains of primary care: access; interpersonal care; anticipatory care planning, and overall satisfaction with primary care services. Depending on the survey measure selected, case-mix adjustment changed the rank of between 0.4% and 29.8% of practices by more than 10 percentile points. Adjusting for case-mix resulted in large increases in score for a small number of practices and small decreases in score for a larger number of practices. Practices with younger patients, more ethnic minority patients and patients living in more socio-economically deprived areas were more likely to gain from case-mix adjustment. Age and race/ethnicity were the most influential adjustors. While its effect is modest for most practices, case-mix adjustment corrects significant underestimation of scores for a small proportion of practices serving vulnerable patients and may reduce the risk that providers would 'cream-skim' by not enrolling patients from vulnerable socio-demographic groups.

Ranking Community Health Status to Stimulate Discussion of Local Public Health Issues: The Wisconsin County Health Rankings

PubMed Central

Peppard, Paul E.; Kindig, David A.; Dranger, Elizabeth; Jovaag, Amanda; Remington, Patrick L.

2008-01-01

United Health Foundation’s America’s Health Rankings, which ranks the states from “least healthy” to “healthiest,” receives wide press coverage and promotes discussion of public health issues. The University of Wisconsin Population Health Institute used the United Health Foundation’s model to develop the Wisconsin County Health Rankings (“Health Rankings”) from existing county-level data. The institute first released the rankings in 2004. A survey of the Wisconsin county health officers indicated that they intend to use the rankings for needs assessment, program planning, and discussion with county health boards. The institute implemented many of the health officers’ suggestions for improvement of the rankings in subsequent editions. The methods employed to create the rankings should be applicable in other states. PMID:18172156

Faculty Hiring at Top-Ranked Higher Education Administration Programs: An Examination Using Social Network Analysis

ERIC Educational Resources Information Center

DiRamio, David; Theroux, Ryan; Guarino, Anthony J.

2009-01-01

Using network analysis we investigated faculty hiring at 21 U. S. News top-ranked programs in higher education administration. Our research questions were as follows. Do top programs hire from each other? Are faculty from the "outside" finding positions at top programs? Mixed results hint at implications for the "health" of the hiring network.…

Web Image Search Re-ranking with Click-based Similarity and Typicality.

PubMed

Yang, Xiaopeng; Mei, Tao; Zhang, Yong Dong; Liu, Jie; Satoh, Shin'ichi

2016-07-20

In image search re-ranking, besides the well known semantic gap, intent gap, which is the gap between the representation of users' query/demand and the real intent of the users, is becoming a major problem restricting the development of image retrieval. To reduce human effects, in this paper, we use image click-through data, which can be viewed as the "implicit feedback" from users, to help overcome the intention gap, and further improve the image search performance. Generally, the hypothesis visually similar images should be close in a ranking list and the strategy images with higher relevance should be ranked higher than others are widely accepted. To obtain satisfying search results, thus, image similarity and the level of relevance typicality are determinate factors correspondingly. However, when measuring image similarity and typicality, conventional re-ranking approaches only consider visual information and initial ranks of images, while overlooking the influence of click-through data. This paper presents a novel re-ranking approach, named spectral clustering re-ranking with click-based similarity and typicality (SCCST). First, to learn an appropriate similarity measurement, we propose click-based multi-feature similarity learning algorithm (CMSL), which conducts metric learning based on clickbased triplets selection, and integrates multiple features into a unified similarity space via multiple kernel learning. Then based on the learnt click-based image similarity measure, we conduct spectral clustering to group visually and semantically similar images into same clusters, and get the final re-rank list by calculating click-based clusters typicality and withinclusters click-based image typicality in descending order. Our experiments conducted on two real-world query-image datasets with diverse representative queries show that our proposed reranking approach can significantly improve initial search results, and outperform several existing re-ranking approaches.

Recurrent fuzzy ranking methods

NASA Astrophysics Data System (ADS)

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.

Reducing Behavior Problems Among Students with Autism Spectrum Disorder: Coaching Teachers in a Mixed-Reality Setting.

PubMed

Pas, Elise T; Johnson, Stacy R; Larson, Kristine E; Brandenburg, Linda; Church, Robin; Bradshaw, Catherine P

2016-12-01

Most approaches aiming to reduce behavior problems among youth with Autism Spectrum Disorder (ASD) focus on individual students; however, school personnel also need professional development to better support students. This study targeted teachers' skill development to promote positive outcomes for students with ASD. The sample included 19 teachers in two non-public special education settings serving students with moderate to severe ASD. Participating teachers received professional development and coaching in classroom management, with guided practice in a mixed-reality simulator. Repeated-measures ANOVAs examining externally-conducted classroom observations revealed statistically significant improvements in teacher management and student behavior over time. Findings suggest that coaching and guided practice in a mixed-reality simulator is perceived as acceptable and may reduce behavior problems among students with ASD.

Mediating the Use of Global University Rankings: Perspectives from Education Facilitators in an International Context

ERIC Educational Resources Information Center

O'Connell, Catherine; Saunders, Murray

2013-01-01

This study explores responses to rankings from a group of staff working as education partnership facilitators for a professional intermediary organisation, the British Council. The study adopts an activity systems perspective from which to view the contexts in which rankings are encountered and the range of practices used to reduce tensions…

AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

PubMed

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

Characterizing thermogenic coalbed gas from Polish coals of different ranks by hydrous pyrolysis

USGS Publications Warehouse

Kotarba, M.J.; Lewan, M.D.

2004-01-01

To provide a better characterization of origin and volume of thermogenic gas generation from coals, hydrous pyrolysis experiments were conducted at 360??C for 72 h on Polish coals ranging in rank from lignite (0.3% R r) to semi-anthracite (2.0% Rr). Under these conditions, the lignites attained a medium-volatile bituminous rank (1.5% Rr), high-volatile bituminous coals attained a low-volatile bituminous rank (1.7% Rr), and the semi-anthracite obtained an anthracite rank (4.0% R r). Hydrous pyrolysis of a coal, irrespective of rank, provides a diagnostic ??13C value for its thermogenic hydrocarbon gases. This value can be used quantitatively to interpret mixing of indigenous thermogenic gas with microbial methane or exogenous thermogenic gas from other sources. Thermogenic methane quantities range from 20 dm3/kg of lignite (0.3% Rr) to 0.35 dm3/kg of semi-anthracite (2.0% Rr). At a vitrinite reflectance of 1.7% Rr, approximately 75% of the maximum potential for a coal to generate thermogenic methane has been expended. At a vitrinite reflectance of 1.7% Rr, more than 90% of the maximum potential for a coal to generate CO2 has been expended. Assuming that these quantities of generated CO2 remain associated with a sourcing coal bed as uplift or erosion provide conditions conducive for microbial methanogenesis, the resulting quantities of microbial methane generated by complete CO2 reduction can exceed the quantities of thermogenic methane generated from the same coal bed by a factor of 2-5. ?? 2004 Elsevier Ltd. All rights reserved.

RANK rewires energy homeostasis in lung cancer cells and drives primary lung cancer

PubMed Central

Rao, Shuan; Sigl, Verena; Wimmer, Reiner Alois; Novatchkova, Maria; Jais, Alexander; Wagner, Gabriel; Handschuh, Stephan; Uribesalgo, Iris; Hagelkruys, Astrid; Kozieradzki, Ivona; Tortola, Luigi; Nitsch, Roberto; Cronin, Shane J.; Orthofer, Michael; Branstetter, Daniel; Canon, Jude; Rossi, John; D'Arcangelo, Manolo; Botling, Johan; Micke, Patrick; Fleur, Linnea La; Edlund, Karolina; Bergqvist, Michael; Ekman, Simon; Lendl, Thomas; Popper, Helmut; Takayanagi, Hiroshi; Kenner, Lukas; Hirsch, Fred R.; Dougall, William

2017-01-01

Lung cancer is the leading cause of cancer deaths. Besides smoking, epidemiological studies have linked female sex hormones to lung cancer in women; however, the underlying mechanisms remain unclear. Here we report that the receptor activator of nuclear factor-kB (RANK), the key regulator of osteoclastogenesis, is frequently expressed in primary lung tumors, an active RANK pathway correlates with decreased survival, and pharmacologic RANK inhibition reduces tumor growth in patient-derived lung cancer xenografts. Clonal genetic inactivation of KRasG12D in mouse lung epithelial cells markedly impairs the progression of KRasG12D-driven lung cancer, resulting in a significant survival advantage. Mechanistically, RANK rewires energy homeostasis in human and murine lung cancer cells and promotes expansion of lung cancer stem-like cells, which is blocked by inhibiting mitochondrial respiration. Our data also indicate survival differences in KRasG12D-driven lung cancer between male and female mice, and we show that female sex hormones can promote lung cancer progression via the RANK pathway. These data uncover a direct role for RANK in lung cancer and may explain why female sex hormones accelerate lung cancer development. Inhibition of RANK using the approved drug denosumab may be a therapeutic drug candidate for primary lung cancer. PMID:29118048

Effect of Hydrogenase and Mixed Sulfate-Reducing Bacterial Populations on the Corrosion of Steel

PubMed Central

Bryant, Richard D.; Jansen, Wayne; Boivin, Joe; Laishley, Edward J.; Costerton, J. William

1991-01-01

The importance of hydrogenase activity to corrosion of steel was assessed by using mixed populations of sulfate-reducing bacteria isolated from corroded and noncorroded oil pipelines. Biofilms which developed on the steel studs contained detectable numbers of sulfate-reducing bacteria (104 increasing to 107/0.5 cm2). However, the biofilm with active hydrogenase activity (i.e., corrosion pipeline organisms), as measured by a semiquantitative commercial kit, was associated with a significantly higher corrosion rate (7.79 mm/year) relative to noncorrosive biofilm (0.48 mm/year) with 105 sulfate-reducing bacteria per 0.5 cm2 but no measurable hydrogenase activity. The importance of hydrogenase and the microbial sulfate-reducing bacterial population making up the biofilm are discussed relative to biocorrosion. Images PMID:16348560

Nongeneric positive partial transpose states of rank five in 3×3 dimensions

NASA Astrophysics Data System (ADS)

Hansen, Leif Ove; Myrheim, Jan

In 3×3 dimensions, entangled mixed states that are positive under partial transposition (PPT states) must have rank at least four. These rank four states are completely understood. We say that they have rank (4,4) since both a state ρ and its partial transpose ρP have rank four. The next problem is to understand the extremal PPT states of rank (5,5). We call two states SL⊗SL-equivalent if they are related by a product transformation. A generic rank (5,5) PPT state ρ is extremal, and both ρ and ρP have six product vectors in their ranges, and no product vectors in their kernels. The three numbers {6,6;0} are SL⊗SL-invariants that help us classify the state. There is no analytical understanding of such states. We have studied numerically a few types of nongeneric rank five PPT states, in particular, states with one or more product vectors in their kernels. We find an interesting new analytical construction of all rank four extremal PPT states, up to SL⊗SL-equivalence, where they appear as boundary states on one single five-dimensional face on the set of normalized PPT states. The interior of the face consists of rank (5,5) states with four common product vectors in their kernels, it is a simplex of separable states surrounded by entangled PPT states. We say that a state ρ is SL⊗SL-symmetric if ρ and ρP are SL⊗SL-equivalent, and is genuinely SL⊗SL-symmetric if it is SL⊗SL-equivalent to a state τ with τ=τP. Genuine SL⊗SL-symmetry implies a special form of SL⊗SL-symmetry. We have produced numerically, by a special method, a random sample of rank (5,5) SL⊗SL-symmetric states. About 50 of these are of type {6,6;0}, among those all are extremal and about half are genuinely SL⊗SL-symmetric. All these genuinely SL⊗SL-symmetric states can be transformed to have a circulant form. We find however that this is not a generic property of genuinely SL⊗SL-symmetric states. The remaining SL⊗SL-symmetric states found in the search have product

Gene Ranking of RNA-Seq Data via Discriminant Non-Negative Matrix Factorization.

PubMed

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.

A Ranking Approach to Genomic Selection.

PubMed

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.

Fracturing ranked surfaces

NASA Astrophysics Data System (ADS)

Schrenk, K. J.; Araújo, N. A. M.; Andrade, J. S., Jr.; Herrmann, H. J.

2012-04-01

Discretized landscapes can be mapped onto ranked surfaces, where every element (site or bond) has a unique rank associated with its corresponding relative height. By sequentially allocating these elements according to their ranks and systematically preventing the occupation of bridges, namely elements that, if occupied, would provide global connectivity, we disclose that bridges hide a new tricritical point at an occupation fraction p = pc, where pc is the percolation threshold of random percolation. For any value of p in the interval pc < p <= 1, our results show that the set of bridges has a fractal dimension dBB ~ 1.22 in two dimensions. In the limit p --> 1, a self-similar fracture is revealed as a singly connected line that divides the system in two domains. We then unveil how several seemingly unrelated physical models tumble into the same universality class and also present results for higher dimensions.

Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation.

PubMed

Niu, Wenjia; Xia, Kewen; Zu, Baokai; Bai, Jianchuan

2017-01-01

Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR). It is well-acknowledged that LRR can reduce dimension while retaining the data features under a global low-rank constraint. Furthermore, we redesign the binary-class MKL as the multiclass MKL based on pairwise strategy. Finally, the recognition effect and efficiency of LR-MKL are verified on the datasets Yale, ORL, LSVT, and Digit. Experimental results show that the proposed LR-MKL algorithm is an efficient kernel weights allocation method in MKL and boosts the performance of MKL largely.

Vapor Phase Hydrogenolysis of Furanics Utilizing Reduced Cobalt Mixed Metal Oxide Catalysts

DOE PAGES

Sulmonetti, Taylor P.; Hu, Bo; Ifkovits, Zachary; ...

2017-03-21

Vapor phase hydrogenolysis of both furfuryl alcohol and furfural were investigated over reduced Co based mixed metal oxides derived from the calcination of a layered double hydroxide precursor. Although a reduced cobalt aluminate sample displays promising selectivity towards 2-methylfuran (2-MF) production, the addition of an Fe dopant into the oxide matrix significantly enhances the activity and selectivity per gram of catalyst. Approximately 82% 2-MF yield is achieved at high conversion when furfuryl alcohol is fed into the reactor at 180 °C over the reduced 3Co-0.25Fe-0.75Al catalyst. Based on structural characterization studies including TPR, XPS, and in-situ XAS it is suggestedmore » that Fe facilitates the reduction of Co, allowing for formation of more metallic species. Altogether, this study demonstrates that non-precious metal catalysts offer promise for the selective conversion of a key biomass oxygenate to a proposed fuel additive.« less

Hitting the Rankings Jackpot

ERIC Educational Resources Information Center

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…

Ranking and clustering of nodes in networks with smart teleportation

NASA Astrophysics Data System (ADS)

Lambiotte, R.; Rosvall, M.

2012-05-01

Random teleportation is a necessary evil for ranking and clustering directed networks based on random walks. Teleportation enables ergodic solutions, but the solutions must necessarily depend on the exact implementation and parametrization of the teleportation. For example, in the commonly used PageRank algorithm, the teleportation rate must trade off a heavily biased solution with a uniform solution. Here we show that teleportation to links rather than nodes enables a much smoother trade-off and effectively more robust results. We also show that, by not recording the teleportation steps of the random walker, we can further reduce the effect of teleportation with dramatic effects on clustering.

Integrating diverse forage sources reduces feed gaps on mixed crop-livestock farms.

PubMed

Bell, L W; Moore, A D; Thomas, D T

2017-12-04

Highly variable climates induce large variability in the supply of forage for livestock and so farmers must manage their livestock systems to reduce the risk of feed gaps (i.e. periods when livestock feed demand exceeds forage supply). However, mixed crop-livestock farmers can utilise a range of feed sources on their farms to help mitigate these risks. This paper reports on the development and application of a simple whole-farm feed-energy balance calculator which is used to evaluate the frequency and magnitude of feed gaps. The calculator matches long-term simulations of variation in forage and metabolisable energy supply from diverse sources against energy demand for different livestock enterprises. Scenarios of increasing the diversity of forage sources in livestock systems is investigated for six locations selected to span Australia's crop-livestock zone. We found that systems relying on only one feed source were prone to higher risk of feed gaps, and hence, would often have to reduce stocking rates to mitigate these risks or use supplementary feed. At all sites, by adding more feed sources to the farm feedbase the continuity of supply of both fresh and carry-over forage was improved, reducing the frequency and magnitude of feed deficits. However, there were diminishing returns from making the feedbase more complex, with combinations of two to three feed sources typically achieving the maximum benefits in terms of reducing the risk of feed gaps. Higher stocking rates could be maintained while limiting risk when combinations of other feed sources were introduced into the feedbase. For the same level of risk, a feedbase relying on a diversity of forage sources could support stocking rates 1.4 to 3 times higher than if they were using a single pasture source. This suggests that there is significant capacity to mitigate both risk of feed gaps at the same time as increasing 'safe' stocking rates through better integration of feed sources on mixed crop

Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

PubMed

Yu, Jun; Yang, Xiaokang; Gao, Fei; Tao, Dacheng

2017-12-01

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.

The Globalization of College and University Rankings

ERIC Educational Resources Information Center

Altbach, Philip G.

2012-01-01

In the era of globalization, accountability, and benchmarking, university rankings have achieved a kind of iconic status. The major ones--the Academic Ranking of World Universities (ARWU, or the "Shanghai rankings"), the QS (Quacquarelli Symonds Limited) World University Rankings, and the "Times Higher Education" World…

RANK rewires energy homeostasis in lung cancer cells and drives primary lung cancer.

PubMed

Rao, Shuan; Sigl, Verena; Wimmer, Reiner Alois; Novatchkova, Maria; Jais, Alexander; Wagner, Gabriel; Handschuh, Stephan; Uribesalgo, Iris; Hagelkruys, Astrid; Kozieradzki, Ivona; Tortola, Luigi; Nitsch, Roberto; Cronin, Shane J; Orthofer, Michael; Branstetter, Daniel; Canon, Jude; Rossi, John; D'Arcangelo, Manolo; Botling, Johan; Micke, Patrick; Fleur, Linnea La; Edlund, Karolina; Bergqvist, Michael; Ekman, Simon; Lendl, Thomas; Popper, Helmut; Takayanagi, Hiroshi; Kenner, Lukas; Hirsch, Fred R; Dougall, William; Penninger, Josef M

2017-10-15

Lung cancer is the leading cause of cancer deaths. Besides smoking, epidemiological studies have linked female sex hormones to lung cancer in women; however, the underlying mechanisms remain unclear. Here we report that the receptor activator of nuclear factor-kB (RANK), the key regulator of osteoclastogenesis, is frequently expressed in primary lung tumors, an active RANK pathway correlates with decreased survival, and pharmacologic RANK inhibition reduces tumor growth in patient-derived lung cancer xenografts. Clonal genetic inactivation of KRas G12D in mouse lung epithelial cells markedly impairs the progression of KRas G12D -driven lung cancer, resulting in a significant survival advantage. Mechanistically, RANK rewires energy homeostasis in human and murine lung cancer cells and promotes expansion of lung cancer stem-like cells, which is blocked by inhibiting mitochondrial respiration. Our data also indicate survival differences in KRas G12D -driven lung cancer between male and female mice, and we show that female sex hormones can promote lung cancer progression via the RANK pathway. These data uncover a direct role for RANK in lung cancer and may explain why female sex hormones accelerate lung cancer development. Inhibition of RANK using the approved drug denosumab may be a therapeutic drug candidate for primary lung cancer. © 2017 Rao et al.; Published by Cold Spring Harbor Laboratory Press.

Method and apparatus for second-rank tensor generation

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor)

1991-01-01

A method and apparatus are disclosed for generation of second-rank tensors using a photorefractive crystal to perform the outer-product between two vectors via four-wave mixing, thereby taking 2n input data to a control n squared output data points. Two orthogonal amplitude modulated coherent vector beams x and y are expanded and then parallel sides of the photorefractive crystal in exact opposition. A beamsplitter is used to direct a coherent pumping beam onto the crystal at an appropriate angle so as to produce a conjugate beam that is the matrix product of the vector beam that propagates in the exact opposite direction from the pumping beam. The conjugate beam thus separated is the tensor output xy (sup T).

Social ranking effects on tooth-brushing behaviour.

PubMed

Maltby, John; Paterson, Kevin; Day, Liz; Jones, Ceri; Kinnear, Hayley; Buchanan, Heather

2016-05-01

A tooth-brushing social rank hypothesis is tested suggesting tooth-brushing duration is influenced when individuals position their behaviour in a rank when comparing their behaviour with other individuals. Study 1 used a correlation design, Study 2 used a semi-experimental design, and Study 3 used a randomized intervention design to examine the tooth-brushing social rank hypothesis in terms of self-reported attitudes, cognitions, and behaviour towards tooth-brushing duration. Study 1 surveyed participants to examine whether the perceived health benefits of tooth-brushing duration could be predicted from the ranking of each person's tooth-brushing duration. Study 2 tested whether manipulating the rank position of the tooth-brushing duration influenced participant-perceived health benefits of tooth-brushing duration. Study 3 used a longitudinal intervention method to examine whether messages relating to the rank positions of tooth-brushing durations causally influenced the self-report tooth-brushing duration. Study 1 demonstrates that perceptions of the health benefits from tooth-brushing duration are predicted by the perceptions of how that behaviour ranks in comparison to other people's behaviour. Study 2 demonstrates that the perceptions of the health benefits of tooth-brushing duration can be manipulated experimentally by changing the ranked position of a person's tooth-brushing duration. Study 3 experimentally demonstrates the possibility of increasing the length of time for which individuals clean their teeth by focusing on how they rank among their peers in terms of tooth-brushing duration. The effectiveness of interventions using social-ranking methods relative to those that emphasize comparisons made against group averages or normative guidelines are discussed. What is already known on this subject? Individual make judgements based on social rank information. Social rank information has been shown to influence positive health behaviours such as exercise

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

PubMed Central

Tao, Chenyang; Nichols, Thomas E.; Hua, Xue; Ching, Christopher R.K.; Rolls, Edmund T.; Thompson, Paul M.; Feng, Jianfeng

2017-01-01

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches. PMID:27666385

Statistical Optimality in Multipartite Ranking and Ordinal Regression.

PubMed

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.

Interval-Valued Rank in Finite Ordered Sets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joslyn, Cliff; Pogel, Alex; Purvine, Emilie

We consider the concept of rank as a measure of the vertical levels and positions of elements of partially ordered sets (posets). We are motivated by the need for algorithmic measures on large, real-world hierarchically-structured data objects like the semantic hierarchies of ontolog- ical databases. These rarely satisfy the strong property of gradedness, which is required for traditional rank functions to exist. Representing such semantic hierarchies as finite, bounded posets, we recognize the duality of ordered structures to motivate rank functions which respect verticality both from the bottom and from the top. Our rank functions are thus interval-valued, and alwaysmore » exist, even for non-graded posets, providing order homomorphisms to an interval order on the interval-valued ranks. The concept of rank width arises naturally, allowing us to identify the poset region with point-valued width as its longest graded portion (which we call the “spindle”). A standard interval rank function is naturally motivated both in terms of its extremality and on pragmatic grounds. Its properties are examined, including the relation- ship to traditional grading and rank functions, and methods to assess comparisons of standard interval-valued ranks.« less

Class Rank Weighs Down True Learning

ERIC Educational Resources Information Center

Guskey, Thomas R.

2014-01-01

The process of determining class rank does not help students achieve more or reach higher levels of proficiency. Evidence indicates ranking students may diminish students' motivation. High school educators argue that they are compelled to rank-order graduating students because selective colleges and universities require information about…

Ranking in evolving complex networks

NASA Astrophysics Data System (ADS)

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.

Large-scale 3-D EM modelling with a Block Low-Rank multifrontal direct solver

NASA Astrophysics Data System (ADS)

Shantsev, Daniil V.; Jaysaval, Piyoosh; de la Kethulle de Ryhove, Sébastien; Amestoy, Patrick R.; Buttari, Alfredo; L'Excellent, Jean-Yves; Mary, Theo

2017-06-01

We put forward the idea of using a Block Low-Rank (BLR) multifrontal direct solver to efficiently solve the linear systems of equations arising from a finite-difference discretization of the frequency-domain Maxwell equations for 3-D electromagnetic (EM) problems. The solver uses a low-rank representation for the off-diagonal blocks of the intermediate dense matrices arising in the multifrontal method to reduce the computational load. A numerical threshold, the so-called BLR threshold, controlling the accuracy of low-rank representations was optimized by balancing errors in the computed EM fields against savings in floating point operations (flops). Simulations were carried out over large-scale 3-D resistivity models representing typical scenarios for marine controlled-source EM surveys, and in particular the SEG SEAM model which contains an irregular salt body. The flop count, size of factor matrices and elapsed run time for matrix factorization are reduced dramatically by using BLR representations and can go down to, respectively, 10, 30 and 40 per cent of their full-rank values for our largest system with N = 20.6 million unknowns. The reductions are almost independent of the number of MPI tasks and threads at least up to 90 × 10 = 900 cores. The BLR savings increase for larger systems, which reduces the factorization flop complexity from O(N2) for the full-rank solver to O(Nm) with m = 1.4-1.6. The BLR savings are significantly larger for deep-water environments that exclude the highly resistive air layer from the computational domain. A study in a scenario where simulations are required at multiple source locations shows that the BLR solver can become competitive in comparison to iterative solvers as an engine for 3-D controlled-source electromagnetic Gauss-Newton inversion that requires forward modelling for a few thousand right-hand sides.

14 CFR 1214.1105 - Final ranking.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 5 2011-01-01 2010-01-01 true Final ranking. 1214.1105 Section 1214.1105 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION SPACE FLIGHT NASA Astronaut Candidate Recruitment and Selection Program § 1214.1105 Final ranking. Final rankings will be based on a combination of...

Should measures of patient experience in primary care be adjusted for case mix? Evidence from the English General Practice Patient Survey

PubMed Central

Paddison, Charlotte; Elliott, Marc; Parker, Richard; Staetsky, Laura; Lyratzopoulos, Georgios; Campbell, John L

2012-01-01

Objectives Uncertainties exist about when and how best to adjust performance measures for case mix. Our aims are to quantify the impact of case-mix adjustment on practice-level scores in a national survey of patient experience, to identify why and when it may be useful to adjust for case mix, and to discuss unresolved policy issues regarding the use of case-mix adjustment in performance measurement in health care. Design/setting Secondary analysis of the 2009 English General Practice Patient Survey. Responses from 2 163 456 patients registered with 8267 primary care practices. Linear mixed effects models were used with practice included as a random effect and five case-mix variables (gender, age, race/ethnicity, deprivation, and self-reported health) as fixed effects. Main outcome measures Primary outcome was the impact of case-mix adjustment on practice-level means (adjusted minus unadjusted) and changes in practice percentile ranks for questions measuring patient experience in three domains of primary care: access; interpersonal care; anticipatory care planning, and overall satisfaction with primary care services. Results Depending on the survey measure selected, case-mix adjustment changed the rank of between 0.4% and 29.8% of practices by more than 10 percentile points. Adjusting for case-mix resulted in large increases in score for a small number of practices and small decreases in score for a larger number of practices. Practices with younger patients, more ethnic minority patients and patients living in more socio-economically deprived areas were more likely to gain from case-mix adjustment. Age and race/ethnicity were the most influential adjustors. Conclusions While its effect is modest for most practices, case-mix adjustment corrects significant underestimation of scores for a small proportion of practices serving vulnerable patients and may reduce the risk that providers would ‘cream-skim’ by not enrolling patients from vulnerable socio

What Contributes More to the Ranking of Higher Education Institutions? A Comparison of Three World University Rankings

ERIC Educational Resources Information Center

Hou, Ya-Wen; Jacob, W. James

2017-01-01

Recently, many universities have drawn attention to world university rankings, which reflect the international competition of universities and represent their relative statuses. This study does not radically contradict types of global university rankings but calls for an examination of the effects of their indicators on the final ranking of…

Scaling laws and reduced-order models for mixing and reactive-transport in heterogeneous anisotropic porous media

NASA Astrophysics Data System (ADS)

Mudunuru, M. K.; Karra, S.; Nakshatrala, K. B.

2016-12-01

Fundamental to enhancement and control of the macroscopic spreading, mixing, and dilution of solute plumes in porous media structures is the topology of flow field and underlying heterogeneity and anisotropy contrast of porous media. Traditionally, in literature, the main focus was limited to the shearing effects of flow field (i.e., flow has zero helical density, meaning that flow is always perpendicular to vorticity vector) on scalar mixing [2]. However, the combined effect of anisotropy of the porous media and the helical structure (or chaotic nature) of the flow field on the species reactive-transport and mixing has been rarely studied. Recently, it has been experimentally shown that there is an irrefutable evidence that chaotic advection and helical flows are inherent in porous media flows [1,2]. In this poster presentation, we present a non-intrusive physics-based model-order reduction framework to quantify the effects of species mixing in-terms of reduced-order models (ROMs) and scaling laws. The ROM framework is constructed based on the recent advancements in non-negative formulations for reactive-transport in heterogeneous anisotropic porous media [3] and non-intrusive ROM methods [4]. The objective is to generate computationally efficient and accurate ROMs for species mixing for different values of input data and reactive-transport model parameters. This is achieved by using multiple ROMs, which is a way to determine the robustness of the proposed framework. Sensitivity analysis is performed to identify the important parameters. Representative numerical examples from reactive-transport are presented to illustrate the importance of the proposed ROMs to accurately describe mixing process in porous media. [1] Lester, Metcalfe, and Trefry, "Is chaotic advection inherent to porous media flow?," PRL, 2013. [2] Ye, Chiogna, Cirpka, Grathwohl, and Rolle, "Experimental evidence of helical flow in porous media," PRL, 2015. [3] Mudunuru, and Nakshatrala, "On

Dynamics of Ranking Processes in Complex Systems

NASA Astrophysics Data System (ADS)

Blumm, Nicholas; Ghoshal, Gourab; Forró, Zalán; Schich, Maximilian; Bianconi, Ginestra; Bouchaud, Jean-Philippe; Barabási, Albert-László

2012-09-01

The world is addicted to ranking: everything, from the reputation of scientists, journals, and universities to purchasing decisions is driven by measured or perceived differences between them. Here, we analyze empirical data capturing real time ranking in a number of systems, helping to identify the universal characteristics of ranking dynamics. We develop a continuum theory that not only predicts the stability of the ranking process, but shows that a noise-induced phase transition is at the heart of the observed differences in ranking regimes. The key parameters of the continuum theory can be explicitly measured from data, allowing us to predict and experimentally document the existence of three phases that govern ranking stability.

On Rank and Nullity

ERIC Educational Resources Information Center

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.

A Universal Rank-Size Law

PubMed Central

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

On the ranking of chemicals based on their PBT characteristics: comparison of different ranking methodologies using selected POPs as an illustrative example.

PubMed

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.

Stabilized thermally beneficiated low rank coal and method of manufacture

DOEpatents

Viall, Arthur J.; Richards, Jeff M.

1999-01-01

A process for reducing the spontaneous combustion tendencies of thermally beneficiated low rank coals employing heat, air or an oxygen containing gas followed by an optional moisture addition. Specific reaction conditions are supplied along with knowledge of equipment types that may be employed on a commercial scale to complete the process.

Stabilized thermally beneficiated low rank coal and method of manufacture

DOEpatents

Viall, Arthur J.; Richards, Jeff M.

2000-01-01

A process for reducing the spontaneous combustion tendencies of thermally beneficiated low rank coals employing heat, air or an oxygen containing gas followed by an optional moisture addition. Specific reaction conditions are supplied along with knowledge of equipment types that may be employed on a commercial scale to complete the process.

Modeling Area-Level Health Rankings.

PubMed

Courtemanche, Charles; Soneji, Samir; Tchernis, Rusty

2015-10-01

Rank county health using a Bayesian factor analysis model. Secondary county data from the National Center for Health Statistics (through 2007) and Behavioral Risk Factor Surveillance System (through 2009). Our model builds on the existing county health rankings (CHRs) by using data-derived weights to compute ranks from mortality and morbidity variables, and by quantifying uncertainty based on population, spatial correlation, and missing data. We apply our model to Wisconsin, which has comprehensive data, and Texas, which has substantial missing information. The data were downloaded from www.countyhealthrankings.org. Our estimated rankings are more similar to the CHRs for Wisconsin than Texas, as the data-derived factor weights are closer to the assigned weights for Wisconsin. The correlations between the CHRs and our ranks are 0.89 for Wisconsin and 0.65 for Texas. Uncertainty is especially severe for Texas given the state's substantial missing data. The reliability of comprehensive CHRs varies from state to state. We advise focusing on the counties that remain among the least healthy after incorporating alternate weighting methods and accounting for uncertainty. Our results also highlight the need for broader geographic coverage in health data. © Health Research and Educational Trust.

Life history in male mandrills (Mandrillus sphinx): physical development, dominance rank, and group association.

PubMed

Setchell, Joanna M; Wickings, E Jean; Knapp, Leslie A

2006-12-01

We assess life history from birth to death in male mandrills (Mandrillus sphinx) living in a semifree-ranging colony in Gabon, using data collected for 82 males that attained at least the age of puberty, including 33 that reached adulthood and 25 that died, yielding data for their entire lifespan. We describe patterns of mortality and injuries, dominance rank, group association, growth and stature, and secondary sexual character expression across the male lifespan. We examine relationships among these variables and investigate potential influences on male life history, including differences in the social environment (maternal rank and group demography) and early development, with the aim of identifying characteristics of successful males. Sons of higher-ranking females were more likely to survive to adulthood than sons of low-ranking females. Adolescent males varied consistently in the rate at which they developed, and this variation was related to a male's own dominance rank. Males with fewer peers and sons of higher-ranking and heavier mothers also matured faster. However, maternal variables were not significantly related to dominance rank during adolescence, the age at which males attained adult dominance rank, or whether a male became alpha male. Among adult males, behavior and morphological development were related to a male's own dominance rank, and sons of high-ranking females were larger than sons of low-ranking females. Alpha males were always the most social, and the most brightly colored males, but were not necessarily the largest males present. Finally, alpha male tenure was related to group demography, with larger numbers of rival adult males and maturing adolescent males reducing the time a male spent as alpha male. Tenure did not appear to be related to characteristics of the alpha male himself. 2006 Wiley-Liss, Inc.

Rank-based decompositions of morphological templates.

PubMed

Sussner, P; Ritter, G X

2000-01-01

Methods for matrix decomposition have found numerous applications in image processing, in particular for the problem of template decomposition. Since existing matrix decomposition techniques are mainly concerned with the linear domain, we consider it timely to investigate matrix decomposition techniques in the nonlinear domain with applications in image processing. The mathematical basis for these investigations is the new theory of rank within minimax algebra. Thus far, only minimax decompositions of rank 1 and rank 2 matrices into outer product expansions are known to the image processing community. We derive a heuristic algorithm for the decomposition of matrices having arbitrary rank.

Atp1a3-deficient heterozygous mice show lower rank in the hierarchy and altered social behavior.

PubMed

Sugimoto, H; Ikeda, K; Kawakami, K

2018-06-01

Atp1a3 is the Na-pump alpha3 subunit gene expressed mainly in neurons of the brain. Atp1a3-deficient heterozygous mice (Atp1a3 +/- ) show altered neurotransmission and deficits of motor function after stress loading. To understand the function of Atp1a3 in a social hierarchy, we evaluated social behaviors (social interaction, aggression, social approach and social dominance) of Atp1a3 +/- and compared the rank and hierarchy structure between Atp1a3 +/- and wild-type mice within a housing cage using the round-robin tube test and barbering observations. Formation of a hierarchy decreases social conflict and promote social stability within the group. The hierarchical rank is a reflection of social dominance within a cage, which is heritable and can be regulated by specific genes in mice. Here we report: (1) The degree of social interaction but not aggression was lower in Atp1a3 +/- than wild-type mice, and Atp1a3 +/- approached Atp1a3 +/- mice more frequently than wild type. (2) The frequency of barbering was lower in the Atp1a3 +/- group than in the wild-type group, while no difference was observed in the mixed-genotype housing condition. (3) Hierarchy formation was not different between Atp1a3 +/- and wild type. (4) Atp1a3 +/- showed a lower rank in the mixed-genotype housing condition than that in the wild type, indicating that Atp1a3 regulates social dominance. In sum, Atp1a3 +/- showed unique social behavior characteristics of lower social interaction and preference to approach the same genotype mice and a lower ranking in the hierarchy. © 2017 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Error analysis of stochastic gradient descent ranking.

PubMed

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.

Early competition shapes maize whole-plant development in mixed stands

PubMed Central

Evers, Jochem B.

2014-01-01

Mixed cropping is practised widely in developing countries and is gaining increasing interest for sustainable agriculture in developed countries. Plants in intercrops grow differently from plants in single crops, due to interspecific plant interactions, but adaptive plant morphological responses to competition in mixed stands have not been studied in detail. Here the maize (Zea mays) response to mixed cultivation with wheat (Triticum aestivum) is described. Evidence is provided that early responses of maize to the modified light environment in mixed stands propagate throughout maize development, resulting in different phenotypes compared with pure stands. Photosynthetically active radiation (PAR), red:far-red ratio (R:FR), leaf development, and final organ sizes of maize grown in three cultivation systems were compared: pure maize, an intercrop with a small distance (25cm) between maize and wheat plants, and an intercop with a large distance (44cm) between the maize and the wheat. Compared with maize in pure stands, maize in the mixed stands had lower leaf and collar appearance rates, increased blade and sheath lengths at low ranks and smaller sizes at high ranks, increased blade elongation duration, and decreased R:FR and PAR at the plant base during early development. Effects were strongest in the treatment with a short distance between wheat and maize strips. The data suggest a feedback between leaf initiation and leaf emergence at the plant level and coordination between blade and sheath growth at the phytomer level. A conceptual model, based on coordination rules, is proposed to explain the development of the maize plant in pure and mixed stands. PMID:24307719

Rank distributions: A panoramic macroscopic outlook

NASA Astrophysics Data System (ADS)

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.

Rank distributions: a panoramic macroscopic outlook.

PubMed

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.

Stabilized thermally beneficiated low rank coal and method of manufacture

DOEpatents

Viall, A.J.; Richards, J.M.

1999-01-26

A process is described for reducing the spontaneous combustion tendencies of thermally beneficiated low rank coals employing heat, air or an oxygen containing gas followed by an optional moisture addition. Specific reaction conditions are supplied along with knowledge of equipment types that may be employed on a commercial scale to complete the process. 3 figs.

Diversifying customer review rankings.

PubMed

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.

Journal Rankings by Health Management Faculty Members: Are There Differences by Rank, Leadership Status, or Area of Expertise?

PubMed

Menachemi, Nir; Hogan, Tory H; DelliFraine, Jami L

2015-01-01

Health administration (HA) faculty members publish in a variety of journals, including journals focused on management, economics, policy, and information technology. HA faculty members are evaluated on the basis of the quality and quantity of their journal publications. However, it is unclear how perceptions of these journals vary by subdiscipline, department leadership role, or faculty rank. It is also not clear how perceptions of journals may have changed over the past decade since the last evaluation of journal rankings in the field was published. The purpose of the current study is to examine how respondents rank journals in the field of HA, as well as the variation in perception by academic rank, department leadership status, and area of expertise. Data were drawn from a survey of HA faculty members at U.S. universities, which was completed in 2012. Different journal ranking patterns were noted for faculty members of different subdisciplines. The health management-oriented journals (Health Care Management Review and Journal of Healthcare Management) were ranked higher than in previous research, suggesting that journal ranking perceptions may have changed over the intervening decade. Few differences in perceptions were noted by academic rank, but we found that department chairs were more likely than others to select Health Affairs in their top three most prestigious journals (β = 0.768; p < .01). Perceived journal prestige varied between a department chair and untenured faculty in different disciplines, and this perceived difference could have implications for promotion and tenure decisions.

Mixing behavior of the rhombic micromixers over a wide Reynolds number range using Taguchi method and 3D numerical simulations.

PubMed

Chung, C K; Shih, T R; Chen, T C; Wu, B H

2008-10-01

A planar micromixer with rhombic microchannels and a converging-diverging element has been systematically investigated by the Taguchi method, CFD-ACE simulations and experiments. To reduce the footprint and extend the operation range of Reynolds number, Taguchi method was used to numerically study the performance of the micromixer in a L(9) orthogonal array. Mixing efficiency is prominently influenced by geometrical parameters and Reynolds number (Re). The four factors in a L(9) orthogonal array are number of rhombi, turning angle, width of the rhombic channel and width of the throat. The degree of sensitivity by Taguchi method can be ranked as: Number of rhombi > Width of the rhombic channel > Width of the throat > Turning angle of the rhombic channel. Increasing the number of rhombi, reducing the width of the rhombic channel and throat and lowering the turning angle resulted in better fluid mixing efficiency. The optimal design of the micromixer in simulations indicates over 90% mixing efficiency at both Re > or = 80 and Re < or = 0.1. Experimental results in the optimal simulations are consistent with the simulated one. This planar rhombic micromixer has simplified the complex fabrication process of the multi-layer or three-dimensional micromixers and improved the performance of a previous rhombic micromixer at a reduced footprint and lower Re.

Fast iterative solution of the Bethe-Salpeter eigenvalue problem using low-rank and QTT tensor approximation

NASA Astrophysics Data System (ADS)

Benner, Peter; Dolgov, Sergey; Khoromskaia, Venera; Khoromskij, Boris N.

2017-04-01

In this paper, we propose and study two approaches to approximate the solution of the Bethe-Salpeter equation (BSE) by using structured iterative eigenvalue solvers. Both approaches are based on the reduced basis method and low-rank factorizations of the generating matrices. We also propose to represent the static screen interaction part in the BSE matrix by a small active sub-block, with a size balancing the storage for rank-structured representations of other matrix blocks. We demonstrate by various numerical tests that the combination of the diagonal plus low-rank plus reduced-block approximation exhibits higher precision with low numerical cost, providing as well a distinct two-sided error estimate for the smallest eigenvalues of the Bethe-Salpeter operator. The complexity is reduced to O (Nb2) in the size of the atomic orbitals basis set, Nb, instead of the practically intractable O (Nb6) scaling for the direct diagonalization. In the second approach, we apply the quantized-TT (QTT) tensor representation to both, the long eigenvectors and the column vectors in the rank-structured BSE matrix blocks, and combine this with the ALS-type iteration in block QTT format. The QTT-rank of the matrix entities possesses almost the same magnitude as the number of occupied orbitals in the molecular systems, No

Augmenting the Deliberative Method for Ranking Risks.

PubMed

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.

Optimizing Estimated Loss Reduction for Active Sampling in Rank Learning

DTIC Science & Technology

2008-01-01

active learning framework for SVM-based and boosting-based rank learning. Our approach suggests sampling based on maximizing the estimated loss differential over unlabeled data. Experimental results on two benchmark corpora show that the proposed model substantially reduces the labeling effort, and achieves superior performance rapidly with as much as 30% relative improvement over the margin-based sampling

A Gaussian-based rank approximation for subspace clustering

NASA Astrophysics Data System (ADS)

Xu, Fei; Peng, Chong; Hu, Yunhong; He, Guoping

2018-04-01

Low-rank representation (LRR) has been shown successful in seeking low-rank structures of data relationships in a union of subspaces. Generally, LRR and LRR-based variants need to solve the nuclear norm-based minimization problems. Beyond the success of such methods, it has been widely noted that the nuclear norm may not be a good rank approximation because it simply adds all singular values of a matrix together and thus large singular values may dominant the weight. This results in far from satisfactory rank approximation and may degrade the performance of lowrank models based on the nuclear norm. In this paper, we propose a novel nonconvex rank approximation based on the Gaussian distribution function, which has demanding properties to be a better rank approximation than the nuclear norm. Then a low-rank model is proposed based on the new rank approximation with application to motion segmentation. Experimental results have shown significant improvements and verified the effectiveness of our method.

Technical Pitfalls in University Rankings

ERIC Educational Resources Information Center

Bougnol, Marie-Laure; Dulá, Jose H.

2015-01-01

Academicians, experts, and other stakeholders have contributed extensively to the literature on university rankings also known as "league tables". Often the tone is critical usually focused on the subjective aspects of the process; e.g., the list of the universities' attributes used in the rankings, their respective weights, and the size…

Rankings matter: nurse graduates from higher-ranked institutions have higher productivity.

PubMed

Yakusheva, Olga; Weiss, Marianne

2017-02-13

Increasing demand for baccalaureate-prepared nurses has led to rapid growth in the number of baccalaureate-granting programs, and to concerns about educational quality and potential effects on productivity of the graduating nursing workforce. We examined the association of individual productivity of a baccalaureate-prepared nurse with the ranking of the degree-granting institution. For a sample of 691 nurses from general medical-surgical units at a large magnet urban hospital between 6/1/2011-12/31/2011, we conducted multivariate regression analysis of nurse productivity on the ranking of the degree-granting institution, adjusted for age, hospital tenure, gender, and unit-specific effects. Nurse productivity was coded as "top"/"average"/"bottom" based on a computation of individual nurse value-added to patient outcomes. Ranking of the baccalaureate-granting institution was derived from the US News and World Report Best Colleges Rankings' categorization of the nurse's institution as the "first tier" or the "second tier", with diploma or associate degree as the reference category. Relative to diploma or associate degree nurses, nurses who had attended first-tier universities had three-times the odds of being in the top productivity category (OR = 3.18, p < 0.001), while second-tier education had a non-significant association with productivity (OR = 1.73, p = 0.11). Being in the bottom productivity category was not associated with having a baccalaureate degree or the quality tier. The productivity boost from a nursing baccalaureate degree depends on the quality of the educational institution. Recognizing differences in educational outcomes, initiatives to build a baccalaureate-educated nursing workforce should be accompanied by improved access to high-quality educational institutions.

Ranking Information in Networks

NASA Astrophysics Data System (ADS)

Eliassi-Rad, Tina; Henderson, Keith

Given a network, we are interested in ranking sets of nodes that score highest on user-specified criteria. For instance in graphs from bibliographic data (e.g. PubMed), we would like to discover sets of authors with expertise in a wide range of disciplines. We present this ranking task as a Top-K problem; utilize fixed-memory heuristic search; and present performance of both the serial and distributed search algorithms on synthetic and real-world data sets.

Model of Decision Making through Consensus in Ranking Case

NASA Astrophysics Data System (ADS)

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).

Deans' Perceptions of Published Rankings of Business Programs

ERIC Educational Resources Information Center

Athavale, Manoj; Bott, Jennifer; Myring, Mark; Richardson, Lynne

2017-01-01

Using a survey of college of business deans, the authors investigate perceptions of published rankings of academic programs. Published rankings have become quite prominent, and anecdotal evidence suggests great efforts are being undertaken to be included in rankings or enhance rankings. The authors conducted a survey of business school deans to…

Test Scores, Class Rank and College Performance: Lessons for Broadening Access and Promoting Success.

PubMed

Niu, Sunny X; Tienda, Marta

2012-04-01

Using administrative data for five Texas universities that differ in selectivity, this study evaluates the relative influence of two key indicators for college success-high school class rank and standardized tests. Empirical results show that class rank is the superior predictor of college performance and that test score advantages do not insulate lower ranked students from academic underperformance. Using the UT-Austin campus as a test case, we conduct a simulation to evaluate the consequences of capping students admitted automatically using both achievement metrics. We find that using class rank to cap the number of students eligible for automatic admission would have roughly uniform impacts across high schools, but imposing a minimum test score threshold on all students would have highly unequal consequences by greatly reduce the admission eligibility of the highest performing students who attend poor high schools while not jeopardizing admissibility of students who attend affluent high schools. We discuss the implications of the Texas admissions experiment for higher education in Europe.

Experienced stigma and its impacts in psychosis: The role of social rank and external shame.

PubMed

Wood, Lisa; Irons, Chris

2017-09-01

Experienced stigma is detrimental to those who experience psychosis and can cause emotional distress and hinder recovery. This study aimed to explore the relationship between experienced stigma with emotional distress and recovery in people with psychosis. It explored the role of external shame and social rank as mediators in these relationships. A cross-sectional design was implemented. Fifty-two service users were administered a battery of questionnaires examining experienced stigma, external shame, social rank, personal recovery, positive symptoms, depression, and anxiety. Correlation and multiple regression analysis were conducted on the data. Where appropriate, mediation analysis was employed to explore social rank and external shame as mediatory variables. Experienced stigma was significantly related to shame (social rank and external shame), positive symptoms, emotional distress (depression and anxiety), and personal recovery. The impact of experienced stigma on depression was mediated by external shame. Social rank was a mediator between experienced stigma and personal recovery only. People with psychosis who have experienced stigma are likely to experience emotional distress and be inhibited in their recovery. This was found to be partly mediated by external shame and low social rank. Clinical approaches to stigma need to target these as potential maintenance factors. Experienced stigma is significantly related to shame (social rank and external shame) emotional distress, and reduced personal recovery. External shame mediated the relationship between experienced stigma and depression in psychosis. Social rank mediated the relationship between experienced stigma and personal recovery. Clinical approaches to stigma should include the assessment of external shame and low social rank. © 2017 The British Psychological Society.

Comparisons of methods for determining dominance rank in male and female prairie voles (Microtus ochrogastor)

USGS Publications Warehouse

Lanctot, Richard B.; Best, Louis B.

2000-01-01

Dominance ranks in male and female prairie voles (Microtus ochrogaster) were determined from 6 measurements that mimicked environmental situations that might be encountered by prairie voles in communal groups, including agonistic interactions resulting from competition for food and water and encounters in burrows. Male and female groups of 6 individuals each were tested against one another in pairwise encounters (i.e., dyads) for 5 of the measurements and together as a group in a 6th measurement. Two types of response variables, aggressive behaviors and possession time of a limiting resource, were collected during trials, and those data were used to determine cardinal ranks and principal component ranks for all animals within each group. Cardinal ranks and principal component ranks seldom yielded similar rankings for each animal across measurements. However, dominance measurements that were conducted in similar environmental contexts, regardless of the response variable recorded, ranked animals similarly. Our results suggest that individual dominance measurements assessed situation- or resource-specific responses. Our study demonstrates problems inherent in determining dominance rankings of individuals within groups, including choosing measurements, response variables, and statistical techniques. Researchers should avoid using a single measurement to represent social dominance until they have first demonstrated that a dominance relationship between 2 individuals has been learned (i.e., subsequent interactions show a reduced response rather than an escalation), that this relationship is relatively constant through time, and that the relationship is not context dependent. Such assessments of dominance status between all dyads then can be used to generate dominance rankings within social groups.

Rapid-mix and chemical quench studies of ferredoxin-reduced stearoyl-acyl carrier protein desaturase.

PubMed

Lyle, Karen S; Haas, Jeffrey A; Fox, Brian G

2003-05-20

Stearoyl-ACP Delta9 desaturase (Delta9D) catalyzes the NADPH- and O(2)-dependent insertion of a cis double bond between the C9 and C10 positions of stearoyl-ACP (18:0-ACP) to produce oleoyl-ACP (18:1-ACP). This work revealed the ability of reduced [2Fe-2S] ferredoxin (Fd) to act as a catalytically competent electron donor during the rapid conversion of 18:0-ACP into 18:1-ACP. Experiments on the order of addition for substrate and reduced Fd showed high conversion of 18:0-ACP to 18:1-ACP (approximately 95% per Delta9D active site in a single turnover) when 18:0-ACP was added prior to reduced Fd. Reactions of the prereduced enzyme-substrate complex with O(2) and the oxidized enzyme-substrate complex with reduced Fd were studied by rapid-mix and chemical quench methods. For reaction of the prereduced enzyme-substrate complex, an exponential burst phase (k(burst) = 95 s(-1)) of product formation accounted for approximately 90% of the turnover expected for one subunit in the dimeric protein. This rapid phase was followed by a slower phase (k(linear) = 4.0 s(-1)) of product formation corresponding to the turnover expected from the second subunit. For reaction of the oxidized enzyme-substrate complex with excess reduced Fd, a slower, linear rate (k(obsd) = 3.4 s(-1)) of product formation was observed over approximately 1.5 turnovers per Delta9D active site potentially corresponding to a third phase of reaction. An analysis of the deuterium isotope effect on the two rapid-mix reaction sequences revealed only a modest effect on k(burst) ((D)k(burst) approximately 1.5) and k(linear) (D)k(linear) approximately 1.4), indicating C-H bond cleavage does not contribute significantly to the rate-limiting steps of pre-steady-state catalysis. These results were used to assemble and evaluate a minimal kinetic model for Delta9D catalysis.

University Ranking as Social Exclusion

ERIC Educational Resources Information Center

Amsler, Sarah S.; Bolsmann, Chris

2012-01-01

In this article we explore the dual role of global university rankings in the creation of a new, knowledge-identified, transnational capitalist class and in facilitating new forms of social exclusion. We examine how and why the practice of ranking universities has become widely defined by national and international organisations as an important…

The Privilege of Ranking: Google Plays Ball.

ERIC Educational Resources Information Center

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;…

14 CFR 1214.1105 - Final ranking.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Recruitment and Selection Program § 1214.1105 Final ranking. Final rankings will be based on a combination of the selection board's initial evaluations and the results of the interview process. Veteran's...

Academic Quality Rankings of American Colleges and Universities.

ERIC Educational Resources Information Center

Webster, David S.

Past and current methods used in academic quality rankings of U.S. colleges and universities are discussed. In addition to a literature and historical review, modern quality rankings are compared with early (pre-1959) rankings, including past rankings of medical, dental, legal and black education. Also considered are the exemplary 1982 evaluation…

Exploiting sparsity and low-rank structure for the recovery of multi-slice breast MRIs with reduced sampling error.

PubMed

Yin, X X; Ng, B W-H; Ramamohanarao, K; Baghai-Wadji, A; Abbott, D

2012-09-01

It has been shown that, magnetic resonance images (MRIs) with sparsity representation in a transformed domain, e.g. spatial finite-differences (FD), or discrete cosine transform (DCT), can be restored from undersampled k-space via applying current compressive sampling theory. The paper presents a model-based method for the restoration of MRIs. The reduced-order model, in which a full-system-response is projected onto a subspace of lower dimensionality, has been used to accelerate image reconstruction by reducing the size of the involved linear system. In this paper, the singular value threshold (SVT) technique is applied as a denoising scheme to reduce and select the model order of the inverse Fourier transform image, and to restore multi-slice breast MRIs that have been compressively sampled in k-space. The restored MRIs with SVT for denoising show reduced sampling errors compared to the direct MRI restoration methods via spatial FD, or DCT. Compressive sampling is a technique for finding sparse solutions to underdetermined linear systems. The sparsity that is implicit in MRIs is to explore the solution to MRI reconstruction after transformation from significantly undersampled k-space. The challenge, however, is that, since some incoherent artifacts result from the random undersampling, noise-like interference is added to the image with sparse representation. These recovery algorithms in the literature are not capable of fully removing the artifacts. It is necessary to introduce a denoising procedure to improve the quality of image recovery. This paper applies a singular value threshold algorithm to reduce the model order of image basis functions, which allows further improvement of the quality of image reconstruction with removal of noise artifacts. The principle of the denoising scheme is to reconstruct the sparse MRI matrices optimally with a lower rank via selecting smaller number of dominant singular values. The singular value threshold algorithm is performed

University Rankings in Critical Perspective

ERIC Educational Resources Information Center

Pusser, Brian; Marginson, Simon

2013-01-01

This article addresses global postsecondary ranking systems by using critical-theoretical perspectives on power. This research suggests rankings are at once a useful lens for studying power in higher education and an important instrument for the exercise of power in service of dominant norms in global higher education. (Contains 1 table and 1…

Stress in Spanish police force depending on occupational rank, sex, age and work-shift.

PubMed

Luceño-Moreno, Lourdes; García-Albuerne, Yolanda; Talavera-Velasco, Beatriz; Martín-García, Jesús

2016-11-01

In the police force, some variables such as occupational rank, sex, age and work-shift are associated with stress in workers. The aim of this paper was to determine possible differences in the perception of occupational stress at work depending on rank, sex, age and work-shift of police agents in the Community of Madrid, Spain. A cross-sectional study was conducted in 24 municipalities of the Community of Madrid. A total number of 565 police agents participated. The ranks of the police agents were: constable, corporal, sergeant and police chief. Occupational stress (psychosocial risk factors at work) was assessed with the DECORE Questionnaire. All police agents perceived psychosocial risk factors adversely; especially agents of lesser rank perceived less control, fewer rewards and scarce support. There were significant differences in the perception of insufficient rewards between constables and other categories; and between constables and corporals in the perception of insufficient organisational support. No significant differences were found in the perception of psychosocial risk factors in relation to the rest of variables. The police rank should be taken into account for the development of preventive measures to reduce stress.

Ranking Theory and Conditional Reasoning.

PubMed

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.

Effect of mixing time, freeze-drying and baking on phenolics, anthocyanins and antioxidant capacity of raspberry juice during processing of muffins.

PubMed

Rosales-Soto, Maria U; Powers, Joseph R; Alldredge, J Richard

2012-05-01

Consumption of baked products constitutes an important part of a daily breakfast considering that people are continually grabbing meals on the go. Among baked products, muffins rank third in breakfast products and attract a broad range of consumers. Incorporation of red raspberry juice into muffins can add value to the product while preserving health benefits to the consumer. The purpose of this study was to evaluate the effect of mixing time, freeze-drying and baking on the phenolic and anthocyanin contents and antioxidant capacity of raspberry juice during the preparation of muffins. Freeze-drying of raspberry batters reduced their phenolic content and antioxidant capacity regardless of mixing time. Non-freeze-dried raspberry batter mixed for 5 min had the highest phenolic content (0.88 mg gallic acid equivalent g(-1) dry matter (DM)). Non-freeze-dried raspberry muffins had the highest antioxidant capacity (0.041 µmol Trolox equivalent g(-1) DM). Freeze-dried raspberry batters mixed for 5 and 10 min had the highest anthocyanin content (0.065 mg cyanidin-3-glucoside g(-1) DM). Baking reduced the anthocyanin content of both non-freeze-dried and freeze-dried raspberry muffins. Despite the reduction in valuable compounds, muffin is a vehicle for the delivery of these compounds. Copyright © 2012 Society of Chemical Industry.

Nominal versus Attained Weights in Universitas 21 Ranking

ERIC Educational Resources Information Center

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…

Ethics: An Indispensable Dimension in the University Rankings.

PubMed

Khaki Sedigh, Ali

2017-02-01

University ranking systems attempt to provide an ordinal gauge to make an expert evaluation of the university's performance for a general audience. University rankings have always had their pros and cons in the higher education community. Some seriously question the usefulness, accuracy, and lack of consensus in ranking systems and therefore multidimensional ranking systems have been proposed to overcome some shortcomings of the earlier systems. Although the present ranking results may rather be rough, they are the only available sources that illustrate the complex university performance in a tangible format. Their relative accuracy has turned the ranking systems into an essential feature of the academic lifecycle within the foreseeable future. The main concern however, is that the present ranking systems totally neglect the ethical issues involved in university performances. Ethics should be a new dimension added into the university ranking systems, as it is an undisputable right of the public and all the parties involved in higher education to have an ethical evaluation of the university's achievements. In this paper, to initiate ethical assessment and rankings, the main factors involved in the university performances are reviewed from an ethical perspective. Finally, a basic benchmarking model for university ethical performance is presented.

A Comprehensive Analysis of Marketing Journal Rankings

ERIC Educational Resources Information Center

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…

Obsession with Rankings Goes Global

ERIC Educational Resources Information Center

Labi, Aisha

2008-01-01

A Chinese list of the world's top universities would seem an unlikely concern for French politicians. But this year, France's legislature took aim at the annual rankings produced by Shanghai Jiao Tong University, which claims to list the 500 best universities in the world. The highest-ranked French entry, Universite Pierre et Marie Curie, comes in…

46 CFR 282.11 - Ranking of flags.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 8 2010-10-01 2010-10-01 false Ranking of flags. 282.11 Section 282.11 Shipping... COMMERCE OF THE UNITED STATES Foreign-Flag Competition § 282.11 Ranking of flags. The operators under each... priority of costs which are representative of the flag. For liner cargo vessels, the ranking of operators...

Low-ranking female Japanese macaques make efforts for social grooming

PubMed Central

Kurihara, Yosuke

2016-01-01

Abstract Grooming is essential to build social relationships in primates. Its importance is universal among animals from different ranks; however, rank-related differences in feeding patterns can lead to conflicts between feeding and grooming in low-ranking animals. Unifying the effects of dominance rank on feeding and grooming behaviors contributes to revealing the importance of grooming. Here, I tested whether the grooming behavior of low-ranking females were similar to that of high-ranking females despite differences in their feeding patterns. I followed 9 Japanese macaques Macaca fuscata fuscata adult females from the Arashiyama group, and analyzed the feeding patterns and grooming behaviors of low- and high-ranking females. Low-ranking females fed on natural foods away from the provisioning site, whereas high-ranking females obtained more provisioned food at the site. Due to these differences in feeding patterns, low-ranking females spent less time grooming than high-ranking females. However, both low- and high-ranking females performed grooming around the provisioning site, which was linked to the number of neighboring individuals for low-ranking females and feeding on provisioned foods at the site for high-ranking females. The similarity in grooming area led to a range and diversity of grooming partners that did not differ with rank. Thus, low-ranking females can obtain small amounts of provisioned foods and perform grooming with as many partners around the provisioning site as high-ranking females. These results highlight the efforts made by low-ranking females to perform grooming and suggest the importance of grooming behavior in group-living primates. PMID:29491896

Low-ranking female Japanese macaques make efforts for social grooming.

PubMed

Kurihara, Yosuke

2016-04-01

Grooming is essential to build social relationships in primates. Its importance is universal among animals from different ranks; however, rank-related differences in feeding patterns can lead to conflicts between feeding and grooming in low-ranking animals. Unifying the effects of dominance rank on feeding and grooming behaviors contributes to revealing the importance of grooming. Here, I tested whether the grooming behavior of low-ranking females were similar to that of high-ranking females despite differences in their feeding patterns. I followed 9 Japanese macaques Macaca fuscata fuscata adult females from the Arashiyama group, and analyzed the feeding patterns and grooming behaviors of low- and high-ranking females. Low-ranking females fed on natural foods away from the provisioning site, whereas high-ranking females obtained more provisioned food at the site. Due to these differences in feeding patterns, low-ranking females spent less time grooming than high-ranking females. However, both low- and high-ranking females performed grooming around the provisioning site, which was linked to the number of neighboring individuals for low-ranking females and feeding on provisioned foods at the site for high-ranking females. The similarity in grooming area led to a range and diversity of grooming partners that did not differ with rank. Thus, low-ranking females can obtain small amounts of provisioned foods and perform grooming with as many partners around the provisioning site as high-ranking females. These results highlight the efforts made by low-ranking females to perform grooming and suggest the importance of grooming behavior in group-living primates.

RANKED SET SAMPLING FOR ECOLOGICAL RESEARCH: ACCOUNTING FOR THE TOTAL COSTS OF SAMPLING

EPA Science Inventory

Researchers aim to design environmental studies that optimize precision and allow for generalization of results, while keeping the costs of associated field and laboratory work at a reasonable level. Ranked set sampling is one method to potentially increase precision and reduce ...

Development of a Reduced-Order Three-Dimensional Flow Model for Thermal Mixing and Stratification Simulation during Reactor Transients

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Rui

2017-09-03

Mixing, thermal-stratification, and mass transport phenomena in large pools or enclosures play major roles for the safety of reactor systems. Depending on the fidelity requirement and computational resources, various modeling methods, from the 0-D perfect mixing model to 3-D Computational Fluid Dynamics (CFD) models, are available. Each is associated with its own advantages and shortcomings. It is very desirable to develop an advanced and efficient thermal mixing and stratification modeling capability embedded in a modern system analysis code to improve the accuracy of reactor safety analyses and to reduce modeling uncertainties. An advanced system analysis tool, SAM, is being developedmore » at Argonne National Laboratory for advanced non-LWR reactor safety analysis. While SAM is being developed as a system-level modeling and simulation tool, a reduced-order three-dimensional module is under development to model the multi-dimensional flow and thermal mixing and stratification in large enclosures of reactor systems. This paper provides an overview of the three-dimensional finite element flow model in SAM, including the governing equations, stabilization scheme, and solution methods. Additionally, several verification and validation tests are presented, including lid-driven cavity flow, natural convection inside a cavity, laminar flow in a channel of parallel plates. Based on the comparisons with the analytical solutions and experimental results, it is demonstrated that the developed 3-D fluid model can perform very well for a wide range of flow problems.« less

Fair ranking of researchers and research teams

PubMed Central

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). PMID:29621316

Fair ranking of researchers and research teams.

PubMed

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).

Toxic chemical release weighted ranking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Petrocchi, A.J.

1989-07-19

The weighted ranking as used in this report is an attempt to combine total air release with recognized exposure limit for each toxic chemical to arrive at a single ranking factor called Release Exposure Index (REI) which takes both release amount and degree of hazard into consideration. The REIs can then be used in decision making to prioritize how these chemicals are addressed. 2 tabs.

Rank diversity of languages: generic behavior in computational linguistics.

PubMed

Cocho, Germinal; Flores, Jorge; Gershenson, Carlos; Pineda, Carlos; Sánchez, Sergio

2015-01-01

Statistical studies of languages have focused on the rank-frequency distribution of words. Instead, we introduce here a measure of how word ranks change in time and call this distribution rank diversity. We calculate this diversity for books published in six European languages since 1800, and find that it follows a universal lognormal distribution. Based on the mean and standard deviation associated with the lognormal distribution, we define three different word regimes of languages: "heads" consist of words which almost do not change their rank in time, "bodies" are words of general use, while "tails" are comprised by context-specific words and vary their rank considerably in time. The heads and bodies reflect the size of language cores identified by linguists for basic communication. We propose a Gaussian random walk model which reproduces the rank variation of words in time and thus the diversity. Rank diversity of words can be understood as the result of random variations in rank, where the size of the variation depends on the rank itself. We find that the core size is similar for all languages studied.

A scoring system for appraising mixed methods research, and concomitantly appraising qualitative, quantitative and mixed methods primary studies in Mixed Studies Reviews.

PubMed

Pluye, Pierre; Gagnon, Marie-Pierre; Griffiths, Frances; Johnson-Lafleur, Janique

2009-04-01

A new form of literature review has emerged, Mixed Studies Review (MSR). These reviews include qualitative, quantitative and mixed methods studies. In the present paper, we examine MSRs in health sciences, and provide guidance on processes that should be included and reported. However, there are no valid and usable criteria for concomitantly appraising the methodological quality of the qualitative, quantitative and mixed methods studies. To propose criteria for concomitantly appraising the methodological quality of qualitative, quantitative and mixed methods studies or study components. A three-step critical review was conducted. 2322 references were identified in MEDLINE, and their titles and abstracts were screened; 149 potentially relevant references were selected and the full-text papers were examined; 59 MSRs were retained and scrutinized using a deductive-inductive qualitative thematic data analysis. This revealed three types of MSR: convenience, reproducible, and systematic. Guided by a proposal, we conducted a qualitative thematic data analysis of the quality appraisal procedures used in the 17 systematic MSRs (SMSRs). Of 17 SMSRs, 12 showed clear quality appraisal procedures with explicit criteria but no SMSR used valid checklists to concomitantly appraise qualitative, quantitative and mixed methods studies. In two SMSRs, criteria were developed following a specific procedure. Checklists usually contained more criteria than needed. In four SMSRs, a reliability assessment was described or mentioned. While criteria for quality appraisal were usually based on descriptors that require specific methodological expertise (e.g., appropriateness), no SMSR described the fit between reviewers' expertise and appraised studies. Quality appraisal usually resulted in studies being ranked by methodological quality. A scoring system is proposed for concomitantly appraising the methodological quality of qualitative, quantitative and mixed methods studies for SMSRs. This

Ranking Surgical Residency Programs: Reputation Survey or Outcomes Measures?

PubMed

Wilson, Adam B; Torbeck, Laura J; Dunnington, Gary L

2015-01-01

The release of general surgery residency program rankings by Doximity and U.S. News & World Report accentuates the need to define and establish measurable standards of program quality. This study evaluated the extent to which program rankings based solely on peer nominations correlated with familiar program outcomes measures. Publicly available data were collected for all 254 general surgery residency programs. To generate a rudimentary outcomes-based program ranking, surgery programs were rank-ordered according to an average percentile rank that was calculated using board pass rates and the prevalence of alumni publications. A Kendall τ-b rank correlation computed the linear association between program rankings based on reputation alone and those derived from outcomes measures to validate whether reputation was a reasonable surrogate for globally judging program quality. For the 218 programs with complete data eligible for analysis, the mean board pass rate was 72% with a standard deviation of 14%. A total of 60 programs were placed in the 75th percentile or above for the number of publications authored by program alumni. The correlational analysis reported a significant correlation of 0.428, indicating only a moderate association between programs ranked by outcomes measures and those ranked according to reputation. Seventeen programs that were ranked in the top 30 according to reputation were also ranked in the top 30 based on outcomes measures. This study suggests that reputation alone does not fully capture a representative snapshot of a program's quality. Rather, the use of multiple quantifiable indicators and attributes unique to programs ought to be given more consideration when assigning ranks to denote program quality. It is advised that the interpretation and subsequent use of program rankings be met with caution until further studies can rigorously demonstrate best practices for awarding program standings. Copyright © 2015 Association of Program

Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal.

PubMed

Ge, Qi; Jing, Xiao-Yuan; Wu, Fei; Wei, Zhi-Hui; Xiao, Liang; Shao, Wen-Ze; Yue, Dong; Li, Hai-Bo

2017-07-01

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms.

Optimal Weights Mixed Filter for removing mixture of Gaussian and impulse noises

PubMed Central

Grama, Ion; Liu, Quansheng

2017-01-01

In this paper we consider the problem of restoration of a image contaminated by a mixture of Gaussian and impulse noises. We propose a new statistic called ROADGI which improves the well-known Rank-Ordered Absolute Differences (ROAD) statistic for detecting points contaminated with the impulse noise in this context. Combining ROADGI statistic with the method of weights optimization we obtain a new algorithm called Optimal Weights Mixed Filter (OWMF) to deal with the mixed noise. Our simulation results show that the proposed filter is effective for mixed noises, as well as for single impulse noise and for single Gaussian noise. PMID:28692667

Optimal Weights Mixed Filter for removing mixture of Gaussian and impulse noises.

PubMed

Jin, Qiyu; Grama, Ion; Liu, Quansheng

2017-01-01

In this paper we consider the problem of restoration of a image contaminated by a mixture of Gaussian and impulse noises. We propose a new statistic called ROADGI which improves the well-known Rank-Ordered Absolute Differences (ROAD) statistic for detecting points contaminated with the impulse noise in this context. Combining ROADGI statistic with the method of weights optimization we obtain a new algorithm called Optimal Weights Mixed Filter (OWMF) to deal with the mixed noise. Our simulation results show that the proposed filter is effective for mixed noises, as well as for single impulse noise and for single Gaussian noise.

5 CFR 451.302 - Ranks for senior career employees.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Ranks for senior career employees. 451... AWARDS Presidential Rank Awards § 451.302 Ranks for senior career employees. (a) The circumstances under which the President may award the rank of Distinguished Senior Professional and Meritorious Senior...

Node Ranking Tool - NoRT

DTIC Science & Technology

2018-03-23

Unclassified Unlimited Unclassified Unlimited Unclassified Unlimited 23 Ira S. Moskowitz (202) 404-7930 This paper gives a description of the Node Ranking Tool...Disease, Virus, Expectation, Pandemic, Close- ness, Graph, Degree, Spectrum. I. INTRODUCTION THis paper gives a description of the Node Ranking Tool...is very much dependent upon which centrality measure we use. Therefore, following [6] and [3], we use TOPSIS to evaluate our decisions about the

Trachomatous Scar Ranking: A Novel Outcome for Trachoma Studies.

PubMed

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.

CNN-based ranking for biomedical entity normalization.

PubMed

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.

Grid-based lattice summation of electrostatic potentials by assembled rank-structured tensor approximation

NASA Astrophysics Data System (ADS)

Khoromskaia, Venera; Khoromskij, Boris N.

2014-12-01

Our recent method for low-rank tensor representation of sums of the arbitrarily positioned electrostatic potentials discretized on a 3D Cartesian grid reduces the 3D tensor summation to operations involving only 1D vectors however retaining the linear complexity scaling in the number of potentials. Here, we introduce and study a novel tensor approach for fast and accurate assembled summation of a large number of lattice-allocated potentials represented on 3D N × N × N grid with the computational requirements only weakly dependent on the number of summed potentials. It is based on the assembled low-rank canonical tensor representations of the collected potentials using pointwise sums of shifted canonical vectors representing the single generating function, say the Newton kernel. For a sum of electrostatic potentials over L × L × L lattice embedded in a box the required storage scales linearly in the 1D grid-size, O(N) , while the numerical cost is estimated by O(NL) . For periodic boundary conditions, the storage demand remains proportional to the 1D grid-size of a unit cell, n = N / L, while the numerical cost reduces to O(N) , that outperforms the FFT-based Ewald-type summation algorithms of complexity O(N3 log N) . The complexity in the grid parameter N can be reduced even to the logarithmic scale O(log N) by using data-sparse representation of canonical N-vectors via the quantics tensor approximation. For justification, we prove an upper bound on the quantics ranks for the canonical vectors in the overall lattice sum. The presented approach is beneficial in applications which require further functional calculus with the lattice potential, say, scalar product with a function, integration or differentiation, which can be performed easily in tensor arithmetics on large 3D grids with 1D cost. Numerical tests illustrate the performance of the tensor summation method and confirm the estimated bounds on the tensor ranks.

Generalization Performance of Regularized Ranking With Multiscale Kernels.

PubMed

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.

Rank-based pooling for deep convolutional neural networks.

PubMed

Shi, Zenglin; Ye, Yangdong; Wu, Yunpeng

2016-11-01

Pooling is a key mechanism in deep convolutional neural networks (CNNs) which helps to achieve translation invariance. Numerous studies, both empirically and theoretically, show that pooling consistently boosts the performance of the CNNs. The conventional pooling methods are operated on activation values. In this work, we alternatively propose rank-based pooling. It is derived from the observations that ranking list is invariant under changes of activation values in a pooling region, and thus rank-based pooling operation may achieve more robust performance. In addition, the reasonable usage of rank can avoid the scale problems encountered by value-based methods. The novel pooling mechanism can be regarded as an instance of weighted pooling where a weighted sum of activations is used to generate the pooling output. This pooling mechanism can also be realized as rank-based average pooling (RAP), rank-based weighted pooling (RWP) and rank-based stochastic pooling (RSP) according to different weighting strategies. As another major contribution, we present a novel criterion to analyze the discriminant ability of various pooling methods, which is heavily under-researched in machine learning and computer vision community. Experimental results on several image benchmarks show that rank-based pooling outperforms the existing pooling methods in classification performance. We further demonstrate better performance on CIFAR datasets by integrating RSP into Network-in-Network. Copyright © 2016 Elsevier Ltd. All rights reserved.

Scalable Faceted Ranking in Tagging Systems

NASA Astrophysics Data System (ADS)

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.

Rank Diversity of Languages: Generic Behavior in Computational Linguistics

PubMed Central

Cocho, Germinal; Flores, Jorge; Gershenson, Carlos; Pineda, Carlos; Sánchez, Sergio

2015-01-01

Statistical studies of languages have focused on the rank-frequency distribution of words. Instead, we introduce here a measure of how word ranks change in time and call this distribution rank diversity. We calculate this diversity for books published in six European languages since 1800, and find that it follows a universal lognormal distribution. Based on the mean and standard deviation associated with the lognormal distribution, we define three different word regimes of languages: “heads” consist of words which almost do not change their rank in time, “bodies” are words of general use, while “tails” are comprised by context-specific words and vary their rank considerably in time. The heads and bodies reflect the size of language cores identified by linguists for basic communication. We propose a Gaussian random walk model which reproduces the rank variation of words in time and thus the diversity. Rank diversity of words can be understood as the result of random variations in rank, where the size of the variation depends on the rank itself. We find that the core size is similar for all languages studied. PMID:25849150

Interhospital differences and case-mix in a nationwide prevalence survey.

PubMed

Kanerva, M; Ollgren, J; Lyytikäinen, O

2010-10-01

A prevalence survey is a time-saving and useful tool for obtaining an overview of healthcare-associated infection (HCAI) either in a single hospital or nationally. Direct comparison of prevalence rates is difficult. We evaluated the impact of case-mix adjustment on hospital-specific prevalences. All five tertiary care, all 15 secondary care and 10 (25% of 40) other acute care hospitals took part in the first national prevalence survey in Finland in 2005. US Centers for Disease Control and Prevention criteria served to define HCAI. The information collected included demographic characteristics, severity of the underlying disease, use of catheters and a respirator, and previous surgery. Patients with HCAI related to another hospital were excluded. Case-mix-adjusted HCAI prevalences were calculated by using a multivariate logistic regression model for HCAI risk and an indirect standardisation method. Altogether, 587 (7.2%) of 8118 adult patients had at least one infection; hospital-specific prevalences ranged between 1.9% and 12.6%. Risk factors for HCAI that were previously known or identified by univariate analysis (age, male gender, intensive care, high Charlson comorbidity and McCabe indices, respirator, central venous or urinary catheters, and surgery during stay) were included in the multivariate analysis for standardisation. Case-mix-adjusted prevalences varied between 2.6% and 17.0%, and ranked the hospitals differently from the observed rates. In 11 (38%) hospitals, the observed prevalence rank was lower than predicted by the case-mix-adjusted figure. Case-mix should be taken into consideration in the interhospital comparison of prevalence rates. Copyright 2010 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved.

Trial type mixing substantially reduces the response set effect in the Stroop task.

PubMed

Hasshim, Nabil; Parris, Benjamin A

2017-03-20

The response set effect refers to the finding that an irrelevant incongruent colour-word produces greater interference when it is one of the response options (referred to as a response set trial), compared to when it is not (a non-response set trial). Despite being a key effect for models of selective attention, the magnitude of the effect varies considerably across studies. We report two within-subjects experiments that tested the hypothesis that presentation format modulates the magnitude of the response set effect. Trial types (e.g. response set, non-response set, neutral) were either presented in separate blocks (pure) or in blocks containing trials from all conditions presented randomly (mixed). In the first experiment we show that the response set effect is substantially reduced in the mixed block context as a result of a decrease in RTs to response set trials. By demonstrating the modulation of the response set effect under conditions of trial type mixing we present evidence that is difficult for models of the effect based on strategic, top-down biasing of attention to explain. In a second experiment we tested a stimulus-driven account of the response set effect by manipulating the number of colour-words that make up the non-response set of distractors. The results show that the greater the number of non-response set colour concepts, the smaller the response set effect. Alternative accounts of the data and its implications for research debating the automaticity of reading are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Lung dynamic MRI deblurring using low-rank decomposition and dictionary learning.

PubMed

Gou, Shuiping; Wang, Yueyue; Wu, Jiaolong; Lee, Percy; Sheng, Ke

2015-04-01

Lung dynamic MRI (dMRI) has emerged to be an appealing tool to quantify lung motion for both planning and treatment guidance purposes. However, this modality can result in blurry images due to intrinsically low signal-to-noise ratio in the lung and spatial/temporal interpolation. The image blurring could adversely affect the image processing that depends on the availability of fine landmarks. The purpose of this study is to reduce dMRI blurring using image postprocessing. To enhance the image quality and exploit the spatiotemporal continuity of dMRI sequences, a low-rank decomposition and dictionary learning (LDDL) method was employed to deblur lung dMRI and enhance the conspicuity of lung blood vessels. Fifty frames of continuous 2D coronal dMRI frames using a steady state free precession sequence were obtained from five subjects including two healthy volunteer and three lung cancer patients. In LDDL, the lung dMRI was decomposed into sparse and low-rank components. Dictionary learning was employed to estimate the blurring kernel based on the whole image, low-rank or sparse component of the first image in the lung MRI sequence. Deblurring was performed on the whole image sequences using deconvolution based on the estimated blur kernel. The deblurring results were quantified using an automated blood vessel extraction method based on the classification of Hessian matrix filtered images. Accuracy of automated extraction was calculated using manual segmentation of the blood vessels as the ground truth. In the pilot study, LDDL based on the blurring kernel estimated from the sparse component led to performance superior to the other ways of kernel estimation. LDDL consistently improved image contrast and fine feature conspicuity of the original MRI without introducing artifacts. The accuracy of automated blood vessel extraction was on average increased by 16% using manual segmentation as the ground truth. Image blurring in dMRI images can be effectively reduced using a

Rehabbing the Rankings: Fool's Errand or the Lord's Work?

ERIC Educational Resources Information Center

Kuh, George D.

2011-01-01

For better or worse, rankings shape public conceptions of collegiate quality. This paper reviews the history of rankings, analyzes what they represent, explores recent efforts to employ indicators in addition to institutional resources and reputation on which the most popular rankings are based, and evaluates the extent to which rankings serve…

FSMRank: feature selection algorithm for learning to rank.

PubMed

Lai, Han-Jiang; Pan, Yan; Tang, Yong; Yu, Rong

2013-06-01

In recent years, there has been growing interest in learning to rank. The introduction of feature selection into different learning problems has been proven effective. These facts motivate us to investigate the problem of feature selection for learning to rank. We propose a joint convex optimization formulation which minimizes ranking errors while simultaneously conducting feature selection. This optimization formulation provides a flexible framework in which we can easily incorporate various importance measures and similarity measures of the features. To solve this optimization problem, we use the Nesterov's approach to derive an accelerated gradient algorithm with a fast convergence rate O(1/T(2)). We further develop a generalization bound for the proposed optimization problem using the Rademacher complexities. Extensive experimental evaluations are conducted on the public LETOR benchmark datasets. The results demonstrate that the proposed method shows: 1) significant ranking performance gain compared to several feature selection baselines for ranking, and 2) very competitive performance compared to several state-of-the-art learning-to-rank algorithms.

Social class rank, threat vigilance, and hostile reactivity.

PubMed

Kraus, Michael W; Horberg, E J; Goetz, Jennifer L; Keltner, Dacher

2011-10-01

Lower-class individuals, because of their lower rank in society, are theorized to be more vigilant to social threats relative to their high-ranking upper-class counterparts. This class-related vigilance to threat, the authors predicted, would shape the emotional content of social interactions in systematic ways. In Study 1, participants engaged in a teasing interaction with a close friend. Lower-class participants--measured in terms of social class rank in society and within the friendship--more accurately tracked the hostile emotions of their friend. As a result, lower-class individuals experienced more hostile emotion contagion relative to upper-class participants. In Study 2, lower-class participants manipulated to experience lower subjective socioeconomic rank showed more hostile reactivity to ambiguous social scenarios relative to upper-class participants and to lower-class participants experiencing elevated socioeconomic rank. The results suggest that class affects expectations, perception, and experience of hostile emotion, particularly in situations in which lower-class individuals perceive their subordinate rank.

Are university rankings useful to improve research? A systematic review.

PubMed

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

Are university rankings useful to improve research? A systematic review

PubMed Central

Momani, Shaher

2018-01-01

Introduction 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. Methods 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. Results 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. Discussion 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

An Optimization-Based Method for Feature Ranking in Nonlinear Regression Problems.

PubMed

Bravi, Luca; Piccialli, Veronica; Sciandrone, Marco

2017-04-01

In this paper, we consider the feature ranking problem, where, given a set of training instances, the task is to associate a score with the features in order to assess their relevance. Feature ranking is a very important tool for decision support systems, and may be used as an auxiliary step of feature selection to reduce the high dimensionality of real-world data. We focus on regression problems by assuming that the process underlying the generated data can be approximated by a continuous function (for instance, a feedforward neural network). We formally state the notion of relevance of a feature by introducing a minimum zero-norm inversion problem of a neural network, which is a nonsmooth, constrained optimization problem. We employ a concave approximation of the zero-norm function, and we define a smooth, global optimization problem to be solved in order to assess the relevance of the features. We present the new feature ranking method based on the solution of instances of the global optimization problem depending on the available training data. Computational experiments on both artificial and real data sets are performed, and point out that the proposed feature ranking method is a valid alternative to existing methods in terms of effectiveness. The obtained results also show that the method is costly in terms of CPU time, and this may be a limitation in the solution of large-dimensional problems.

25 CFR 1001.3 - Priority ranking for negotiations.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 25 Indians 2 2010-04-01 2010-04-01 false Priority ranking for negotiations. 1001.3 Section 1001.3... PROGRAM § 1001.3 Priority ranking for negotiations. In addition to the eligibility criteria identified above, a tribe or consortium of tribes seeking priority ranking for negotiations must submit a...

25 CFR 1001.3 - Priority ranking for negotiations.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 25 Indians 2 2012-04-01 2012-04-01 false Priority ranking for negotiations. 1001.3 Section 1001.3... PROGRAM § 1001.3 Priority ranking for negotiations. In addition to the eligibility criteria identified above, a tribe or consortium of tribes seeking priority ranking for negotiations must submit a...

25 CFR 1001.3 - Priority ranking for negotiations.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 25 Indians 2 2013-04-01 2013-04-01 false Priority ranking for negotiations. 1001.3 Section 1001.3... PROGRAM § 1001.3 Priority ranking for negotiations. In addition to the eligibility criteria identified above, a tribe or consortium of tribes seeking priority ranking for negotiations must submit a...

25 CFR 1001.3 - Priority ranking for negotiations.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 25 Indians 2 2011-04-01 2011-04-01 false Priority ranking for negotiations. 1001.3 Section 1001.3... PROGRAM § 1001.3 Priority ranking for negotiations. In addition to the eligibility criteria identified above, a tribe or consortium of tribes seeking priority ranking for negotiations must submit a...

25 CFR 1001.3 - Priority ranking for negotiations.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 25 Indians 2 2014-04-01 2014-04-01 false Priority ranking for negotiations. 1001.3 Section 1001.3... PROGRAM § 1001.3 Priority ranking for negotiations. In addition to the eligibility criteria identified above, a tribe or consortium of tribes seeking priority ranking for negotiations must submit a...

Ranking Quality in Higher Education: Guiding or Misleading?

ERIC Educational Resources Information Center

Bergseth, Brita; Petocz, Peter; Abrandt Dahlgren, Madeleine

2014-01-01

The study examines two different models of measuring, assessing and ranking quality in higher education. Do different systems of quality assessment lead to equivalent conclusions about the quality of education? This comparative study is based on the rankings of 24 Swedish higher education institutions. Two ranking actors have independently…

Ending the Reign of the Fraser Institute's School Rankings

ERIC Educational Resources Information Center

Raptis, Helen

2012-01-01

The Fraser Institute "Report Card" of school rankings has won the hearts of parents and the press. For over a decade, the rankings have been particularly burdensome for low-ranking (usually low socio-economic status, high-poverty) schools when parents of high-achieving children move them to higher-ranking schools. In February 2010, after…

Feasibility of reducing fines in S-5 mixes.

DOT National Transportation Integrated Search

1975-01-01

The study investigated the feasibility of eliminating aggregate particulates passing the #200 and #100 sieves from a surface mix (S-5) gradation. Feasibility was to be determined on the basis of test mixtures, with particulates deleted, meeting Virgi...

An R package for analyzing and modeling ranking data

PubMed Central

2013-01-01

Background In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty’s and Koczkodaj’s inconsistencies), probability models (Luce model, distance-based model, and rank-ordered logit model), and the visualization of ranking data with multidimensional preference analysis. Results Examples of the use of package pmr are given using a real ranking dataset from medical informatics, in which 566 Hong Kong physicians ranked the top five incentives (1: competitive pressures; 2: increased savings; 3: government regulation; 4: improved efficiency; 5: improved quality care; 6: patient demand; 7: financial incentives) to the computerization of clinical practice. The mean rank showed that item 4 is the most preferred item and item 3 is the least preferred item, and significance difference was found between physicians’ preferences with respect to their monthly income. A multidimensional preference analysis identified two dimensions that explain 42% of the total variance. The first can be interpreted as the overall preference of the seven items (labeled as “internal/external”), and the second dimension can be interpreted as their overall variance of (labeled as “push/pull factors”). Various statistical models were fitted, and the best were found to be weighted distance-based models with Spearman’s footrule distance. Conclusions In this paper, we presented the R package pmr, the first package for analyzing and modeling ranking data. The package provides insight to users through descriptive statistics of ranking data. Users can also visualize

An R package for analyzing and modeling ranking data.

PubMed

Lee, Paul H; Yu, Philip L H

2013-05-14

In medical informatics, psychology, market research and many other fields, researchers often need to analyze and model ranking data. However, there is no statistical software that provides tools for the comprehensive analysis of ranking data. Here, we present pmr, an R package for analyzing and modeling ranking data with a bundle of tools. The pmr package enables descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce model, distance-based model, and rank-ordered logit model), and the visualization of ranking data with multidimensional preference analysis. Examples of the use of package pmr are given using a real ranking dataset from medical informatics, in which 566 Hong Kong physicians ranked the top five incentives (1: competitive pressures; 2: increased savings; 3: government regulation; 4: improved efficiency; 5: improved quality care; 6: patient demand; 7: financial incentives) to the computerization of clinical practice. The mean rank showed that item 4 is the most preferred item and item 3 is the least preferred item, and significance difference was found between physicians' preferences with respect to their monthly income. A multidimensional preference analysis identified two dimensions that explain 42% of the total variance. The first can be interpreted as the overall preference of the seven items (labeled as "internal/external"), and the second dimension can be interpreted as their overall variance of (labeled as "push/pull factors"). Various statistical models were fitted, and the best were found to be weighted distance-based models with Spearman's footrule distance. In this paper, we presented the R package pmr, the first package for analyzing and modeling ranking data. The package provides insight to users through descriptive statistics of ranking data. Users can also visualize ranking data by applying a thought

Sigmund Freud and Otto Rank: debates and confrontations about anxiety and birth.

PubMed

Pizarro Obaid, Francisco

2012-06-01

The publication of Otto Rank's The Trauma of Birth (1924) gave rise to an intense debate within the secret Committee and confronted Freud with one of his most beloved disciples. After analyzing the letters that the Professor exchanged with his closest collaborators and reviewing the works he published during this period, it is clear that anxiety was a crucial element among the topics in dispute. His reflections linked to the signal anxiety concept allowed Freud to refute Rank's thesis that defined birth trauma as the paradigmatic key to understanding neurosis, and, in turn, was a way of confirming the validity of the concepts of Oedipus complex, repression and castration in the conceptualization of anxiety. The reasons for the modifications of anxiety theory in the mid-1920s cannot be reduced, as Freud would affirm officially in his work of 1926, to the detection of internal contradictions in his theory or to the desire to establish a metapsychological version of the problem, for they gain their essential impulse from the debate with Rank. Copyright © 2012 Institute of Psychoanalysis.

Global network centrality of university rankings

NASA Astrophysics Data System (ADS)

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.

Quantum probability ranking principle for ligand-based virtual screening.

PubMed

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

NASA Astrophysics Data System (ADS)

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.

You Cannot Judge a Book by Its Cover: The Problems with Journal Rankings

ERIC Educational Resources Information Center

Sangster, Alan

2015-01-01

Journal rankings lists have impacted and are impacting accounting educators and accounting education researchers around the world. Nowhere is the impact positive. It ranges from slight constraints on academic freedom to admonition, censure, reduced research allowances, non-promotion, non-short-listing for jobs, increased teaching loads, and…

A novel three-stage distance-based consensus ranking method

NASA Astrophysics Data System (ADS)

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.

Learning to rank figures within a biomedical article.

PubMed

Liu, Feifan; Yu, Hong

2014-01-01

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. This ever-increasing sheer volume has made it difficult for scientists to effectively and accurately access figures of their interest, the process of which is crucial for validating research facts and for formulating or testing novel research hypotheses. Current figure search applications can't fully meet this challenge as the "bag of figures" assumption doesn't take into account the relationship among figures. In our previous study, hundreds of biomedical researchers have annotated articles in which they serve as corresponding authors. They ranked each figure in their paper based on a figure's importance at their discretion, referred to as "figure ranking". Using this collection of annotated data, we investigated computational approaches to automatically rank figures. We exploited and extended the state-of-the-art listwise learning-to-rank algorithms and developed a new supervised-learning model BioFigRank. The cross-validation results show that BioFigRank yielded the best performance compared with other state-of-the-art computational models, and the greedy feature selection can further boost the ranking performance significantly. Furthermore, we carry out the evaluation by comparing BioFigRank with three-level competitive domain-specific human experts: (1) First Author, (2) Non-Author-In-Domain-Expert who is not the author nor co-author of an article but who works in the same field of the corresponding author of the article, and (3) Non-Author-Out-Domain-Expert who is not the author nor co-author of an article and who may or may not work in the same field of the corresponding author of an article. Our results show that BioFigRank outperforms Non-Author-Out-Domain-Expert and performs as well as Non-Author-In-Domain-Expert. Although BioFigRank underperforms First Author, since most biomedical researchers are either in- or out

Diversity rankings among bacterial lineages in soil.

PubMed

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.

Image Re-Ranking Based on Topic Diversity.

PubMed

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.

Social class rank, essentialism, and punitive judgment.

PubMed

Kraus, Michael W; Keltner, Dacher

2013-08-01

Recent evidence suggests that perceptions of social class rank influence a variety of social cognitive tendencies, from patterns of causal attribution to moral judgment. In the present studies we tested the hypotheses that upper-class rank individuals would be more likely to endorse essentialist lay theories of social class categories (i.e., that social class is founded in genetically based, biological differences) than would lower-class rank individuals and that these beliefs would decrease support for restorative justice--which seeks to rehabilitate offenders, rather than punish unlawful action. Across studies, higher social class rank was associated with increased essentialism of social class categories (Studies 1, 2, and 4) and decreased support for restorative justice (Study 4). Moreover, manipulated essentialist beliefs decreased preferences for restorative justice (Study 3), and the association between social class rank and class-based essentialist theories was explained by the tendency to endorse beliefs in a just world (Study 2). Implications for how class-based essentialist beliefs potentially constrain social opportunity and mobility are discussed.

A Different Approach to University Rankings

ERIC Educational Resources Information Center

Tofallis, Chris

2012-01-01

Educationalists are well able to find fault with rankings on numerous grounds and may reject them outright. However, given that they are here to stay, we could also try to improve them wherever possible. All currently published university rankings combine various measures to produce an overall score using an additive approach. The individual…

Compressive Sensing via Nonlocal Smoothed Rank Function

PubMed Central

Fan, Ya-Ru; Liu, Jun; Zhao, Xi-Le

2016-01-01

Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction. PMID:27583683

Benchmarking Jiangsu University to Improve Its Academic Ranking

ERIC Educational Resources Information Center

Li, Xinchao; Thige, Joseph Muiruri

2017-01-01

This paper collates research on global ranking through U.S.News.com in relation to Jiangsu University's nonappearance in global ranking of higher education institutions. The author critiques the Academic set up of the University in comparison with universities Ranked as World Class. The author navigates the study largely through descriptive and…

Sign rank versus Vapnik-Chervonenkis dimension

NASA Astrophysics Data System (ADS)

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.

Rankings and the Global Reputation Race

ERIC Educational Resources Information Center

Hazelkorn, Ellen

2014-01-01

This chapter delves into the growing influence and impact of rankings on higher education, as a lens through which to view how the race for reputation and status is changing the higher education landscape, both globally and nationally. The author considers the extent to which rankings are driving policy choices and institutional decisions and the…

The Rankings Game: Who's Playing Whom?

ERIC Educational Resources Information Center

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…

College Rankings: History, Criticism and Reform

ERIC Educational Resources Information Center

Myers, Luke; Robe, Jonathan

2009-01-01

Today, college quality rankings in news magazines and guidebooks are a big business with tangible impacts on the operation of higher education institutions. The college rankings published annually by "U.S. News and World Report" ("U.S. News") are so influential that Don Hossler of Indiana University derisively claims that higher education is the…

Public Perception of Cancer Survival Rankings

ERIC Educational Resources Information Center

Jensen, Jakob D.; Scherr, Courtney L.; Brown, Natasha; Jones, Christina; Christy, Katheryn

2013-01-01

Past research has observed that certain subgroups (e.g., individuals who are overweight/obese) have inaccurate estimates of survival rates for particular cancers (e.g., colon cancer). However, no study has examined whether the lay public can accurately rank cancer survival rates in comparison with one another (i.e., rank cancers from most deadly…

Thermolysis of phenethyl phenyl ether: A model of ether linkages in low rank coal

DOE Office of Scientific and Technical Information (OSTI.GOV)

Britt, P.F.; Buchanan, A.C. III; Malcolm, E.A.

Currently, an area of interest and frustration for coal chemists has been the direct liquefaction of low rank coal. Although low rank coals are more reactive than bituminous coals, they are more difficult to liquefy and offer lower liquefaction yields under conditions optimized for bituminous coals. Solomon, Serio, and co-workers have shown that: in the pyrolysis and liquefaction of low rank coals, a low temperature cross-linking reaction associated with oxygen functional groups occurs before tar evolution. A variety of pretreatments (demineralization, alkylation, and ion-exchange) have been shown to reduce these retrogressive reactions and increase tar yields, but the actual chemicalmore » reactions responsible for these processes have not been defined. In order to gain insight into the thermochemical reactions leading to cross-linking in low rank coal, we have undertaken a study of the pyrolysis of oxygen containing coal model compounds. Solid state NMR studies suggest that the alkyl aryl ether linkage may be present in modest amounts in low rank coal. Therefore, in this paper, we will investigate the thermolysis of phenethyl phenyl ether (PPE) as a model of 0-aryl ether linkages found in low rank coal, lignites, and lignin, an evolutionary precursor of coal. Our results have uncovered a new reaction channel that can account for 25% of the products formed. The impact of reaction conditions, including restricted mass transport, on this new reaction pathway and the role of oxygen functional groups in cross-linking reactions will be investigated.« less

Dietary patterns by reduced rank regression are associated with obesity and hypertension in Australian adults.

PubMed

Livingstone, Katherine M; McNaughton, Sarah A

2017-01-01

Evidence linking dietary patterns (DP) and obesity and hypertension prevalence is inconsistent. We aimed to identify DP derived from energy density, fibre and sugar intakes, as well as Na, K, fibre, SFA and PUFA, and investigate associations with obesity and hypertension. Adults (n 4908) were included from the cross-sectional Australian Health Survey 2011-2013. Two 24-h dietary recalls estimated food and nutrient intakes. Reduced rank regression derived DP with dietary energy density (DED), fibre density and total sugar intake as response variables for obesity and Na:K, SFA:PUFA and fibre density as variables for hypertension. Poisson regression investigated relationships between DP and prevalence ratios (PR) of overweight/obesity (BMI≥25 kg/m2) and hypertension (blood pressure≥140/90 mmHg). Obesity-DP1 was positively correlated with fibre density and sugars and inversely with DED. Obesity-DP2 was positively correlated with sugars and inversely with fibre density. Individuals in the highest tertile of Obesity-DP1 and Obesity-DP2, compared with the lowest, had lower (PR 0·88; 95 % CI 0·81, 0·95) and higher (PR 1·09; 95 % CI 1·01, 1·18) prevalence of obesity, respectively. Na:K and SFA:PUFA were positively correlated with Hypertension-DP1 and inversely correlated with Hypertension-DP2, respectively. There was a trend towards higher hypertension prevalence in the highest tertile of Hypertension-DP1 compared with the lowest (PR 1·18; 95 % CI 0·99, 1·41). Hypertension-DP2 was not associated with hypertension. Obesity prevalence was inversely associated with low-DED, high-fibre and high-sugar (natural sugars) diets and positively associated with low-fibre and high-sugar (added sugars) diets. Hypertension prevalence was higher on low-fibre and high-Na and SFA diets.

Assessing significance in a Markov chain without mixing

PubMed Central

Chikina, Maria; Frieze, Alan; Pegden, Wesley

2017-01-01

We present a statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution. In particular, given a value function for the states of the Markov chain, we would like to show rigorously that the presented state is an outlier with respect to the values, by establishing a p value under the null hypothesis that it was chosen from a stationary distribution of the chain. A simple heuristic used in practice is to sample ranks of states from long random trajectories on the Markov chain and compare these with the rank of the presented state; if the presented state is a 0.1% outlier compared with the sampled ranks (its rank is in the bottom 0.1% of sampled ranks), then this observation should correspond to a p value of 0.001. This significance is not rigorous, however, without good bounds on the mixing time of the Markov chain. Our test is the following: Given the presented state in the Markov chain, take a random walk from the presented state for any number of steps. We prove that observing that the presented state is an ε-outlier on the walk is significant at p=2ε under the null hypothesis that the state was chosen from a stationary distribution. We assume nothing about the Markov chain beyond reversibility and show that significance at p≈ε is best possible in general. We illustrate the use of our test with a potential application to the rigorous detection of gerrymandering in Congressional districting. PMID:28246331

A Case-Based Reasoning Method with Rank Aggregation

NASA Astrophysics Data System (ADS)

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.

Global network centrality of university rankings

PubMed Central

Del Vecchio, Marco; Pogrebna, Ganna

2017-01-01

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. PMID:29134105

System and method for regeneration and recirculation of a reducing agent using highly exothermic reactions induced by mixed industrial slags

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakano, Jinichiro; Bennett, James P.; Nakano, Anna

Embodiments relate to systems and methods for regenerating and recirculating a CO, H.sub.2 or combinations thereof utilized for metal oxide reduction in a reduction furnace. The reduction furnace receives the reducing agent, reduces the metal oxide, and generates an exhaust of the oxidized product. The oxidized product is transferred to a mixing vessel, where the oxidized product, a calcium oxide, and a vanadium oxide interact to regenerate the reducing agent from the oxidized product. The regenerated reducing agent is transferred back to the reduction furnace for continued metal oxide reductions.

Diagnostic tools for mixing models of stream water chemistry

USGS Publications Warehouse

Hooper, Richard P.

2003-01-01

Mixing models provide a useful null hypothesis against which to evaluate processes controlling stream water chemical data. Because conservative mixing of end‐members with constant concentration is a linear process, a number of simple mathematical and multivariate statistical methods can be applied to this problem. Although mixing models have been most typically used in the context of mixing soil and groundwater end‐members, an extension of the mathematics of mixing models is presented that assesses the “fit” of a multivariate data set to a lower dimensional mixing subspace without the need for explicitly identified end‐members. Diagnostic tools are developed to determine the approximate rank of the data set and to assess lack of fit of the data. This permits identification of processes that violate the assumptions of the mixing model and can suggest the dominant processes controlling stream water chemical variation. These same diagnostic tools can be used to assess the fit of the chemistry of one site into the mixing subspace of a different site, thereby permitting an assessment of the consistency of controlling end‐members across sites. This technique is applied to a number of sites at the Panola Mountain Research Watershed located near Atlanta, Georgia.

Rank-preserving regression: a more robust rank regression model against outliers.

PubMed

Chen, Tian; Kowalski, Jeanne; Chen, Rui; Wu, Pan; Zhang, Hui; Feng, Changyong; Tu, Xin M

2016-08-30

Mean-based semi-parametric regression models such as the popular generalized estimating equations are widely used to improve robustness of inference over parametric models. Unfortunately, such models are quite sensitive to outlying observations. The Wilcoxon-score-based rank regression (RR) provides more robust estimates over generalized estimating equations against outliers. However, the RR and its extensions do not sufficiently address missing data arising in longitudinal studies. In this paper, we propose a new approach to address outliers under a different framework based on the functional response models. This functional-response-model-based alternative not only addresses limitations of the RR and its extensions for longitudinal data, but, with its rank-preserving property, even provides more robust estimates than these alternatives. The proposed approach is illustrated with both real and simulated data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Aggregate Interview Method of ranking orthopedic applicants predicts future performance.

PubMed

Geissler, Jacqueline; VanHeest, Ann; Tatman, Penny; Gioe, Terence

2013-07-01

This article evaluates and describes a process of ranking orthopedic applicants using what the authors term the Aggregate Interview Method. The authors hypothesized that higher-ranking applicants using this method at their institution would perform better than those ranked lower using multiple measures of resident performance. A retrospective review of 115 orthopedic residents was performed at the authors' institution. Residents were grouped into 3 categories by matching rank numbers: 1-5, 6-14, and 15 or higher. Each rank group was compared with resident performance as measured by faculty evaluations, the Orthopaedic In-Training Examination (OITE), and American Board of Orthopaedic Surgery (ABOS) test results. Residents ranked 1-5 scored significantly better on patient care, behavior, and overall competence by faculty evaluation (P<.05). Residents ranked 1-5 scored higher on the OITE compared with those ranked 6-14 during postgraduate years 2 and 3 (P⩽.5). Graduates who had been ranked 1-5 had a 100% pass rate on the ABOS part 1 examination on the first attempt. The most favorably ranked residents performed at or above the level of other residents in the program; they did not score inferiorly on any measure. These results support the authors' method of ranking residents. The rigorous Aggregate Interview Method for ranking applicants consistently identified orthopedic resident candidates who scored highly on the Accreditation Council for Graduate Medical Education resident core competencies as measured by faculty evaluations, performed above the national average on the OITE, and passed the ABOS part 1 examination at rates exceeding the national average. Copyright 2013, SLACK Incorporated.

Learning to Rank Figures within a Biomedical Article

PubMed Central

Liu, Feifan; Yu, Hong

2014-01-01

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. This ever-increasing sheer volume has made it difficult for scientists to effectively and accurately access figures of their interest, the process of which is crucial for validating research facts and for formulating or testing novel research hypotheses. Current figure search applications can't fully meet this challenge as the “bag of figures” assumption doesn't take into account the relationship among figures. In our previous study, hundreds of biomedical researchers have annotated articles in which they serve as corresponding authors. They ranked each figure in their paper based on a figure's importance at their discretion, referred to as “figure ranking”. Using this collection of annotated data, we investigated computational approaches to automatically rank figures. We exploited and extended the state-of-the-art listwise learning-to-rank algorithms and developed a new supervised-learning model BioFigRank. The cross-validation results show that BioFigRank yielded the best performance compared with other state-of-the-art computational models, and the greedy feature selection can further boost the ranking performance significantly. Furthermore, we carry out the evaluation by comparing BioFigRank with three-level competitive domain-specific human experts: (1) First Author, (2) Non-Author-In-Domain-Expert who is not the author nor co-author of an article but who works in the same field of the corresponding author of the article, and (3) Non-Author-Out-Domain-Expert who is not the author nor co-author of an article and who may or may not work in the same field of the corresponding author of an article. Our results show that BioFigRank outperforms Non-Author-Out-Domain-Expert and performs as well as Non-Author-In-Domain-Expert. Although BioFigRank underperforms First Author, since most biomedical researchers are either in- or out

Diversity efforts, admissions, and national rankings: can we align priorities?

PubMed

Heller, Caren A; Rúa, Sandra Hurtado; Mazumdar, Madhu; Moon, Jennifer E; Bardes, Charles; Gotto, Antonio M

2014-01-01

Increasing student body diversity is a priority for national health education and professional organizations and for many medical schools. However, national rankings of medical schools, such as those published by U.S. News & World Report, place a heavy emphasis on grade point average (GPA) and Medical College Admissions Test (MCAT) scores, without considering student body diversity. These rankings affect organizational reputation and admissions outcomes, even though there is considerable controversy surrounding the predictive value of GPA and MCAT scores. Our aim in this article was to explore the relationship between standard admissions practices, which typically aim to attract students with the highest academic scores, and student body diversity. We examined how changes in GPA and MCAT scores over 5 years correlated with the percentage of enrolled students who are underrepresented in medicine. In a majority of medical schools in the United States from 2005 to 2009, average GPA and MCAT scores of applicants increased, whereas the percentage of enrolled students who are underrepresented in medicine decreased. Our findings suggest that efforts to increase the diversity of medical school student bodies may be complicated by a desire to maintain high average GPA and MCAT scores. We propose that U.S. News revise its ranking methodology by incorporating a new diversity score into its student selectivity score and by reducing the weight placed on GPA and MCAT scores.

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

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

Visualizing Rank Time Series of Wikipedia Top-Viewed Pages.

PubMed

Xia, Jing; Hou, Yumeng; Chen, Yingjie Victor; Qian, Zhenyu Cheryl; Ebert, David S; Chen, Wei

2017-01-01

Visual clutter is a common challenge when visualizing large rank time series data. WikiTopReader, a reader of Wikipedia page rank, lets users explore connections among top-viewed pages by connecting page-rank behaviors with page-link relations. Such a combination enhances the unweighted Wikipedia page-link network and focuses attention on the page of interest. A set of user evaluations shows that the system effectively represents evolving ranking patterns and page-wise correlation.

Implicating Receptor Activator of NF-κB (RANK)/RANK Ligand Signalling in Microglial Responses to Toll-Like Receptor Stimuli

PubMed Central

Kichev, Anton; Eede, Pascale; Gressens, Pierre; Thornton, Claire; Hagberg, Henrik

2017-01-01

Inflammation in the perinatal brain caused by maternal or intrauterine fetal infection is now well established as an important contributor to the development of perinatal brain injury. Exposure to inflammatory products can impair perinatal brain development and act as a risk factor for neurological dysfunction, cognitive disorders, cerebral palsy, or preterm birth. Pre-exposure to inflammation significantly exacerbates brain injury caused by hypoxic/ischaemic insult. Tumour necrosis factor (TNF) is a family of cytokines largely involved in inflammation signalling. In our previous study, we identified the importance of TNF-related apoptosis-inducing ligand (TRAIL) signalling in the development of perinatal brain injury. We observed a significant increase in the expression levels of a soluble decoy receptor for TRAIL, osteoprotegerin (OPG). Besides TRAIL, OPG is able to bind the receptor activator of the NF-κB (RANK) ligand (RANKL) and inhibit its signalling. The function of the RANK/RANKL/OPG system in the brain has not come under much scrutiny. The aim of this research study was to elucidate the role of RANK, RANKL, and OPG in microglial responses to the proinflammatory stimuli lipopolysaccharide (LPS) and polyinosinic-polycytidylic acid (Poly I:C). Here, we show that RANK signalling is important for regulating the activation of the BV2 microglial cell line. We found that LPS treatment causes a significant decrease in the expression of RANK in the BV2 cell line while significantly increasing the expression of OPG, Toll-like receptor (TLR)3, and the adaptor proteins MyD88 and TRIF. We found that pretreatment of BV2 cells with RANKL for 24 h before the LPS or Poly I:C exposure decreases the expression of inflammatory markers such as inducible nitric oxide synthase and cyclooxygenase. This is accompanied by a decreased expression of the TLR adaptor proteins MyD88 and TRIF, which we observed after RANKL treatment. Similar results were obtained in our experiments with

Implicating Receptor Activator of NF-κB (RANK)/RANK Ligand Signalling in Microglial Responses to Toll-Like Receptor Stimuli.

PubMed

Kichev, Anton; Eede, Pascale; Gressens, Pierre; Thornton, Claire; Hagberg, Henrik

2017-01-01

Inflammation in the perinatal brain caused by maternal or intrauterine fetal infection is now well established as an important contributor to the development of perinatal brain injury. Exposure to inflammatory products can impair perinatal brain development and act as a risk factor for neurological dysfunction, cognitive disorders, cerebral palsy, or preterm birth. Pre-exposure to inflammation significantly exacerbates brain injury caused by hypoxic/ischaemic insult. Tumour necrosis factor (TNF) is a family of cytokines largely involved in inflammation signalling. In our previous study, we identified the importance of TNF-related apoptosis-inducing ligand (TRAIL) signalling in the development of perinatal brain injury. We observed a significant increase in the expression levels of a soluble decoy receptor for TRAIL, osteoprotegerin (OPG). Besides TRAIL, OPG is able to bind the receptor activator of the NF-κB (RANK) ligand (RANKL) and inhibit its signalling. The function of the RANK/RANKL/OPG system in the brain has not come under much scrutiny. The aim of this research study was to elucidate the role of RANK, RANKL, and OPG in microglial responses to the proinflammatory stimuli lipopolysaccharide (LPS) and polyinosinic-polycytidylic acid (Poly I:C). Here, we show that RANK signalling is important for regulating the activation of the BV2 microglial cell line. We found that LPS treatment causes a significant decrease in the expression of RANK in the BV2 cell line while significantly increasing the expression of OPG, Toll-like receptor (TLR)3, and the adaptor proteins MyD88 and TRIF. We found that pretreatment of BV2 cells with RANKL for 24 h before the LPS or Poly I:C exposure decreases the expression of inflammatory markers such as inducible nitric oxide synthase and cyclooxygenase. This is accompanied by a decreased expression of the TLR adaptor proteins MyD88 and TRIF, which we observed after RANKL treatment. Similar results were obtained in our experiments with

Sensitivity ranking for freshwater invertebrates towards hydrocarbon contaminants.

PubMed

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.

Ranking of sabotage/tampering avoidance technology alternatives

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrews, W.B.; Tabatabai, A.S.; Powers, T.B.

1986-01-01

Pacific Northwest Laboratory conducted a study to evaluate alternatives to the design and operation of nuclear power plants, emphasizing a reduction of their vulnerability to sabotage. Estimates of core melt accident frequency during normal operations and from sabotage/tampering events were used to rank the alternatives. Core melt frequency for normal operations was estimated using sensitivity analysis of results of probabilistic risk assessments. Core melt frequency for sabotage/tampering was estimated by developing a model based on probabilistic risk analyses, historic data, engineering judgment, and safeguards analyses of plant locations where core melt events could be initiated. Results indicate the most effectivemore » alternatives focus on large areas of the plant, increase safety system redundancy, and reduce reliance on single locations for mitigation of transients. Less effective options focus on specific areas of the plant, reduce reliance on some plant areas for safe shutdown, and focus on less vulnerable targets.« less

Dominance-based ranking functions for interval-valued intuitionistic fuzzy sets.

PubMed

Chen, Liang-Hsuan; Tu, Chien-Cheng

2014-08-01

The ranking of interval-valued intuitionistic fuzzy sets (IvIFSs) is difficult since they include the interval values of membership and nonmembership. This paper proposes ranking functions for IvIFSs based on the dominance concept. The proposed ranking functions consider the degree to which an IvIFS dominates and is not dominated by other IvIFSs. Based on the bivariate framework and the dominance concept, the functions incorporate not only the boundary values of membership and nonmembership, but also the relative relations among IvIFSs in comparisons. The dominance-based ranking functions include bipolar evaluations with a parameter that allows the decision-maker to reflect his actual attitude in allocating the various kinds of dominance. The relationship for two IvIFSs that satisfy the dual couple is defined based on four proposed ranking functions. Importantly, the proposed ranking functions can achieve a full ranking for all IvIFSs. Two examples are used to demonstrate the applicability and distinctiveness of the proposed ranking functions.

The Augmented Lagrange Multipliers Method for Matrix Completion from Corrupted Samplings with Application to Mixed Gaussian-Impulse Noise Removal

PubMed Central

Meng, Fan; Yang, Xiaomei; Zhou, Chenghu

2014-01-01

This paper studies the problem of the restoration of images corrupted by mixed Gaussian-impulse noise. In recent years, low-rank matrix reconstruction has become a research hotspot in many scientific and engineering domains such as machine learning, image processing, computer vision and bioinformatics, which mainly involves the problem of matrix completion and robust principal component analysis, namely recovering a low-rank matrix from an incomplete but accurate sampling subset of its entries and from an observed data matrix with an unknown fraction of its entries being arbitrarily corrupted, respectively. Inspired by these ideas, we consider the problem of recovering a low-rank matrix from an incomplete sampling subset of its entries with an unknown fraction of the samplings contaminated by arbitrary errors, which is defined as the problem of matrix completion from corrupted samplings and modeled as a convex optimization problem that minimizes a combination of the nuclear norm and the -norm in this paper. Meanwhile, we put forward a novel and effective algorithm called augmented Lagrange multipliers to exactly solve the problem. For mixed Gaussian-impulse noise removal, we regard it as the problem of matrix completion from corrupted samplings, and restore the noisy image following an impulse-detecting procedure. Compared with some existing methods for mixed noise removal, the recovery quality performance of our method is dominant if images possess low-rank features such as geometrically regular textures and similar structured contents; especially when the density of impulse noise is relatively high and the variance of Gaussian noise is small, our method can outperform the traditional methods significantly not only in the simultaneous removal of Gaussian noise and impulse noise, and the restoration ability for a low-rank image matrix, but also in the preservation of textures and details in the image. PMID:25248103

International ranking systems for universities and institutions: a critical appraisal

PubMed Central

Ioannidis, John PA; Patsopoulos, Nikolaos A; Kavvoura, Fotini K; Tatsioni, Athina; Evangelou, Evangelos; Kouri, Ioanna; Contopoulos-Ioannidis, Despina G; Liberopoulos, George

2007-01-01

Background Ranking of universities and institutions has attracted wide attention recently. Several systems have been proposed that attempt to rank academic institutions worldwide. Methods We review the two most publicly visible ranking systems, the Shanghai Jiao Tong University 'Academic Ranking of World Universities' and the Times Higher Education Supplement 'World University Rankings' and also briefly review other ranking systems that use different criteria. We assess the construct validity for educational and research excellence and the measurement validity of each of the proposed ranking criteria, and try to identify generic challenges in international ranking of universities and institutions. Results None of the reviewed criteria for international ranking seems to have very good construct validity for both educational and research excellence, and most don't have very good construct validity even for just one of these two aspects of excellence. Measurement error for many items is also considerable or is not possible to determine due to lack of publication of the relevant data and methodology details. The concordance between the 2006 rankings by Shanghai and Times is modest at best, with only 133 universities shared in their top 200 lists. The examination of the existing international ranking systems suggests that generic challenges include adjustment for institutional size, definition of institutions, implications of average measurements of excellence versus measurements of extremes, adjustments for scientific field, time frame of measurement and allocation of credit for excellence. Conclusion Naïve lists of international institutional rankings that do not address these fundamental challenges with transparent methods are misleading and should be abandoned. We make some suggestions on how focused and standardized evaluations of excellence could be improved and placed in proper context. PMID:17961208

Robust Covariate-Adjusted Log-Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data

PubMed Central

Song, Rui; Kosorok, Michael R.; Cai, Jianwen

2009-01-01

Summary Recurrent events data are frequently encountered in clinical trials. This article develops robust covariate-adjusted log-rank statistics applied to recurrent events data with arbitrary numbers of events under independent censoring and the corresponding sample size formula. The proposed log-rank tests are robust with respect to different data-generating processes and are adjusted for predictive covariates. It reduces to the Kong and Slud (1997, Biometrika 84, 847–862) setting in the case of a single event. The sample size formula is derived based on the asymptotic normality of the covariate-adjusted log-rank statistics under certain local alternatives and a working model for baseline covariates in the recurrent event data context. When the effect size is small and the baseline covariates do not contain significant information about event times, it reduces to the same form as that of Schoenfeld (1983, Biometrics 39, 499–503) for cases of a single event or independent event times within a subject. We carry out simulations to study the control of type I error and the comparison of powers between several methods in finite samples. The proposed sample size formula is illustrated using data from an rhDNase study. PMID:18162107

The sensitivity of relative toxicity rankings by the USF/NASA test method to some test variables

NASA Technical Reports Server (NTRS)

Hilado, C. J.; Labossiere, L. A.; Leon, H. A.; Kourtides, D. A.; Parker, J. A.; Hsu, M.-T. S.

1976-01-01

Pyrolysis temperature and the distance between the source and sensor of effluents are two important variables in tests for relative toxicity. Modifications of the USF/NASA toxicity screening test method to increase the upper temperature limit of pyrolysis, reduce the distance between the sample and the test animals, and increase the chamber volume available for animal occupancy, did not significantly alter rankings of relative toxicity of four representative materials. The changes rendered some differences no longer significant, but did not reverse any rankings. The materials studied were cotton, wool, aromatic polyamide, and polybenzimidazole.

Learning to rank-based gene summary extraction.

PubMed

Shang, Yue; Hao, Huihui; Wu, Jiajin; Lin, Hongfei

2014-01-01

In recent years, the biomedical literature has been growing rapidly. These articles provide a large amount of information about proteins, genes and their interactions. Reading such a huge amount of literature is a tedious task for researchers to gain knowledge about a gene. As a result, it is significant for biomedical researchers to have a quick understanding of the query concept by integrating its relevant resources. In the task of gene summary generation, we regard automatic summary as a ranking problem and apply the method of learning to rank to automatically solve this problem. This paper uses three features as a basis for sentence selection: gene ontology relevance, topic relevance and TextRank. From there, we obtain the feature weight vector using the learning to rank algorithm and predict the scores of candidate summary sentences and obtain top sentences to generate the summary. ROUGE (a toolkit for summarization of automatic evaluation) was used to evaluate the summarization result and the experimental results showed that our method outperforms the baseline techniques. According to the experimental result, the combination of three features can improve the performance of summary. The application of learning to rank can facilitate the further expansion of features for measuring the significance of sentences.

World University Rankings: Take with a Large Pinch of Salt

ERIC Educational Resources Information Center

Cheng, Soh Kay

2011-01-01

Equating the unequal is misleading, and this happens consistently in comparing rankings from different university ranking systems, as the NUT saga shows. This article illustrates the problem by analyzing the 2011 rankings of the top 100 universities in the AWUR, QSWUR and THEWUR ranking results. It also discusses the reasons why the rankings…

On Classification of Modular Categories by Rank: Table A.1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bruillard, Paul; Ng, Siu-Hung; Rowell, Eric C.

2016-04-10

The feasibility of a classification-by-rank program for modular categories follows from the Rank-Finiteness Theorem. We develop arithmetic, representation theoretic and algebraic methods for classifying modular categories by rank. As an application, we determine all possible fusion rules for all rank=5 modular categories and describe the corresponding monoidal equivalence classes.

A Mixed-Method Exploration of Functioning in Safe Schools/Healthy Students Partnerships

ERIC Educational Resources Information Center

Merrill, Marina L.; Taylor, Nicole L.; Martin, Alison J.; Maxim, Lauren A.; D'Ambrosio, Ryan; Gabriel, Roy M.; Wendt, Staci J.; Mannix, Danyelle; Wells, Michael E.

2012-01-01

This paper presents a mixed-method approach to measuring the functioning of Safe Schools/Healthy Students (SS/HS) Initiative partnerships. The SS/HS national evaluation team developed a survey to collect partners' perceptions of functioning within SS/HS partnerships. Average partnership functioning scores were used to rank each site from lowest to…

Learning oncogenetic networks by reducing to mixed integer linear programming.

PubMed

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

Long-term operation of microbial electrosynthesis cell reducing CO2 to multi-carbon chemicals with a mixed culture avoiding methanogenesis.

PubMed

Bajracharya, Suman; Yuliasni, Rustiana; Vanbroekhoven, Karolien; Buisman, Cees J N; Strik, David P B T B; Pant, Deepak

2017-02-01

In microbial electrosynthesis (MES), CO 2 can be reduced preferably to multi-carbon chemicals by a biocathode-based process which uses electrochemically active bacteria as catalysts. A mixed anaerobic consortium from biological origin typically produces methane from CO 2 reduction which circumvents production of multi-carbon compounds. This study aimed to develop a stable and robust CO 2 reducing biocathode from a mixed culture inoculum avoiding the methane generation. An effective approach was demonstrated based on (i) an enrichment procedure involving inoculum pre-treatment and several culture transfers in H 2 :CO 2 media, (ii) a transfer from heterotrophic to autotrophic growth and (iii) a sequential batch operation. Biomass growth and gradual acclimation to CO 2 electro-reduction accomplished a maximum acetate production rate of 400mgL catholyte -1 d -1 at -1V (vs. Ag/AgCl). Methane was never detected in more than 300days of operation. Accumulation of acetate up to 7-10gL -1 was repeatedly attained by supplying (80:20) CO 2 :N 2 mixture at -0.9 to -1V (vs. Ag/AgCl). In addition, ethanol and butyrate were also produced from CO 2 reduction. Thus, a robust CO 2 reducing biocathode can be developed from a mixed culture avoiding methane generation by adopting the specific culture enrichment and operation procedures without the direct addition of chemical inhibitor. Copyright © 2016 Elsevier B.V. All rights reserved.

Cross-modal learning to rank via latent joint representation.

PubMed

Wu, Fei; Jiang, Xinyang; Li, Xi; Tang, Siliang; Lu, Weiming; Zhang, Zhongfei; Zhuang, Yueting

2015-05-01

Cross-modal ranking is a research topic that is imperative to many applications involving multimodal data. Discovering a joint representation for multimodal data and learning a ranking function are essential in order to boost the cross-media retrieval (i.e., image-query-text or text-query-image). In this paper, we propose an approach to discover the latent joint representation of pairs of multimodal data (e.g., pairs of an image query and a text document) via a conditional random field and structural learning in a listwise ranking manner. We call this approach cross-modal learning to rank via latent joint representation (CML²R). In CML²R, the correlations between multimodal data are captured in terms of their sharing hidden variables (e.g., topics), and a hidden-topic-driven discriminative ranking function is learned in a listwise ranking manner. The experiments show that the proposed approach achieves a good performance in cross-media retrieval and meanwhile has the capability to learn the discriminative representation of multimodal data.

Low-rank structure learning via nonconvex heuristic recovery.

PubMed

Deng, Yue; Dai, Qionghai; Liu, Risheng; Zhang, Zengke; Hu, Sanqing

2013-03-01

In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex measurements to enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions. We will, respectively, introduce how to combine the widely used ℓp norm (0 < p < 1) and log-sum term into the framework of low-rank structure learning. Although the proposed optimization is no longer convex, it still can be effectively solved by a majorization-minimization (MM)-type algorithm, with which the nonconvex objective function is iteratively replaced by its convex surrogate and the nonconvex problem finally falls into the general framework of reweighed approaches. We prove that the MM-type algorithm can converge to a stationary point after successive iterations. The proposed model is applied to solve two typical problems: robust principal component analysis and low-rank representation. Experimental results on low-rank structure learning demonstrate that our nonconvex heuristic methods, especially the log-sum heuristic recovery algorithm, generally perform much better than the convex-norm-based method (0 < p < 1) for both data with higher rank and with denser corruptions.

Low rank magnetic resonance fingerprinting.

PubMed

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.

The Rankings of Marketing Programs in China.

ERIC Educational Resources Information Center

Siu, Wai-sum

1996-01-01

Nineteen marketing faculty and administrators in China ranked 10 universities offering business administration education and indicated their criteria. Results of the rankings and evaluative criteria are presented, and implications for marketing education in China discussed. It was found that most respondents were more concerned about input…

Rankings in Institutional Strategies and Processes: Impact or Illusion?

ERIC Educational Resources Information Center

Hazelkorn, Ellen; Loukkola, Tia; Zhang, Thérèse

2014-01-01

The "Rankings in Institutional Strategies and Processes" (RISP) project is the first pan-European study of the impact and influence of rankings on European higher education institutions. The project has sought to build understanding of how rankings impact and influence the development of institutional strategies and processes and its…

Revisiting the Relationship between Institutional Rank and Student Engagement

ERIC Educational Resources Information Center

Zilvinskis, John; Louis Rocconi

2018-01-01

College rankings dominate the conversation regarding quality in postsecondary education. However, the criteria used to rank institutions often have nothing to do with the quality of education students receive. A decade ago, Pike (2004) demonstrated that institutional rank had little association with student involvement in educational activities.…

Technical Note: Interleaved Bipolar Acquisition and Low-rank Reconstruction for Water-Fat Separation in MRI.

PubMed

Cho, JaeJin; Park, HyunWook

2018-05-17

To acquire interleaved bipolar data and reconstruct the full data using low-rank property for water fat separation. Bipolar acquisition suffers from issues related to gradient switching, the opposite gradient polarities, and other system imperfections, which prevent accurate water-fat separation. In this study, an interleaved bipolar acquisition scheme and a low-rank reconstruction method were proposed to reduce issues from the bipolar gradients while achieving a short imaging time. The proposed interleaved bipolar acquisition scheme collects echo-time signals from both gradient polarities; however, the sequence increases the imaging time. To reduce the imaging time, the signals were subsampled at every dimension of k-space. The low-rank property of the bipolar acquisition was defined and exploited to estimate the full data from the acquired subsampled data. To eliminate the bipolar issues, in the proposed method, the water-fat separation was performed separately for each gradient polarity, and the results for the positive and negative gradient polarities were combined after the water-fat separation. A phantom study and in-vivo experiments were conducted on a 3T Siemens Verio system. The results for the proposed method were compared with the results of the fully sampled interleaved bipolar acquisition and Soliman's method, which was the previous water-fat separation approach for reducing the issues of bipolar gradients and accelerating the interleaved bipolar acquisition. The proposed method provided accurate water and fat images without the issues of bipolar gradients and demonstrated a better performance compared with the results of the previous methods. The water-fat separation using the bipolar acquisition has several benefits including a short echo-spacing time. However, it suffers from bipolar-gradient issues such as strong gradient switching, system imperfection, and eddy current effects. This study demonstrated that accurate water-fat separated images can

Bayesian Inference of Natural Rankings in Incomplete Competition Networks

NASA Astrophysics Data System (ADS)

Park, Juyong; Yook, Soon-Hyung

2014-08-01

Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest - essential in determining reward and penalty - is frequently an ambiguous task due to the incomplete (partially filled) nature of competition networks. Here we introduce the ``Natural Ranking,'' an unambiguous ranking method applicable to a round robin tournament, and formulate an analytical model based on the Bayesian formula for inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in resolving important issues of ranking by applying it to real-world competition networks.

Assessing significance in a Markov chain without mixing.

PubMed

Chikina, Maria; Frieze, Alan; Pegden, Wesley

2017-03-14

We present a statistical test to detect that a presented state of a reversible Markov chain was not chosen from a stationary distribution. In particular, given a value function for the states of the Markov chain, we would like to show rigorously that the presented state is an outlier with respect to the values, by establishing a [Formula: see text] value under the null hypothesis that it was chosen from a stationary distribution of the chain. A simple heuristic used in practice is to sample ranks of states from long random trajectories on the Markov chain and compare these with the rank of the presented state; if the presented state is a [Formula: see text] outlier compared with the sampled ranks (its rank is in the bottom [Formula: see text] of sampled ranks), then this observation should correspond to a [Formula: see text] value of [Formula: see text] This significance is not rigorous, however, without good bounds on the mixing time of the Markov chain. Our test is the following: Given the presented state in the Markov chain, take a random walk from the presented state for any number of steps. We prove that observing that the presented state is an [Formula: see text]-outlier on the walk is significant at [Formula: see text] under the null hypothesis that the state was chosen from a stationary distribution. We assume nothing about the Markov chain beyond reversibility and show that significance at [Formula: see text] is best possible in general. We illustrate the use of our test with a potential application to the rigorous detection of gerrymandering in Congressional districting.

Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges

NASA Astrophysics Data System (ADS)

Wingert, Bentley M.; Oerlemans, Rick; Camacho, Carlos J.

2018-01-01

The goal of virtual screening is to generate a substantially reduced and enriched subset of compounds from a large virtual chemistry space. Critical in these efforts are methods to properly rank the binding affinity of compounds. Prospective evaluations of ranking strategies in the D3R grand challenges show that for targets with deep pockets the best correlations (Spearman ρ 0.5) were obtained by our submissions that docked compounds to the holo-receptors with the most chemically similar ligand. On the other hand, for targets with open pockets using multiple receptor structures is not a good strategy. Instead, docking to a single optimal receptor led to the best correlations (Spearman ρ 0.5), and overall performs better than any other method. Yet, choosing a suboptimal receptor for crossdocking can significantly undermine the affinity rankings. Our submissions that evaluated the free energy of congeneric compounds were also among the best in the community experiment. Error bars of around 1 kcal/mol are still too large to significantly improve the overall rankings. Collectively, our top of the line predictions show that automated virtual screening with rigid receptors perform better than flexible docking and other more complex methods.

Academic Ranking--From Its Genesis to Its International Expansion

ERIC Educational Resources Information Center

Vieira, Rosilene C.; Lima, Manolita C.

2015-01-01

Given the visibility and popularity of rankings that encompass the measurement of quality of post-graduate courses, for instance, the MBA (Master of Business Administration) or graduate studies program (MSc and PhD) as do global academic rankings--Academic Ranking of World Universities-ARWU, Times Higher/Thomson Reuters World University Ranking…

Academic Ranking of World Universities by Broad Subject Fields

ERIC Educational Resources Information Center

Cheng, Ying; Liu, Nian Cai

2007-01-01

Upon numerous requests to provide ranking of world universities by broad subject fields/schools/colleges and by subject fields/programs/departments, the authors present the ranking methodologies and problems that arose from the research by the Institute of Higher Education, Shanghai Jiao Tong University on the Academic Ranking of World…

RANK/RANKL/OPG Signalization Implication in Periodontitis: New Evidence from a RANK Transgenic Mouse Model

PubMed Central

Sojod, Bouchra; Chateau, Danielle; Mueller, Christopher G.; Babajko, Sylvie; Berdal, Ariane; Lézot, Frédéric; Castaneda, Beatriz

2017-01-01

Periodontitis is based on a complex inflammatory over-response combined with possible genetic predisposition factors. The RANKL/RANK/OPG signaling pathway is implicated in bone resorption through its key function in osteoclast differentiation and activation, as well as in the inflammatory response. This central element of osteo-immunology has been suggested to be perturbed in several diseases, including periodontitis, as it is a predisposing factor for this disease. The aim of the present study was to validate this hypothesis using a transgenic mouse line, which over-expresses RANK (RTg) and develops a periodontitis-like phenotype at 5 months of age. RTg mice exhibited severe alveolar bone loss, an increased number of TRAP positive cells, and disorganization of periodontal ligaments. This phenotype was more pronounced in females. We also observed dental root resorption lacunas. Hyperplasia of the gingival epithelium, including Malassez epithelial rests, was visible as early as 25 days, preceding any other symptoms. These results demonstrate that perturbations of the RANKL/RANK/OPG system constitute a core element of periodontitis, and more globally, osteo-immune diseases. PMID:28596739

Rank on emotional intelligence, unlearning and self-leadership.

PubMed

Kramer, Robert

2012-12-01

Propelled from the inner circle after publishing The Trauma of Birth (1924), Otto Rank jettisoned Freud's science of knowing because it denied the intelligence of the emotions. Transforming therapy from knowing to being-in-relationship, Rank invented modern object-relations theory, which advocates continual learning, unlearning and relearning: that is, cutting the chains that bind us to the past. Separating, no matter how anxiety-provoking, from outworn phases of life, including previously taken-for-granted ideologies and internalized others, is essential for self-leadership. In 1926, Rank coined the terms "here-and-now" and "pre-Oedipal." By 1926, Rank had formulated a model of "creative willing"-self-leadership infused with the intelligence of the emotions-as the optimal way of being-in-relationship with others.

Extreme learning machine for ranking: generalization analysis and applications.

PubMed

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.

World University Rankings: Ambiguous Signals. Go8 Backgrounder 30

ERIC Educational Resources Information Center

Group of Eight (NJ1), 2012

2012-01-01

The current main world university rankings broadly group the leading research universities of nations. Australia's Go8 universities are generally within the top 250 ranked universities, with several institutions in the top 50-100 on some measures. This recognition is commendable, however imperfect the individual rankings may be. Use is made of…

A Rational Method for Ranking Engineering Programs.

ERIC Educational Resources Information Center

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)

Mining Feedback in Ranking and Recommendation Systems

ERIC Educational Resources Information Center

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…

Energy savings by reduced mixing in aeration tanks: results from a full scale investigation and long term implementation at Avedoere wastewater treatment plant.

PubMed

Sharma, A K; Guildal, T; Thomsen, H R; Jacobsen, B N

2011-01-01

The aim of this project was to investigate the potential of reducing number of mixers in the biological treatment process and thereby achieve energy and economical savings and contribute to cleaner environment. The project was carried out at Avedoere wastewater treatment plant and a full scale investigation was conducted to study the effect of reduced mixing on flow velocity, suspended solid sedimentation, concentration gradients of oxygen and SS with depth and treatment efficiency. The only negative effect observed was on flow velocity; however the velocity was above the critical velocity. The plant has been operating with 50% of its designed number of mixers since September 2007 and long term results also confirm that reduced mixing did not have any negative effect on treatment efficiency. The estimated yearly electricity saving is 0.75 GWh/year.

Comparative Case Studies on Indonesian Higher Education Rankings

NASA Astrophysics Data System (ADS)

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.

Case-mix adjustment and the comparison of community health center performance on patient experience measures.

PubMed

Johnson, M Laura; Rodriguez, Hector P; Solorio, M Rosa

2010-06-01

To assess the effect of case-mix adjustment on community health center (CHC) performance on patient experience measures. A Medicaid-managed care plan in Washington State collected patient survey data from 33 CHCs over three fiscal quarters during 2007-2008. The survey included three composite patient experience measures (6-month reports) and two overall ratings of care. The analytic sample includes 2,247 adult patients and 2,859 adults reporting for child patients. We compared the relative importance of patient case-mix adjusters by calculating each adjuster's predictive power and variability across CHCs. We then evaluated the impact of case-mix adjustment on the relative ranking of CHCs. Important case-mix adjusters included adult self-reported health status or parent-reported child health status, adult age, and educational attainment. The effects of case-mix adjustment on patient reports and ratings were different in the adult and child samples. Adjusting for race/ethnicity and language had a greater impact on parent reports than adult reports, but it impacted ratings similarly across the samples. The impact of adjustment on composites and ratings was modest, but it affected the relative ranking of CHCs. To ensure equitable comparison of CHC performance on patient experience measures, reports and ratings should be adjusted for adult self-reported health status or parent-reported child health status, adult age, education, race/ethnicity, and survey language. Because of the differential impact of case-mix adjusters for child and adult surveys, initiatives should consider measuring and reporting adult and child scores separately.

Bayesian Inference of Natural Rankings in Incomplete Competition Networks

PubMed Central

Park, Juyong; Yook, Soon-Hyung

2014-01-01

Competition between a complex system's constituents and a corresponding reward mechanism based on it have profound influence on the functioning, stability, and evolution of the system. But determining the dominance hierarchy or ranking among the constituent parts from the strongest to the weakest – essential in determining reward and penalty – is frequently an ambiguous task due to the incomplete (partially filled) nature of competition networks. Here we introduce the “Natural Ranking,” an unambiguous ranking method applicable to a round robin tournament, and formulate an analytical model based on the Bayesian formula for inferring the expected mean and error of the natural ranking of nodes from an incomplete network. We investigate its potential and uses in resolving important issues of ranking by applying it to real-world competition networks. PMID:25163528

Econophysics of a ranked demand and supply resource allocation problem

NASA Astrophysics Data System (ADS)

Priel, Avner; Tamir, Boaz

2018-01-01

We present a two sided resource allocation problem, between demands and supplies, where both parties are ranked. For example, in Big Data problems where a set of different computational tasks is divided between a set of computers each with its own resources, or between employees and employers where both parties are ranked, the employees by their fitness and the employers by their package benefits. The allocation process can be viewed as a repeated game where in each iteration the strategy is decided by a meta-rule, based on the ranks of both parties and the results of the previous games. We show the existence of a phase transition between an absorbing state, where all demands are satisfied, and an active one where part of the demands are always left unsatisfied. The phase transition is governed by the ratio between supplies and demand. In a job allocation problem we find positive correlation between the rank of the workers and the rank of the factories; higher rank workers are usually allocated to higher ranked factories. These all suggest global emergent properties stemming from local variables. To demonstrate the global versus local relations, we introduce a local inertial force that increases the rank of employees in proportion to their persistence time in the same factory. We show that such a local force induces non trivial global effects, mostly to benefit the lower ranked employees.

Tutorial: Calculating Percentile Rank and Percentile Norms Using SPSS

ERIC Educational Resources Information Center

Baumgartner, Ted A.

2009-01-01

Practitioners can benefit from using norms, but they often have to develop their own percentile rank and percentile norms. This article is a tutorial on how to quickly and easily calculate percentile rank and percentile norms using SPSS, and this information is presented for a data set. Some issues in calculating percentile rank and percentile…

University Rankings 2.0: New Frontiers in Institutional Comparisons

ERIC Educational Resources Information Center

Usher, Alex

2009-01-01

The number of university rankings systems in use around the world has increased dramatically over the last decade. As they have spread, they have mutated; no longer are ranking systems simply clones of the original ranking systems such as "US News" and "World Report". A number of different types of "mutation" have occurred, so that there are now…

Maximising information recovery from rank-order codes

NASA Astrophysics Data System (ADS)

Sen, B.; Furber, S.

2007-04-01

The central nervous system encodes information in sequences of asynchronously generated voltage spikes, but the precise details of this encoding are not well understood. Thorpe proposed rank-order codes as an explanation of the observed speed of information processing in the human visual system. The work described in this paper is inspired by the performance of SpikeNET, a biologically inspired neural architecture using rank-order codes for information processing, and is based on the retinal model developed by VanRullen and Thorpe. This model mimics retinal information processing by passing an input image through a bank of Difference of Gaussian (DoG) filters and then encoding the resulting coefficients in rank-order. To test the effectiveness of this encoding in capturing the information content of an image, the rank-order representation is decoded to reconstruct an image that can be compared with the original. The reconstruction uses a look-up table to infer the filter coefficients from their rank in the encoded image. Since the DoG filters are approximately orthogonal functions, they are treated as their own inverses in the reconstruction process. We obtained a quantitative measure of the perceptually important information retained in the reconstructed image relative to the original using a slightly modified version of an objective metric proposed by Petrovic. It is observed that around 75% of the perceptually important information is retained in the reconstruction. In the present work we reconstruct the input using a pseudo-inverse of the DoG filter-bank with the aim of improving the reconstruction and thereby extracting more information from the rank-order encoded stimulus. We observe that there is an increase of 10 - 15% in the information retrieved from a reconstructed stimulus as a result of inverting the filter-bank.

Embedded feature ranking for ensemble MLP classifiers.

PubMed

Windeatt, Terry; Duangsoithong, Rakkrit; Smith, Raymond

2011-06-01

A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stopping criterion based upon the out-of-bootstrap estimate. To solve multi-class problems feature ranking is combined with modified error-correcting output coding. Experimental results on benchmark data demonstrate the versatility of the MLP base classifier in removing irrelevant features.

A Ranking Method for Evaluating Constructed Responses

ERIC Educational Resources Information Center

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…

Efficacy of fibre additions to flatbread flour mixes for reducing post-meal glucose and insulin responses in healthy Indian subjects.

PubMed

Boers, Hanny M; MacAulay, Katrina; Murray, Peter; Dobriyal, Rajendra; Mela, David J; Spreeuwenberg, Maria A M

2017-02-01

The incidence of type 2 diabetes mellitus (T2DM) is increasing worldwide, including in developing countries, particularly in South Asia. Intakes of foods generating a high postprandial glucose (PPG) response have been positively associated with T2DM. As part of efforts to identify effective and feasible strategies to reduce the glycaemic impact of carbohydrate-rich staples, we previously found that addition of guar gum (GG) and chickpea flour (CPF) to wheat flour could significantly reduce the PPG response to flatbread products. On the basis of the results of an exploratory study with Caucasian subjects, we have now tested the effect of additions of specific combinations of CPF with low doses of GG to a flatbread flour mix for their impacts on PPG and postprandial insulin (PPI) responses in a South-Asian population. In a randomised, placebo-controlled full-cross-over design, fifty-six healthy Indian adults consumed flatbreads made with a commercial flatbread mix (100 % wheat flour) with no further additions (control) or incorporating 15 % CPF in combination with 2, 3 or 4 % GG. The flatbreads with CPF and 3 or 4 % GG significantly reduced PPG (both ≥15 % reduction in positive incremental AUC, P<0·01) and PPI (both ≥28 % reduction in total AUC, P<0·0001) compared with flatbreads made from control flour. These results confirm the efficacy and feasibility of the addition of CPF with GG to flatbread flour mixes to achieve significant reductions in both PPG and PPI in Indian subjects.

Low rank approximation methods for MR fingerprinting with large scale dictionaries.

PubMed

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.

Reducing the Strength: a mixed methods evaluation of alcohol retailers' willingness to voluntarily reduce the availability of low cost, high strength beers and ciders in two UK local authorities.

PubMed

Sumpter, Colin; McGill, Elizabeth; Dickie, Esther; Champo, Enes; Romeri, Ester; Egan, Matt

2016-05-26

Reducing the Strength is an increasingly popular intervention in which local authorities ask retailers to stop selling 'super-strength' beers and ciders. The intervention cannot affect alcohol availability, nor consumption, unless retailers participate. In this paper, we ask whether and why retailers choose or refuse to self-impose restrictions on alcohol sales in this way. Mixed method assessment of retailers' participation in Reducing the Strength in two London (UK) local authorities. Compliance rates and the cheapest available unit of alcohol at each store were assessed. Qualitative interviews with retailer managers and staff (n = 39) explored attitudes towards the intervention and perceptions of its impacts. Shops selling super-strength across both areas fell from 78 to 25 (18 % of all off-licences). The median price of the cheapest unit of alcohol available across all retailers increased from £0.29 to £0.33 and in shops that participated in Reducing the Strength it rose from £0.33 to £0.43. The project received a mixed response from retailers. Retailers said they participated to deter disruptive customers, reduce neighbourhood disruptions and to maintain a good relationship with the local authority. Reducing the Strength participants and non-participants expressed concern about its perceived financial impact due to customers shopping elsewhere for super-strength. Some felt that customers' ability to circumvent the intervention would limit its effectiveness and that a larger scale compulsory approach would be more effective. Reducing the Strength can achieve high rates of voluntary compliance, reduce availability of super-strength and raise the price of the cheapest available unit of alcohol in participating shops. Questions remain over the extent to which voluntary interventions of this type can achieve wider social or health goals if non-participating shops attract customers from those who participate.

Associations of dietary intake patterns identified using reduced rank regression with markers of arterial stiffness among youth with type 1 diabetes.

PubMed

Lamichhane, A P; Liese, A D; Urbina, E M; Crandell, J L; Jaacks, L M; Dabelea, D; Black, M H; Merchant, A T; Mayer-Davis, E J

2014-12-01

Youth with type 1 diabetes (T1DM) are at substantially increased risk for adverse vascular outcomes, but little is known about the influence of dietary behavior on cardiovascular disease (CVD) risk profile. We aimed to identify dietary intake patterns associated with CVD risk factors and evaluate their impact on arterial stiffness (AS) measures collected thereafter in a cohort of youth with T1DM. Baseline diet data from a food frequency questionnaire and CVD risk factors (triglycerides, low density lipoprotein-cholesterol, systolic blood pressure, hemoglobin A1c, C-reactive protein and waist circumference) were available for 1153 youth aged ⩾10 years with T1DM from the SEARCH for Diabetes in Youth Study. A dietary intake pattern was identified using 33 food groups as predictors and six CVD risk factors as responses in reduced rank regression (RRR) analysis. Associations of this RRR-derived dietary pattern with AS measures (augmentation index (AIx75), n=229; pulse wave velocity, n=237; and brachial distensibility, n=228) were then assessed using linear regression. The RRR-derived pattern was characterized by high intakes of sugar-sweetened beverages (SSB) and diet soda, eggs, potatoes and high-fat meats and low intakes of sweets/desserts and low-fat dairy; major contributors were SSB and diet soda. This pattern captured the largest variability in adverse CVD risk profile and was subsequently associated with AIx75 (β=0.47; P<0.01). The mean difference in AIx75 concentration between the highest and the lowest dietary pattern quartiles was 4.3% in fully adjusted model. Intervention strategies to reduce consumption of unhealthy foods and beverages among youth with T1DM may significantly improve CVD risk profile and ultimately reduce the risk for AS.

Reduction from cost-sensitive ordinal ranking to weighted binary classification.

PubMed

Lin, Hsuan-Tien; Li, Ling

2012-05-01

We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.

Highlighting entanglement of cultures via ranking of multilingual Wikipedia articles.

PubMed

Eom, Young-Ho; Shepelyansky, Dima L

2013-01-01

How different cultures evaluate a person? Is an important person in one culture is also important in the other culture? We address these questions via ranking of multilingual Wikipedia articles. With three ranking algorithms based on network structure of Wikipedia, we assign ranking to all articles in 9 multilingual editions of Wikipedia and investigate general ranking structure of PageRank, CheiRank and 2DRank. In particular, we focus on articles related to persons, identify top 30 persons for each rank among different editions and analyze distinctions of their distributions over activity fields such as politics, art, science, religion, sport for each edition. We find that local heroes are dominant but also global heroes exist and create an effective network representing entanglement of cultures. The Google matrix analysis of network of cultures shows signs of the Zipf law distribution. This approach allows to examine diversity and shared characteristics of knowledge organization between cultures. The developed computational, data driven approach highlights cultural interconnections in a new perspective. Dated: June 26, 2013.

Highlighting Entanglement of Cultures via Ranking of Multilingual Wikipedia Articles

PubMed Central

Eom, Young-Ho; Shepelyansky, Dima L.

2013-01-01

How different cultures evaluate a person? Is an important person in one culture is also important in the other culture? We address these questions via ranking of multilingual Wikipedia articles. With three ranking algorithms based on network structure of Wikipedia, we assign ranking to all articles in 9 multilingual editions of Wikipedia and investigate general ranking structure of PageRank, CheiRank and 2DRank. In particular, we focus on articles related to persons, identify top 30 persons for each rank among different editions and analyze distinctions of their distributions over activity fields such as politics, art, science, religion, sport for each edition. We find that local heroes are dominant but also global heroes exist and create an effective network representing entanglement of cultures. The Google matrix analysis of network of cultures shows signs of the Zipf law distribution. This approach allows to examine diversity and shared characteristics of knowledge organization between cultures. The developed computational, data driven approach highlights cultural interconnections in a new perspective. Dated: June 26, 2013 PMID:24098338

Likelihoods for fixed rank nomination networks

PubMed Central

HOFF, PETER; FOSDICK, BAILEY; VOLFOVSKY, ALEX; STOVEL, KATHERINE

2014-01-01

Many studies that gather social network data use survey methods that lead to censored, missing, or otherwise incomplete information. For example, the popular fixed rank nomination (FRN) scheme, often used in studies of schools and businesses, asks study participants to nominate and rank at most a small number of contacts or friends, leaving the existence of other relations uncertain. However, most statistical models are formulated in terms of completely observed binary networks. Statistical analyses of FRN data with such models ignore the censored and ranked nature of the data and could potentially result in misleading statistical inference. To investigate this possibility, we compare Bayesian parameter estimates obtained from a likelihood for complete binary networks with those obtained from likelihoods that are derived from the FRN scheme, and therefore accommodate the ranked and censored nature of the data. We show analytically and via simulation that the binary likelihood can provide misleading inference, particularly for certain model parameters that relate network ties to characteristics of individuals and pairs of individuals. We also compare these different likelihoods in a data analysis of several adolescent social networks. For some of these networks, the parameter estimates from the binary and FRN likelihoods lead to different conclusions, indicating the importance of analyzing FRN data with a method that accounts for the FRN survey design. PMID:25110586

Optimized mixed oils remarkably reduce the amount of surfactants in microemulsions without affecting oral bioavailability of ibuprofen by simultaneously enlarging microemulsion areas and enhancing drug solubility.

PubMed

Chen, Yizhen; Tuo, Jue; Huang, Huizhi; Liu, Dan; You, Xiuhua; Mai, Jialuo; Song, Jiaqi; Xie, Yanqi; Wu, Chuanbin; Hu, Haiyan

2015-06-20

The toxicity and irritation associated with high amounts of surfactants restrict the extensive utilization of microemulsions. To address these shortcomings, employing mixed oils to enlarge microemulsion areas therefore reducing surfactant contents is a promising strategy. However, what kinds of mixed oils are more efficient in enlarging microemulsion areas still remains unclear. In this research, we found that the chain length and degree of unsaturation of oils play a key role in enlarging microemulsion areas. The combination of moderate chain saturated oil caprylic/capric triglyceride (GTCC) with long chain unsaturated oil glycerol trioleate significantly increased the microemulsion areas. Solubility of ibuprofen in the mixed oils was unexpectedly and remarkably increased (almost 300mg/mL) compared with that (around 100mg/mL) of the single oil (GTCC), which also resulted in greatly increased solubility of ibuprofen in mixed oils-containing microemulsions. By optimizing the mixed oil formulation, the absolute amount of surfactant in drug-loaded microemulsions was reduced but increased drug oral bioavailability in rats was maintained. It could be concluded that the combined use of moderate chain oils and long chain unsaturated oils could not only acquire enlarged microemulsion areas but also enhanced drug solubility, therefore doubly reducing surfactant amount, which is extremely beneficial for developing safe microemulsions. Copyright © 2015. Published by Elsevier B.V.

Rank-frequency relation for Chinese characters

NASA Astrophysics Data System (ADS)

Deng, Weibing; Allahverdyan, Armen E.; Li, Bo; Wang, Qiuping A.

2014-02-01

We show that the Zipf's law for Chinese characters perfectly holds for sufficiently short texts (few thousand different characters). The scenario of its validity is similar to the Zipf's law for words in short English texts. For long Chinese texts (or for mixtures of short Chinese texts), rank-frequency relations for Chinese characters display a two-layer, hierarchic structure that combines a Zipfian power-law regime for frequent characters (first layer) with an exponential-like regime for less frequent characters (second layer). For these two layers we provide different (though related) theoretical descriptions that include the range of low-frequency characters (hapax legomena). We suggest that this hierarchic structure of the rank-frequency relation connects to semantic features of Chinese characters (number of different meanings and homographies). The comparative analysis of rank-frequency relations for Chinese characters versus English words illustrates the extent to which the characters play for Chinese writers the same role as the words for those writing within alphabetical systems.

Ranking benchmarks of top 100 players in men's professional tennis.

PubMed

Reid, Machar; Morris, Craig

2013-01-01

In men's professional tennis, players aspire to hold the top ranking position. On the way to the top spot, reaching the top 100 can be seen as a significant career milestone. National Federations undertake extensive efforts to assist their players to reach the top 100. However, objective data considering reasonable ranking yardsticks for top 100 success in men's professional tennis are lacking. Therefore, it is difficult for National Federations and those involved in player development to give empirical programming advice to young players. By taking a closer look at the ranking history of professional male tennis players, this article tries to provide those involved in player development a more objective basis for decision-making. The 100 names, countries, birthdates and ranking histories of the top 100 players listed in the Association of Tennis Professionals (ATP) at 31 December 2009 were recorded from websites in the public domain. Descriptive statistics were reported for the ranking milestones of interest. Results confirmed the merits of the International Tennis Federation's junior tour with 91% of the top 100 professionals earning a junior ranking, the mean peak of which was 94.1, s=148.9. On average, top 100 professionals achieved their best junior rankings and earned their first ATP point at similar ages, suggesting that players compete on both the junior and professional tours during their transition. Once professionally ranked, players took an average 4.5, s=2.1 years to reach the ATP top 100 at the mean age of 21.5, s=2.6 years, which contrasts with the mean current age of the top 100 of 26.8, s=3.2. The best professional rankings of players born in 1982 or earlier were positively related to the ages at which players earned their first ATP point and then entered the top 100, suggesting that the ages associated with these ranking milestones may have some forecasting potential. Future work should focus on the change in top 100 demographics over time as well

Determining hospital performance based on rank ordering: is it appropriate?

PubMed

Anderson, Judy; Hackman, Mark; Burnich, Jeff; Gurgiolo, Thomas R

2007-01-01

An increasing number of "pay for performance" initiatives for hospitals and physicians ascribe performance by ranking hospitals or physicians on quality of care measures. Payment is subsequently based on where a hospital or physician ranks among peers. This study examines the variability of ranking hospitals on quality of care measures and its impact on comparing hospital performance. Variability in the ranks of 3 quality of care measures was examined: discharge instruction for congestive heart failure, use of beta-blockers at discharge for heart attack, and timing of initial antibiotic therapy within 4 hours of admission to the hospital for pneumonia. The data are available on the Centers for Medicare and Medicaid Services Web site as part of the Hospital Quality Alliance project. We found that considerable uncertainty exists in ranking of hospitals on these measures, which calls into question the use of rank ordering as a determinant of performance.

Social Rank, Stress, Fitness, and Life Expectancy in Wild Rabbits

NASA Astrophysics Data System (ADS)

von Holst, Dietrich; Hutzelmeyer, Hans; Kaetzke, Paul; Khaschei, Martin; Schönheiter, Ronald

Wild rabbits of the two sexes have separate linear rank orders, which are established and maintained by intensive fights. The social rank of individuals strongly influence their fitness: males and females that gain a high social rank, at least at the outset of their second breeding season, have a much higher lifetime fitness than subordinate individuals. This is because of two separate factors: a much higher fecundity and annual reproductive success and a 50% longer reproductive life span. These results are in contrast to the view in evolutionary biology that current reproduction can be increased only at the expense of future survival and/or fecundity. These concepts entail higher physiological costs in high-ranking mammals, which is not supported by our data: In wild rabbits the physiological costs of social positions are caused predominantly by differential psychosocial stress responses that are much lower in high-ranking than in low-ranking individuals.

Rank-frequency distributions of Romanian words

NASA Astrophysics Data System (ADS)

Cocioceanu, Adrian; Raportaru, Carina Mihaela; Nicolin, Alexandru I.; Jakimovski, Dragan

2017-12-01

The calibration of voice biometrics solutions requires detailed analyses of spoken texts and in this context we investigate by computational means the rank-frequency distributions of Romanian words and word series to determine the most common words and word series of the language. To this end, we have constructed a corpus of approximately 2.5 million words and then determined that the rank-frequency distributions of the Romanian words, as well as series of two, and three subsequent words, obey the celebrated Zipf law.

AGU journals continue to rank highly in Impact Factors

NASA Astrophysics Data System (ADS)

Sears, Jon; Warner, Mary

2012-07-01

AGU journals continue to rank highly in the 2011 Journal Citation Reports (JCR), which was released by Thomson Reuters on 28 June. The impact factor of several AGU journals increased significantly, continuing their trend over the previous 5 years, while others remained consistent with the previous year's ranking. Paleoceanography is an outstanding performer in both the Paleontology and Oceanography categories. Since 1995, Paleoceanography has been the top-ranked journal in the Paleontology category (of 49 titles in 2011), with an Impact Factor of 3.357. In the Oceanography group (59 journals total), Paleoceanography ranks third in Impact Factor. Reviews of Geophysics, with an Impact Factor of 12.364 (an increase of 2.826 from the prior year's score of 9.538), ranks second in Geochemistry and Geophysics out of a total of 77 journals in this cohort. Water Resources Research comes in at second place in the Limnology group, with 19 titles, and third place in the Water Resources group, which has a cohort of 78 titles.

Case-mix adjustment of consumer reports about managed behavioral health care and health plans.

PubMed

Eselius, Laura L; Cleary, Paul D; Zaslavsky, Alan M; Huskamp, Haiden A; Busch, Susan H

2008-12-01

To develop a model for adjusting patients' reports of behavioral health care experiences on the Experience of Care and Health Outcomes (ECHO) survey to allow for fair comparisons across health plans. Survey responses from 4,068 individuals enrolled in 21 managed behavioral health plans who received behavioral health care within the previous year (response rate = 48 percent). Potential case-mix adjustors were evaluated by combining information about their predictive power and the amount of within- and between-plan variability. Changes in plan scores and rankings due to case-mix adjustment were quantified. The final case-mix adjustment model included self-reported mental health status, self-reported general health status, alcohol/drug treatment, age, education, and race/ethnicity. The impact of adjustment on plan report scores was modest, but large enough to change some plan rankings. Adjusting plan report scores on the ECHO survey for differences in patient characteristics had modest effects, but still may be important to maintain the credibility of patient reports as a quality metric. Differences between those with self-reported fair/poor health compared with those in excellent/very good health varied by plan, suggesting quality differences associated with health status and underscoring the importance of collecting quality information.

Improved magnetic resonance fingerprinting reconstruction with low-rank and subspace modeling.

PubMed

Zhao, Bo; Setsompop, Kawin; Adalsteinsson, Elfar; Gagoski, Borjan; Ye, Huihui; Ma, Dan; Jiang, Yun; Ellen Grant, P; Griswold, Mark A; Wald, Lawrence L

2018-02-01

This article introduces a constrained imaging method based on low-rank and subspace modeling to improve the accuracy and speed of MR fingerprinting (MRF). A new model-based imaging method is developed for MRF to reconstruct high-quality time-series images and accurate tissue parameter maps (e.g., T 1 , T 2 , and spin density maps). Specifically, the proposed method exploits low-rank approximations of MRF time-series images, and further enforces temporal subspace constraints to capture magnetization dynamics. This allows the time-series image reconstruction problem to be formulated as a simple linear least-squares problem, which enables efficient computation. After image reconstruction, tissue parameter maps are estimated via dictionary-based pattern matching, as in the conventional approach. The effectiveness of the proposed method was evaluated with in vivo experiments. Compared with the conventional MRF reconstruction, the proposed method reconstructs time-series images with significantly reduced aliasing artifacts and noise contamination. Although the conventional approach exhibits some robustness to these corruptions, the improved time-series image reconstruction in turn provides more accurate tissue parameter maps. The improvement is pronounced especially when the acquisition time becomes short. The proposed method significantly improves the accuracy of MRF, and also reduces data acquisition time. Magn Reson Med 79:933-942, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Semi-quantitative spectrographic analysis and rank correlation in geochemistry

USGS Publications Warehouse

Flanagan, F.J.

1957-01-01

The rank correlation coefficient, rs, which involves less computation than the product-moment correlation coefficient, r, can be used to indicate the degree of relationship between two elements. The method is applicable in situations where the assumptions underlying normal distribution correlation theory may not be satisfied. Semi-quantitative spectrographic analyses which are reported as grouped or partly ranked data can be used to calculate rank correlations between elements. ?? 1957.

Discriminative Multi-View Interactive Image Re-Ranking.

PubMed

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

A new mixed subgrid-scale model for large eddy simulation of turbulent drag-reducing flows of viscoelastic fluids

NASA Astrophysics Data System (ADS)

Li, Feng-Chen; Wang, Lu; Cai, Wei-Hua

2015-07-01

A mixed subgrid-scale (SGS) model based on coherent structures and temporal approximate deconvolution (MCT) is proposed for turbulent drag-reducing flows of viscoelastic fluids. The main idea of the MCT SGS model is to perform spatial filtering for the momentum equation and temporal filtering for the conformation tensor transport equation of turbulent flow of viscoelastic fluid, respectively. The MCT model is suitable for large eddy simulation (LES) of turbulent drag-reducing flows of viscoelastic fluids in engineering applications since the model parameters can be easily obtained. The LES of forced homogeneous isotropic turbulence (FHIT) with polymer additives and turbulent channel flow with surfactant additives based on MCT SGS model shows excellent agreements with direct numerical simulation (DNS) results. Compared with the LES results using the temporal approximate deconvolution model (TADM) for FHIT with polymer additives, this mixed SGS model MCT behaves better, regarding the enhancement of calculating parameters such as the Reynolds number. For scientific and engineering research, turbulent flows at high Reynolds numbers are expected, so the MCT model can be a more suitable model for the LES of turbulent drag-reducing flows of viscoelastic fluid with polymer or surfactant additives. Project supported by the China Postdoctoral Science Foundation (Grant No. 2011M500652), the National Natural Science Foundation of China (Grant Nos. 51276046 and 51206033), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20112302110020).

Using Concept Relations to Improve Ranking in Information Retrieval

PubMed Central

Price, Susan L.; Delcambre, Lois M.

2005-01-01

Despite improved search engine technology, most searches return numerous documents not directly related to the query. This problem is mitigated if relevant documents appear high on a ranked list of search results. We propose that some queries and the underlying information needs can be modeled as relationships between concepts (relations), and we match relations in queries to relations in documents to try to improve ranking of search results. We investigate four techniques to identify two relationships important in medicine, causes and treats, to improve the ranking of medical text documents relevant to clinical questions about causation and treatment. Preliminary results suggest that identifying relation instances can improve the ranking of search results. PMID:16779114

A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.

PubMed

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.

The Distribution of the Sum of Signed Ranks

ERIC Educational Resources Information Center

Albright, Brian

2012-01-01

We describe the calculation of the distribution of the sum of signed ranks and develop an exact recursive algorithm for the distribution as well as an approximation of the distribution using the normal. The results have applications to the non-parametric Wilcoxon signed-rank test.

A cautionary note on the rank product statistic.

PubMed

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.

Biomarkers of Fatigue: Ranking Mental Fatigue Susceptibility

DTIC Science & Technology

2010-12-10

expected declines in performance during the 36-hour, 15-minute period of sleep deprivation without caffeine. The simple change from baseline results...rankings for fatigue resistance were then determined via a percent- change rule similar to that used in Chaiken, Harville, Harrison, Fischer, Fisher...and Whitmore (2008). This rule ranks subjects on percent change of cognitive performance from a baseline performance (before fatigue) to a fatigue

Potential value of phosphate compounds in enhancing immobilization and reducing bioavailability of mixed heavy metal contaminants in shooting range soil.

PubMed

Seshadri, B; Bolan, N S; Choppala, G; Kunhikrishnan, A; Sanderson, P; Wang, H; Currie, L D; Tsang, Daniel C W; Ok, Y S; Kim, G

2017-10-01

Shooting range soils contain mixed heavy metal contaminants including lead (Pb), cadmium (Cd), and zinc (Zn). Phosphate (P) compounds have been used to immobilize these metals, particularly Pb, thereby reducing their bioavailability. However, research on immobilization of Pb's co-contaminants showed the relative importance of soluble and insoluble P compounds, which is critical in evaluating the overall success of in situ stabilization practice in the sustainable remediation of mixed heavy metal contaminated soils. Soluble synthetic P fertilizer (diammonium phosphate; DAP) and reactive (Sechura; SPR) and unreactive (Christmas Island; CPR) natural phosphate rocks (PR) were tested for Cd, Pb and Zn immobilization and later their mobility and bioavailability in a shooting range soil. The addition of P compounds resulted in the immobilization of Cd, Pb and Zn by 1.56-76.2%, 3.21-83.56%, and 2.31-74.6%, respectively. The reactive SPR significantly reduced Cd, Pb and Zn leaching while soluble DAP increased their leachate concentrations. The SPR reduced the bioaccumulation of Cd, Pb and Zn in earthworms by 7.13-23.4% and 14.3-54.6% in comparison with earthworms in the DAP and control treatment, respectively. Bioaccessible Cd, Pb and Zn concentrations as determined using a simplified bioaccessibility extraction test showed higher long-term stability of P-immobilized Pb and Zn than Cd. The differential effect of P-induced immobilization between P compounds and metals is due to the variation in the solubility characteristics of P compounds and nature of metal phosphate compounds formed. Therefore, Pb and Zn immobilization by P compounds is an effective long-term remediation strategy for mixed heavy metal contaminated soils. Copyright © 2017 Elsevier Ltd. All rights reserved.

Video denoising using low rank tensor decomposition

NASA Astrophysics Data System (ADS)

Gui, Lihua; Cui, Gaochao; Zhao, Qibin; Wang, Dongsheng; Cichocki, Andrzej; Cao, Jianting

2017-03-01

Reducing noise in a video sequence is of vital important in many real-world applications. One popular method is block matching collaborative filtering. However, the main drawback of this method is that noise standard deviation for the whole video sequence is known in advance. In this paper, we present a tensor based denoising framework that considers 3D patches instead of 2D patches. By collecting the similar 3D patches non-locally, we employ the low-rank tensor decomposition for collaborative filtering. Since we specify the non-informative prior over the noise precision parameter, the noise variance can be inferred automatically from observed video data. Therefore, our method is more practical, which does not require knowing the noise variance. The experimental on video denoising demonstrates the effectiveness of our proposed method.

RANK ligand as a potential target for breast cancer prevention in BRCA1-mutation carriers.

PubMed

Nolan, Emma; Vaillant, François; Branstetter, Daniel; Pal, Bhupinder; Giner, Göknur; Whitehead, Lachlan; Lok, Sheau W; Mann, Gregory B; Rohrbach, Kathy; Huang, Li-Ya; Soriano, Rosalia; Smyth, Gordon K; Dougall, William C; Visvader, Jane E; Lindeman, Geoffrey J

2016-08-01

Individuals who have mutations in the breast-cancer-susceptibility gene BRCA1 (hereafter referred to as BRCA1-mutation carriers) frequently undergo prophylactic mastectomy to minimize their risk of breast cancer. The identification of an effective prevention therapy therefore remains a 'holy grail' for the field. Precancerous BRCA1(mut/+) tissue harbors an aberrant population of luminal progenitor cells, and deregulated progesterone signaling has been implicated in BRCA1-associated oncogenesis. Coupled with the findings that tumor necrosis factor superfamily member 11 (TNFSF11; also known as RANKL) is a key paracrine effector of progesterone signaling and that RANKL and its receptor TNFRSF11A (also known as RANK) contribute to mammary tumorigenesis, we investigated a role for this pathway in the pre-neoplastic phase of BRCA1-mutation carriers. We identified two subsets of luminal progenitors (RANK(+) and RANK(-)) in histologically normal tissue of BRCA1-mutation carriers and showed that RANK(+) cells are highly proliferative, have grossly aberrant DNA repair and bear a molecular signature similar to that of basal-like breast cancer. These data suggest that RANK(+) and not RANK(-) progenitors are a key target population in these women. Inhibition of RANKL signaling by treatment with denosumab in three-dimensional breast organoids derived from pre-neoplastic BRCA1(mut/+) tissue attenuated progesterone-induced proliferation. Notably, proliferation was markedly reduced in breast biopsies from BRCA1-mutation carriers who were treated with denosumab. Furthermore, inhibition of RANKL in a Brca1-deficient mouse model substantially curtailed mammary tumorigenesis. Taken together, these findings identify a targetable pathway in a putative cell-of-origin population in BRCA1-mutation carriers and implicate RANKL blockade as a promising strategy in the prevention of breast cancer.

Mining User Dwell Time for Personalized Web Search Re-Ranking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Songhua; Jiang, Hao; Lau, Francis

We propose a personalized re-ranking algorithm through mining user dwell times derived from a user's previously online reading or browsing activities. We acquire document level user dwell times via a customized web browser, from which we then infer conceptword level user dwell times in order to understand a user's personal interest. According to the estimated concept word level user dwell times, our algorithm can estimate a user's potential dwell time over a new document, based on which personalized webpage re-ranking can be carried out. We compare the rankings produced by our algorithm with rankings generated by popular commercial search enginesmore » and a recently proposed personalized ranking algorithm. The results clearly show the superiority of our method. In this paper, we propose a new personalized webpage ranking algorithmthrough mining dwell times of a user. We introduce a quantitative model to derive concept word level user dwell times from the observed document level user dwell times. Once we have inferred a user's interest over the set of concept words the user has encountered in previous readings, we can then predict the user's potential dwell time over a new document. Such predicted user dwell time allows us to carry out personalized webpage re-ranking. To explore the effectiveness of our algorithm, we measured the performance of our algorithm under two conditions - one with a relatively limited amount of user dwell time data and the other with a doubled amount. Both evaluation cases put our algorithm for generating personalized webpage rankings to satisfy a user's personal preference ahead of those by Google, Yahoo!, and Bing, as well as a recent personalized webpage ranking algorithm.« less

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection

PubMed Central

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-01-01

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. PMID:25177107

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

PubMed

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.

Resolution of ranking hierarchies in directed networks.

PubMed

Letizia, Elisa; 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.

Resolution of ranking hierarchies in directed networks

PubMed Central

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

Factors Impacting Faculty Research Productivity at a Highly-Ranked University

ERIC Educational Resources Information Center

Fung, Jin Lung Michael

2017-01-01

Universities around the world are facing increasing pressure to perform well in rankings, and rankings results have been shown to impact institutional reputation, ability to secure funding, and recruitment of students and faculty. Faculty research productivity is one of the main factors impacting rankings performance, and the aim of this project…

Ranked set sampling: cost and optimal set size.

PubMed

Nahhas, Ramzi W; Wolfe, Douglas A; Chen, Haiying

2002-12-01

McIntyre (1952, Australian Journal of Agricultural Research 3, 385-390) introduced ranked set sampling (RSS) as a method for improving estimation of a population mean in settings where sampling and ranking of units from the population are inexpensive when compared with actual measurement of the units. Two of the major factors in the usefulness of RSS are the set size and the relative costs of the various operations of sampling, ranking, and measurement. In this article, we consider ranking error models and cost models that enable us to assess the effect of different cost structures on the optimal set size for RSS. For reasonable cost structures, we find that the optimal RSS set sizes are generally larger than had been anticipated previously. These results will provide a useful tool for determining whether RSS is likely to lead to an improvement over simple random sampling in a given setting and, if so, what RSS set size is best to use in this case.

An ensemble rank learning approach for gene prioritization.

PubMed

Lee, Po-Feng; Soo, Von-Wun

2013-01-01

Several different computational approaches have been developed to solve the gene prioritization problem. We intend to use the ensemble boosting learning techniques to combine variant computational approaches for gene prioritization in order to improve the overall performance. In particular we add a heuristic weighting function to the Rankboost algorithm according to: 1) the absolute ranks generated by the adopted methods for a certain gene, and 2) the ranking relationship between all gene-pairs from each prioritization result. We select 13 known prostate cancer genes in OMIM database as training set and protein coding gene data in HGNC database as test set. We adopt the leave-one-out strategy for the ensemble rank boosting learning. The experimental results show that our ensemble learning approach outperforms the four gene-prioritization methods in ToppGene suite in the ranking results of the 13 known genes in terms of mean average precision, ROC and AUC measures.

Max-margin multiattribute learning with low-rank constraint.

PubMed

Zhang, Qiang; Chen, Lin; Li, Baoxin

2014-07-01

Attribute learning has attracted a lot of interests in recent years for its advantage of being able to model high-level concepts with a compact set of midlevel attributes. Real-world objects often demand multiple attributes for effective modeling. Most existing methods learn attributes independently without explicitly considering their intrinsic relatedness. In this paper, we propose max margin multiattribute learning with low-rank constraint, which learns a set of attributes simultaneously, using only relative ranking of the attributes for the data. By learning all the attributes simultaneously through low-rank constraint, the proposed method is able to capture their intrinsic correlation for improved learning; by requiring only relative ranking, the method avoids restrictive binary labels of attributes that are often assumed by many existing techniques. The proposed method is evaluated on both synthetic data and real visual data including a challenging video data set. Experimental results demonstrate the effectiveness of the proposed method.

Discrepancies between multicriteria decision analysis-based ranking and intuitive ranking for pharmaceutical benefit-risk profiles in a hypothetical setting.

PubMed

Hoshikawa, K; Ono, S

2017-02-01

Multicriteria decision analysis (MCDA) has been generally considered a promising decision-making methodology for the assessment of drug benefit-risk profiles. There have been many discussions in both public and private sectors on its feasibility and applicability, but it has not been employed in official decision-makings. For the purpose of examining to what extent MCDA would reflect the first-hand, intuitive preference of evaluators in practical pharmaceutical assessments, we conducted a questionnaire survey involving the participation of employees of pharmaceutical companies. Showing profiles of the efficacy and safety of four hypothetical drugs, each respondent was asked to rank them following the standard MCDA process and then to rank them intuitively (i.e. without applying any analytical framework). These two approaches resulted in substantially different ranking patterns from the same individuals, and the concordance rate was surprisingly low (17%). Although many respondents intuitively showed a preference for mild, balanced risk-benefit profiles over profiles with a conspicuous advantage in either risk or benefit, the ranking orders based on MCDA scores did not reflect the intuitive preference. Observed discrepancies between the rankings seemed to be primarily attributed to the structural characteristics of MCDA, which assumes that evaluation on each benefit and risk component should have monotonic impact on final scores. It would be difficult for MCDA to reflect commonly observed non-monotonic preferences for risk and benefit profiles. Possible drawbacks of MCDA should be further investigated prior to the real-world application of its benefit-risk assessment. © 2016 John Wiley & Sons Ltd.

5 CFR 451.301 - Ranks for the Senior Executive Service.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Ranks for the Senior Executive Service... REGULATIONS AWARDS Presidential Rank Awards § 451.301 Ranks for the Senior Executive Service. (a) The... to a Senior Executive Service (SES) career appointee are set forth in 5 U.S.C. 4507. (b) To be...

Universality in the tail of musical note rank distribution

NASA Astrophysics Data System (ADS)

Beltrán del Río, M.; Cocho, G.; Naumis, G. G.

2008-09-01

Although power laws have been used to fit rank distributions in many different contexts, they usually fail at the tails. Languages as sequences of symbols have been a popular subject for ranking distributions, and for this purpose, music can be treated as such. Here we show that more than 1800 musical compositions are very well fitted by the first kind two parameter beta distribution, which arises in the ranking of multiplicative stochastic processes. The parameters a and b are obtained for classical, jazz and rock music, revealing interesting features. Specially, we have obtained a clear trend in the values of the parameters for major and minor tonal modes. Finally, we discuss the distribution of notes for each octave and its connection with the ranking of the notes.

Top-d Rank Aggregation in Web Meta-search Engine

NASA Astrophysics Data System (ADS)

Fang, Qizhi; Xiao, Han; Zhu, Shanfeng

In this paper, we consider the rank aggregation problem for information retrieval over Web making use of a kind of metric, the coherence, which considers both the normalized Kendall-τ distance and the size of overlap between two partial rankings. In general, the top-d coherence aggregation problem is defined as: given collection of partial rankings Π = {τ 1,τ 2, ⋯ , τ K }, how to find a final ranking π with specific length d, which maximizes the total coherence Φ(π,Pi)=sum_{i=1}^K Φ(π,tau_i). The corresponding complexity and algorithmic issues are discussed in this paper. Our main technical contribution is a polynomial time approximation scheme (PTAS) for a restricted top-d coherence aggregation problem.

Time-Aware Service Ranking Prediction in the Internet of Things Environment

PubMed Central

Huang, Yuze; Huang, Jiwei; Cheng, Bo; He, Shuqing; Chen, Junliang

2017-01-01

With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets. PMID:28448451

Time-Aware Service Ranking Prediction in the Internet of Things Environment.

PubMed

Huang, Yuze; Huang, Jiwei; Cheng, Bo; He, Shuqing; Chen, Junliang

2017-04-27

With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.

Ranking Regime and the Future of Vernacular Scholarship

ERIC Educational Resources Information Center

Ishikawa, Mayumi

2014-01-01

World university rankings and their global popularity present a number of far-reaching impacts for vernacular scholarship. This article employs a multidimensional approach to analyze the ranking regime's threat to local scholarship and knowledge construction through a study of Japanese research universities. First, local conditions that have led…

Influence of membrane fouling reducers (MFRs) on filterability of disperse mixed liquor of jet loop bioreactors.

PubMed

Koseoglu-Imer, Derya Yuksel; Dizge, Nadir; Karagunduz, Ahmet; Keskinler, Bulent

2011-07-01

The effects of membrane fouling reducers (MFRs) (the cationic polyelectrolyte (CPE) and FeCI(3)) on membrane fouling were studied in a lab-scale jet loop submerged membrane bioreactor (JL-SMBR) system. The optimum dosages of MFRs (CPE dosage=20 mg g(-1)MLSS, FeCI(3) dosage=14 mg g(-1)MLSS) were continuously fed to JL-SMBR system. The soluble and bound EPS concentrations as well as MLSS concentration in the mixed liquor of JL-SMBR were not changed substantially by the addition of MFRs. However, significant differences were observed in particle size and relative hydrophobicity. Filtration tests were performed by using different membrane types (polycarbonate (PC) and nitrocellulose mixed ester (ME)) and various pore sizes (0.45-0.22-0.1 μm). The steady state fluxes (J(ss)) of membranes increased at all membranes after MFRs addition to JL-SMBR. The filtration results showed that MFRs addition was an effective approach in terms of improvement in filtration performance for both membrane types. Copyright © 2011 Elsevier Ltd. All rights reserved.

Expanding the landscape of $$ \\mathcal{N} $$ = 2 rank 1 SCFTs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Argyres, Philip C.; Lotito, Matteo; Lu, Yongchao

Here, we refine our previous proposal [1-3] for systematically classifying 4d rank-1 N = 2 SCFTs by constructing their possible Coulomb branch geometries. Four new recently discussed rank-1 theories [4, 5], including novel N = 3 SCFTs, sit beautifully in our refined classification framework. By arguing for the consistency of their RG flows we can make a strong case for the existence of at least four additional rank-1 SCFTs, nearly doubling the number of known rank-1 SCFTs. The refinement consists of relaxing the assumption that the flavor symmetries of the SCFTs have no discrete factors. This results in an enlargedmore » (but finite) set of possible rank-1 SCFTs. Their existence can be further constrained using consistency of their central charges and RG flows.« less

Expanding the landscape of $$ \\mathcal{N} $$ = 2 rank 1 SCFTs

DOE PAGES

Argyres, Philip C.; Lotito, Matteo; Lu, Yongchao; ...

2016-05-16

Here, we refine our previous proposal [1-3] for systematically classifying 4d rank-1 N = 2 SCFTs by constructing their possible Coulomb branch geometries. Four new recently discussed rank-1 theories [4, 5], including novel N = 3 SCFTs, sit beautifully in our refined classification framework. By arguing for the consistency of their RG flows we can make a strong case for the existence of at least four additional rank-1 SCFTs, nearly doubling the number of known rank-1 SCFTs. The refinement consists of relaxing the assumption that the flavor symmetries of the SCFTs have no discrete factors. This results in an enlargedmore » (but finite) set of possible rank-1 SCFTs. Their existence can be further constrained using consistency of their central charges and RG flows.« less

Rank-Order and Paired Comparisons as the Basis for Measurement.

ERIC Educational Resources Information Center

Linacre, John M.

Three case studies are presented demonstrating the application of straight-forward Rasch techniques to rank order data. Paired comparisons are the simplest form of rank ordering. A consumer preference test with 56 pairs of cups of coffee tasted by each of 26 consumers illustrates analysis of these rankings. When subjects are allowed the option of…

CFD simulation of vertical linear motion mixing in anaerobic digester tanks.

PubMed

Meroney, Robert N; Sheker, Robert E

2014-09-01

Computational fluid dynamics (CFD) was used to simulate the mixing characteristics of a small circular anaerobic digester tank (diameter 6 m) equipped sequentially with 13 different plunger type vertical linear motion mixers and two different type internal draft-tube mixers. Rates of mixing of step injection of tracers were calculated from which active volume (AV) and hydraulic retention time (HRT) could be calculated. Washout characteristics were compared to analytic formulae to estimate any presence of partial mixing, dead volume, short-circuiting, or piston flow. Active volumes were also estimated based on tank regions that exceeded minimum velocity criteria. The mixers were ranked based on an ad hoc criteria related to the ratio of AV to unit power (UP) or AV/UP. The best plunger mixers were found to behave about the same as the conventional draft-tube mixers of similar UP.

Learning to rank image tags with limited training examples.

PubMed

Songhe Feng; Zheyun Feng; Rong Jin

2015-04-01

With an increasing number of images that are available in social media, image annotation has emerged as an important research topic due to its application in image matching and retrieval. Most studies cast image annotation into a multilabel classification problem. The main shortcoming of this approach is that it requires a large number of training images with clean and complete annotations in order to learn a reliable model for tag prediction. We address this limitation by developing a novel approach that combines the strength of tag ranking with the power of matrix recovery. Instead of having to make a binary decision for each tag, our approach ranks tags in the descending order of their relevance to the given image, significantly simplifying the problem. In addition, the proposed method aggregates the prediction models for different tags into a matrix, and casts tag ranking into a matrix recovery problem. It introduces the matrix trace norm to explicitly control the model complexity, so that a reliable prediction model can be learned for tag ranking even when the tag space is large and the number of training images is limited. Experiments on multiple well-known image data sets demonstrate the effectiveness of the proposed framework for tag ranking compared with the state-of-the-art approaches for image annotation and tag ranking.

Higher Education Ranking and Leagues Tables: Lessons Learned from Benchmarking

ERIC Educational Resources Information Center

Proulx, Roland

2007-01-01

The paper intends to contribute to the debate on ranking and league tables by adopting a critical approach to ranking methodologies from the point of view of a university benchmarking exercise. The absence of a strict benchmarking exercise in the ranking process has been, in the opinion of the author, one of the major problems encountered in the…

Control by Numbers: New Managerialism and Ranking in Higher Education

ERIC Educational Resources Information Center

Lynch, Kathleen

2015-01-01

This paper analyses the role of rankings as an instrument of new managerialism. It shows how rankings are reconstituting the purpose of universities, the role of academics and the definition of what it is to be a student. The paper opens by examining the forces that have facilitated the emergence of the ranking industry and the ideologies…

Multimodal biometric system using rank-level fusion approach.

PubMed

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.

Reissner-Mindlin Legendre Spectral Finite Elements with Mixed Reduced Quadrature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brito, K. D.; Sprague, M. A.

2012-10-01

Legendre spectral finite elements (LSFEs) are examined through numerical experiments for static and dynamic Reissner-Mindlin plate bending and a mixed-quadrature scheme is proposed. LSFEs are high-order Lagrangian-interpolant finite elements with nodes located at the Gauss-Lobatto-Legendre quadrature points. Solutions on unstructured meshes are examined in terms of accuracy as a function of the number of model nodes and total operations. While nodal-quadrature LSFEs have been shown elsewhere to be free of shear locking on structured grids, locking is demonstrated here on unstructured grids. LSFEs with mixed quadrature are, however, locking free and are significantly more accurate than low-order finite-elements for amore » given model size or total computation time.« less

Review assessment support in Open Journal System using TextRank

NASA Astrophysics Data System (ADS)

Manalu, S. R.; Willy; Sundjaja, A. M.; Noerlina

2017-01-01

In this paper, a review assessment support in Open Journal System (OJS) using TextRank is proposed. OJS is an open-source journal management platform that provides a streamlined journal publishing workflow. TextRank is an unsupervised, graph-based ranking model commonly used as extractive auto summarization of text documents. This study applies the TextRank algorithm to summarize 50 article reviews from an OJS-based international journal. The resulting summaries are formed using the most representative sentences extracted from the reviews. The summaries are then used to help OJS editors in assessing a review’s quality.

Constrained dictionary learning and probabilistic hypergraph ranking for person re-identification

NASA Astrophysics Data System (ADS)

He, You; Wu, Song; Pu, Nan; Qian, Li; Xiao, Guoqiang

2018-04-01

Person re-identification is a fundamental and inevitable task in public security. In this paper, we propose a novel framework to improve the performance of this task. First, two different types of descriptors are extracted to represent a pedestrian: (1) appearance-based superpixel features, which are constituted mainly by conventional color features and extracted from the supepixel rather than a whole picture and (2) due to the limitation of discrimination of appearance features, the deep features extracted by feature fusion Network are also used. Second, a view invariant subspace is learned by dictionary learning constrained by the minimum negative sample (termed as DL-cMN) to reduce the noise in appearance-based superpixel feature domain. Then, we use deep features and sparse codes transformed by appearancebased features to establish the hyperedges respectively by k-nearest neighbor, rather than jointing different features simply. Finally, a final ranking is performed by probabilistic hypergraph ranking algorithm. Extensive experiments on three challenging datasets (VIPeR, PRID450S and CUHK01) demonstrate the advantages and effectiveness of our proposed algorithm.

Beyond Zipf's Law: The Lavalette Rank Function and Its Properties.

PubMed

Fontanelli, Oscar; Miramontes, Pedro; Yang, Yaning; Cocho, Germinal; Li, Wentian

Although Zipf's law is widespread in natural and social data, one often encounters situations where one or both ends of the ranked data deviate from the power-law function. Previously we proposed the Beta rank function to improve the fitting of data which does not follow a perfect Zipf's law. Here we show that when the two parameters in the Beta rank function have the same value, the Lavalette rank function, the probability density function can be derived analytically. We also show both computationally and analytically that Lavalette distribution is approximately equal, though not identical, to the lognormal distribution. We illustrate the utility of Lavalette rank function in several datasets. We also address three analysis issues on the statistical testing of Lavalette fitting function, comparison between Zipf's law and lognormal distribution through Lavalette function, and comparison between lognormal distribution and Lavalette distribution.

Global University Rankings--Impacts and Unintended Side Effects

ERIC Educational Resources Information Center

Kehm, Barbara M.

2014-01-01

In this article, global and other university rankings are critically assessed with regard to their unintended side effects and their impacts on the European and national landscape of universities, as well as on individual institutions. An emphasis is put on the effects of ranking logics rather than on criticising their methodology. Nevertheless,…

Impact of case-mix on comparisons of patient-reported experience in NHS acute hospital trusts in England.

PubMed

Raleigh, Veena; Sizmur, Steve; Tian, Yang; Thompson, James

2015-04-01

To examine the impact of patient-mix on National Health Service (NHS) acute hospital trust scores in two national NHS patient surveys. Secondary analysis of 2012 patient survey data for 57,915 adult inpatients at 142 NHS acute hospital trusts and 45,263 adult emergency department attendees at 146 NHS acute hospital trusts in England. Changes in trust scores for selected questions, ranks, inter-trust variance and score-based performance bands were examined using three methods: no adjustment for case-mix; the current standardization method with weighting for age, sex and, for inpatients only, admission method; and a regression model adjusting in addition for ethnicity, presence of a long-term condition, proxy response (inpatients only) and previous emergency attendances (emergency department survey only). For both surveys, all the variables examined were associated with patients' responses and affected inter-trust variance in scores, although the direction and strength of impact differed between variables. Inter-trust variance was generally greatest for the unadjusted scores and lowest for scores derived from the full regression model. Although trust scores derived from the three methods were highly correlated (Kendall's tau coefficients 0.70-0.94), up to 14% of trusts had discordant ranks of when the standardization and regression methods were compared. Depending on the survey and question, up to 14 trusts changed performance bands when the regression model with its fuller case-mix adjustment was used rather than the current standardization method. More comprehensive case-mix adjustment of patient survey data than the current limited adjustment reduces performance variation between NHS acute hospital trusts and alters the comparative performance bands of some trusts. Given the use of these data for high-impact purposes such as performance assessment, regulation, commissioning, quality improvement and patient choice, a review of the long-standing method for analysing

A TNF receptor loop peptide mimic blocks RANK ligand–induced signaling, bone resorption, and bone loss

PubMed Central

Aoki, Kazuhiro; Saito, Hiroaki; Itzstein, Cecile; Ishiguro, Masaji; Shibata, Tatsuya; Blanque, Roland; Mian, Anower Hussain; Takahashi, Mariko; Suzuki, Yoshifumi; Yoshimatsu, Masako; Yamaguchi, Akira; Deprez, Pierre; Mollat, Patrick; Murali, Ramachandran; Ohya, Keiichi; Horne, William C.; Baron, Roland

2006-01-01

Activating receptor activator of NF-κB (RANK) and TNF receptor (TNFR) promote osteoclast differentiation. A critical ligand contact site on the TNFR is partly conserved in RANK. Surface plasmon resonance studies showed that a peptide (WP9QY) that mimics this TNFR contact site and inhibits TNF-α–induced activity bound to RANK ligand (RANKL). Changing a single residue predicted to play an important role in the interaction reduced the binding significantly. WP9QY, but not the altered control peptide, inhibited the RANKL-induced activation of RANK-dependent signaling in RAW 264.7 cells but had no effect on M-CSF–induced activation of some of the same signaling events. WP9QY but not the control peptide also prevented RANKL-induced bone resorption and osteoclastogenesis, even when TNFRs were absent or blocked. In vivo, where both RANKL and TNF-α promote osteoclastogenesis, osteoclast activity, and bone loss, WP9QY prevented the increased osteoclastogenesis and bone loss induced in mice by ovariectomy or low dietary calcium, in the latter case in both wild-type and TNFR double-knockout mice. These results suggest that a peptide that mimics a TNFR ligand contact site blocks bone resorption by interfering with recruitment and activation of osteoclasts by both RANKL and TNF. PMID:16680194

SortNet: learning to rank by a neural preference function.

PubMed

Rigutini, Leonardo; Papini, Tiziano; Maggini, Marco; Scarselli, Franco

2011-09-01

Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the use of a scoring function, learned by examples, that evaluates a feature-based representation of each object yielding an absolute relevance score, a pairwise approach, where a preference function is learned to determine the object that has to be ranked first in a given pair. In this paper, we present a preference learning method for learning to rank. A neural network, the comparative neural network (CmpNN), is trained from examples to approximate the comparison function for a pair of objects. The CmpNN adopts a particular architecture designed to implement the symmetries naturally present in a preference function. The learned preference function can be embedded as the comparator into a classical sorting algorithm to provide a global ranking of a set of objects. To improve the ranking performances, an active-learning procedure is devised, that aims at selecting the most informative patterns in the training set. The proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state-of-the-art algorithms.

College Rankings. ERIC Digest.

ERIC Educational Resources Information Center

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…

Can Mixed-Species Groups Reduce Individual Parasite Load? A Field Test with Two Closely Related Poeciliid Fishes (Poecilia reticulata and Poecilia picta)

PubMed Central

Dargent, Felipe; Torres-Dowdall, Julián; Scott, Marilyn E.; Ramnarine, Indar; Fussmann, Gregor F.

2013-01-01

Predation and parasitism are two of the most important sources of mortality in nature. By forming groups, individuals can gain protection against predators but may increase their risk of being infected with contagious parasites. Animals might resolve this conflict by forming mixed-species groups thereby reducing the costs associated with parasites through a relative decrease in available hosts. We tested this hypothesis in a system with two closely related poeciliid fishes (Poecilia reticulata and Poecilia picta) and their host-specific monogenean ectoparasites (Gyrodactylus spp.) in Trinidad. Fish from three different rivers were sampled from single and mixed-species groups, measured and scanned for Gyrodactylus. The presence and abundance of Gyrodactylus were lower when fish of both species were part of mixed-species groups relative to single-species groups. This is consistent with the hypothesis that mixed-species groups provide a level of protection against contagious parasites. We discuss the importance of potentially confounding factors such as salinity and individual fish size. PMID:23437237

Efficacy of different fibres and flour mixes in South-Asian flatbreads for reducing post-prandial glucose responses in healthy adults.

PubMed

Boers, Hanny M; MacAulay, Katrina; Murray, Peter; Seijen Ten Hoorn, Jack; Hoogenraad, Anne-Roos; Peters, Harry P F; Vente-Spreeuwenberg, Maria A M; Mela, David J

2017-09-01

Type 2 diabetes (T2DM) is increasing, particularly in South-East Asia. Intake of high-glycaemic foods has been positively associated with T2DM, and feasible routes to reduce the glycaemic response to carbohydrate-rich staple foods are needed. The research question was whether different fibre and legume flour mixes in flatbreads lower postprandial glucose (PPG) responses. Using a balanced incomplete block design, we tested the inclusion of guar gum (GG), konjac mannan (KM) and chickpea flour (CPF) in 10 combinations (2/4/6 g GG; 2/4 g KM; 15 g CPF, and 10 or 15 g CPF plus 2 or 4 g GG) in 100 g total of a control commercial high-fibre flatbread flour mix ("atta") on PPG in 38 normal-weight adults. Self-reported appetite was an additional exploratory outcome. An in vitro digestion assay was adapted for flatbreads and assessed for prediction of in vivo PPG. Flatbreads with 6 g GG, 4 g KM, and 15 g CPF plus 2 or 4 g GG reduced PPG ≥30 % (p < 0.01), while no other combinations differed significantly from the control. A statistical model with four in vitro parameters (rate of digestion, %RDS, AUC, carbohydrate level) was highly predictive of PPG results (adjusted R 2  = 0.89). Test products were similar to the control for appetite-related measures. The results confirm the efficacy of specific additions to flatbread flour mixes for reducing PPG and the value of the in vitro model as a predictive tool with these ingredients and product format. This trial is registered at ClinicalTrials.gov with identifier NCT02671214.

Mixed Polyanion Glass Cathodes: Mixed Alkali Effect

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kercher, A. K.; Chapel, A. S.; Kolopus, J. A.

2017-01-01

In lithium-ion batteries, mixed polyanion glass cathodes have demonstrated high capacities (200-500 mAh/g) by undergoing conversion and intercalation reactions. Mixed polyanion glasses typically have the same fundamental issues as other conversion cathodes, i.e.: large hysteresis, capacity fade, and 1st-cycle irreversible loss. A key advantage of glass cathodes is the ability to tailor their composition to optimize the desired physical properties and electrochemical performance. The strong dependence of glass physical properties (e.g., ionic diffusivity, electrical conductivity, and chemical durability) on the composition of alkali mixtures in a glass is well known and has been named the mixed alkali effect. The mixedmore » alkali effect on battery electrochemical properties is reported here for the first time. Depending on glass composition, the mixed alkali effect is shown to improve capacity retention during cycling (from 39% to 50% after 50 cycle test), to reduce the 1st-cycle irreversible loss (from 41% to 22%), and improve the high power (500 mA/g) capacity (from 50% to 67% of slow discharge capacity).« less

Analysis of Duplicated Multiple-Samples Rank Data Using the Mack-Skillings Test.

PubMed

Carabante, Kennet Mariano; Alonso-Marenco, Jose Ramon; Chokumnoyporn, Napapan; Sriwattana, Sujinda; Prinyawiwatkul, Witoon

2016-07-01

Appropriate analysis for duplicated multiple-samples rank data is needed. This study compared analysis of duplicated rank preference data using the Friedman versus Mack-Skillings tests. Panelists (n = 125) ranked twice 2 orange juice sets: different-samples set (100%, 70%, vs. 40% juice) and similar-samples set (100%, 95%, vs. 90%). These 2 sample sets were designed to get contrasting differences in preference. For each sample set, rank sum data were obtained from (1) averaged rank data of each panelist from the 2 replications (n = 125), (2) rank data of all panelists from each of the 2 separate replications (n = 125 each), (3) jointed rank data of all panelists from the 2 replications (n = 125), and (4) rank data of all panelists pooled from the 2 replications (n = 250); rank data (1), (2), and (4) were separately analyzed by the Friedman test, although those from (3) by the Mack-Skillings test. The effect of sample sizes (n = 10 to 125) was evaluated. For the similar-samples set, higher variations in rank data from the 2 replications were observed; therefore, results of the main effects were more inconsistent among methods and sample sizes. Regardless of analysis methods, the larger the sample size, the higher the χ(2) value, the lower the P-value (testing H0 : all samples are not different). Analyzing rank data (2) separately by replication yielded inconsistent conclusions across sample sizes, hence this method is not recommended. The Mack-Skillings test was more sensitive than the Friedman test. Furthermore, it takes into account within-panelist variations and is more appropriate for analyzing duplicated rank data. © 2016 Institute of Food Technologists®

Charting taxonomic knowledge through ontologies and ranking algorithms

NASA Astrophysics Data System (ADS)

Huber, Robert; Klump, Jens

2009-04-01

Since the inception of geology as a modern science, paleontologists have described a large number of fossil species. This makes fossilized organisms an important tool in the study of stratigraphy and past environments. Since taxonomic classifications of organisms, and thereby their names, change frequently, the correct application of this tool requires taxonomic expertise in finding correct synonyms for a given species name. Much of this taxonomic information has already been published in journals and books where it is compiled in carefully prepared synonymy lists. Because this information is scattered throughout the paleontological literature, it is difficult to find and sometimes not accessible. Also, taxonomic information in the literature is often difficult to interpret for non-taxonomists looking for taxonomic synonymies as part of their research. The highly formalized structure makes Open Nomenclature synonymy lists ideally suited for computer aided identification of taxonomic synonyms. Because a synonymy list is a list of citations related to a taxon name, its bibliographic nature allows the application of bibliometric techniques to calculate the impact of synonymies and taxonomic concepts. TaxonRank is a ranking algorithm based on bibliometric analysis and Internet page ranking algorithms. TaxonRank uses published synonymy list data stored in TaxonConcept, a taxonomic information system. The basic ranking algorithm has been modified to include a measure of confidence on species identification based on the Open Nomenclature notation used in synonymy list, as well as other synonymy specific criteria. The results of our experiments show that the output of the proposed ranking algorithm gives a good estimate of the impact a published taxonomic concept has on the taxonomic opinions in the geological community. Also, our results show that treating taxonomic synonymies as part of on an ontology is a way to record and manage taxonomic knowledge, and thus contribute

A Global Comparison of Business Journal Ranking Systems

ERIC Educational Resources Information Center

Alexander, Jennifer K.; Scherer, Robert F.; Lecoutre, Marc

2007-01-01

The authors compared business journal ranking systems from 6 countries. Results revealed a low degree of agreement among the systems, and a low to moderate relationship between pairs of systems. In addition, the French and United Kingdom ranking systems were different from each other and from the systems in Australia, Germany, Hong Kong, and the…

Multicolinearity and Indicator Redundancy Problem in World University Rankings: An Example Using Times Higher Education World University Ranking 2013-2014 Data

ERIC Educational Resources Information Center

Kaycheng, Soh

2015-01-01

World university ranking systems used the weight-and-sum approach to combined indicator scores into overall scores on which the universities are then ranked. This approach assumes that the indicators all independently contribute to the overall score in the specified proportions. In reality, this assumption is doubtful as the indicators tend to…

An Investigation of the Relationship between University Rankings and Graduate Starting Wages

ERIC Educational Resources Information Center

Carroll, David

2014-01-01

The rise of global university rankings has garnered much attention in recent years. Various ranking systems exist, but all are conceptually similar in that universities are evaluated and ranked on the basis of comparable indicators, with a focus on research performance. Although these rankings are widely criticised as over-simplistic and…

Automatic Figure Ranking and User Interfacing for Intelligent Figure Search

PubMed Central

Yu, Hong; Liu, Feifan; Ramesh, Balaji Polepalli

2010-01-01

Background Figures are important experimental results that are typically reported in full-text bioscience articles. Bioscience researchers need to access figures to validate research facts and to formulate or to test novel research hypotheses. On the other hand, the sheer volume of bioscience literature has made it difficult to access figures. Therefore, we are developing an intelligent figure search engine (http://figuresearch.askhermes.org). Existing research in figure search treats each figure equally, but we introduce a novel concept of “figure ranking”: figures appearing in a full-text biomedical article can be ranked by their contribution to the knowledge discovery. Methodology/Findings We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP) approaches to automatically rank figures. Evaluating on a collection of 202 full-text articles in which authors have ranked the figures based on importance, our best system achieved a weighted error rate of 0.2, which is significantly better than several other baseline systems we explored. We further explored a user interfacing application in which we built novel user interfaces (UIs) incorporating figure ranking, allowing bioscience researchers to efficiently access important figures. Our evaluation results show that 92% of the bioscience researchers prefer as the top two choices the user interfaces in which the most important figures are enlarged. With our automatic figure ranking NLP system, bioscience researchers preferred the UIs in which the most important figures were predicted by our NLP system than the UIs in which the most important figures were randomly assigned. In addition, our results show that there was no statistical difference in bioscience researchers' preference in the UIs generated by automatic figure ranking and UIs by human ranking annotation. Conclusion/Significance The evaluation results

Ranking network of a captive rhesus macaque society: a sophisticated corporative kingdom.

PubMed

Fushing, Hsieh; McAssey, Michael P; Beisner, Brianne; McCowan, Brenda

2011-03-15

We develop a three-step computing approach to explore a hierarchical ranking network for a society of captive rhesus macaques. The computed network is sufficiently informative to address the question: Is the ranking network for a rhesus macaque society more like a kingdom or a corporation? Our computations are based on a three-step approach. These steps are devised to deal with the tremendous challenges stemming from the transitivity of dominance as a necessary constraint on the ranking relations among all individual macaques, and the very high sampling heterogeneity in the behavioral conflict data. The first step simultaneously infers the ranking potentials among all network members, which requires accommodation of heterogeneous measurement error inherent in behavioral data. Our second step estimates the social rank for all individuals by minimizing the network-wide errors in the ranking potentials. The third step provides a way to compute confidence bounds for selected empirical features in the social ranking. We apply this approach to two sets of conflict data pertaining to two captive societies of adult rhesus macaques. The resultant ranking network for each society is found to be a sophisticated mixture of both a kingdom and a corporation. Also, for validation purposes, we reanalyze conflict data from twenty longhorn sheep and demonstrate that our three-step approach is capable of correctly computing a ranking network by eliminating all ranking error.

Online Low-Rank Representation Learning for Joint Multi-subspace Recovery and Clustering.

PubMed

Li, Bo; Liu, Risheng; Cao, Junjie; Zhang, Jie; Lai, Yu-Kun; Liua, Xiuping

2017-10-06

Benefiting from global rank constraints, the lowrank representation (LRR) method has been shown to be an effective solution to subspace learning. However, the global mechanism also means that the LRR model is not suitable for handling large-scale data or dynamic data. For large-scale data, the LRR method suffers from high time complexity, and for dynamic data, it has to recompute a complex rank minimization for the entire data set whenever new samples are dynamically added, making it prohibitively expensive. Existing attempts to online LRR either take a stochastic approach or build the representation purely based on a small sample set and treat new input as out-of-sample data. The former often requires multiple runs for good performance and thus takes longer time to run, and the latter formulates online LRR as an out-ofsample classification problem and is less robust to noise. In this paper, a novel online low-rank representation subspace learning method is proposed for both large-scale and dynamic data. The proposed algorithm is composed of two stages: static learning and dynamic updating. In the first stage, the subspace structure is learned from a small number of data samples. In the second stage, the intrinsic principal components of the entire data set are computed incrementally by utilizing the learned subspace structure, and the low-rank representation matrix can also be incrementally solved by an efficient online singular value decomposition (SVD) algorithm. The time complexity is reduced dramatically for large-scale data, and repeated computation is avoided for dynamic problems. We further perform theoretical analysis comparing the proposed online algorithm with the batch LRR method. Finally, experimental results on typical tasks of subspace recovery and subspace clustering show that the proposed algorithm performs comparably or better than batch methods including the batch LRR, and significantly outperforms state-of-the-art online methods.

A new mutually reinforcing network node and link ranking algorithm

PubMed Central

Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.

2015-01-01

This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity. PMID:26492958

Third-rank chromatic aberrations of electron lenses.

PubMed

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.

Cross ranking of cities and regions: population versus income

NASA Astrophysics Data System (ADS)

Cerqueti, Roy; Ausloos, Marcel

2015-07-01

This paper explores the relationship between the inner economical structure of communities and their population distribution through a rank-rank analysis of official data, along statistical physics ideas within two techniques. The data is taken on Italian cities. The analysis is performed both at a global (national) and at a more local (regional) level in order to distinguish ‘macro’ and ‘micro’ aspects. First, the rank-size rule is found not to be a standard power law, as in many other studies, but a doubly decreasing power law. Next, the Kendall τ and the Spearman ρ rank correlation coefficients which measure pair concordance and the correlation between fluctuations in two rankings, respectively,—as a correlation function does in thermodynamics, are calculated for finding rank correlation (if any) between demography and wealth. Results show non only global disparities for the whole (country) set, but also (regional) disparities, when comparing the number of cities in regions, the number of inhabitants in cities and that in regions, as well as when comparing the aggregated tax income of the cities and that of regions. Different outliers are pointed out and justified. Interestingly, two classes of cities in the country and two classes of regions in the country are found. ‘Common sense’ social, political, and economic considerations sustain the findings. More importantly, the methods show that they allow to distinguish communities, very clearly, when specific criteria are numerically sound. A specific modeling for the findings is presented, i.e. for the doubly decreasing power law and the two phase system, based on statistics theory, e.g. urn filling. The model ideas can be expected to hold when similar rank relationship features are observed in fields. It is emphasized that the analysis makes more sense than one through a Pearson Π value-value correlation analysis

Learning Robust and Discriminative Subspace With Low-Rank Constraints.

PubMed

Li, Sheng; Fu, Yun

2016-11-01

In this paper, we aim at learning robust and discriminative subspaces from noisy data. Subspace learning is widely used in extracting discriminative features for classification. However, when data are contaminated with severe noise, the performance of most existing subspace learning methods would be limited. Recent advances in low-rank modeling provide effective solutions for removing noise or outliers contained in sample sets, which motivates us to take advantage of low-rank constraints in order to exploit robust and discriminative subspace for classification. In particular, we present a discriminative subspace learning method called the supervised regularization-based robust subspace (SRRS) approach, by incorporating the low-rank constraint. SRRS seeks low-rank representations from the noisy data, and learns a discriminative subspace from the recovered clean data jointly. A supervised regularization function is designed to make use of the class label information, and therefore to enhance the discriminability of subspace. Our approach is formulated as a constrained rank-minimization problem. We design an inexact augmented Lagrange multiplier optimization algorithm to solve it. Unlike the existing sparse representation and low-rank learning methods, our approach learns a low-dimensional subspace from recovered data, and explicitly incorporates the supervised information. Our approach and some baselines are evaluated on the COIL-100, ALOI, Extended YaleB, FERET, AR, and KinFace databases. The experimental results demonstrate the effectiveness of our approach, especially when the data contain considerable noise or variations.

LCK rank of locally conformally Kähler manifolds with potential

NASA Astrophysics Data System (ADS)

Ornea, Liviu; Verbitsky, Misha

2016-09-01

An LCK manifold with potential is a quotient of a Kähler manifold X equipped with a positive Kähler potential f, such that the monodromy group acts on X by holomorphic homotheties and multiplies f by a character. The LCK rank is the rank of the image of this character, considered as a function from the monodromy group to real numbers. We prove that an LCK manifold with potential can have any rank between 1 and b1(M) . Moreover, LCK manifolds with proper potential (ones with rank 1) are dense. Two errata to our previous work are given in the last section.

Rankings & Estimates: Rankings of the States 2010 and Estimates of School Statistics 2011

ERIC Educational Resources Information Center

National Education Association Research Department, 2010

2010-01-01

The data presented in this combined report--"Rankings & Estimates"--provide facts about the extent to which local, state, and national governments commit resources to public education. As one might expect in a nation as diverse as the United States--with respect to economics, geography, and politics--the level of commitment to…

Rankings & Estimates: Rankings of the States 2015 and Estimates of School Statistics 2016

ERIC Educational Resources Information Center

National Education Association, 2016

2016-01-01

The data presented in this combined report--"Rankings & Estimates"--provide facts about the extent to which local, state, and national governments commit resources to public education. As one might expect in a nation as diverse as the United States--with respect to economics, geography, and politics--the level of commitment to…

Constrained Low-Rank Learning Using Least Squares-Based Regularization.

PubMed

Li, Ping; Yu, Jun; Wang, Meng; Zhang, Luming; Cai, Deng; Li, Xuelong

2017-12-01

Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization. Most low-rank methods are incapable of capturing low-dimensional subspace for supervised learning tasks, e.g., classification and regression. This paper aims to learn both the discriminant low-rank representation (LRR) and the robust projecting subspace in a supervised manner. To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization. Naturally, the data label structure tends to resemble that of the corresponding low-dimensional representation, which is derived from the robust subspace projection of clean data by low-rank learning. Moreover, the low-dimensional representation of original data can be paired with some informative structure by imposing an appropriate constraint, e.g., Laplacian regularizer. Therefore, we propose a novel constrained LRR method. The objective function is formulated as a constrained nuclear norm minimization problem, which can be solved by the inexact augmented Lagrange multiplier algorithm. Extensive experiments on image classification, human pose estimation, and robust face recovery have confirmed the superiority of our method.

Complete hazard ranking to analyze right-censored data: An ALS survival study.

PubMed

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.

A General Class of Signed Rank Tests for Clustered Data when the Cluster Size is Potentially Informative.

PubMed

Datta, Somnath; Nevalainen, Jaakko; Oja, Hannu

2012-09-01

Rank based tests are alternatives to likelihood based tests popularized by their relative robustness and underlying elegant mathematical theory. There has been a serge in research activities in this area in recent years since a number of researchers are working to develop and extend rank based procedures to clustered dependent data which include situations with known correlation structures (e.g., as in mixed effects models) as well as more general form of dependence.The purpose of this paper is to test the symmetry of a marginal distribution under clustered data. However, unlike most other papers in the area, we consider the possibility that the cluster size is a random variable whose distribution is dependent on the distribution of the variable of interest within a cluster. This situation typically arises when the clusters are defined in a natural way (e.g., not controlled by the experimenter or statistician) and in which the size of the cluster may carry information about the distribution of data values within a cluster.Under the scenario of an informative cluster size, attempts to use some form of variance adjusted sign or signed rank tests would fail since they would not maintain the correct size under the distribution of marginal symmetry. To overcome this difficulty Datta and Satten (2008; Biometrics, 64, 501-507) proposed a Wilcoxon type signed rank test based on the principle of within cluster resampling. In this paper we study this problem in more generality by introducing a class of valid tests employing a general score function. Asymptotic null distribution of these tests is obtained. A simulation study shows that a more general choice of the score function can sometimes result in greater power than the Datta and Satten test; furthermore, this development offers the user a wider choice. We illustrate our tests using a real data example on spinal cord injury patients.

Sparse Contextual Activation for Efficient Visual Re-Ranking.

PubMed

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.

The Publication Ranking Score for pediatric urology: quantifying thought leadership within the subspecialty.

PubMed

Lloyd, Jessica C; Madden-Fuentes, Ramiro J; Nelson, Caleb P; Kokorowski, Paul J; Wiener, John S; Ross, Sherry S; Kutikov, Alexander; Routh, Jonathan C

2013-12-01

Clinical care parameters are frequently assessed by national ranking systems. However, these rankings do little to comment on institutions' academic contributions. The Publication Ranking Score (PRS) was developed to allow for objective comparisons of scientific thought-leadership at various pediatric urology institutions. Faculty lists were compiled for each of the US News & World Report (USNWR) top-50 pediatric urology hospitals. A list of all faculty publications (2006-2011) was then compiled, after adjusting for journal impact factor, and summed to derive a Publication Ranking Score (PRS). PRS rankings were then compared to the USNWR pediatric urology top-50 hospital list. A total of 1811 publications were indexed. PRS rankings resulted in a mean change in rank of 12 positions, compared to USNWR ranks. Of the top-10 USNWR hospitals, only 4 were ranked in the top-10 by the PRS. There was little correlation between the USNWR and PRS ranks for either top-10 (r = 0.42, p = 0.23) or top-50 (r = 0.48, p = 0.0004) hospitals. PRS institutional ranking differs significantly from the USNWR top-50 hospital list in pediatric urology. While not a replacement, we believe the PRS to be a useful adjunct to the USNWR rankings of pediatric urology hospitals. Copyright © 2013 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

Structure-preserving and rank-revealing QR-factorizations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bischof, C.H.; Hansen, P.C.

1991-11-01

The rank-revealing QR-factorization (RRQR-factorization) is a special QR-factorization that is guaranteed to reveal the numerical rank of the matrix under consideration. This makes the RRQR-factorization a useful tool in the numerical treatment of many rank-deficient problems in numerical linear algebra. In this paper, a framework is presented for the efficient implementation of RRQR algorithms, in particular, for sparse matrices. A sparse RRQR-algorithm should seek to preserve the structure and sparsity of the matrix as much as possible while retaining the ability to capture safely the numerical rank. To this end, the paper proposes to compute an initial QR-factorization using amore » restricted pivoting strategy guarded by incremental condition estimation (ICE), and then applies the algorithm suggested by Chan and Foster to this QR-factorization. The column exchange strategy used in the initial QR factorization will exploit the fact that certain column exchanges do not change the sparsity structure, and compute a sparse QR-factorization that is a good approximation of the sought-after RRQR-factorization. Due to quantities produced by ICE, the Chan/Foster RRQR algorithm can be implemented very cheaply, thus verifying that the sought-after RRQR-factorization has indeed been computed. Experimental results on a model problem show that the initial QR-factorization is indeed very likely to produce RRQR-factorization.« less

Effect of different mixing methods on the bacterial microleakage of calcium-enriched mixture cement.

PubMed

Shahi, Shahriar; Jeddi Khajeh, Soniya; Rahimi, Saeed; Yavari, Hamid R; Jafari, Farnaz; Samiei, Mohammad; Ghasemi, Negin; Milani, Amin S

2016-10-01

Calcium-enriched mixture (CEM) cement is used in the field of endodontics. It is similar to mineral trioxide aggregate in its main ingredients. The present study investigated the effect of different mixing methods on the bacterial microleakage of CEM cement. A total of 55 human single-rooted human permanent teeth were decoronated so that 14-mm-long samples were obtained and obturated with AH26 sealer and gutta-percha using lateral condensation technique. Three millimeters of the root end were cut off and randomly divided into 3 groups of 15 each (3 mixing methods of amalgamator, ultrasonic and conventional) and 2 negative and positive control groups (each containing 5 samples). BHI (brain-heart infusion agar) suspension containing Enterococcus faecalis was used for bacterial leakage assessment. Statistical analysis was carried out using descriptive statistics, Kaplan-Meier survival analysis with censored data and log rank test. Statistical significance was set at P<0.05. The survival means for conventional, amalgamator and ultrasonic methods were 62.13±12.44, 68.87±12.79 and 77.53±12.52 days, respectively. The log rank test showed no significant differences between the groups. Based on the results of the present study it can be concluded that different mixing methods had no significant effect on the bacterial microleakage of CEM cement.

Playing the Rankings Game

ERIC Educational Resources Information Center

Farrell, Elizabeth F.; Van Der Werf, Martin

2007-01-01

While some colleges claim not to care what "U.S. News & World Report" says, and experts cite problems in the way its annual rankings are done, many institutions scramble to improve their positions. There are well-documented examples of institutions that have solicited nominal donations from alumni to boost their percentage of giving, encouraged…

Ranking Practice Variability in the Medical Student Performance Evaluation: So Bad, It's "Good".

PubMed

Boysen Osborn, Megan; Mattson, James; Yanuck, Justin; Anderson, Craig; Tekian, Ara; Fox, John Christian; Harris, Ilene B

2016-11-01

To examine the variability among medical schools in ranking systems used in medical student performance evaluations (MSPEs). The authors reviewed MSPEs from U.S. MD-granting medical schools received by the University of California, Irvine emergency medicine and internal medicine residency programs during 2012-2013 and 2014-2015. They recorded whether the school used a ranking system, the type of ranking system used, the size and description of student categories, the location of the ranking statement and category legend, and whether nonranking schools used language suggestive of rank. Of the 134 medical schools in the study sample, the majority (n = 101; 75%) provided ranks for students in the MSPE. Most of the ranking schools (n = 63; 62%) placed students into named category groups, but the number and size of groups varied. The most common descriptors used for these 63 schools' top, second, third, and lowest groups were "outstanding," "excellent," "very good," and "good," respectively, but each of these terms was used across a broad range of percentile ranks. Student ranks and school category legends were found in various locations. Many of the 33 schools that did not rank students included language suggestive of rank. There is extensive variation in ranking systems used in MSPEs. Program directors may find it difficult to use MSPEs to compare applicants, which may diminish the MSPE's value in the residency application process and negatively affect high-achieving students. A consistent approach to ranking students would benefit program directors, students, and student affairs officers.

Student Practices, Learning, and Attitudes When Using Computerized Ranking Tasks

NASA Astrophysics Data System (ADS)

Lee, Kevin M.; Prather, E. E.; Collaboration of Astronomy Teaching Scholars CATS

2011-01-01

Ranking Tasks are a novel type of conceptual exercise based on a technique called rule assessment. Ranking Tasks present students with a series of four to eight icons that describe slightly different variations of a basic physical situation. Students are then asked to identify the order, or ranking, of the various situations based on some physical outcome or result. The structure of Ranking Tasks makes it difficult for students to rely strictly on memorized answers and mechanical substitution of formulae. In addition, by changing the presentation of the different scenarios (e.g., photographs, line diagrams, graphs, tables, etc.) we find that Ranking Tasks require students to develop mental schema that are more flexible and robust. Ranking tasks may be implemented on the computer which requires students to order the icons through drag-and-drop. Computer implementation allows the incorporation of background material, grading with feedback, and providing additional similar versions of the task through randomization so that students can build expertise through practice. This poster will summarize the results of a study of student usage of computerized ranking tasks. We will investigate 1) student practices (How do they make use of these tools?), 2) knowledge and skill building (Do student scores improve with iteration and are there diminishing returns?), and 3) student attitudes toward using computerized Ranking Tasks (Do they like using them?). This material is based upon work supported by the National Science Foundation under Grant No. 0715517, a CCLI Phase III Grant for the Collaboration of Astronomy Teaching Scholars (CATS). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Identification of significant features by the Global Mean Rank test.

PubMed

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.

Ranking State Fiscal Structures Using Theory and Evidence

ERIC Educational Resources Information Center

Bania, Neil; Stone, Joe A.

2008-01-01

This paper offers unique rankings of the extent to which fiscal structures of U.S. states contribute to economic growth. The rankings are novel in two key respects: They are well grounded in established growth theory, in which the effect of taxes depends both on the level of taxes and on the composition of expenditures; and they are derived from…

Chemical comminution and deashing of low-rank coals

DOEpatents

Quigley, David R.

1992-01-01

A method of chemically comminuting a low-rank coal while at the same time increasing the heating value of the coal. A strong alkali solution is added to a low-rank coal to solubilize the carbonaceous portion of the coal, leaving behind the noncarbonaceous mineral matter portion. The solubilized coal is precipitated from solution by a multivalent cation, preferably calcium.

Chemical comminution and deashing of low-rank coals

DOEpatents

Quigley, David R.

1992-12-01

A method of chemically comminuting a low-rank coal while at the same time increasing the heating value of the coal. A strong alkali solution is added to a low-rank coal to solubilize the carbonaceous portion of the coal, leaving behind the noncarbonaceous mineral matter portion. The solubilized coal is precipitated from solution by a multivalent cation, preferably calcium.

Reducing Physical Risk Factors in Construction Work Through a Participatory Intervention: Protocol for a Mixed-Methods Process Evaluation.

PubMed

Ajslev, Jeppe; Brandt, Mikkel; Møller, Jeppe Lykke; Skals, Sebastian; Vinstrup, Jonas; Jakobsen, Markus Due; Sundstrup, Emil; Madeleine, Pascal; Andersen, Lars Louis

2016-05-26

Previous research has shown that reducing physical workload among workers in the construction industry is complicated. In order to address this issue, we developed a process evaluation in a formative mixed-methods design, drawing on existing knowledge of the potential barriers for implementation. We present the design of a mixed-methods process evaluation of the organizational, social, and subjective practices that play roles in the intervention study, integrating technical measurements to detect excessive physical exertion measured with electromyography and accelerometers, video documentation of working tasks, and a 3-phased workshop program. The evaluation is designed in an adapted process evaluation framework, addressing recruitment, reach, fidelity, satisfaction, intervention delivery, intervention received, and context of the intervention companies. Observational studies, interviews, and questionnaires among 80 construction workers organized in 20 work gangs, as well as health and safety staff, contribute to the creation of knowledge about these phenomena. At the time of publication, the process of participant recruitment is underway. Intervention studies are challenging to conduct and evaluate in the construction industry, often because of narrow time frames and ever-changing contexts. The mixed-methods design presents opportunities for obtaining detailed knowledge of the practices intra-acting with the intervention, while offering the opportunity to customize parts of the intervention.

Poisson statistics of PageRank probabilities of Twitter and Wikipedia networks

NASA Astrophysics Data System (ADS)

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.

[2013 research ranking of Spanish public universities].

PubMed

Buela-Casal, Gualberto; Quevedo-Blasco, Raúl; Guillén-Riquelme, Alejandro

2015-01-01

The evaluation of research production and productivity is becoming increasingly necessary for universities. Having reliable and clear data is extremely useful in order to uncover strengths and weaknesses. The objective of this article is to update the research ranking of Spanish public universities with the 2013 data. Assessment was carried out based on articles in journals indexed in the JCR, research periods, R+D projects, doctoral theses, FPU grants, doctoral studies awarded with a citation of excellence, and patents, providing a rating, both for each individual indicator and globally, in production and productivity. The same methodology as previous editions was followed. In the global ranking, the universities with a higher production are Barcelona, Complutense of Madrid, and Granada. In productivity, the first positions are held by the universities Pompeu Fabra, Pablo de Olavide, and the Autonomous University of Barcelona. Differences can be found between the universities in production and productivity, while there are also certain similarities with regard to the position of Spanish universities in international rankings.

A network-based dynamical ranking system for competitive sports

NASA Astrophysics Data System (ADS)

Motegi, Shun; Masuda, Naoki

2012-12-01

From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.

Optimization of the two-sample rank Neyman-Pearson detector

NASA Astrophysics Data System (ADS)

Akimov, P. S.; Barashkov, V. M.

1984-10-01

The development of optimal algorithms concerned with rank considerations in the case of finite sample sizes involves considerable mathematical difficulties. The present investigation provides results related to the design and the analysis of an optimal rank detector based on a utilization of the Neyman-Pearson criteria. The detection of a signal in the presence of background noise is considered, taking into account n observations (readings) x1, x2, ... xn in the experimental communications channel. The computation of the value of the rank of an observation is calculated on the basis of relations between x and the variable y, representing interference. Attention is given to conditions in the absence of a signal, the probability of the detection of an arriving signal, details regarding the utilization of the Neyman-Pearson criteria, the scheme of an optimal rank, multichannel, incoherent detector, and an analysis of the detector.

Weighted Discriminative Dictionary Learning based on Low-rank Representation

NASA Astrophysics Data System (ADS)

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.

A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment

NASA Astrophysics Data System (ADS)

Wu, Qingbo; Li, Hongliang; Meng, Fanman; Ngan, King N.

2018-05-01

In the field of objective image quality assessment (IQA), the Spearman's $\\rho$ and Kendall's $\\tau$ are two most popular rank correlation indicators, which straightforwardly assign uniform weight to all quality levels and assume each pair of images are sortable. They are successful for measuring the average accuracy of an IQA metric in ranking multiple processed images. However, two important perceptual properties are ignored by them as well. Firstly, the sorting accuracy (SA) of high quality images are usually more important than the poor quality ones in many real world applications, where only the top-ranked images would be pushed to the users. Secondly, due to the subjective uncertainty in making judgement, two perceptually similar images are usually hardly sortable, whose ranks do not contribute to the evaluation of an IQA metric. To more accurately compare different IQA algorithms, we explore a perceptually weighted rank correlation indicator in this paper, which rewards the capability of correctly ranking high quality images, and suppresses the attention towards insensitive rank mistakes. More specifically, we focus on activating `valid' pairwise comparison towards image quality, whose difference exceeds a given sensory threshold (ST). Meanwhile, each image pair is assigned an unique weight, which is determined by both the quality level and rank deviation. By modifying the perception threshold, we can illustrate the sorting accuracy with a more sophisticated SA-ST curve, rather than a single rank correlation coefficient. The proposed indicator offers a new insight for interpreting visual perception behaviors. Furthermore, the applicability of our indicator is validated in recommending robust IQA metrics for both the degraded and enhanced image data.

Probabilistic Low-Rank Multitask Learning.

PubMed

Kong, Yu; Shao, Ming; Li, Kang; Fu, Yun

2018-03-01

In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multitask learning (MTL) that can automatically balance between low-rank and sparsity constraints. The former assumes a low-rank structure of the underlying predictive hypothesis space to explicitly capture the relationship of different tasks and the latter learns the incoherent sparse patterns private to each task. We derive and perform inference via variational Bayesian methods. Experimental results on both regression and classification tasks on real-world applications demonstrate the effectiveness of the proposed method in dealing with the MTL problems.

An intertwined method for making low-rank, sum-of-product basis functions that makes it possible to compute vibrational spectra of molecules with more than 10 atoms

PubMed Central

Thomas, Phillip S.

2017-01-01

We propose a method for solving the vibrational Schrödinger equation with which one can compute spectra for molecules with more than ten atoms. It uses sum-of-product (SOP) basis functions stored in a canonical polyadic tensor format and generated by evaluating matrix-vector products. By doing a sequence of partial optimizations, in each of which the factors in a SOP basis function for a single coordinate are optimized, the rank of the basis functions is reduced as matrix-vector products are computed. This is better than using an alternating least squares method to reduce the rank, as is done in the reduced-rank block power method. Partial optimization is better because it speeds up the calculation by about an order of magnitude and allows one to significantly reduce the memory cost. We demonstrate the effectiveness of the new method by computing vibrational spectra of two molecules, ethylene oxide (C2H4O) and cyclopentadiene (C5H6), with 7 and 11 atoms, respectively. PMID:28571348

An intertwined method for making low-rank, sum-of-product basis functions that makes it possible to compute vibrational spectra of molecules with more than 10 atoms.

PubMed

Thomas, Phillip S; Carrington, Tucker

2017-05-28

We propose a method for solving the vibrational Schrödinger equation with which one can compute spectra for molecules with more than ten atoms. It uses sum-of-product (SOP) basis functions stored in a canonical polyadic tensor format and generated by evaluating matrix-vector products. By doing a sequence of partial optimizations, in each of which the factors in a SOP basis function for a single coordinate are optimized, the rank of the basis functions is reduced as matrix-vector products are computed. This is better than using an alternating least squares method to reduce the rank, as is done in the reduced-rank block power method. Partial optimization is better because it speeds up the calculation by about an order of magnitude and allows one to significantly reduce the memory cost. We demonstrate the effectiveness of the new method by computing vibrational spectra of two molecules, ethylene oxide (C 2 H 4 O) and cyclopentadiene (C 5 H 6 ), with 7 and 11 atoms, respectively.

Using both Principal Component Analysis and Reduced Rank Regression to Study Dietary Patterns and Diabetes in Chinese Adults

PubMed Central

Batis, Carolina; Mendez, Michelle A.; Gordon-Larsen, Penny; Sotres-Alvarez, Daniela; Adair, Linda; Popkin, Barry

2014-01-01

Objective We examined the association between dietary patterns and diabetes using the strengths of two methods: principal component analysis (PCA) to identify the eating patterns of the population and reduced rank regression (RRR) to derive a pattern that explains the variation in hemoglobin A1c (HbA1c), homeostasis model of insulin resistance (HOMA-IR), and fasting glucose. Design We measured diet over a 3-day period with 24-hour recalls and a household food inventory in 2006 and used it to derive PCA and RRR dietary patterns. The outcomes were measured in 2009. Setting Adults (n = 4,316) from the China Health and Nutrition Survey. Results The adjusted odds ratio for diabetes prevalence (HbA1c ≥ 6.5%), comparing the highest dietary pattern score quartile to the lowest, was 1.26 (0.76, 2.08) for a modern high-wheat pattern (PCA; wheat products, fruits, eggs, milk, instant noodles and frozen dumplings), 0.76 (0.49, 1.17) for a traditional southern pattern (PCA; rice, meat, poultry, and fish), and 2.37 (1.56, 3.60) for the pattern derived with RRR. By comparing the dietary pattern structures of RRR and PCA, we found that the RRR pattern was also behaviorally meaningful. It combined the deleterious effects of the modern high-wheat (high intake of wheat buns and breads, deep-fried wheat, and soy milk) with the deleterious effects of consuming the opposite of the traditional southern (low intake of rice, poultry and game, fish and seafood). Conclusions Our findings suggest that using both PCA and RRR provided useful insights when studying the association of dietary patterns with diabetes. PMID:26784586

A Social Rank Explanation of How Money Influences Health

PubMed Central

2014-01-01

Objective: Financial resources are a potent determinant of health, yet it remains unclear why this is the case. We aimed to identify whether the frequently observed association between absolute levels of monetary resources and health may occur because money acts an indirect proxy for a person’s social rank. Method: To address this question we examined over 230,000 observations on 40,400 adults drawn from two representative national panel studies; the British Household Panel Survey and the English Longitudinal Study of Ageing. We identified each person’s absolute income/wealth and their objective ranked position of income/wealth within a social reference-group. Absolute and rank income/wealth variables were then used to predict a series of self-reported and objectively recorded health outcomes in cross-sectional and longitudinal analyses. Results: As anticipated, those with higher levels of absolute income/wealth were found to have better health than others, after adjustment for age, gender, education, marital status, and labor force status. When evaluated simultaneously the ranked position of income/wealth but not absolute income/wealth predicted all health outcomes examined including: objective measures of allostatic load and obesity, the presence of long-standing illness, and ratings of health, physical functioning, role limitations, and pain. The health benefits of high rank were consistent in cross-sectional and longitudinal analyses and did not depend on the reference-group used to rank participants. Conclusions: This is the first study to demonstrate that social position rather than material conditions may explain the impact of money on human health. PMID:25133843

Low-rank coal oil agglomeration

DOEpatents

Knudson, Curtis L.; Timpe, Ronald C.

1991-01-01

A low-rank coal oil agglomeration process. High mineral content, a high ash content subbituminous coals are effectively agglomerated with a bridging oil which is partially water soluble and capable of entering the pore structure, and usually coal derived.

Anchoring Effects in World University Rankings: Exploring Biases in Reputation Scores

ERIC Educational Resources Information Center

Bowman, Nicholas A.; Bastedo, Michael N.

2011-01-01

Despite ongoing debates about their uses and validity, university rankings are a popular means to compare institutions within a country and around the world. Anchoring theory suggests that these rankings may influence assessments of institutional reputation, and this effect may be particularly strong when a new rankings system is introduced. We…

Relationship between Journal-Ranking Metrics for a Multidisciplinary Set of Journals

ERIC Educational Resources Information Center

Perera, Upeksha; Wijewickrema, Manjula

2018-01-01

Ranking of scholarly journals is important to many parties. Studying the relationships among various ranking metrics is key to understanding the significance of one metric based on another. This research investigates the relationship among four major journal-ranking indicators: the impact factor (IF), the Eigenfactor score (ES), the "h."…

Warm-mix asphalt : European practice.

DOT National Transportation Integrated Search

2008-02-01

Warm-mix asphalt (WMA) is a group of technologies that allow a reduction in the temperatures at which : asphalt mixes are produced and placed. These technologies tend to reduce the viscosity of the asphalt and : provide for the complete coating of ag...

Low-rank regularization for learning gene expression programs.

PubMed

Ye, Guibo; Tang, Mengfan; Cai, Jian-Feng; Nie, Qing; Xie, Xiaohui

2013-01-01

Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.

Hazard-ranking of agricultural pesticides for chronic health effects in Yuma County, Arizona.

PubMed

Sugeng, Anastasia J; Beamer, Paloma I; Lutz, Eric A; Rosales, Cecilia B

2013-10-01

With thousands of pesticides registered by the United States Environmental Protection Agency, it not feasible to sample for all pesticides applied in agricultural communities. Hazard-ranking pesticides based on use, toxicity, and exposure potential can help prioritize community-specific pesticide hazards. This study applied hazard-ranking schemes for cancer, endocrine disruption, and reproductive/developmental toxicity in Yuma County, Arizona. An existing cancer hazard-ranking scheme was modified, and novel schemes for endocrine disruption and reproductive/developmental toxicity were developed to rank pesticide hazards. The hazard-ranking schemes accounted for pesticide use, toxicity, and exposure potential based on chemical properties of each pesticide. Pesticides were ranked as hazards with respect to each health effect, as well as overall chronic health effects. The highest hazard-ranked pesticides for overall chronic health effects were maneb, metam-sodium, trifluralin, pronamide, and bifenthrin. The relative pesticide rankings were unique for each health effect. The highest hazard-ranked pesticides differed from those most heavily applied, as well as from those previously detected in Yuma homes over a decade ago. The most hazardous pesticides for cancer in Yuma County, Arizona were also different from a previous hazard-ranking applied in California. Hazard-ranking schemes that take into account pesticide use, toxicity, and exposure potential can help prioritize pesticides of greatest health risk in agricultural communities. This study is the first to provide pesticide hazard-rankings for endocrine disruption and reproductive/developmental toxicity based on use, toxicity, and exposure potential. These hazard-ranking schemes can be applied to other agricultural communities for prioritizing community-specific pesticide hazards to target decreasing health risk. Copyright © 2013 Elsevier B.V. All rights reserved.

Hazard-Ranking of Agricultural Pesticides for Chronic Health Effects in Yuma County, Arizona

PubMed Central

Sugeng, Anastasia J.; Beamer, Paloma I.; Lutz, Eric A.; Rosales, Cecilia B.

2013-01-01

With thousands of pesticides registered by the United States Environmental Protection Agency, it not feasible to sample for all pesticides applied in agricultural communities. Hazard-ranking pesticides based on use, toxicity, and exposure potential can help prioritize community-specific pesticide hazards. This study applied hazard-ranking schemes for cancer, endocrine disruption, and reproductive/developmental toxicity in Yuma County, Arizona. An existing cancer hazard-ranking scheme was modified, and novel schemes for endocrine disruption and reproductive/developmental toxicity were developed to rank pesticide hazards. The hazard-ranking schemes accounted for pesticide use, toxicity, and exposure potential based on chemical properties of each pesticide. Pesticides were ranked as hazards with respect to each health effect, as well as overall chronic health effects. The highest hazard-ranked pesticides for overall chronic health effects were maneb, metam sodium, trifluralin, pronamide, and bifenthrin. The relative pesticide rankings were unique for each health effect. The highest hazard-ranked pesticides differed from those most heavily applied, as well as from those previously detected in Yuma homes over a decade ago. The most hazardous pesticides for cancer in Yuma County, Arizona were also different from a previous hazard-ranking applied in California. Hazard-ranking schemes that take into account pesticide use, toxicity, and exposure potential can help prioritize pesticides of greatest health risk in agricultural communities. This study is the first to provide pesticide hazard-rankings for endocrine disruption and reproductive/developmental toxicity based on use, toxicity, and exposure potential. These hazard-ranking schemes can be applied to other agricultural communities for prioritizing community-specific pesticide hazards to target decreasing health risk. PMID:23783270

Application of learning to rank to protein remote homology detection.

PubMed

Liu, Bin; Chen, Junjie; Wang, Xiaolong

2015-11-01

Protein remote homology detection is one of the fundamental problems in computational biology, aiming to find protein sequences in a database of known structures that are evolutionarily related to a given query protein. Some computational methods treat this problem as a ranking problem and achieve the state-of-the-art performance, such as PSI-BLAST, HHblits and ProtEmbed. This raises the possibility to combine these methods to improve the predictive performance. In this regard, we are to propose a new computational method called ProtDec-LTR for protein remote homology detection, which is able to combine various ranking methods in a supervised manner via using the Learning to Rank (LTR) algorithm derived from natural language processing. Experimental results on a widely used benchmark dataset showed that ProtDec-LTR can achieve an ROC1 score of 0.8442 and an ROC50 score of 0.9023 outperforming all the individual predictors and some state-of-the-art methods. These results indicate that it is correct to treat protein remote homology detection as a ranking problem, and predictive performance improvement can be achieved by combining different ranking approaches in a supervised manner via using LTR. For users' convenience, the software tools of three basic ranking predictors and Learning to Rank algorithm were provided at http://bioinformatics.hitsz.edu.cn/ProtDec-LTR/home/ bliu@insun.hit.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

RELATIVE POTENCY RANKING FOR CHLOROPHENOLS

EPA Science Inventory

Recently the National Center for Environmental Assessment-Cincinnati completed a feasibility study for developing a toxicity related relative potency ranking scheme for chlorophenols. In this study it was concluded that a large data base exists pertaining to the relative toxicity...

The Impact of Ranking Systems on Higher Education and Its Stakeholders

ERIC Educational Resources Information Center

Thakur, Marian

2007-01-01

The arrival of university ranking has changed the landscape of higher education all over the world and is likely to continue to influence further development nationally and internationally. This article provides an overview of rankings systems in which Australian universities feature and it goes on further to discuss the impact ranking systems…

Complex sources of variance in female dominance rank in a nepotistic society

PubMed Central

Lea, Amanda J.; Learn, Niki H.; Theus, Marcus J.; Altmann, Jeanne; Alberts, Susan C.

2016-01-01

Many mammalian societies are structured by dominance hierarchies, and an individual’s position within this hierarchy can influence reproduction, behaviour, physiology and health. In nepotistic hierarchies, which are common in cercopithecine primates and also seen in spotted hyaenas, Crocuta crocuta, adult daughters are expected to rank immediately below their mother, and in reverse age order (a phenomenon known as ‘youngest ascendancy’). This pattern is well described, but few studies have systematically examined the frequency or causes of departures from the expected pattern. Using a longitudinal data set from a natural population of yellow baboons, Papio cynocephalus, we measured the influence of maternal kin, paternal kin and group size on female rank positions at two life history milestones, menarche and first live birth. At menarche, most females (73%) ranked adjacent to their family members (i.e. the female held an ordinal rank in consecutive order with other members of her maternal family); however, only 33% of females showed youngest ascendancy within their matriline at menarche. By the time they experienced their first live birth, many females had improved their dominance rank: 78% ranked adjacent to their family members and 49% showed youngest ascendancy within their matriline. The presence of mothers and maternal sisters exerted a powerful influence on rank outcomes. However, the presence of fathers, brothers and paternal siblings did not produce a clear effect on female dominance rank in our analyses, perhaps because females in our data set co-resided with variable numbers and types of paternal and male relatives. Our results also raise the possibility that female body size or competitive ability may influence dominance rank, even in this classically nepotistic species. In total, our analyses reveal that the predictors of dominance rank in nepotistic rank systems are much more complex than previously thought. PMID:26997663

Health systems around the world - a comparison of existing health system rankings.

PubMed

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.

Rationality of moduli space of torsion-free sheaves over reducible curve

NASA Astrophysics Data System (ADS)

Dey, Arijit; Suhas, B. N.

2018-06-01

Let M(2 , w ̲ , χ) be the moduli space of rank 2 torsion-free sheaves of fixed determinant and odd Euler characteristic over a reducible nodal curve with each irreducible component having utmost two nodal singularities. We show that in each irreducible component of M(2 , w ̲ , χ) , the closure of rank 2 vector bundles is rational.

Moving up in the U.S. News and World Report Rankings

ERIC Educational Resources Information Center

Martin, Jeremy P.

2015-01-01

Rankings are a powerful force in higher education, swaying the enrollment decisions of prospective students and affecting the opinions of parents, board members, and policymakers. In the words of one provost, "The rankings matter to our university because they matter to people who matter to us." Rankings are also a business--one that is…

Biological solubilization of low-rank coal

DOE Office of Scientific and Technical Information (OSTI.GOV)