WorldWideScience

Sample records for learning hypothesis ranking

  1. Evaluating the Stage Learning Hypothesis.

    Science.gov (United States)

    Thomas, Hoben

    1980-01-01

    A procedure for evaluating the Genevan stage learning hypothesis is illustrated by analyzing Inhelder, Sinclair, and Bovet's guided learning experiments (in "Learning and the Development of Cognition." Cambridge: Harvard University Press, 1974). (Author/MP)

  2. Testing the encoding elaboration hypothesis: The effects of exemplar ranking on recognition and recall.

    Science.gov (United States)

    Schnur, P

    1977-11-01

    Two experiments investigated the effects of exemplar ranking on retention. High-ranking exemplars are words judged to be prototypical of a given category; low-ranking exemplars are words judged to be atypical of a given category. In Experiment 1, an incidental learning paradigm was used to measure reaction time to answer an encoding question as well as subsequent recognition. It was found that low-ranking exemplars were classified more slowly but recognized better than high-ranking exemplars. Other comparisons of the effects of category encoding, rhyme encoding, and typescript encoding on response latency and recognition replicated the results of Craik and Tulving (1975). In Experiment 2, unanticipated free recall of live previously learned paired associate lists revealed that a list composed of low-ranking exemplars was better recalled than a comparable list composed of high-ranking exemplars. Moreover, this was true only when the lists were studied in the context of appropriate category cues. These findings are discussed in terms of the encoding elaboration hypothesis.

  3. Extreme learning machine for ranking: generalization analysis and applications.

    Science.gov (United States)

    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.

  4. Lerot: An Online Learning to Rank Framework

    NARCIS (Netherlands)

    Schuth, A.; Hofmann, K.; Whiteson, S.; de Rijke, M.

    2013-01-01

    Online learning to rank methods for IR allow retrieval systems to optimize their own performance directly from interactions with users via click feedback. In the software package Lerot, presented in this paper, we have bundled all ingredients needed for experimenting with online learning to rank for

  5. Contextual effects on the perceived health benefits of exercise: the exercise rank hypothesis.

    Science.gov (United States)

    Maltby, John; Wood, Alex M; Vlaev, Ivo; Taylor, Michael J; Brown, Gordon D A

    2012-12-01

    Many accounts of social influences on exercise participation describe how people compare their behaviors to those of others. We develop and test a novel hypothesis, the exercise rank hypothesis, of how this comparison can occur. The exercise rank hypothesis, derived from evolutionary theory and the decision by sampling model of judgment, suggests that individuals' perceptions of the health benefits of exercise are influenced by how individuals believe the amount of exercise ranks in comparison with other people's amounts of exercise. Study 1 demonstrated that individuals' perceptions of the health benefits of their own current exercise amounts were as predicted by the exercise rank hypothesis. Study 2 demonstrated that the perceptions of the health benefits of an amount of exercise can be manipulated by experimentally changing the ranked position of the amount within a comparison context. The discussion focuses on how social norm-based interventions could benefit from using rank information.

  6. Preference Learning and Ranking by Pairwise Comparison

    Science.gov (United States)

    Fürnkranz, Johannes; Hüllermeier, Eyke

    This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.

  7. Reverse hypothesis machine learning a practitioner's perspective

    CERN Document Server

    Kulkarni, Parag

    2017-01-01

    This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as ...

  8. Mastery Learning and the Decreasing Variability Hypothesis.

    Science.gov (United States)

    Livingston, Jennifer A.; Gentile, J. Ronald

    1996-01-01

    This report results from studies that tested two variations of Bloom's decreasing variability hypothesis using performance on successive units of achievement in four graduate classrooms that used mastery learning procedures. Data do not support the decreasing variability hypothesis; rather, they show no change over time. (SM)

  9. Balancing exploration and exploitation in learning to rank online

    NARCIS (Netherlands)

    Hofmann, K.; Whiteson, S.; de Rijke, M.

    2011-01-01

    As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches, retrieval systems can learn directly from implicit feedback, while they are running. In such an online setting, algorithms need

  10. When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

    KAUST Repository

    Wang, Jim Jing-Yan

    2017-06-28

    Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

  11. Learning to rank for information retrieval and natural language processing

    CERN Document Server

    Li, Hang

    2014-01-01

    Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work.The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as tw

  12. When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

    KAUST Repository

    Wang, Jim Jing-Yan; Cui, Xuefeng; Yu, Ge; Guo, Lili; Gao, Xin

    2017-01-01

    Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays

  13. Learning to Rank for Information Retrieval from User Interactions

    NARCIS (Netherlands)

    Hofmann, K.; Whiteson, S.; Schuth, A.; de Rijke, M.

    2014-01-01

    In this article we give an overview of our recent work on online learning to rank for information retrieval (IR). This work addresses IR from a reinforcement learning (RL) point of view, with the aim to enable systems that can learn directly from interactions with their users. Learning directly from

  14. Learning to rank for information retrieval

    CERN Document Server

    Liu, Tie-Yan

    2011-01-01

    Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as coll

  15. Learning of Rule Ensembles for Multiple Attribute Ranking Problems

    Science.gov (United States)

    Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

    In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

  16. Weighted Discriminative Dictionary Learning based on Low-rank Representation

    International Nuclear Information System (INIS)

    Chang, Heyou; Zheng, Hao

    2017-01-01

    Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods. (paper)

  17. Social learning and evolution: the cultural intelligence hypothesis

    Science.gov (United States)

    van Schaik, Carel P.; Burkart, Judith M.

    2011-01-01

    If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer. PMID:21357223

  18. Social learning and evolution: the cultural intelligence hypothesis.

    Science.gov (United States)

    van Schaik, Carel P; Burkart, Judith M

    2011-04-12

    If social learning is more efficient than independent individual exploration, animals should learn vital cultural skills exclusively, and routine skills faster, through social learning, provided they actually use social learning preferentially. Animals with opportunities for social learning indeed do so. Moreover, more frequent opportunities for social learning should boost an individual's repertoire of learned skills. This prediction is confirmed by comparisons among wild great ape populations and by social deprivation and enculturation experiments. These findings shaped the cultural intelligence hypothesis, which complements the traditional benefit hypotheses for the evolution of intelligence by specifying the conditions in which these benefits can be reaped. The evolutionary version of the hypothesis argues that species with frequent opportunities for social learning should more readily respond to selection for a greater number of learned skills. Because improved social learning also improves asocial learning, the hypothesis predicts a positive interspecific correlation between social-learning performance and individual learning ability. Variation among primates supports this prediction. The hypothesis also predicts that more heavily cultural species should be more intelligent. Preliminary tests involving birds and mammals support this prediction too. The cultural intelligence hypothesis can also account for the unusual cognitive abilities of humans, as well as our unique mechanisms of skill transfer.

  19. A cross-benchmark comparison of 87 learning to rank methods

    NARCIS (Netherlands)

    Tax, N.; Bockting, S.; Hiemstra, D.

    2015-01-01

    Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered

  20. Wilcoxon's signed-rank statistic: what null hypothesis and why it matters.

    Science.gov (United States)

    Li, Heng; Johnson, Terri

    2014-01-01

    In statistical literature, the term 'signed-rank test' (or 'Wilcoxon signed-rank test') has been used to refer to two distinct tests: a test for symmetry of distribution and a test for the median of a symmetric distribution, sharing a common test statistic. To avoid potential ambiguity, we propose to refer to those two tests by different names, as 'test for symmetry based on signed-rank statistic' and 'test for median based on signed-rank statistic', respectively. The utility of such terminological differentiation should become evident through our discussion of how those tests connect and contrast with sign test and one-sample t-test. Published 2014. This article is a U.S. Government work and is in the public domain in the USA. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

  1. Learning-Related Changes in Adolescents' Neural Networks during Hypothesis-Generating and Hypothesis-Understanding Training

    Science.gov (United States)

    Lee, Jun-Ki; Kwon, Yongju

    2012-01-01

    Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were…

  2. Generating pseudo test collections for learning to rank scientific articles

    NARCIS (Netherlands)

    Berendsen, R.; Tsagkias, M.; de Rijke, M.; Meij, E.

    2012-01-01

    Pseudo test collections are automatically generated to provide training material for learning to rank methods. We propose a method for generating pseudo test collections in the domain of digital libraries, where data is relatively sparse, but comes with rich annotations. Our intuition is that

  3. Improve Biomedical Information Retrieval using Modified Learning to Rank Methods.

    Science.gov (United States)

    Xu, Bo; Lin, Hongfei; Lin, Yuan; Ma, Yunlong; Yang, Liang; Wang, Jian; Yang, Zhihao

    2016-06-14

    In these years, the number of biomedical articles has increased exponentially, which becomes a problem for biologists to capture all the needed information manually. Information retrieval technologies, as the core of search engines, can deal with the problem automatically, providing users with the needed information. However, it is a great challenge to apply these technologies directly for biomedical retrieval, because of the abundance of domain specific terminologies. To enhance biomedical retrieval, we propose a novel framework based on learning to rank. Learning to rank is a series of state-of-the-art information retrieval techniques, and has been proved effective in many information retrieval tasks. In the proposed framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents, but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate the effectiveness of our framework for biomedical information retrieval.

  4. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

    KAUST Repository

    Zhang, Tianzhu; Liu, Si; Ahuja, Narendra; Yang, Ming-Hsuan; Ghanem, Bernard

    2014-01-01

    and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25

  5. Learning to rank figures within a biomedical article.

    Directory of Open Access Journals (Sweden)

    Feifan Liu

    Full Text Available 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

  6. Learning to rank figures within a biomedical article.

    Science.gov (United States)

    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

  7. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu

    2012-01-01

    In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

  8. Balancing Speed and Quality in Online Learning to Rank for Information Retrieval

    NARCIS (Netherlands)

    Oosterhuis, H.; de Rijke, M.

    2017-01-01

    In Online Learning to Rank (OLTR) the aim is to find an optimal ranking model by interacting with users. When learning from user behavior, systems must interact with users while simultaneously learning from those interactions. Unlike other Learning to Rank (LTR) settings, existing research in this

  9. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

    KAUST Repository

    Zhang, Tianzhu

    2014-06-19

    Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.

  10. Fast Low-Rank Shared Dictionary Learning for Image Classification.

    Science.gov (United States)

    Tiep Huu Vu; Monga, Vishal

    2017-11-01

    Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

  11. Selection of suitable e-learning approach using TOPSIS technique with best ranked criteria weights

    Science.gov (United States)

    Mohammed, Husam Jasim; Kasim, Maznah Mat; Shaharanee, Izwan Nizal Mohd

    2017-11-01

    This paper compares the performances of four rank-based weighting assessment techniques, Rank Sum (RS), Rank Reciprocal (RR), Rank Exponent (RE), and Rank Order Centroid (ROC) on five identified e-learning criteria to select the best weights method. A total of 35 experts in a public university in Malaysia were asked to rank the criteria and to evaluate five e-learning approaches which include blended learning, flipped classroom, ICT supported face to face learning, synchronous learning, and asynchronous learning. The best ranked criteria weights are defined as weights that have the least total absolute differences with the geometric mean of all weights, were then used to select the most suitable e-learning approach by using TOPSIS method. The results show that RR weights are the best, while flipped classroom approach implementation is the most suitable approach. This paper has developed a decision framework to aid decision makers (DMs) in choosing the most suitable weighting method for solving MCDM problems.

  12. The Literal Translation Hypothesis in ESP Teaching/Learning Environments

    Directory of Open Access Journals (Sweden)

    Pedro A. Fuertes-Olivera

    2015-11-01

    Full Text Available Research on the characteristics of specialized vocabulary usually replicates studies that deal with general words, e.g. they typically describe frequent terms and focus on their linguistic characteristics to aid in the learning and acquisition of the terms. We dispute this practise, as we believe that the basic characteristic of terms is that they are coined to restrict meaning, i.e. to be as precise and as specific as possible in a particular context. For instance, around 70% of English and Spanish accounting terms are multi-word terms, most of which contain more than three orthographic words that syntactically behave in a way that is very different from the syntactic behaviour of the node on which they are formed (Fuertes-Olivera and Tarp, forthcoming. This has prompted us to propose a research framework that investigates whether or not the literal translation hypothesis, which has been addressed in several areas of translation studies, can also be applied in ESP teaching/learning environments. If plausible, the assumptions on which this hypothesis is based can shed light on how learners disambiguate terms they encounter. Within this framework, this paper presents evidence that the literal translation hypothesis is possible in ESP; it offers the results of a pilot study that sheds light on how this hypothesis may work, and also discusses its usability in the context of ESP learning. In particular, this paper presents strategies for teaching multi-word terms that are different from those currently based on corpus data. We believe that exercises such as “cloze”, “fill in” and similar “guessing” exercises must be abandoned in ESP teaching/learning environments. Instead, we propose exercises that reproduce L1 teaching and learning activities, i.e., exercises that are typically used when acquiring specialised knowledge and skills in any domain, e.g. taking part in meetings and giving presentations in a business context.

  13. Research on B Cell Algorithm for Learning to Rank Method Based on Parallel Strategy.

    Science.gov (United States)

    Tian, Yuling; Zhang, Hongxian

    2016-01-01

    For the purposes of information retrieval, users must find highly relevant documents from within a system (and often a quite large one comprised of many individual documents) based on input query. Ranking the documents according to their relevance within the system to meet user needs is a challenging endeavor, and a hot research topic-there already exist several rank-learning methods based on machine learning techniques which can generate ranking functions automatically. This paper proposes a parallel B cell algorithm, RankBCA, for rank learning which utilizes a clonal selection mechanism based on biological immunity. The novel algorithm is compared with traditional rank-learning algorithms through experimentation and shown to outperform the others in respect to accuracy, learning time, and convergence rate; taken together, the experimental results show that the proposed algorithm indeed effectively and rapidly identifies optimal ranking functions.

  14. A rank-based Prediction Algorithm of Learning User's Intention

    Science.gov (United States)

    Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing

    Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

  15. Learning Preference Models from Data: On the Problem of Label Ranking and Its Variants

    Science.gov (United States)

    Hüllermeier, Eyke; Fürnkranz, Johannes

    The term “preference learning” refers to the application of machine learning methods for inducing preference models from empirical data. In the recent literature, corresponding problems appear in various guises. After a brief overview of the field, this work focuses on a particular learning scenario called label ranking where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a ranking function, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data, using a natural extension of pairwise classification. A ranking is then derived from this relation by means of a ranking procedure. This paper elaborates on a key advantage of such an approach, namely the fact that our learner can be adapted to different loss functions by using different ranking procedures on the same underlying order relations. In particular, the Spearman rank correlation is minimized by using a simple weighted voting procedure. Moreover, we discuss a loss function suitable for settings where candidate labels must be tested successively until a target label is found. In this context, we propose the idea of “empirical conditioning” of class probabilities. A related ranking procedure, called “ranking through iterated choice”, is investigated experimentally.

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

    Science.gov (United States)

    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.

  17. Enabling multi-level relevance feedback on PubMed by integrating rank learning into DBMS.

    Science.gov (United States)

    Yu, Hwanjo; Kim, Taehoon; Oh, Jinoh; Ko, Ilhwan; Kim, Sungchul; Han, Wook-Shin

    2010-04-16

    Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into RDBMS to support both keyword queries and the multi-level relevance feedback in real time; the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at http://dm.postech.ac.kr/refmed. RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time.

  18. Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

    Science.gov (United States)

    Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

    2017-09-12

    In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

  19. A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits

    OpenAIRE

    Simmonds, Anna J.

    2015-01-01

    Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred fr...

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

    Science.gov (United States)

    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.

  1. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

    2014-01-01

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

  2. A hypothesis on a role of oxytocin in the social mechanisms of speech and vocal learning.

    Science.gov (United States)

    Theofanopoulou, Constantina; Boeckx, Cedric; Jarvis, Erich D

    2017-08-30

    Language acquisition in humans and song learning in songbirds naturally happen as a social learning experience, providing an excellent opportunity to reveal social motivation and reward mechanisms that boost sensorimotor learning. Our knowledge about the molecules and circuits that control these social mechanisms for vocal learning and language is limited. Here we propose a hypothesis of a role for oxytocin (OT) in the social motivation and evolution of vocal learning and language. Building upon existing evidence, we suggest specific neural pathways and mechanisms through which OT might modulate vocal learning circuits in specific developmental stages. © 2017 The Authors.

  3. Is it better to select or to receive? Learning via active and passive hypothesis testing.

    Science.gov (United States)

    Markant, Douglas B; Gureckis, Todd M

    2014-02-01

    People can test hypotheses through either selection or reception. In a selection task, the learner actively chooses observations to test his or her beliefs, whereas in reception tasks data are passively encountered. People routinely use both forms of testing in everyday life, but the critical psychological differences between selection and reception learning remain poorly understood. One hypothesis is that selection learning improves learning performance by enhancing generic cognitive processes related to motivation, attention, and engagement. Alternatively, we suggest that differences between these 2 learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test his or her own individual hypothesis. Drawing on influential models of sequential hypothesis-testing behavior, we show that such a bias (a) can lead to the collection of data that facilitates learning compared with reception learning and (b) can be more effective than observing the selections of another person. We then report a novel experiment based on a popular category learning paradigm that compares reception and selection learning. We additionally compare selection learners to a set of "yoked" participants who viewed the exact same sequence of observations under reception conditions. The results revealed systematic differences in performance that depended on the learner's role in collecting information and the abstract structure of the problem.

  4. Does learning performance in horses relate to fearfulness, baseline stress hormone, and social rank?

    DEFF Research Database (Denmark)

    Christensen, Janne Winther; Ahrendt, Line Peerstrup; Lintrup, Randi

    2012-01-01

    The ability of horses to learn and remember new tasks is fundamentally important for their use by humans. Fearfulness may, however, interfere with learning, because stimuli in the environment can overshadow signals from the rider or handler. In addition, prolonged high levels of stress hormones c...... to behavioural responses in a standardised fear test. Learning performance in the home environment, however, appears unrelated to fearfulness, social rank and baseline FCM levels.......The ability of horses to learn and remember new tasks is fundamentally important for their use by humans. Fearfulness may, however, interfere with learning, because stimuli in the environment can overshadow signals from the rider or handler. In addition, prolonged high levels of stress hormones can...... affect neurons within the hippocampus; a brain region central to learning and memory. In a series of experiments, we aimed to investigate the link between performance in two learning tests, the baseline level of stress hormones, measured as faecal cortisol metabolites (FCM), fearfulness, and social rank...

  5. A hypothesis on improving foreign accents by optimizing variability in vocal learning brain circuits.

    Science.gov (United States)

    Simmonds, Anna J

    2015-01-01

    Rapid vocal motor learning is observed when acquiring a language in early childhood, or learning to speak another language later in life. Accurate pronunciation is one of the hardest things for late learners to master and they are almost always left with a non-native accent. Here, I propose a novel hypothesis that this accent could be improved by optimizing variability in vocal learning brain circuits during learning. Much of the neurobiology of human vocal motor learning has been inferred from studies on songbirds. Jarvis (2004) proposed the hypothesis that as in songbirds there are two pathways in humans: one for learning speech (the striatal vocal learning pathway), and one for production of previously learnt speech (the motor pathway). Learning new motor sequences necessary for accurate non-native pronunciation is challenging and I argue that in late learners of a foreign language the vocal learning pathway becomes inactive prematurely. The motor pathway is engaged once again and learners maintain their original native motor patterns for producing speech, resulting in speaking with a foreign accent. Further, I argue that variability in neural activity within vocal motor circuitry generates vocal variability that supports accurate non-native pronunciation. Recent theoretical and experimental work on motor learning suggests that variability in the motor movement is necessary for the development of expertise. I propose that there is little trial-by-trial variability when using the motor pathway. When using the vocal learning pathway variability gradually increases, reflecting an exploratory phase in which learners try out different ways of pronouncing words, before decreasing and stabilizing once the "best" performance has been identified. The hypothesis proposed here could be tested using behavioral interventions that optimize variability and engage the vocal learning pathway for longer, with the prediction that this would allow learners to develop new motor

  6. A ranking method for the concurrent learning of compounds with various activity profiles.

    Science.gov (United States)

    Dörr, Alexander; Rosenbaum, Lars; Zell, Andreas

    2015-01-01

    In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each. The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods. SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

  7. The effects of age, rank and neophobia on social learning in horses.

    Science.gov (United States)

    Krueger, Konstanze; Farmer, Kate; Heinze, Jürgen

    2014-05-01

    Social learning is said to meet the demands of complex environments in which individuals compete over resources and cooperate to share resources. Horses (Equus caballus) were thought to lack social learning skills because they feed on homogenously distributed resources with few reasons for conflict. However, the horse's social environment is complex, which raises the possibility that its capacity for social transfer of feeding behaviour has been underestimated. We conducted a social learning experiment using 30 socially kept horses of different ages. Five horses, one from each group, were chosen as demonstrators, and the remaining 25 horses were designated observers. Observers from each group were allowed to watch their group demonstrator opening a feeding apparatus. We found that young, low-ranking and more exploratory horses learned by observing older members of their own group, and the older the horse, the more slowly it appeared to learn. Social learning may be an adaptive specialisation to the social environment. Older animals may avoid the potential costs of acquiring complex and potentially disadvantageous feeding behaviours from younger group members. We argue that horses show social learning in the context of their social ecology and that research procedures must take such contexts into account. Misconceptions about the horse's sociality may have hampered earlier studies.

  8. Generalization Performance of Regularized Ranking With Multiscale Kernels.

    Science.gov (United States)

    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.

  9. Optimizing Earth Data Search Ranking using Deep Learning and Real-time User Behaviour

    Science.gov (United States)

    Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; McGibbney, L. J.; Greguska, F. R., III

    2017-12-01

    Finding Earth science data has been a challenging problem given both the quantity of data available and the heterogeneity of the data across a wide variety of domains. Current search engines in most geospatial data portals tend to induce end users to focus on one single data characteristic dimension (e.g., term frequency-inverse document frequency (TF-IDF) score, popularity, release date, etc.). This approach largely fails to take account of users' multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset out of a vast range of available datasets. With users interacting with search engines, sufficient information is already hidden in the log files. Compared with explicit feedback data, information that can be derived/extracted from log files is virtually free and substantially more timely. In this dissertation, I propose an online deep learning framework that can quickly update the learning function based on real-time user clickstream data. The contributions of this framework include 1) a log processor that can ingest, process and create training data from web logs in a real-time manner; 2) a query understanding module to better interpret users' search intent using web log processing results and metadata; 3) a feature extractor that identifies ranking features representing users' multidimensional interests of geospatial data; and 4) a deep learning based ranking algorithm that can be trained incrementally using user behavior data. The search ranking results will be evaluated using precision at K and normalized discounted cumulative gain (NDCG).

  10. How revealing rankings affects student attitude and rerformance in a peer review learning environment

    DEFF Research Database (Denmark)

    Papadopoulos, Pantelis M.; Lagkas, Thomas D.; Demetriadis, Stavros N.

    2015-01-01

    This paper investigates the possible benefits as well as the overall impact on the behaviour of students within a learning environment, which is based on double-blinding reviewing of freely selected peer works. Fifty-six sophomore students majoring in Informatics and Telecommunications Engi....... The students that participated in the other two conditions were provided with their usage information (logins, peer work viewed/reviewed, etc.), while members of the last group could also have access to ranking information about their positioning in their group, based on their usage data. According to our...

  11. Test-potentiated learning: three independent replications, a disconfirmed hypothesis, and an unexpected boundary condition.

    Science.gov (United States)

    Wissman, Kathryn T; Rawson, Katherine A

    2018-04-01

    Arnold and McDermott [(2013). Test-potentiated learning: Distinguishing between direct and indirect effects of testing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 940-945] isolated the indirect effects of testing and concluded that encoding is enhanced to a greater extent following more versus fewer practice tests, referred to as test-potentiated learning. The current research provided further evidence for test-potentiated learning and evaluated the covert retrieval hypothesis as an alternative explanation for the observed effect. Learners initially studied foreign language word pairs and then completed either one or five practice tests before restudy occurred. Results of greatest interest concern performance on test trials following restudy for items that were not correctly recalled on the test trials that preceded restudy. Results replicate Arnold and McDermott (2013) by demonstrating that more versus fewer tests potentiate learning when trial time is limited. Results also provide strong evidence against the covert retrieval hypothesis concerning why the effect occurs (i.e., it does not reflect differential covert retrieval during pre-restudy trials). In addition, outcomes indicate that the magnitude of the test-potentiated learning effect decreases as trial length increases, revealing an unexpected boundary condition to test-potentiated learning.

  12. Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

    Directory of Open Access Journals (Sweden)

    Lei Guo

    2017-02-01

    Full Text Available Point-of-interest (POI recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method.

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

    Science.gov (United States)

    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.

  14. Linking neurogenetics and individual differences in language learning: the dopamine hypothesis.

    Science.gov (United States)

    Wong, Patrick C M; Morgan-Short, Kara; Ettlinger, Marc; Zheng, Jing

    2012-10-01

    Fundamental advances in neuroscience have come from investigations into neuroplasticity and learning. These investigations often focus on identifying universal principles across different individuals of the same species. Increasingly, individual differences in learning success have also been observed, such that any seemingly universal principle might only be applicable to a certain extent within a particular learner. One potential source of this variation is individuals' genetic differences. Adult language learning provides a unique opportunity for understanding individual differences and genetic bases of neuroplasticity because of the large individual differences in learning success that have already been documented, and because of the body of empirical work connecting language learning and neurocognition. In this article, we review the literature on the genetic bases of neurocognition, especially studies examining polymorphisms of dopamine (DA)-related genes and procedural learning. This review leads us to hypothesize that there may be an association between DA-related genetic variation and language learning differences. If this hypothesis is supported by future empirical findings we suggest that it may point to neurogenetic markers that allow for language learning to be personalized. Copyright © 2012 Elsevier Srl. All rights reserved.

  15. Analysis of the Financial Times ranking "master in management" with machine learning

    OpenAIRE

    Jansen, Arthur

    2017-01-01

    University rankings play nowadays a major role in the decision of many students with regards to their future schools. Nonetheless, these rankings often remain quite opaque: not all data are made available, the methodology behind the rankings is not well defined, etc. One of the main ranking centred on business schools is the "Master in Management" from the Financial Times. This work aims to study the relevance of this ranking and its possible flaws. Several techniques are conducted, as a robu...

  16. Use and limitations of learning curves for energy technology policy: A component-learning hypothesis

    International Nuclear Information System (INIS)

    Ferioli, F.; Schoots, K.; Zwaan, B.C.C. van der

    2009-01-01

    In this paper, we investigate the use of learning curves for the description of observed cost reductions for a variety of energy technologies. Starting point of our analysis is the representation of energy processes and technologies as the sum of different components. While we recognize that in many cases 'learning-by-doing' may improve the overall costs or efficiency of a technology, we argue that so far insufficient attention has been devoted to study the effects of single component improvements that together may explain an aggregated form of learning. Indeed, for an entire technology the phenomenon of learning-by-doing may well result from learning of one or a few individual components only. We analyze under what conditions it is possible to combine learning curves for single components to derive one comprehensive learning curve for the total product. The possibility that for certain technologies some components (e.g., the primary natural resources that serve as essential input) do not exhibit cost improvements might account for the apparent time dependence of learning rates reported in several studies (the learning rate might also change considerably over time depending on the data set considered, a crucial issue to be aware of when one uses the learning curve methodology). Such an explanation may have important consequences for the extent to which learning curves can be extrapolated into the future. This argumentation suggests that cost reductions may not continue indefinitely and that well-behaved learning curves do not necessarily exist for every product or technology. In addition, even for diffusing and maturing technologies that display clear learning effects, market and resource constraints can eventually significantly reduce the scope for further improvements in their fabrication or use. It appears likely that some technologies, such as wind turbines and photovoltaic cells, are significantly more amenable than others to industry-wide learning. For such

  17. A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network.

    Science.gov (United States)

    Zhao, Jianwei; Lv, Yongbiao; Zhou, Zhenghua; Cao, Feilong

    2017-10-01

    There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel convolution neural network frame, named a low-rank-recovery network (LRRNet), is proposed to conquer the difficulty effectively inspired by matrix completion and deep learning techniques. The proposed LRRNet first recovers the incomplete face images via an approach of matrix completion with the truncated nuclear norm regularization solution, and then extracts some low-rank parts of the recovered images as the filters. With these filters, some important features are obtained by means of the binaryzation and histogram algorithms. Finally, these features are classified with the classical support vector machines (SVMs). The proposed LRRNet method has high face recognition rate for the heavily corrupted images, especially for the images in the large databases. The proposed LRRNet performs well and efficiently for the images with heavily corrupted, especially in the case of large databases. Extensive experiments on several benchmark databases demonstrate that the proposed LRRNet performs better than some other excellent robust face recognition methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Ontogeny of Foraging Competence in Capuchin Monkeys (Cebus capucinus for Easy versus Difficult to Acquire Fruits: A Test of the Needing to Learn Hypothesis.

    Directory of Open Access Journals (Sweden)

    Elizabeth Christine Eadie

    Full Text Available Which factors select for long juvenile periods in some species is not well understood. One potential reason to delay the onset of reproduction is slow food acquisition rates, either due to competition (part of the ecological risk avoidance hypothesis, or due to a decreased foraging efficiency (a version of the needing to learn hypothesis. Capuchins provide a useful genus to test the needing to learn hypothesis because they are known for having long juvenile periods and a difficult-to-acquire diet. Generalized, linear, mixed models with data from 609 fruit forage focal follows on 49, habituated, wild Cebus capucinus were used to test two predictions from the needing-to-learn hypothesis as it applies to fruit foraging skills: 1 capuchin monkeys do not achieve adult foraging return rates for difficult-to-acquire fruits before late in the juvenile period; and 2 variance in return rates for these fruits is at least partially associated with differences in foraging skill. In support of the first prediction, adults, compared with all younger age classes, had significantly higher foraging return rates when foraging for fruits that were ranked as difficult-to-acquire (return rates relative to adults: 0.30-0.41, p-value range 0.008-0.016, indicating that the individuals in the group who have the most foraging experience also achieve the highest return rates. In contrast, and in support of the second prediction, there were no significant differences between age classes for fruits that were ranked as easy to acquire (return rates relative to adults: 0.97-1.42, p-value range 0.086-0.896, indicating that strength and/or skill are likely to affect return rates. In addition, fruits that were difficult to acquire were foraged at nearly identical rates by adult males and significantly smaller (and presumably weaker adult females (males relative to females: 1.01, p = 0.978, while subadult females had much lower foraging efficiency than the similarly-sized but more

  19. Ontogeny of Foraging Competence in Capuchin Monkeys (Cebus capucinus) for Easy versus Difficult to Acquire Fruits: A Test of the Needing to Learn Hypothesis.

    Science.gov (United States)

    Eadie, Elizabeth Christine

    2015-01-01

    Which factors select for long juvenile periods in some species is not well understood. One potential reason to delay the onset of reproduction is slow food acquisition rates, either due to competition (part of the ecological risk avoidance hypothesis), or due to a decreased foraging efficiency (a version of the needing to learn hypothesis). Capuchins provide a useful genus to test the needing to learn hypothesis because they are known for having long juvenile periods and a difficult-to-acquire diet. Generalized, linear, mixed models with data from 609 fruit forage focal follows on 49, habituated, wild Cebus capucinus were used to test two predictions from the needing-to-learn hypothesis as it applies to fruit foraging skills: 1) capuchin monkeys do not achieve adult foraging return rates for difficult-to-acquire fruits before late in the juvenile period; and 2) variance in return rates for these fruits is at least partially associated with differences in foraging skill. In support of the first prediction, adults, compared with all younger age classes, had significantly higher foraging return rates when foraging for fruits that were ranked as difficult-to-acquire (return rates relative to adults: 0.30-0.41, p-value range 0.008-0.016), indicating that the individuals in the group who have the most foraging experience also achieve the highest return rates. In contrast, and in support of the second prediction, there were no significant differences between age classes for fruits that were ranked as easy to acquire (return rates relative to adults: 0.97-1.42, p-value range 0.086-0.896), indicating that strength and/or skill are likely to affect return rates. In addition, fruits that were difficult to acquire were foraged at nearly identical rates by adult males and significantly smaller (and presumably weaker) adult females (males relative to females: 1.01, p = 0.978), while subadult females had much lower foraging efficiency than the similarly-sized but more experienced

  20. The procedural learning deficit hypothesis of language learning disorders: we see some problems.

    Science.gov (United States)

    West, Gillian; Vadillo, Miguel A; Shanks, David R; Hulme, Charles

    2018-03-01

    Impaired procedural learning has been suggested as a possible cause of developmental dyslexia (DD) and specific language impairment (SLI). This study examined the relationship between measures of verbal and non-verbal implicit and explicit learning and measures of language, literacy and arithmetic attainment in a large sample of 7 to 8-year-old children. Measures of verbal explicit learning were correlated with measures of attainment. In contrast, no relationships between measures of implicit learning and attainment were found. Critically, the reliability of the implicit learning tasks was poor. Our results show that measures of procedural learning, as currently used, are typically unreliable and insensitive to individual differences. A video abstract of this article can be viewed at: https://www.youtube.com/watch?v=YnvV-BvNWSo. 2017 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

  1. Perceptual Learning Style and Learning Proficiency: A Test of the Hypothesis

    Science.gov (United States)

    Kratzig, Gregory P.; Arbuthnott, Katherine D.

    2006-01-01

    Given the potential importance of using modality preference with instruction, the authors tested whether learning style preference correlated with memory performance in each of 3 sensory modalities: visual, auditory, and kinesthetic. In Study 1, participants completed objective measures of pictorial, auditory, and tactile learning and learning…

  2. Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

    Science.gov (United States)

    Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

    2018-03-01

    Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

  3. Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning

    OpenAIRE

    Lai, Rongjie; Li, Jia

    2017-01-01

    Low-rank structures play important role in recent advances of many problems in image science and data science. As a natural extension of low-rank structures for data with nonlinear structures, the concept of the low-dimensional manifold structure has been considered in many data processing problems. Inspired by this concept, we consider a manifold based low-rank regularization as a linear approximation of manifold dimension. This regularization is less restricted than the global low-rank regu...

  4. Learning to Learn: A Case for the Heterogeneous Expectations Hypothesis in Industrialized Markets

    Directory of Open Access Journals (Sweden)

    Desmond W. Ng

    2016-07-01

    Full Text Available A cobweb model is developed where the heterogeneous expectation hypothesis is examined. An agent’s heterogeneous expectation involves the development of a “higher ordered learning” process in which agents over time develop expectations that are consistent with rational expectations. In addition, as cob web models are production based systems, an agents’ heterogeneous expectations are influenced by a specialization of activities. The case of the industrialization of the U.S. hog-pork industry is used to illustrate the influence of these features on the equilibrium and non-equilibrium properties of a modified cob-web model.

  5. Stages in learning motor synergies: a view based on the equilibrium-point hypothesis.

    Science.gov (United States)

    Latash, Mark L

    2010-10-01

    This review describes a novel view on stages in motor learning based on recent developments of the notion of synergies, the uncontrolled manifold hypothesis, and the equilibrium-point hypothesis (referent configuration) that allow to merge these notions into a single scheme of motor control. The principle of abundance and the principle of minimal final action form the foundation for analyses of natural motor actions performed by redundant sets of elements. Two main stages of motor learning are introduced corresponding to (1) discovery and strengthening of motor synergies stabilizing salient performance variable(s) and (2) their weakening when other aspects of motor performance are optimized. The first stage may be viewed as consisting of two steps, the elaboration of an adequate referent configuration trajectory and the elaboration of multi-joint (multi-muscle) synergies stabilizing the referent configuration trajectory. Both steps are expected to lead to more variance in the space of elemental variables that is compatible with a desired time profile of the salient performance variable ("good variability"). Adjusting control to other aspects of performance during the second stage (for example, esthetics, energy expenditure, time, fatigue, etc.) may lead to a drop in the "good variability". Experimental support for the suggested scheme is reviewed. Copyright © 2009 Elsevier B.V. All rights reserved.

  6. [Possible evolutionary mechanisms of 'culture' in animals: The hypothesis of distributed social learning].

    Science.gov (United States)

    Reznikova, Zh I; Panteleeva, S N

    2015-01-01

    There is a plethora of works on the origin and genesis of behavioral traditions in different animal species. Nevertheless, it still remains unclear as for which factors facilitate and which factors hinder the spreading those forms of behavior that are new for a population. Here, we present an analytical review on the topic, considering also the results of studies on 'culture' in animals and analyzing contradictions that arise when attempting to clarify the ethological mechanisms of cultural succession. The hypothesis of 'distributed social learning' is formulated, meaning that for spreading of complex behavioral stereotypes in a population the presence of few carriers of consistent stereotypes is enough under the condition that the rest of animals carry incomplete genetic programmes that start up these stereotypes. Existence of 'dormant' fragments of such programmes determines an inborn predisposition of their bearer to perform a certain sequence of acts. To complete the consistent stereotype, the simplest forms of social learning ('social alleviation') turn to be enough. The hypothesis is examined at the behavioral level and supported by experimental data obtained when studying the scenarios of hunting behavior development in ants Myrmica rubra L. It makes possible to explain the spreading of behavioral models in animal communities in a simpler way than cultural succession.

  7. The active learning hypothesis of the job-demand-control model: an experimental examination.

    Science.gov (United States)

    Häusser, Jan Alexander; Schulz-Hardt, Stefan; Mojzisch, Andreas

    2014-01-01

    The active learning hypothesis of the job-demand-control model [Karasek, R. A. 1979. "Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign." Administration Science Quarterly 24: 285-307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed-accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.

  8. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-04-17

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

  9. SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

    Science.gov (United States)

    Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

    2017-01-01

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  10. Measuring the learning effectiveness of Web-based teacher professional development in the hypothesis based learning method of teaching science

    Science.gov (United States)

    Wilson, Penne L.

    2007-12-01

    This study was conducted as part of the five year evaluation of the Star Schools grant awarded to Oklahoma State University for the development on online teacher professional development in the Hypothesis Based Learning (HbL) method of science instruction. Participants in this research were five teachers who had completed the online workshop, submitted a lesson plan, and who allowed this researcher and other members of the University of New Mexico Evaluation Team into their classrooms to observe and to determine if the learning of the method from the online HbL workshop had translated into practice. These teachers worked in inner city, suburban, metropolitan, and rural communities in the U.S. Southwest. This study was conducted to determine if teachers learned the HbL method from the online HbL workshop, to examine the relationship of satisfaction to learning, and to determine the elements of the online workshop that led to teacher learning. To measure learning of HbL, three different assessment instruments were used: embedded assessments within the online HbL workshop that gave teachers a scenario and asked them to generate questions to facilitate the HbL process; the analysis of a lesson plan provided by teachers using a science concept that they wished to incorporate in their curriculum using an HbL lesson template provided at the HbL website; and, observations of teachers facilitating the HbL process conducted at three different times during the year that they began the HbL online workshop. To determine if teachers were satisfied with the learning environment, the online HbL workshop, and the product, HbL Method for Teaching Science, and to determine if teachers could identify the elements of the online workshop that led to learning, interviews with the participants were conducted. The research findings were presented in two parts: Part I is an analysis of data provided by the assessment instruments and a content analysis of the transcripts of the teacher

  11. Effects of arousal on cognitive control: empirical tests of the conflict-modulated Hebbian-learning hypothesis.

    Science.gov (United States)

    Brown, Stephen B R E; van Steenbergen, Henk; Kedar, Tomer; Nieuwenhuis, Sander

    2014-01-01

    An increasing number of empirical phenomena that were previously interpreted as a result of cognitive control, turn out to reflect (in part) simple associative-learning effects. A prime example is the proportion congruency effect, the finding that interference effects (such as the Stroop effect) decrease as the proportion of incongruent stimuli increases. While this was previously regarded as strong evidence for a global conflict monitoring-cognitive control loop, recent evidence has shown that the proportion congruency effect is largely item-specific and hence must be due to associative learning. The goal of our research was to test a recent hypothesis about the mechanism underlying such associative-learning effects, the conflict-modulated Hebbian-learning hypothesis, which proposes that the effect of conflict on associative learning is mediated by phasic arousal responses. In Experiment 1, we examined in detail the relationship between the item-specific proportion congruency effect and an autonomic measure of phasic arousal: task-evoked pupillary responses. In Experiment 2, we used a task-irrelevant phasic arousal manipulation and examined the effect on item-specific learning of incongruent stimulus-response associations. The results provide little evidence for the conflict-modulated Hebbian-learning hypothesis, which requires additional empirical support to remain tenable.

  12. Effects of arousal on cognitive control: Empirical tests of the conflict-modulated Hebbian-learning hypothesis

    Directory of Open Access Journals (Sweden)

    Stephen B.R.E. Brown

    2014-01-01

    Full Text Available An increasing number of empirical phenomena that were previously interpreted as a result of cognitive control, turn out to reflect (in part simple associative-learning effects. A prime example is the proportion congruency effect, the finding that interference effects (such as the Stroop effect decrease as the proportion of incongruent stimuli increases. While this was previously regarded as strong evidence for a global conflict monitoring-cognitive control loop, recent evidence has shown that the proportion congruency effect is largely item-specific and hence must be due to associative learning. The goal of our research was to test a recent hypothesis about the mechanism underlying such associative-learning effects, the conflict-modulated Hebbian-learning hypothesis, which proposes that the effect of conflict on associative learning is mediated by phasic arousal responses. In Experiment 1, we examined in detail the relationship between the item-specific proportion congruency effect and an autonomic measure of phasic arousal: task-evoked pupillary responses. In Experiment 2, we used a task-irrelevant phasic arousal manipulation and examined the effect on item-specific learning of incongruent stimulus-response associations. The results provide little evidence for the conflict-modulated Hebbian-learning hypothesis, which requires additional empirical support to remain tenable.

  13. Multimodal Detection of Engagement in Groups of Children Using Rank Learning

    NARCIS (Netherlands)

    Kim, Jaebok; Truong, Khiet Phuong; Charisi, Vasiliki; Zaga, Cristina; Evers, Vanessa; Chetouani, Mohamed

    2016-01-01

    In collaborative play, children exhibit different levels of engagement. Some children are engaged with other children while some play alone. In this study, we investigated multimodal detection of individual levels of engagement using a ranking method and non-verbal features: turn-taking and body

  14. Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

    Science.gov (United States)

    Zheng, Jiaping; Yu, Hong

    2016-01-01

    Background Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients’ notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. Objective We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. Methods First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians’ agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Results Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen’s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term

  15. COLLABORATIVE RANKING TASKS (CRT BERBANTUAN e-LEARNING UNTUK MENINGKATAN KETERAMPILAN GENERIK SAINS MAHASISWA CALON GURU FISIKA

    Directory of Open Access Journals (Sweden)

    A. F.C. Wijaya

    2012-07-01

    Full Text Available Kurangnya kemampuan mahasiswa dalam proses membangun konsep dan keterampilan generik sains membuat kompetensi mereka tidaklah utuh sebagai calon guru. Collaborative Ranking Tasks (CRT sebagai format baru dari latihan konseptual dan dilakukan secara kolaboratif dan ditunjang multimedia pembelajaran dalam sistem managemen e-Learning diharapkan dapat menjadi solusinya. Penelitian kuasi eksperimen pada 120 mahasiswa perkuliahan IPBA semester genap 2010-2011 yang dipilih secara purposive sampling dan terbagi menjadi dua kelompok kontrol dan eksperimen, penerapan CRT berbantuan e-Learning diarahkan untuk mengetahui pengaruhnya terhadap penguasaan konsep dan keterampilan generik sains (KGS mereka. Dengan menggunakan instrumen pilihan ganda pada kelompok eksperimen dan kontrol, penguasaan konsep dan KGS mahasiswa dianalisis berdasarkan nilai rata-rata gain yang dinormalisasi mereka. Hasil penelitian menunjukkan peningkatan penguasaan konsep pada kelompok eksperimen berbeda secara signifikan dibandingkan dengan peningkatan penguasaan konsep pada kelompok kontrol. Dengan demikian penerapan CRT berbantuan e-learning pada perkuliahan IPBA berpengaruh positif dan signifikan terhadap peningkatan penguasaan konsep dan KGS mahasiswa. Lackof developing mastery concept and generic science skills of physics university students causes them as a prospective physics teacher having incomplete competence. Collaborative Ranking Tasks (CRTas a new form of conceptual exercise that is built collaborativelyby means of multimedia assistance hopefuly becomes the solution.By employing quasi-experimental research method, this study was aimed to investigate the effectiveness of CRT with e-Learning support system to improve university students’ mastery concepts and generic skills. The subjects of the study were 120 university students which were chosen through purposive sampling and divided into experimental group and control group. The research instruments were

  16. Rank Dynamics

    Science.gov (United States)

    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

  17. MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank.

    Science.gov (United States)

    Mao, Yuqing; Lu, Zhiyong

    2017-04-17

    MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other scientific investigations in biomedical research. Unfortunately, given its manual nature, the process of MeSH indexing is both time-consuming (new articles are not immediately indexed until 2 or 3 months later) and costly (approximately ten dollars per article). In response, automatic indexing by computers has been previously proposed and attempted but remains challenging. In order to advance the state of the art in automatic MeSH indexing, a community-wide shared task called BioASQ was recently organized. We propose MeSH Now, an integrated approach that first uses multiple strategies to generate a combined list of candidate MeSH terms for a target article. Through a novel learning-to-rank framework, MeSH Now then ranks the list of candidate terms based on their relevance to the target article. Finally, MeSH Now selects the highest-ranked MeSH terms via a post-processing module. We assessed MeSH Now on two separate benchmarking datasets using traditional precision, recall and F 1 -score metrics. In both evaluations, MeSH Now consistently achieved over 0.60 in F-score, ranging from 0.610 to 0.612. Furthermore, additional experiments show that MeSH Now can be optimized by parallel computing in order to process MEDLINE documents on a large scale. We conclude that MeSH Now is a robust approach with state-of-the-art performance for automatic MeSH indexing and that MeSH Now is capable of processing PubMed scale documents within a reasonable time frame. http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/MeSHNow/ .

  18. Examining the linguistic coding differences hypothesis to explain individual differences in foreign language learning.

    Science.gov (United States)

    Sparks, R L

    1995-01-01

    In this paper, it is suggested that foreign language learning problems result from difficulties with native language learning and hypothesized that difficulties with phonological processing may be the locus of foreign language learning difficulties for some poor foreign language learners. Evidence is described that supports these positions. It is argued that conceptualizing foreign language learning problems as alanguage problem allows researchers to more clearly specify deficits related to the learning of a foreign language. Research evidence which shows that good and poor foreign language learners exhibit significantly different levels of native language skill and phonological processing is summarized. Finally, potential challenges to my hypotheses as an explanation for foreign language learning problems are reviewed.

  19. A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity

    Science.gov (United States)

    Nguyen-Vu, TD Barbara; Zhao, Grace Q; Lahiri, Subhaneil; Kimpo, Rhea R; Lee, Hanmi; Ganguli, Surya; Shatz, Carla J; Raymond, Jennifer L

    2017-01-01

    Across many studies, animals with enhanced synaptic plasticity exhibit either enhanced or impaired learning, raising a conceptual puzzle: how enhanced plasticity can yield opposite learning outcomes? Here, we show that the recent history of experience can determine whether mice with enhanced plasticity exhibit enhanced or impaired learning in response to the same training. Mice with enhanced cerebellar LTD, due to double knockout (DKO) of MHCI H2-Kb/H2-Db (KbDb−/−), exhibited oculomotor learning deficits. However, the same mice exhibited enhanced learning after appropriate pre-training. Theoretical analysis revealed that synapses with history-dependent learning rules could recapitulate the data, and suggested that saturation may be a key factor limiting the ability of enhanced plasticity to enhance learning. Optogenetic stimulation designed to saturate LTD produced the same impairment in WT as observed in DKO mice. Overall, our results suggest that the recent history of activity and the threshold for synaptic plasticity conspire to effect divergent learning outcomes. DOI: http://dx.doi.org/10.7554/eLife.20147.001 PMID:28234229

  20. The hypothesis of matching about styles of learning and teaching at Higher Education

    Directory of Open Access Journals (Sweden)

    Ana Clara Ventura

    2013-12-01

    Full Text Available A review of learning and teaching styles matching at university over the last ten years is presented. For this purpose, a review was carried out of the Dialnet, Redalyc, Scielo and Doaj data bases due to their scope and importance in the Hispanic context. The descriptors used in the search for information were the key words: learning styles, teaching styles and higher education. The data bases consulted produced a total of 53 papers, with only 17 of these complying with all the criteria for inclusion (paper: empirical research; sample: university;object: correspondence learning and teaching styles; time period 2002-2012; Spanish language. The literature review showed that the matching is researched and understanding from two approaches: (1 adaptive instruction or (2 stylistics. Results are discussed.

  1. Latin American students and language learning in Catalonia: what does the linguistic interdependence hypothesis show us?

    Science.gov (United States)

    Huguet, Ángel

    2014-01-01

    The massive arrival in Spain of students of immigrant origin has visibly altered the traditional configuration of schools, where ethnic, cultural and linguistic diversity is becoming increasingly manifest. This situation is worth being mentioned insofar as it affects all the different autonomous communities in the country, even more clearly Catalonia, where the educational system is organized under the parameters of bilingual education. One of the theoretical constructs supporting this educational model is the Linguistic Interdependence Hypothesis, developed by Jim Cummins at the beginning of the 1980s. According to the author, whenever the instruction in a given language (Lx) takes place under certain conditions, competence acquired in this language can be transferred onto another (Ly). Bearing this theoretical construct in mind, our study focuses on a sample of 237 Spanish-speaking subjects (123 native and 114 immigrant students) who completed a series of parallel tests evaluating their skills in Catalan and Spanish. Drawing on the data analyzed we can conclude that the Hypothesis accounts for the results in both native and immigrant students with the same L1.

  2. A Test of the Teaching-Learning Style Mesh Hypothesis in a Chinese MBA

    Science.gov (United States)

    Andres, Hayward P.; Akan, Obasi H.

    2015-01-01

    Purpose: The purpose of this paper is to determine if "fit" and "non-fit" between authoritarian versus demonstrator teaching and visual versus verbal learning preferences differ in impact on Chinese MBA student academic performance in a large local urban Chinese university setting. In addition, the role of Chinese cultural…

  3. Understanding dopamine and reinforcement learning: the dopamine reward prediction error hypothesis.

    Science.gov (United States)

    Glimcher, Paul W

    2011-09-13

    A number of recent advances have been achieved in the study of midbrain dopaminergic neurons. Understanding these advances and how they relate to one another requires a deep understanding of the computational models that serve as an explanatory framework and guide ongoing experimental inquiry. This intertwining of theory and experiment now suggests very clearly that the phasic activity of the midbrain dopamine neurons provides a global mechanism for synaptic modification. These synaptic modifications, in turn, provide the mechanistic underpinning for a specific class of reinforcement learning mechanisms that now seem to underlie much of human and animal behavior. This review describes both the critical empirical findings that are at the root of this conclusion and the fantastic theoretical advances from which this conclusion is drawn.

  4. Effects of musicality and motivational orientation on auditory category learning: a test of a regulatory-fit hypothesis.

    Science.gov (United States)

    McAuley, J Devin; Henry, Molly J; Wedd, Alan; Pleskac, Timothy J; Cesario, Joseph

    2012-02-01

    Two experiments investigated the effects of musicality and motivational orientation on auditory category learning. In both experiments, participants learned to classify tone stimuli that varied in frequency and duration according to an initially unknown disjunctive rule; feedback involved gaining points for correct responses (a gains reward structure) or losing points for incorrect responses (a losses reward structure). For Experiment 1, participants were told at the start that musicians typically outperform nonmusicians on the task, and then they were asked to identify themselves as either a "musician" or a "nonmusician." For Experiment 2, participants were given either a promotion focus prime (a performance-based opportunity to gain entry into a raffle) or a prevention focus prime (a performance-based criterion that needed to be maintained to avoid losing an entry into a raffle) at the start of the experiment. Consistent with a regulatory-fit hypothesis, self-identified musicians and promotion-primed participants given a gains reward structure made more correct tone classifications and were more likely to discover the optimal disjunctive rule than were musicians and promotion-primed participants experiencing losses. Reward structure (gains vs. losses) had inconsistent effects on the performance of nonmusicians, and a weaker regulatory-fit effect was found for the prevention focus prime. Overall, the findings from this study demonstrate a regulatory-fit effect in the domain of auditory category learning and show that motivational orientation may contribute to musician performance advantages in auditory perception.

  5. CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

    Energy Technology Data Exchange (ETDEWEB)

    Saz Parkinson, P. M. [Department of Physics, The University of Hong Kong, Pokfulam Road, Hong Kong (China); Xu, H.; Yu, P. L. H. [Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong (China); Salvetti, D.; Marelli, M. [INAF—Istituto di Astrofisica Spaziale e Fisica Cosmica Milano, via E. Bassini 15, I-20133, Milano (Italy); Falcone, A. D. [Department of Astronomy and Astrophysics, The Pennsylvania State University, University Park, PA 16802 (United States)

    2016-03-20

    We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

  6. CLASSIFICATION AND RANKING OF FERMI LAT GAMMA-RAY SOURCES FROM THE 3FGL CATALOG USING MACHINE LEARNING TECHNIQUES

    International Nuclear Information System (INIS)

    Saz Parkinson, P. M.; Xu, H.; Yu, P. L. H.; Salvetti, D.; Marelli, M.; Falcone, A. D.

    2016-01-01

    We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies

  7. University Rankings: The Web Ranking

    Science.gov (United States)

    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…

  8. The sequential hypothesis of sleep function. IV. A correlative analysis of sleep variables in learning and nonlearning rats.

    Science.gov (United States)

    Langella, M; Colarieti, L; Ambrosini, M V; Giuditta, A

    1992-02-01

    Female adult rats were trained for a two-way active avoidance task (4 h), and allowed free sleep (3 h). Control rats (C) were left in their home cages during the acquisition period. Dural electrodes and an intraventricular cannula, implanted one week in advance, were used for EEG recording during the period of sleep and for the injection of [3H]thymidine at the beginning of the training session, respectively. Rats were killed at the end of the sleep period, and the DNA-specific activity was determined in the main brain regions and in liver. Correlations among sleep, behavioral and biochemical variables were assessed using Spearman's nonparametric method. In learning rats (L), the number of avoidances was negatively correlated with SS-W variables, and positively correlated with SS-PS variables (episodes of synchronized sleep followed by wakefulness or paradoxical sleep, respectively) and with PS variables. An inverse pattern of correlations was shown by the number of escapes or freezings. No correlations occurred in rats unable to achieve the learning criterion (NL). In L rats, the specific activity of brain DNA was negatively correlated with SS-W variables and positively correlated with SS-PS variables, while essentially no correlation concerned PS variables. On the other hand, in NL rats, comparable correlations were positive with SS-W variables and negative with SS-PS and PS variables. Few and weak correlations occurred in C rats. The data support a role of SS in brain information processing, as postulated by the sequential hypothesis on the function of sleep. In addition, they suggest that the elimination of nonadaptive memory traces may require several SS-W episodes and a terminal SS-PS episode. During PS episodes, adaptive memory traces cleared of nonadaptive components may be copied in more suitable brain sites.

  9. Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

    Science.gov (United States)

    Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

    2016-01-01

    The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. PMID:27892471

  10. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

    Science.gov (United States)

    Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

    2015-01-01

    Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

  11. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

    Directory of Open Access Journals (Sweden)

    Xin Tang

    Full Text Available Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC. Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our

  12. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

    Science.gov (United States)

    Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

    2015-01-01

    Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

  13. Fourth-rank cosmology

    International Nuclear Information System (INIS)

    Marrakchi, A.E.L.; Tapia, V.

    1992-05-01

    Some cosmological implications of the recently proposed fourth-rank theory of gravitation are studied. The model exhibits the possibility of being free from the horizon and flatness problems at the price of introducing a negative pressure. The field equations we obtain are compatible with k obs =0 and Ω obs t clas approx. 10 20 t Planck approx. 10 -23 s. When interpreted at the light of General Relativity the treatment is shown to be almost equivalent to that of the standard model of cosmology combined with the inflationary scenario. Hence, an interpretation of the negative pressure hypothesis is provided. (author). 8 refs

  14. Multiple graph regularized protein domain ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2012-11-19

    Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

  15. Multiple graph regularized protein domain ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2012-01-01

    Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

  16. Multiple graph regularized protein domain ranking.

    Science.gov (United States)

    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.

  17. Multiple graph regularized protein domain ranking

    Directory of Open Access Journals (Sweden)

    Wang Jim

    2012-11-01

    Full Text Available Abstract Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

  18. Ranking Support Vector Machine with Kernel Approximation

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2017-01-01

    Full Text Available Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels can give higher accuracy than linear RankSVM (RankSVM with a linear kernel for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  19. Ranking Support Vector Machine with Kernel Approximation.

    Science.gov (United States)

    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.

  20. Ranking factors involved in diabetes remission after bariatric surgery using machine-learning integrating clinical and genomic biomarkers

    DEFF Research Database (Denmark)

    Pedersen, Helle Krogh; Gudmundsdottir, Valborg; Pedersen, Mette Krogh

    2016-01-01

    , use of insulin-sensitising agents and baseline serum insulin levels, as the most informative variables with a decent internal validation performance (74% accuracy, area under the curve (AUC) 0.81). Adding information for the eight top-ranked single nucleotide polymorphisms (SNPs) significantly boosted...... classification performance to 84% accuracy (AUC 0.92). The eight SNPs mapped to eight genes — ABCA1, ARHGEF12, CTNNBL1, GLI3, PROK2, RYBP, SMUG1 and STXBP5 — three of which are known to have a role in insulin secretion, insulin sensitivity or obesity, but have not been indicated for diabetes remission after...

  1. MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

    International Nuclear Information System (INIS)

    Xu, Q; Liu, H; Xing, L; Yu, H; Wang, G

    2016-01-01

    Purpose: Spectral CT enabled by an energy-resolved photon-counting detector outperforms conventional CT in terms of material discrimination, contrast resolution, etc. One reconstruction method for spectral CT is to generate a color image from a reconstructed component in each energy channel. However, given the radiation dose, the number of photons in each channel is limited, which will result in strong noise in each channel and affect the final color reconstruction. Here we propose a novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channel and to address the inter-channel correlations to further improve the reconstruction. Methods: The proposed method has two important features: (1) guarantee of the patch based sparsity in each energy channel, which is the result of the dictionary based sparse representation constraint; (2) the explicit consideration of the correlations among different energy channels, which is realized by patch-by-patch nuclear norm-based low-rank constraint. For each channel, the dictionary consists of two sub-dictionaries. One is learned from the average of the images in all energy channels, and the other is learned from the average of the images in all energy channels except the current channel. With average operation to reduce noise, these two dictionaries can effectively preserve the structural details and get rid of artifacts caused by noise. Combining them together can express all structural information in current channel. Results: Dictionary learning based methods can obtain better results than FBP and the TV-based method. With low-rank constraint, the image quality can be further improved in the channel with more noise. The final color result by the proposed method has the best visual quality. Conclusion: The proposed method can effectively improve the image quality of low-dose spectral

  2. MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Q [Xi’an Jiaotong University, Xi’an (China); Stanford University School of Medicine, Stanford, CA (United States); Liu, H; Xing, L [Stanford University School of Medicine, Stanford, CA (United States); Yu, H [University of Massachusetts Lowell, Lowell, MA (United States); Wang, G [Rensselaer Polytechnic Instute., Troy, NY (United States)

    2016-06-15

    Purpose: Spectral CT enabled by an energy-resolved photon-counting detector outperforms conventional CT in terms of material discrimination, contrast resolution, etc. One reconstruction method for spectral CT is to generate a color image from a reconstructed component in each energy channel. However, given the radiation dose, the number of photons in each channel is limited, which will result in strong noise in each channel and affect the final color reconstruction. Here we propose a novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channel and to address the inter-channel correlations to further improve the reconstruction. Methods: The proposed method has two important features: (1) guarantee of the patch based sparsity in each energy channel, which is the result of the dictionary based sparse representation constraint; (2) the explicit consideration of the correlations among different energy channels, which is realized by patch-by-patch nuclear norm-based low-rank constraint. For each channel, the dictionary consists of two sub-dictionaries. One is learned from the average of the images in all energy channels, and the other is learned from the average of the images in all energy channels except the current channel. With average operation to reduce noise, these two dictionaries can effectively preserve the structural details and get rid of artifacts caused by noise. Combining them together can express all structural information in current channel. Results: Dictionary learning based methods can obtain better results than FBP and the TV-based method. With low-rank constraint, the image quality can be further improved in the channel with more noise. The final color result by the proposed method has the best visual quality. Conclusion: The proposed method can effectively improve the image quality of low-dose spectral

  3. An Extension to the Constructivist Coding Hypothesis as a Learning Model for Selective Feedback when the Base Rate Is High

    Science.gov (United States)

    Ghaffarzadegan, Navid; Stewart, Thomas R.

    2011-01-01

    Elwin, Juslin, Olsson, and Enkvist (2007) and Henriksson, Elwin, and Juslin (2010) offered the constructivist coding hypothesis to describe how people code the outcomes of their decisions when availability of feedback is conditional on the decision. They provided empirical evidence only for the 0.5 base rate condition. This commentary argues that…

  4. Fractional cointegration rank estimation

    DEFF Research Database (Denmark)

    Lasak, Katarzyna; Velasco, Carlos

    the parameters of the model under the null hypothesis of the cointegration rank r = 1, 2, ..., p-1. This step provides consistent estimates of the cointegration degree, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup......-likelihood ratio test of no-cointegration on the estimated p - r common trends that are not cointegrated under the null. The cointegration degree is re-estimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step...... to control for stochastic trend estimation effects from the first step. The critical values of the tests proposed depend only on the number of common trends under the null, p - r, and on the interval of the cointegration degrees b allowed, but not on the true cointegration degree b0. Hence, no additional...

  5. Reduced Rank Regression

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

  6. Error analysis of stochastic gradient descent ranking.

    Science.gov (United States)

    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.

  7. Testing the hypothesis on cognitive evolution of modern humans' learning ability: current status of past-climatic approaches.

    Science.gov (United States)

    Yoneda, Minoru; Abe-Ouchi, Ayako; Kawahata, Hodaka; Yokoyama, Yusuke; Oguchi, Takashi

    2014-05-01

    The impact of climate change on human evolution is important and debating topic for many years. Since 2010, we have involved in a general joint project entitled "Replacement of Neanderthal by Modern Humans: Testing Evolutional Models of Learning", which based on a theoretical prediction that the cognitive ability related to individual and social learning divide fates of ancient humans in very unstable Late Pleistocene climate. This model predicts that the human populations which experienced a series of environmental changes would have higher rate of individual learners, while detailed reconstructions of global climate change have reported fluent and drastic change based on ice cores and stalagmites. However, we want to understand the difference between anatomically modern human which survived and the other archaic extinct humans including European Neanderthals and Asian Denisovans. For this purpose the global synchronized change is not useful for understanding but the regional difference in the amplitude and impact of climate change is the information required. Hence, we invited a geophysicist busing Global Circulation Model to reconstruct the climatic distribution and temporal change in a continental scale. At the same time, some geochemists and geographers construct a database of local climate changes recorded in different proxies. At last, archaeologists and anthropologists tried to interpret the emergence and disappearance of human species in Europe and Asia on the reconstructed past climate maps using some tools, such as Eco-cultural niche model. Our project will show the regional difference in climate change and related archaeological events and its impact on the evolution of learning ability of modern humans.

  8. Neural Ranking Models with Weak Supervision

    NARCIS (Netherlands)

    Dehghani, M.; Zamani, H.; Severyn, A.; Kamps, J.; Croft, W.B.

    2017-01-01

    Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from

  9. Statistical Optimality in Multipartite Ranking and Ordinal Regression.

    Science.gov (United States)

    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.

  10. Ranking Operations Management conferences

    NARCIS (Netherlands)

    Steenhuis, H.J.; de Bruijn, E.J.; Gupta, Sushil; Laptaned, U

    2007-01-01

    Several publications have appeared in the field of Operations Management which rank Operations Management related journals. Several ranking systems exist for journals based on , for example, perceived relevance and quality, citation, and author affiliation. Many academics also publish at conferences

  11. Validating rankings in soccer championships

    Directory of Open Access Journals (Sweden)

    Annibal Parracho Sant'Anna

    2012-08-01

    Full Text Available The final ranking of a championship is determined by quality attributes combined with other factors which should be filtered out of any decision on relegation or draft for upper level tournaments. Factors like referees' mistakes and difficulty of certain matches due to its accidental importance to the opponents should have their influence reduced. This work tests approaches to combine classification rules considering the imprecision of the number of points as a measure of quality and of the variables that provide reliable explanation for it. Two home-advantage variables are tested and shown to be apt to enter as explanatory variables. Independence between the criteria is checked against the hypothesis of maximal correlation. The importance of factors and of composition rules is evaluated on the basis of correlation between rank vectors, number of classes and number of clubs in tail classes. Data from five years of the Brazilian Soccer Championship are analyzed.

  12. Ranking of patient and surgeons' perspectives for endpoints in randomized controlled trials--lessons learned from the POVATI trial [ISRCTN 60734227].

    Science.gov (United States)

    Fischer, Lars; Deckert, Andreas; Diener, Markus K; Zimmermann, Johannes B; Büchler, Markus W; Seiler, Christoph M

    2011-10-01

    Surgical trials focus mainly on mortality and morbidity rates, which may be not the most important endpoints from the patient's perspective. Evaluation of expectations and needs of patients enrolled in clinical trials can be analyzed using a procedure called ranking. Within the Postsurgical Pain Outcome of Vertical and Transverse Abdominal Incision randomized trial (POVATI), the perspectives of participating patients and surgeons were assessed as well as the influence of the surgical intervention on patients' needs. All included patients of the POVATI trial were asked preoperatively and postoperatively to rank predetermined outcome variables concerning the upcoming surgical procedure (e.g., pain, complication, cosmetic result) hierarchically according to their importance. Preoperatively, the surgeons were asked to do the same. One hundred eighty two out of 200 randomized patients (71 females, 111 males; mean age 59 years) returned the ranking questionnaire preoperatively and 152 patients (67 females, 85 males; mean age 60 years) on the day of discharge. There were no differences between the two groups with respect to the distribution of ranking variables (p > 0.05). Thirty-five surgeons (7 residents, 6 fellows, and 22 consultants) completed the same ranking questionnaire. The order of the four most important ranking variables for both patients and surgeons were death, avoiding of postoperative complications, avoiding of intraoperative complications, and pain. Surgeons ranked the variable "cosmetic result" significantly as more important compared to patients (p = 0.034, Fisher's exact test). Patients and surgeons did not differ in ranking predetermined outcomes in the POVATI trial. Only the variable "cosmetic result" is significantly more important from the surgeon's than from the patient's perspective. Ranking of outcomes might be a beneficial tool and can be a proper addition to RCTs.

  13. How to Rank Journals.

    Science.gov (United States)

    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.

  14. Ranking docking poses by graph matching of protein-ligand interactions: lessons learned from the D3R Grand Challenge 2

    Science.gov (United States)

    da Silva Figueiredo Celestino Gomes, Priscila; Da Silva, Franck; Bret, Guillaume; Rognan, Didier

    2018-01-01

    A novel docking challenge has been set by the Drug Design Data Resource (D3R) in order to predict the pose and affinity ranking of a set of Farnesoid X receptor (FXR) agonists, prior to the public release of their bound X-ray structures and potencies. In a first phase, 36 agonists were docked to 26 Protein Data Bank (PDB) structures of the FXR receptor, and next rescored using the in-house developed GRIM method. GRIM aligns protein-ligand interaction patterns of docked poses to those of available PDB templates for the target protein, and rescore poses by a graph matching method. In agreement with results obtained during the previous 2015 docking challenge, we clearly show that GRIM rescoring improves the overall quality of top-ranked poses by prioritizing interaction patterns already visited in the PDB. Importantly, this challenge enables us to refine the applicability domain of the method by better defining the conditions of its success. We notably show that rescoring apolar ligands in hydrophobic pockets leads to frequent GRIM failures. In the second phase, 102 FXR agonists were ranked by decreasing affinity according to the Gibbs free energy of the corresponding GRIM-selected poses, computed by the HYDE scoring function. Interestingly, this fast and simple rescoring scheme provided the third most accurate ranking method among 57 contributions. Although the obtained ranking is still unsuitable for hit to lead optimization, the GRIM-HYDE scoring scheme is accurate and fast enough to post-process virtual screening data.

  15. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

    Science.gov (United States)

    Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A; Fells, James I; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

    2018-01-01

    The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

  16. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

    Science.gov (United States)

    Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A.; Fells, James I.; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

    2018-01-01

    The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

  17. On Page Rank

    NARCIS (Netherlands)

    Hoede, C.

    In this paper the concept of page rank for the world wide web is discussed. The possibility of describing the distribution of page rank by an exponential law is considered. It is shown that the concept is essentially equal to that of status score, a centrality measure discussed already in 1953 by

  18. On Rank and Nullity

    Science.gov (United States)

    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.

  19. Hitting the Rankings Jackpot

    Science.gov (United States)

    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…

  20. Block models and personalized PageRank

    OpenAIRE

    Kloumann, Isabel M.; Ugander, Johan; Kleinberg, Jon

    2016-01-01

    Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods though the seed set expansion problem: given a subset $S$ of nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate...

  1. Is there a 'Mid-Rank Trap' for Universities'

    OpenAIRE

    Chang Da Wan

    2015-01-01

    The middle-income trap is an economic phenomenon to describe economies that have stagnated at the middle-income level and failed to progress into the high-income level. Inspired by this economic concept, this paper explores a hypothesis: is there a 'mid-rank trap' for universities in the exercise to rank universities globally' Using the rankings between 2004 and 2014 that were jointly and separately developed by Times Higher Education and Quacquarelli Symonds Company, this paper argues that t...

  2. Recurrent fuzzy ranking methods

    Science.gov (United States)

    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.

  3. Ranking as parameter estimation

    Czech Academy of Sciences Publication Activity Database

    Kárný, Miroslav; Guy, Tatiana Valentine

    2009-01-01

    Roč. 4, č. 2 (2009), s. 142-158 ISSN 1745-7645 R&D Projects: GA MŠk 2C06001; GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ranking * Bayesian estimation * negotiation * modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/AS/karny- ranking as parameter estimation.pdf

  4. Hierarchical partial order ranking

    International Nuclear Information System (INIS)

    Carlsen, Lars

    2008-01-01

    Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritisation of polluted sites is given. - Hierarchical partial order ranking of polluted sites has been developed for prioritization based on a large number of parameters

  5. Low-rank quadratic semidefinite programming

    KAUST Repository

    Yuan, Ganzhao

    2013-04-01

    Low rank matrix approximation is an attractive model in large scale machine learning problems, because it can not only reduce the memory and runtime complexity, but also provide a natural way to regularize parameters while preserving learning accuracy. In this paper, we address a special class of nonconvex quadratic matrix optimization problems, which require a low rank positive semidefinite solution. Despite their non-convexity, we exploit the structure of these problems to derive an efficient solver that converges to their local optima. Furthermore, we show that the proposed solution is capable of dramatically enhancing the efficiency and scalability of a variety of concrete problems, which are of significant interest to the machine learning community. These problems include the Top-k Eigenvalue problem, Distance learning and Kernel learning. Extensive experiments on UCI benchmarks have shown the effectiveness and efficiency of our proposed method. © 2012.

  6. Low-rank quadratic semidefinite programming

    KAUST Repository

    Yuan, Ganzhao; Zhang, Zhenjie; Ghanem, Bernard; Hao, Zhifeng

    2013-01-01

    Low rank matrix approximation is an attractive model in large scale machine learning problems, because it can not only reduce the memory and runtime complexity, but also provide a natural way to regularize parameters while preserving learning accuracy. In this paper, we address a special class of nonconvex quadratic matrix optimization problems, which require a low rank positive semidefinite solution. Despite their non-convexity, we exploit the structure of these problems to derive an efficient solver that converges to their local optima. Furthermore, we show that the proposed solution is capable of dramatically enhancing the efficiency and scalability of a variety of concrete problems, which are of significant interest to the machine learning community. These problems include the Top-k Eigenvalue problem, Distance learning and Kernel learning. Extensive experiments on UCI benchmarks have shown the effectiveness and efficiency of our proposed method. © 2012.

  7. A Ranking Approach to Genomic Selection.

    Science.gov (United States)

    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.

  8. Odor preference learning and memory modify GluA1 phosphorylation and GluA1 distribution in the neonate rat olfactory bulb: testing the AMPA receptor hypothesis in an appetitive learning model.

    Science.gov (United States)

    Cui, Wen; Darby-King, Andrea; Grimes, Matthew T; Howland, John G; Wang, Yu Tian; McLean, John H; Harley, Carolyn W

    2011-01-01

    An increase in synaptic AMPA receptors is hypothesized to mediate learning and memory. AMPA receptor increases have been reported in aversive learning models, although it is not clear if they are seen with memory maintenance. Here we examine AMPA receptor changes in a cAMP/PKA/CREB-dependent appetitive learning model: odor preference learning in the neonate rat. Rat pups were given a single pairing of peppermint and 2 mg/kg isoproterenol, which produces a 24-h, but not a 48-h, peppermint preference in the 7-d-old rat pup. GluA1 PKA-dependent phosphorylation peaked 10 min after the 10-min training trial and returned to baseline within 90 min. At 24 h, GluA1 subunits did not change overall but were significantly increased in synaptoneurosomes, consistent with increased membrane insertion. Immunohistochemistry revealed a significant increase in GluA1 subunits in olfactory bulb glomeruli, the targets of olfactory nerve axons. Glomerular increases were seen at 3 and 24 h after odor exposure in trained pups, but not in control pups. GluA1 increases were not seen as early as 10 min after training and were no longer observed 48 h after training when odor preference is no longer expressed behaviorally. Thus, the pattern of increased GluA1 membrane expression closely follows the memory timeline. Further, blocking GluA1 insertion using an interference peptide derived from the carboxyl tail of the GluA1 subunit inhibited 24 h odor preference memory providing causative support for our hypothesis. PKA-mediated GluA1 phosphorylation and later GluA1 insertion could, conjointly, provide increased AMPA function to support both short-term and long-term appetitive memory.

  9. Multiplex PageRank.

    Science.gov (United States)

    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.

  10. Multiplex PageRank.

    Directory of Open Access Journals (Sweden)

    Arda Halu

    Full Text Available Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

  11. THE FRACTAL MARKET HYPOTHESIS

    OpenAIRE

    FELICIA RAMONA BIRAU

    2012-01-01

    In this article, the concept of capital market is analysed using Fractal Market Hypothesis which is a modern, complex and unconventional alternative to classical finance methods. Fractal Market Hypothesis is in sharp opposition to Efficient Market Hypothesis and it explores the application of chaos theory and fractal geometry to finance. Fractal Market Hypothesis is based on certain assumption. Thus, it is emphasized that investors did not react immediately to the information they receive and...

  12. Variability: A Pernicious Hypothesis.

    Science.gov (United States)

    Noddings, Nel

    1992-01-01

    The hypothesis of greater male variability in test results is discussed in its historical context, and reasons feminists have objected to the hypothesis are considered. The hypothesis acquires political importance if it is considered that variability results from biological, rather than cultural, differences. (SLD)

  13. Groundwater contaminant plume ranking

    International Nuclear Information System (INIS)

    1988-08-01

    Containment plumes at Uranium Mill Tailings Remedial Action (UMTRA) Project sites were ranked to assist in Subpart B (i.e., restoration requirements of 40 CFR Part 192) compliance strategies for each site, to prioritize aquifer restoration, and to budget future requests and allocations. The rankings roughly estimate hazards to the environment and human health, and thus assist in determining for which sites cleanup, if appropriate, will provide the greatest benefits for funds available. The rankings are based on the scores that were obtained using the US Department of Energy's (DOE) Modified Hazard Ranking System (MHRS). The MHRS and HRS consider and score three hazard modes for a site: migration, fire and explosion, and direct contact. The migration hazard mode score reflects the potential for harm to humans or the environment from migration of a hazardous substance off a site by groundwater, surface water, and air; it is a composite of separate scores for each of these routes. For ranking the containment plumes at UMTRA Project sites, it was assumed that each site had been remediated in compliance with the EPA standards and that relict contaminant plumes were present. Therefore, only the groundwater route was scored, and the surface water and air routes were not considered. Section 2.0 of this document describes the assumptions and procedures used to score the groundwater route, and Section 3.0 provides the resulting scores for each site. 40 tabs

  14. THE FRACTAL MARKET HYPOTHESIS

    Directory of Open Access Journals (Sweden)

    FELICIA RAMONA BIRAU

    2012-05-01

    Full Text Available In this article, the concept of capital market is analysed using Fractal Market Hypothesis which is a modern, complex and unconventional alternative to classical finance methods. Fractal Market Hypothesis is in sharp opposition to Efficient Market Hypothesis and it explores the application of chaos theory and fractal geometry to finance. Fractal Market Hypothesis is based on certain assumption. Thus, it is emphasized that investors did not react immediately to the information they receive and of course, the manner in which they interpret that information may be different. Also, Fractal Market Hypothesis refers to the way that liquidity and investment horizons influence the behaviour of financial investors.

  15. Ranking economic history journals

    DEFF Research Database (Denmark)

    Di Vaio, Gianfranco; Weisdorf, Jacob Louis

    2010-01-01

    This study ranks-for the first time-12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We also...... compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential for economic...... history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....

  16. Ranking Economic History Journals

    DEFF Research Database (Denmark)

    Di Vaio, Gianfranco; Weisdorf, Jacob Louis

    This study ranks - for the first time - 12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We...... also compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential...... for economic history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....

  17. Dynamic Matrix Rank

    DEFF Research Database (Denmark)

    Frandsen, Gudmund Skovbjerg; Frandsen, Peter Frands

    2009-01-01

    We consider maintaining information about the rank of a matrix under changes of the entries. For n×n matrices, we show an upper bound of O(n1.575) arithmetic operations and a lower bound of Ω(n) arithmetic operations per element change. The upper bound is valid when changing up to O(n0.575) entries...... in a single column of the matrix. We also give an algorithm that maintains the rank using O(n2) arithmetic operations per rank one update. These bounds appear to be the first nontrivial bounds for the problem. The upper bounds are valid for arbitrary fields, whereas the lower bound is valid for algebraically...... closed fields. The upper bound for element updates uses fast rectangular matrix multiplication, and the lower bound involves further development of an earlier technique for proving lower bounds for dynamic computation of rational functions....

  18. Ranking Workplace Competencies: Student and Graduate Perceptions.

    Science.gov (United States)

    Rainsbury, Elizabeth; Hodges, Dave; Burchell, Noel; Lay, Mark

    2002-01-01

    New Zealand business students and graduates made similar rankings of the five most important workplace competencies: computer literacy, customer service orientation, teamwork and cooperation, self-confidence, and willingness to learn. Graduates placed greater importance on most of the 24 competencies, resulting in a statistically significant…

  19. Generalidad de la Hipótesis de la Flexibilidad de Procesamiento en el Aprendizaje Humano: Del Condicionamiento al Aprendizaje Predictivo Generality of the Flexibility of Processing Hypothesis in Human Learning: From Conditioning to Predictive Learning

    Directory of Open Access Journals (Sweden)

    Gonzalo R Quintana

    2010-11-01

    each negative cue was examined. The results indicated that the predictive ratings of the new cues were similar to those of their respective learned cues when the dimensions were perceptually separable (size and angle, indicating elemental processing, but not when they were integral (brightness and saturation, indicating configural processing. These results support the hypothesis that stimulus characteristics determine the type of processing in learning.

  20. Diversifying customer review rankings.

    Science.gov (United States)

    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.

  1. College Rankings. ERIC Digest.

    Science.gov (United States)

    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…

  2. OutRank

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Steinhausen, Uwe

    2008-01-01

    Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed...

  3. Improving Ranking Using Quantum Probability

    OpenAIRE

    Melucci, Massimo

    2011-01-01

    The paper shows that ranking information units by quantum probability differs from ranking them by classical probability provided the same data used for parameter estimation. As probability of detection (also known as recall or power) and probability of false alarm (also known as fallout or size) measure the quality of ranking, we point out and show that ranking by quantum probability yields higher probability of detection than ranking by classical probability provided a given probability of ...

  4. Physiopathological Hypothesis of Cellulite

    Science.gov (United States)

    de Godoy, José Maria Pereira; de Godoy, Maria de Fátima Guerreiro

    2009-01-01

    A series of questions are asked concerning this condition including as regards to its name, the consensus about the histopathological findings, physiological hypothesis and treatment of the disease. We established a hypothesis for cellulite and confirmed that the clinical response is compatible with this hypothesis. Hence this novel approach brings a modern physiological concept with physiopathologic basis and clinical proof of the hypothesis. We emphasize that the choice of patient, correct diagnosis of cellulite and the technique employed are fundamental to success. PMID:19756187

  5. Life Origination Hydrate Hypothesis (LOH-Hypothesis

    Directory of Open Access Journals (Sweden)

    Victor Ostrovskii

    2012-01-01

    Full Text Available The paper develops the Life Origination Hydrate Hypothesis (LOH-hypothesis, according to which living-matter simplest elements (LMSEs, which are N-bases, riboses, nucleosides, nucleotides, DNA- and RNA-like molecules, amino-acids, and proto-cells repeatedly originated on the basis of thermodynamically controlled, natural, and inevitable processes governed by universal physical and chemical laws from CH4, niters, and phosphates under the Earth's surface or seabed within the crystal cavities of the honeycomb methane-hydrate structure at low temperatures; the chemical processes passed slowly through all successive chemical steps in the direction that is determined by a gradual decrease in the Gibbs free energy of reacting systems. The hypothesis formulation method is based on the thermodynamic directedness of natural movement and consists ofan attempt to mentally backtrack on the progression of nature and thus reveal principal milestones alongits route. The changes in Gibbs free energy are estimated for different steps of the living-matter origination process; special attention is paid to the processes of proto-cell formation. Just the occurrence of the gas-hydrate periodic honeycomb matrix filled with LMSEs almost completely in its final state accounts for size limitation in the DNA functional groups and the nonrandom location of N-bases in the DNA chains. The slowness of the low-temperature chemical transformations and their “thermodynamic front” guide the gross process of living matter origination and its successive steps. It is shown that the hypothesis is thermodynamically justified and testable and that many observed natural phenomena count in its favor.

  6. Block models and personalized PageRank.

    Science.gov (United States)

    Kloumann, Isabel M; Ugander, Johan; Kleinberg, Jon

    2017-01-03

    Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods through the "seed set expansion problem": given a subset [Formula: see text] of nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate in the space of "landing probabilities" of a random walk rooted at the seed set, ranking nodes according to weighted sums of landing probabilities of different length walks. Both schemes, however, lack an a priori relationship to the seed set objective. In this work, we develop a principled framework for evaluating ranking methods by studying seed set expansion applied to the stochastic block model. We derive the optimal gradient for separating the landing probabilities of two classes in a stochastic block model and find, surprisingly, that under reasonable assumptions the gradient is asymptotically equivalent to personalized PageRank for a specific choice of the PageRank parameter [Formula: see text] that depends on the block model parameters. This connection provides a formal motivation for the success of personalized PageRank in seed set expansion and node ranking generally. We use this connection to propose more advanced techniques incorporating higher moments of landing probabilities; our advanced methods exhibit greatly improved performance, despite being simple linear classification rules, and are even competitive with belief propagation.

  7. 1991 Acceptance priority ranking

    International Nuclear Information System (INIS)

    1991-12-01

    The Standard Contract for Disposal of Spent Nuclear Fuel and/or High- Level Radioactive Waste (10 CFR Part 961) that the Department of Energy (DOE) has executed with the owners and generators of civilian spent nuclear fuel requires annual publication of the Acceptance Priority Ranking (APR). The 1991 APR details the order in which DOE will allocate Federal waste acceptance capacity. As required by the Standard Contract, the ranking is based on the age of permanently discharged spent nuclear fuel (SNF), with the owners of the oldest SNF, on an industry-wide basis, given the highest priority. the 1991 APR will be the basis for the annual allocation of waste acceptance capacity to the Purchasers in the 1991 Annual Capacity Report (ACR), to be issued later this year. This document is based on SNF discharges as of December 31, 1990, and reflects Purchaser comments and corrections, as appropriate, to the draft APR issued on May 15, 1991

  8. Around power law for PageRank components in Buckley-Osthus model of web graph

    OpenAIRE

    Gasnikov, Alexander; Zhukovskii, Maxim; Kim, Sergey; Noskov, Fedor; Plaunov, Stepan; Smirnov, Daniil

    2017-01-01

    In the paper we investigate power law for PageRank components for the Buckley-Osthus model for web graph. We compare different numerical methods for PageRank calculation. With the best method we do a lot of numerical experiments. These experiments confirm the hypothesis about power law. At the end we discuss real model of web-ranking based on the classical PageRank approach.

  9. Effect of harmonic rank on the streaming of complex tones

    DEFF Research Database (Denmark)

    Madsen, Sara Miay Kim; Dau, Torsten; Moore, Brian C.J.

    2015-01-01

    The effect of the rank of the harmonics on sequential stream segregation of complex tones was investigated for normal-hearing participants with no musical training. It was hypothesized that stream segregation would be greater for tones with high pitch salience, as assessed by fundamental frequency....... There was a significant trend for less stream segregation with increasing harmonic rank. The amount of stream segregation was inversely correlated with the f0 difference limens, consistent with the hypothesis....

  10. Low Rank Approximation Algorithms, Implementation, Applications

    CERN Document Server

    Markovsky, Ivan

    2012-01-01

    Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory. Applications described include: system and control theory: approximate realization, model reduction, output error, and errors-in-variables identification; signal processing: harmonic retrieval, sum-of-damped exponentials, finite impulse response modeling, and array processing; machine learning: multidimensional scaling and recommender system; computer vision: algebraic curve fitting and fundamental matrix estimation; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; ...

  11. Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture

    Science.gov (United States)

    Sanfilippo, Antonio P [Richland, WA; Cowell, Andrew J [Kennewick, WA; Gregory, Michelle L [Richland, WA; Baddeley, Robert L [Richland, WA; Paulson, Patrick R [Pasco, WA; Tratz, Stephen C [Richland, WA; Hohimer, Ryan E [West Richland, WA

    2012-03-20

    Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture are described according to some aspects. In one aspect, a hypothesis analysis method includes providing a hypothesis, providing an indicator which at least one of supports and refutes the hypothesis, using the indicator, associating evidence with the hypothesis, weighting the association of the evidence with the hypothesis, and using the weighting, providing information regarding the accuracy of the hypothesis.

  12. On the Keyhole Hypothesis

    DEFF Research Database (Denmark)

    Mikkelsen, Kaare B.; Kidmose, Preben; Hansen, Lars Kai

    2017-01-01

    simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG "keyhole," furthermore we......We propose and test the keyhole hypothesis that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10...

  13. Ranking Baltic States Researchers

    Directory of Open Access Journals (Sweden)

    Gyula Mester

    2017-10-01

    Full Text Available In this article, using the h-index and the total number of citations, the best 10 Lithuanian, Latvian and Estonian researchers from several disciplines are ranked. The list may be formed based on the h-index and the total number of citations, given in Web of Science, Scopus, Publish or Perish Program and Google Scholar database. Data for the first 10 researchers are presented. Google Scholar is the most complete. Therefore, to define a single indicator, h-index calculated by Google Scholar may be a good and simple one. The author chooses the Google Scholar database as it is the broadest one.

  14. University Rankings and Social Science

    OpenAIRE

    Marginson, S.

    2014-01-01

    University rankings widely affect the behaviours of prospective students and their families, university executive leaders, academic faculty, governments and investors in higher education. Yet the social science foundations of global rankings receive little scrutiny. Rankings that simply recycle reputation without any necessary connection to real outputs are of no common value. It is necessary that rankings be soundly based in scientific terms if a virtuous relationship between performance and...

  15. University Rankings and Social Science

    Science.gov (United States)

    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…

  16. Rankings, creatividad y urbanismo

    Directory of Open Access Journals (Sweden)

    JOAQUÍN SABATÉ

    2008-08-01

    Full Text Available La competencia entre ciudades constituye uno de los factores impulsores de procesos de renovación urbana y los rankings han devenido instrumentos de medida de la calidad de las ciudades. Nos detendremos en el caso de un antiguo barrio industrial hoy en vías de transformación en distrito "creativo" por medio de una intervención urbanística de gran escala. Su análisis nos descubre tres claves críticas. En primer lugar, nos obliga a plantearnos la definición de innovación urbana y cómo se integran el pasado, la identidad y la memoria en la construcción del futuro. Nos lleva a comprender que la innovación y el conocimiento no se "dan" casualmente, sino que son el fruto de una larga y compleja red en la que participan saberes, espacios, actores e instituciones diversas en naturaleza, escala y magnitud. Por último nos obliga a reflexionar sobre el valor que se le otorga a lo local en los procesos de renovación urbana.Competition among cities constitutes one ofthe main factors o furban renewal, and rankings have become instruments to indícate cities quality. Studying the transformation of an old industrial quarter into a "creative district" by the means ofa large scale urban project we highlight three main conclusions. First, itasks us to reconsider the notion ofurban innovation and hoto past, identity and memory should intégrate the future development. Second, it shows that innovation and knowledge doesn't yield per chance, but are the result ofa large and complex grid of diverse knowledges, spaces, agents and institutions. Finally itforces us to reflect about the valué attributed to the "local" in urban renewalprocesses.

  17. Odor Preference Learning and Memory Modify GluA1 Phosphorylation and GluA1 Distribution in the Neonate Rat Olfactory Bulb: Testing the AMPA Receptor Hypothesis in an Appetitive Learning Model

    Science.gov (United States)

    Cui, Wen; Darby-King, Andrea; Grimes, Matthew T.; Howland, John G.; Wang, Yu Tian; McLean, John H.; Harley, Carolyn W.

    2011-01-01

    An increase in synaptic AMPA receptors is hypothesized to mediate learning and memory. AMPA receptor increases have been reported in aversive learning models, although it is not clear if they are seen with memory maintenance. Here we examine AMPA receptor changes in a cAMP/PKA/CREB-dependent appetitive learning model: odor preference learning in…

  18. The Qualitative Expectations Hypothesis

    DEFF Research Database (Denmark)

    Frydman, Roman; Johansen, Søren; Rahbek, Anders

    2017-01-01

    We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and financial outcomes. Building on John Muth's seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market's forecasts to be consistent with the predictions...... of an economistís model. However, by assuming that outcomes lie within stochastic intervals, QEH, unlike REH, recognizes the ambiguity faced by an economist and market participants alike. Moreover, QEH leaves the model open to ambiguity by not specifying a mechanism determining specific values that outcomes take...

  19. The Qualitative Expectations Hypothesis

    DEFF Research Database (Denmark)

    Frydman, Roman; Johansen, Søren; Rahbek, Anders

    We introduce the Qualitative Expectations Hypothesis (QEH) as a new approach to modeling macroeconomic and financial outcomes. Building on John Muth's seminal insight underpinning the Rational Expectations Hypothesis (REH), QEH represents the market's forecasts to be consistent with the predictions...... of an economist's model. However, by assuming that outcomes lie within stochastic intervals, QEH, unlike REH, recognizes the ambiguity faced by an economist and market participants alike. Moreover, QEH leaves the model open to ambiguity by not specifying a mechanism determining specific values that outcomes take...

  20. Ranking nodes in growing networks: When PageRank fails.

    Science.gov (United States)

    Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng

    2015-11-10

    PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm's efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank's performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

  1. Sign rank versus Vapnik-Chervonenkis dimension

    Science.gov (United States)

    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.

  2. Neophilia Ranking of Scientific Journals.

    Science.gov (United States)

    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.

  3. Revisiting the Dutch hypothesis

    NARCIS (Netherlands)

    Postma, Dirkje S.; Weiss, Scott T.; van den Berge, Maarten; Kerstjens, Huib A. M.; Koppelman, Gerard H.

    The Dutch hypothesis was first articulated in 1961, when many novel and advanced scientific techniques were not available, such as genomics techniques for pinpointing genes, gene expression, lipid and protein profiles, and the microbiome. In addition, computed tomographic scans and advanced analysis

  4. The Lehman Sisters Hypothesis

    NARCIS (Netherlands)

    I.P. van Staveren (Irene)

    2014-01-01

    markdownabstract__Abstract__ This article explores the Lehman Sisters Hypothesis. It reviews empirical literature about gender differences in behavioral, experimental, and neuro-economics as well as in other fields of behavioral research. It discusses gender differences along three dimensions of

  5. Low-rank coal research

    Energy Technology Data Exchange (ETDEWEB)

    Weber, G. F.; Laudal, D. L.

    1989-01-01

    This work is a compilation of reports on ongoing research at the University of North Dakota. Topics include: Control Technology and Coal Preparation Research (SO{sub x}/NO{sub x} control, waste management), Advanced Research and Technology Development (turbine combustion phenomena, combustion inorganic transformation, coal/char reactivity, liquefaction reactivity of low-rank coals, gasification ash and slag characterization, fine particulate emissions), Combustion Research (fluidized bed combustion, beneficiation of low-rank coals, combustion characterization of low-rank coal fuels, diesel utilization of low-rank coals), Liquefaction Research (low-rank coal direct liquefaction), and Gasification Research (hydrogen production from low-rank coals, advanced wastewater treatment, mild gasification, color and residual COD removal from Synfuel wastewaters, Great Plains Gasification Plant, gasifier optimization).

  6. Ranking Specific Sets of Objects.

    Science.gov (United States)

    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.

  7. Wikipedia ranking of world universities

    Science.gov (United States)

    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.

  8. Ranking by Medians

    Science.gov (United States)

    Martin, Brian

    2016-01-01

    Brian Martin describes a difficult committee meeting he once attended which consisted of one representative from each department. When the meeting ended it left a bitter taste for many who participated. Having learned from this experience, Martin became a chair of the committee and tried a new system that overcame many of the previous problems.…

  9. Statistical methods for ranking data

    CERN Document Server

    Alvo, Mayer

    2014-01-01

    This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.

  10. Ranking nodes in growing networks: When PageRank fails

    Science.gov (United States)

    Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng

    2015-11-01

    PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm’s efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank’s performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

  11. Bayesian Hypothesis Testing

    Energy Technology Data Exchange (ETDEWEB)

    Andrews, Stephen A. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sigeti, David E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-11-15

    These are a set of slides about Bayesian hypothesis testing, where many hypotheses are tested. The conclusions are the following: The value of the Bayes factor obtained when using the median of the posterior marginal is almost the minimum value of the Bayes factor. The value of τ2 which minimizes the Bayes factor is a reasonable choice for this parameter. This allows a likelihood ratio to be computed with is the least favorable to H0.

  12. The Drift Burst Hypothesis

    OpenAIRE

    Christensen, Kim; Oomen, Roel; Renò, Roberto

    2016-01-01

    The Drift Burst Hypothesis postulates the existence of short-lived locally explosive trends in the price paths of financial assets. The recent US equity and Treasury flash crashes can be viewed as two high profile manifestations of such dynamics, but we argue that drift bursts of varying magnitude are an expected and regular occurrence in financial markets that can arise through established mechanisms such as feedback trading. At a theoretical level, we show how to build drift bursts into the...

  13. Hypothesis in research

    Directory of Open Access Journals (Sweden)

    Eudaldo Enrique Espinoza Freire

    2018-01-01

    Full Text Available It is intended with this work to have a material with the fundamental contents, which enable the university professor to formulate the hypothesis, for the development of an investigation, taking into account the problem to be solved. For its elaboration, the search of information in primary documents was carried out, such as thesis of degree and reports of research results, selected on the basis of its relevance with the analyzed subject, current and reliability, secondary documents, as scientific articles published in journals of recognized prestige, the selection was made with the same terms as in the previous documents. It presents a conceptualization of the updated hypothesis, its characterization and an analysis of the structure of the hypothesis in which the determination of the variables is deepened. The involvement of the university professor in the teaching-research process currently faces some difficulties, which are manifested, among other aspects, in an unstable balance between teaching and research, which leads to a separation between them.

  14. Toward optimal feature selection using ranking methods and classification algorithms

    Directory of Open Access Journals (Sweden)

    Novaković Jasmina

    2011-01-01

    Full Text Available We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.

  15. The Effectiveness of the Comprehension Hypothesis: A Review on the Current Research on Incidental Vocabulary Acquisition

    Science.gov (United States)

    Ponniah, Joseph

    2011-01-01

    The Comprehension Hypothesis (CH) is the most powerful hypothesis in the field of Second Language Acquisition despite the presence of the rivals the skill-building hypothesis, the output hypothesis, and the interaction hypothesis. The competing hypotheses state that consciously learned linguistic knowledge is a necessary step for the development…

  16. PageRank tracker: from ranking to tracking.

    Science.gov (United States)

    Gong, Chen; Fu, Keren; Loza, Artur; Wu, Qiang; Liu, Jia; Yang, Jie

    2014-06-01

    Video object tracking is widely used in many real-world applications, and it has been extensively studied for over two decades. However, tracking robustness is still an issue in most existing methods, due to the difficulties with adaptation to environmental or target changes. In order to improve adaptability, this paper formulates the tracking process as a ranking problem, and the PageRank algorithm, which is a well-known webpage ranking algorithm used by Google, is applied. Labeled and unlabeled samples in tracking application are analogous to query webpages and the webpages to be ranked, respectively. Therefore, determining the target is equivalent to finding the unlabeled sample that is the most associated with existing labeled set. We modify the conventional PageRank algorithm in three aspects for tracking application, including graph construction, PageRank vector acquisition and target filtering. Our simulations with the use of various challenging public-domain video sequences reveal that the proposed PageRank tracker outperforms mean-shift tracker, co-tracker, semiboosting and beyond semiboosting trackers in terms of accuracy, robustness and stability.

  17. Asynchronous Gossip for Averaging and Spectral Ranking

    Science.gov (United States)

    Borkar, Vivek S.; Makhijani, Rahul; Sundaresan, Rajesh

    2014-08-01

    We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.

  18. Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers

    NARCIS (Netherlands)

    Eisinga, R.N.; Heskes, T.M.; Pelzer, B.J.; Grotenhuis, H.F. te

    2017-01-01

    Background: The Friedman rank sum test is a widely-used nonparametric method in computational biology. In addition to examining the overall null hypothesis of no significant difference among any of the rank sums, it is typically of interest to conduct pairwise comparison tests. Current approaches to

  19. Is there a 'Mid-Rank Trap' for Universities'

    Directory of Open Access Journals (Sweden)

    Chang Da Wan

    2015-10-01

    Full Text Available The middle-income trap is an economic phenomenon to describe economies that have stagnated at the middle-income level and failed to progress into the high-income level. Inspired by this economic concept, this paper explores a hypothesis: is there a 'mid-rank trap' for universities in the exercise to rank universities globally' Using the rankings between 2004 and 2014 that were jointly and separately developed by Times Higher Education and Quacquarelli Symonds Company, this paper argues that there is indeed a phenomenon, which I term as 'mid-rank trap' whereby universities remain stagnant for a decade in a similar band of the rankings. Having established the hypothesis for universities, the paper examines policies and interventions that have been successfully carried out to elevate economies away from the middle-income trap, and importantly, to draw out the underlying principles of these economic policies and interventions that can be incorporated into policymaking and strategic planning for universities using the Malaysian higher education system as a case study.

  20. Universal scaling in sports ranking

    International Nuclear Information System (INIS)

    Deng Weibing; Li Wei; Cai Xu; Bulou, Alain; Wang Qiuping A

    2012-01-01

    Ranking is a ubiquitous phenomenon in human society. On the web pages of Forbes, one may find all kinds of rankings, such as the world's most powerful people, the world's richest people, the highest-earning tennis players, and so on and so forth. Herewith, we study a specific kind—sports ranking systems in which players' scores and/or prize money are accrued based on their performances in different matches. By investigating 40 data samples which span 12 different sports, we find that the distributions of scores and/or prize money follow universal power laws, with exponents nearly identical for most sports. In order to understand the origin of this universal scaling we focus on the tennis ranking systems. By checking the data we find that, for any pair of players, the probability that the higher-ranked player tops the lower-ranked opponent is proportional to the rank difference between the pair. Such a dependence can be well fitted to a sigmoidal function. By using this feature, we propose a simple toy model which can simulate the competition of players in different matches. The simulations yield results consistent with the empirical findings. Extensive simulation studies indicate that the model is quite robust with respect to the modifications of some parameters. (paper)

  1. [Dilemma of null hypothesis in ecological hypothesis's experiment test.

    Science.gov (United States)

    Li, Ji

    2016-06-01

    Experimental test is one of the major test methods of ecological hypothesis, though there are many arguments due to null hypothesis. Quinn and Dunham (1983) analyzed the hypothesis deduction model from Platt (1964) and thus stated that there is no null hypothesis in ecology that can be strictly tested by experiments. Fisher's falsificationism and Neyman-Pearson (N-P)'s non-decisivity inhibit statistical null hypothesis from being strictly tested. Moreover, since the null hypothesis H 0 (α=1, β=0) and alternative hypothesis H 1 '(α'=1, β'=0) in ecological progresses are diffe-rent from classic physics, the ecological null hypothesis can neither be strictly tested experimentally. These dilemmas of null hypothesis could be relieved via the reduction of P value, careful selection of null hypothesis, non-centralization of non-null hypothesis, and two-tailed test. However, the statistical null hypothesis significance testing (NHST) should not to be equivalent to the causality logistical test in ecological hypothesis. Hence, the findings and conclusions about methodological studies and experimental tests based on NHST are not always logically reliable.

  2. A Rank Test on Equality of Population Medians

    OpenAIRE

    Pooi Ah Hin

    2012-01-01

    The Kruskal-Wallis test is a non-parametric test for the equality of K population medians. The test statistic involved is a measure of the overall closeness of the K average ranks in the individual samples to the average rank in the combined sample. The resulting acceptance region of the test however may not be the smallest region with the required acceptance probability under the null hypothesis. Presently an alternative acceptance region is constructed such that it has the smallest size, ap...

  3. PageRank of integers

    International Nuclear Information System (INIS)

    Frahm, K M; Shepelyansky, D L; Chepelianskii, A D

    2012-01-01

    We up a directed network tracing links from a given integer to its divisors and analyze the properties of the Google matrix of this network. The PageRank vector of this matrix is computed numerically and it is shown that its probability is approximately inversely proportional to the PageRank index thus being similar to the Zipf law and the dependence established for the World Wide Web. The spectrum of the Google matrix of integers is characterized by a large gap and a relatively small number of nonzero eigenvalues. A simple semi-analytical expression for the PageRank of integers is derived that allows us to find this vector for matrices of billion size. This network provides a new PageRank order of integers. (paper)

  4. Freudenthal ranks: GHZ versus W

    International Nuclear Information System (INIS)

    Borsten, L

    2013-01-01

    The Hilbert space of three-qubit pure states may be identified with a Freudenthal triple system. Every state has an unique Freudenthal rank ranging from 1 to 4, which is determined by a set of automorphism group covariants. It is shown here that the optimal success rates for winning a three-player non-local game, varying over all local strategies, are strictly ordered by the Freudenthal rank of the shared three-qubit resource. (paper)

  5. Ranking Queries on Uncertain Data

    CERN Document Server

    Hua, Ming

    2011-01-01

    Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorith

  6. Ranking in evolving complex networks

    Science.gov (United States)

    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.

  7. Explorations in Statistics: Hypothesis Tests and P Values

    Science.gov (United States)

    Curran-Everett, Douglas

    2009-01-01

    Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of "Explorations in Statistics" delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what…

  8. The Bergschrund Hypothesis Revisited

    Science.gov (United States)

    Sanders, J. W.; Cuffey, K. M.; MacGregor, K. R.

    2009-12-01

    After Willard Johnson descended into the Lyell Glacier bergschrund nearly 140 years ago, he proposed that the presence of the bergschrund modulated daily air temperature fluctuations and enhanced freeze-thaw processes. He posited that glaciers, through their ability to birth bergschrunds, are thus able to induce rapid cirque headwall retreat. In subsequent years, many researchers challenged the bergschrund hypothesis on grounds that freeze-thaw events did not occur at depth in bergschrunds. We propose a modified version of Johnson’s original hypothesis: that bergschrunds maintain subfreezing temperatures at values that encourage rock fracture via ice lensing because they act as a cold air trap in areas that would otherwise be held near zero by temperate glacial ice. In support of this claim we investigated three sections of the bergschrund at the West Washmawapta Glacier, British Columbia, Canada, which sits in an east-facing cirque. During our bergschrund reconnaissance we installed temperature sensors at multiple elevations, light sensors at depth in 2 of the 3 locations and painted two 1 m2 sections of the headwall. We first emphasize bergschrunds are not wanting for ice: verglas covers significant fractions of the headwall and icicles dangle from the base of bödens or overhanging rocks. If temperature, rather than water availability, is the limiting factor governing ice-lensing rates, our temperature records demonstrate that the bergschrund provides a suitable environment for considerable rock fracture. At the three sites (north, west, and south walls), the average temperature at depth from 9/3/2006 to 8/6/2007 was -3.6, -3.6, and -2.0 °C, respectively. During spring, when we observed vast amounts of snow melt trickle in to the bergschrund, temperatures averaged -3.7, -3.8, and -2.2 °C, respectively. Winter temperatures are even lower: -8.5, -7.3, and -2.4 °C, respectively. Values during the following year were similar. During the fall, diurnal

  9. RANK and RANK ligand expression in primary human osteosarcoma

    Directory of Open Access Journals (Sweden)

    Daniel Branstetter

    2015-09-01

    Our results demonstrate RANKL expression was observed in the tumor element in 68% of human OS using IHC. However, the staining intensity was relatively low and only 37% (29/79 of samples exhibited≥10% RANKL positive tumor cells. RANK expression was not observed in OS tumor cells. In contrast, RANK expression was clearly observed in other cells within OS samples, including the myeloid osteoclast precursor compartment, osteoclasts and in giant osteoclast cells. The intensity and frequency of RANKL and RANK staining in OS samples were substantially less than that observed in GCTB samples. The observation that RANKL is expressed in OS cells themselves suggests that these tumors may mediate an osteoclastic response, and anti-RANKL therapy may potentially be protective against bone pathologies in OS. However, the absence of RANK expression in primary human OS cells suggests that any autocrine RANKL/RANK signaling in human OS tumor cells is not operative, and anti-RANKL therapy would not directly affect the tumor.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    Bouchra Sojod

    2017-05-01

    Full Text Available 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.

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

    Science.gov (United States)

    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

  13. RUSSIAN UNIVERSITIES IN THE LOOP OF THE WORLD EDUCATION RANKINGS

    Directory of Open Access Journals (Sweden)

    Екатерина Сергеевна Иноземцева

    2013-04-01

    Full Text Available Purpose: a research on different sociological and economic aspects of world education rankings (THE, ARWU, QS, evaluation of their role and impact on the world education market’s main consumers (i.e. students and academic staff as a subject to discussion in terms of the customers’ preferences and choice motivators.  Methodology: general scientific research tools were applied throughout the research: analysis, synthesis, deduction.Results: world ranking approach and methodology was assessed, defined and systemized, a unique general ranking of the countries was developed and performed (based on the researched body – the US ranked #1, Russia #30, expert recommendations for Russian universities have been developed and concluded.Practical implications: the main statements could be used within learning courses on the internationalization of higher education and applied in sociological and economic research dedicated to macroeconomic problems and issues analysis.DOI: http://dx.doi.org/10.12731/2218-7405-2013-2-18

  14. RUSSIAN UNIVERSITIES IN THE LOOP OF THE WORLD EDUCATION RANKINGS

    Directory of Open Access Journals (Sweden)

    Inozemtseva Ekaterina Sergeevna

    2013-02-01

    Full Text Available Purpose: a research on different sociological and economic aspects of world education rankings (THE, ARWU, QS, evaluation of their role and impact on the world education market’s main consumers (i.e. students and academic staff as a subject to discussion in terms of the customers’ preferences and choice motivators. Methodology: general scientific research tools were applied throughout the research: analysis, synthesis, deduction. Results: world ranking approach and methodology was assessed, defined and systemized, a unique general ranking of the countries was developed and performed (based on the researched body – the US ranked #1, Russia #30, expert recommendations for Russian universities have been developed and concluded. Practical implications: the main statements could be used within learning courses on the internationalization of higher education and applied in sociological and economic research dedicated to macroeconomic problems and issues analysis.

  15. Multi-agent sequential hypothesis testing

    KAUST Repository

    Kim, Kwang-Ki K.

    2014-12-15

    This paper considers multi-agent sequential hypothesis testing and presents a framework for strategic learning in sequential games with explicit consideration of both temporal and spatial coordination. The associated Bayes risk functions explicitly incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well-defined value functions with respect to (a) the belief states for the case of conditional independent private noisy measurements that are also assumed to be independent identically distributed over time, and (b) the information states for the case of correlated private noisy measurements. A sequential investment game of strategic coordination and delay is also discussed as an application of the proposed strategic learning rules.

  16. Ranking species in mutualistic networks

    Science.gov (United States)

    Domínguez-García, Virginia; Muñoz, Miguel A.

    2015-02-01

    Understanding the architectural subtleties of ecological networks, believed to confer them enhanced stability and robustness, is a subject of outmost relevance. Mutualistic interactions have been profusely studied and their corresponding bipartite networks, such as plant-pollinator networks, have been reported to exhibit a characteristic ``nested'' structure. Assessing the importance of any given species in mutualistic networks is a key task when evaluating extinction risks and possible cascade effects. Inspired in a recently introduced algorithm -similar in spirit to Google's PageRank but with a built-in non-linearity- here we propose a method which -by exploiting their nested architecture- allows us to derive a sound ranking of species importance in mutualistic networks. This method clearly outperforms other existing ranking schemes and can become very useful for ecosystem management and biodiversity preservation, where decisions on what aspects of ecosystems to explicitly protect need to be made.

  17. Ranking Theory and Conditional Reasoning.

    Science.gov (United States)

    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.

  18. University rankings in computer science

    DEFF Research Database (Denmark)

    Ehret, Philip; Zuccala, Alesia Ann; Gipp, Bela

    2017-01-01

    This is a research-in-progress paper concerning two types of institutional rankings, the Leiden and QS World ranking, and their relationship to a list of universities’ ‘geo-based’ impact scores, and Computing Research and Education Conference (CORE) participation scores in the field of computer...... science. A ‘geo-based’ impact measure examines the geographical distribution of incoming citations to a particular university’s journal articles for a specific period of time. It takes into account both the number of citations and the geographical variability in these citations. The CORE participation...... score is calculated on the basis of the number of weighted proceedings papers that a university has contributed to either an A*, A, B, or C conference as ranked by the Computing Research and Education Association of Australasia. In addition to calculating the correlations between the distinct university...

  19. Subtracting a best rank-1 approximation may increase tensor rank

    NARCIS (Netherlands)

    Stegeman, Alwin; Comon, Pierre

    2010-01-01

    It has been shown that a best rank-R approximation of an order-k tensor may not exist when R >= 2 and k >= 3. This poses a serious problem to data analysts using tensor decompositions it has been observed numerically that, generally, this issue cannot be solved by consecutively computing and

  20. Consistent ranking of volatility models

    DEFF Research Database (Denmark)

    Hansen, Peter Reinhard; Lunde, Asger

    2006-01-01

    We show that the empirical ranking of volatility models can be inconsistent for the true ranking if the evaluation is based on a proxy for the population measure of volatility. For example, the substitution of a squared return for the conditional variance in the evaluation of ARCH-type models can...... variance in out-of-sample evaluations rather than the squared return. We derive the theoretical results in a general framework that is not specific to the comparison of volatility models. Similar problems can arise in comparisons of forecasting models whenever the predicted variable is a latent variable....

  1. Automatic figure ranking and user interfacing for intelligent figure search.

    Directory of Open Access Journals (Sweden)

    Hong Yu

    2010-10-01

    Full Text Available 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.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.The evaluation results conclude that automatic figure ranking and user

  2. Automatic Generation and Ranking of Questions for Critical Review

    Science.gov (United States)

    Liu, Ming; Calvo, Rafael A.; Rus, Vasile

    2014-01-01

    Critical review skill is one important aspect of academic writing. Generic trigger questions have been widely used to support this activity. When students have a concrete topic in mind, trigger questions are less effective if they are too general. This article presents a learning-to-rank based system which automatically generates specific trigger…

  3. Adaptive Game Level Creation through Rank-based Interactive Evolution

    DEFF Research Database (Denmark)

    Liapis, Antonios; Martínez, Héctor Pérez; Togelius, Julian

    2013-01-01

    as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using...

  4. Discriminative Multi-View Interactive Image Re-Ranking.

    Science.gov (United States)

    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.

  5. Let Us Rank Journalism Programs

    Science.gov (United States)

    Weber, Joseph

    2014-01-01

    Unlike law, business, and medical schools, as well as universities in general, journalism schools and journalism programs have rarely been ranked. Publishers such as "U.S. News & World Report," "Forbes," "Bloomberg Businessweek," and "Washington Monthly" do not pay them much mind. What is the best…

  6. On Rank Driven Dynamical Systems

    Science.gov (United States)

    Veerman, J. J. P.; Prieto, F. J.

    2014-08-01

    We investigate a class of models related to the Bak-Sneppen (BS) model, initially proposed to study evolution. The BS model is extremely simple and yet captures some forms of "complex behavior" such as self-organized criticality that is often observed in physical and biological systems. In this model, random fitnesses in are associated to agents located at the vertices of a graph . Their fitnesses are ranked from worst (0) to best (1). At every time-step the agent with the worst fitness and some others with a priori given rank probabilities are replaced by new agents with random fitnesses. We consider two cases: The exogenous case where the new fitnesses are taken from an a priori fixed distribution, and the endogenous case where the new fitnesses are taken from the current distribution as it evolves. We approximate the dynamics by making a simplifying independence assumption. We use Order Statistics and Dynamical Systems to define a rank-driven dynamical system that approximates the evolution of the distribution of the fitnesses in these rank-driven models, as well as in the BS model. For this simplified model we can find the limiting marginal distribution as a function of the initial conditions. Agreement with experimental results of the BS model is excellent.

  7. PageRank (II): Mathematics

    African Journals Online (AJOL)

    maths/stats

    ... GAUSS SEIDEL'S. NUMERICAL ALGORITHMS IN PAGE RANK ANALYSIS. ... The convergence is guaranteed, if the absolute value of the largest eigen ... improved Gauss-Seidel iteration algorithm, based on the decomposition. U. L. D. M. +. +. = ..... This corresponds to determine the eigen vector of T with eigen value 1.

  8. 14 CFR 1214.1105 - Final ranking.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Final ranking. 1214.1105 Section 1214.1105... Recruitment and Selection Program § 1214.1105 Final ranking. Final rankings will be based on a combination of... preference will be included in this final ranking in accordance with applicable regulations. ...

  9. A Survey on PageRank Computing

    OpenAIRE

    Berkhin, Pavel

    2005-01-01

    This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much mor...

  10. Is the Aluminum Hypothesis Dead?

    Science.gov (United States)

    2014-01-01

    The Aluminum Hypothesis, the idea that aluminum exposure is involved in the etiology of Alzheimer disease, dates back to a 1965 demonstration that aluminum causes neurofibrillary tangles in the brains of rabbits. Initially the focus of intensive research, the Aluminum Hypothesis has gradually been abandoned by most researchers. Yet, despite this current indifference, the Aluminum Hypothesis continues to attract the attention of a small group of scientists and aluminum continues to be viewed with concern by some of the public. This review article discusses reasons that mainstream science has largely abandoned the Aluminum Hypothesis and explores a possible reason for some in the general public continuing to view aluminum with mistrust. PMID:24806729

  11. Time evolution of Wikipedia network ranking

    Science.gov (United States)

    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.

  12. LogDet Rank Minimization with Application to Subspace Clustering

    Directory of Open Access Journals (Sweden)

    Zhao Kang

    2015-01-01

    Full Text Available Low-rank matrix is desired in many machine learning and computer vision problems. Most of the recent studies use the nuclear norm as a convex surrogate of the rank operator. However, all singular values are simply added together by the nuclear norm, and thus the rank may not be well approximated in practical problems. In this paper, we propose using a log-determinant (LogDet function as a smooth and closer, though nonconvex, approximation to rank for obtaining a low-rank representation in subspace clustering. Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. By making use of the angular information of principal directions of the resultant low-rank representation, an affinity graph matrix is constructed for spectral clustering. Experimental results on motion segmentation and face clustering data demonstrate that the proposed method often outperforms state-of-the-art subspace clustering algorithms.

  13. Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination.

    Science.gov (United States)

    Zhao, Qibin; Zhang, Liqing; Cichocki, Andrzej

    2015-09-01

    CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank . In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.

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

    Directory of Open Access Journals (Sweden)

    Zhengnan Huang

    2017-12-01

    Full Text Available Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

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

    Science.gov (United States)

    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.

  16. Low-rank and sparse modeling for visual analysis

    CERN Document Server

    Fu, Yun

    2014-01-01

    This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applic

  17. Minkowski metrics in creating universal ranking algorithms

    Directory of Open Access Journals (Sweden)

    Andrzej Ameljańczyk

    2014-06-01

    Full Text Available The paper presents a general procedure for creating the rankings of a set of objects, while the relation of preference based on any ranking function. The analysis was possible to use the ranking functions began by showing the fundamental drawbacks of commonly used functions in the form of a weighted sum. As a special case of the ranking procedure in the space of a relation, the procedure based on the notion of an ideal element and generalized Minkowski distance from the element was proposed. This procedure, presented as universal ranking algorithm, eliminates most of the disadvantages of ranking functions in the form of a weighted sum.[b]Keywords[/b]: ranking functions, preference relation, ranking clusters, categories, ideal point, universal ranking algorithm

  18. Functional Multiplex PageRank

    Science.gov (United States)

    Iacovacci, Jacopo; Rahmede, Christoph; Arenas, Alex; Bianconi, Ginestra

    2016-10-01

    Recently it has been recognized that many complex social, technological and biological networks have a multilayer nature and can be described by multiplex networks. Multiplex networks are formed by a set of nodes connected by links having different connotations forming the different layers of the multiplex. Characterizing the centrality of the nodes in a multiplex network is a challenging task since the centrality of the node naturally depends on the importance associated to links of a certain type. Here we propose to assign to each node of a multiplex network a centrality called Functional Multiplex PageRank that is a function of the weights given to every different pattern of connections (multilinks) existent in the multiplex network between any two nodes. Since multilinks distinguish all the possible ways in which the links in different layers can overlap, the Functional Multiplex PageRank can describe important non-linear effects when large relevance or small relevance is assigned to multilinks with overlap. Here we apply the Functional Page Rank to the multiplex airport networks, to the neuronal network of the nematode C. elegans, and to social collaboration and citation networks between scientists. This analysis reveals important differences existing between the most central nodes of these networks, and the correlations between their so-called pattern to success.

  19. Low rank magnetic resonance fingerprinting.

    Science.gov (United States)

    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.

  20. Hypothesis Designs for Three-Hypothesis Test Problems

    OpenAIRE

    Yan Li; Xiaolong Pu

    2010-01-01

    As a helpful guide for applications, the alternative hypotheses of the three-hypothesis test problems are designed under the required error probabilities and average sample number in this paper. The asymptotic formulas and the proposed numerical quadrature formulas are adopted, respectively, to obtain the hypothesis designs and the corresponding sequential test schemes under the Koopman-Darmois distributions. The example of the normal mean test shows that our methods are qu...

  1. Hypothesis-driven physical examination curriculum.

    Science.gov (United States)

    Allen, Sharon; Olson, Andrew; Menk, Jeremiah; Nixon, James

    2017-12-01

    Medical students traditionally learn physical examination skills as a rote list of manoeuvres. Alternatives like hypothesis-driven physical examination (HDPE) may promote students' understanding of the contribution of physical examination to diagnostic reasoning. We sought to determine whether first-year medical students can effectively learn to perform a physical examination using an HDPE approach, and then tailor the examination to specific clinical scenarios. Medical students traditionally learn physical examination skills as a rote list of manoeuvres CONTEXT: First-year medical students at the University of Minnesota were taught both traditional and HDPE approaches during a required 17-week clinical skills course in their first semester. The end-of-course evaluation assessed HDPE skills: students were assigned one of two cardiopulmonary cases. Each case included two diagnostic hypotheses. During an interaction with a standardised patient, students were asked to select physical examination manoeuvres in order to make a final diagnosis. Items were weighted and selection order was recorded. First-year students with minimal pathophysiology performed well. All students selected the correct diagnosis. Importantly, students varied the order when selecting examination manoeuvres depending on the diagnoses under consideration, demonstrating early clinical decision-making skills. An early introduction to HDPE may reinforce physical examination skills for hypothesis generation and testing, and can foster early clinical decision-making skills. This has important implications for further research in physical examination instruction. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

  2. Tests of the lunar hypothesis

    Science.gov (United States)

    Taylor, S. R.

    1984-01-01

    The concept that the Moon was fissioned from the Earth after core separation is the most readily testable hypothesis of lunar origin, since direct comparisons of lunar and terrestrial compositions can be made. Differences found in such comparisons introduce so many ad hoc adjustments to the fission hypothesis that it becomes untestable. Further constraints may be obtained from attempting to date the volatile-refractory element fractionation. The combination of chemical and isotopic problems suggests that the fission hypothesis is no longer viable, and separate terrestrial and lunar accretion from a population of fractionated precursor planetesimals provides a more reasonable explanation.

  3. The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

    Directory of Open Access Journals (Sweden)

    Lange Matthias

    2010-06-01

    Full Text Available Search engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases.

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

    Science.gov (United States)

    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.

  5. The Purchasing Power Parity Hypothesis:

    African Journals Online (AJOL)

    2011-10-02

    Oct 2, 2011 ... reject the unit root hypothesis in real exchange rates may simply be due to the shortness ..... Violations of Purchasing Power Parity and Their Implications for Efficient ... Official Intervention in the Foreign Exchange Market:.

  6. A folk-psychological ranking of personality facets

    Directory of Open Access Journals (Sweden)

    Eka Roivainen

    2016-10-01

    Full Text Available Background Which personality facets should a general personality test measure? No consensus exists on the facet structure of personality, the nature of facets, or the correct method of identifying the most significant facets. However, it can be hypothesized (the lexical hypothesis that high frequency personality describing words more likely represent important personality facets and rarely used words refer to less significant aspects of personality. Participants and procedure A ranking of personality facets was performed by studying the frequency of the use of popular personality adjectives in causal clauses (because he is a kind person on the Internet and in books as attributes of the word person (kind person. Results In Study 1, the 40 most frequently used adjectives had a cumulative usage frequency equal to that of the rest of the 295 terms studied. When terms with a higher-ranking dictionary synonym or antonym were eliminated, 23 terms remained, which represent 23 different facets. In Study 2, clusters of synonymous terms were examined. Within the top 30 clusters, personality terms were used 855 times compared to 240 for the 70 lower-ranking clusters. Conclusions It is hypothesized that personality facets represented by the top-ranking terms and clusters of terms are important and impactful independent of their correlation with abstract underlying personality factors (five/six factor models. Compared to hierarchical personality models, lists of important facets probably better cover those aspects of personality that are situated between the five or six major domains.

  7. Rank Two Affine Manifolds in Genus 3

    OpenAIRE

    Aulicino, David; Nguyen, Duc-Manh

    2016-01-01

    We complete the classification of rank two affine manifolds in the moduli space of translation surfaces in genus three. Combined with a recent result of Mirzakhani and Wright, this completes the classification of higher rank affine manifolds in genus three.

  8. Podium: Ranking Data Using Mixed-Initiative Visual Analytics.

    Science.gov (United States)

    Wall, Emily; Das, Subhajit; Chawla, Ravish; Kalidindi, Bharath; Brown, Eli T; Endert, Alex

    2018-01-01

    People often rank and order data points as a vital part of making decisions. Multi-attribute ranking systems are a common tool used to make these data-driven decisions. Such systems often take the form of a table-based visualization in which users assign weights to the attributes representing the quantifiable importance of each attribute to a decision, which the system then uses to compute a ranking of the data. However, these systems assume that users are able to quantify their conceptual understanding of how important particular attributes are to a decision. This is not always easy or even possible for users to do. Rather, people often have a more holistic understanding of the data. They form opinions that data point A is better than data point B but do not necessarily know which attributes are important. To address these challenges, we present a visual analytic application to help people rank multi-variate data points. We developed a prototype system, Podium, that allows users to drag rows in the table to rank order data points based on their perception of the relative value of the data. Podium then infers a weighting model using Ranking SVM that satisfies the user's data preferences as closely as possible. Whereas past systems help users understand the relationships between data points based on changes to attribute weights, our approach helps users to understand the attributes that might inform their understanding of the data. We present two usage scenarios to describe some of the potential uses of our proposed technique: (1) understanding which attributes contribute to a user's subjective preferences for data, and (2) deconstructing attributes of importance for existing rankings. Our proposed approach makes powerful machine learning techniques more usable to those who may not have expertise in these areas.

  9. Low-Rank Sparse Coding for Image Classification

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Xu, Changsheng; Ahuja, Narendra

    2013-01-01

    In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

  10. Low-Rank Sparse Coding for Image Classification

    KAUST Repository

    Zhang, Tianzhu

    2013-12-01

    In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

  11. Artistic talent in dyslexia--a hypothesis.

    Science.gov (United States)

    Chakravarty, Ambar

    2009-10-01

    The present article hints at a curious neurocognitive phenomenon of development of artistic talents in some children with dyslexia. The article also takes note of the phenomenon of creating in the midst of language disability as observed in the lives of such creative people like Leonardo da Vinci and Albert Einstein who were most probably affected with developmental learning disorders. It has been hypothesised that a developmental delay in the dominant hemisphere most likely 'disinhibits' the non-dominant parietal lobe to unmask talents, artistic or otherwise, in some such individuals. The present hypothesis follows the phenomenon of paradoxical functional facilitation described earlier. It has been suggested that children with learning disorders be encouraged to develop such hidden talents to full capacity, rather than be subjected to overemphasising on the correction of the disturbed coded symbol operations, in remedial training.

  12. The Privilege of Ranking: Google Plays Ball.

    Science.gov (United States)

    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;…

  13. A Comprehensive Analysis of Marketing Journal Rankings

    Science.gov (United States)

    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…

  14. The atomic hypothesis: physical consequences

    International Nuclear Information System (INIS)

    Rivas, Martin

    2008-01-01

    The hypothesis that matter is made of some ultimate and indivisible objects, together with the restricted relativity principle, establishes a constraint on the kind of variables we are allowed to use for the variational description of elementary particles. We consider that the atomic hypothesis not only states the indivisibility of elementary particles, but also that these ultimate objects, if not annihilated, cannot be modified by any interaction so that all allowed states of an elementary particle are only kinematical modifications of any one of them. Therefore, an elementary particle cannot have excited states. In this way, the kinematical group of spacetime symmetries not only defines the symmetries of the system, but also the variables in terms of which the mathematical description of the elementary particles can be expressed in either the classical or the quantum mechanical description. When considering the interaction of two Dirac particles, the atomic hypothesis restricts the interaction Lagrangian to a kind of minimal coupling interaction

  15. Two-dimensional ranking of Wikipedia articles

    Science.gov (United States)

    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.

  16. 24 CFR 599.401 - Ranking of applications.

    Science.gov (United States)

    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...

  17. Multiple sclerosis: a geographical hypothesis.

    Science.gov (United States)

    Carlyle, I P

    1997-12-01

    Multiple sclerosis remains a rare neurological disease of unknown aetiology, with a unique distribution, both geographically and historically. Rare in equatorial regions, it becomes increasingly common in higher latitudes; historically, it was first clinically recognized in the early nineteenth century. A hypothesis, based on geographical reasoning, is here proposed: that the disease is the result of a specific vitamin deficiency. Different individuals suffer the deficiency in separate and often unique ways. Evidence to support the hypothesis exists in cultural considerations, in the global distribution of the disease, and in its historical prevalence.

  18. Discussion of the Porter hypothesis

    International Nuclear Information System (INIS)

    1999-11-01

    In the reaction to the long-range vision of RMNO, published in 1996, The Dutch government posed the question whether a far-going and progressive modernization policy will lead to competitive advantages of high-quality products on partly new markets. Such a question is connected to the so-called Porter hypothesis: 'By stimulating innovation, strict environmental regulations can actually enhance competitiveness', from which statement it can be concluded that environment and economy can work together quite well. A literature study has been carried out in order to determine under which conditions that hypothesis is endorsed in the scientific literature and policy documents. Recommendations are given for further studies. refs

  19. The thrifty phenotype hypothesis revisited

    DEFF Research Database (Denmark)

    Vaag, A A; Grunnet, L G; Arora, G P

    2012-01-01

    Twenty years ago, Hales and Barker along with their co-workers published some of their pioneering papers proposing the 'thrifty phenotype hypothesis' in Diabetologia (4;35:595-601 and 3;36:62-67). Their postulate that fetal programming could represent an important player in the origin of type 2...... of the underlying molecular mechanisms. Type 2 diabetes is a multiple-organ disease, and developmental programming, with its idea of organ plasticity, is a plausible hypothesis for a common basis for the widespread organ dysfunctions in type 2 diabetes and the metabolic syndrome. Only two among the 45 known type 2...

  20. Agro-tourism and ranking

    Science.gov (United States)

    Cioca, L. I.; Giurea, R.; Precazzini, I.; Ragazzi, M.; Achim, M. I.; Schiavon, M.; Rada, E. C.

    2018-05-01

    Nowadays the global tourism growth has caused a significant interest in research focused on the impact of the tourism on environment and community. The purpose of this study is to introduce a new ranking for the classification of tourist accommodation establishments with the functions of agro-tourism boarding house type by examining the sector of agro-tourism based on a research aimed to improve the economic, socio-cultural and environmental performance of agrotourism structures. This paper links the criteria for the classification of agro-tourism boarding houses (ABHs) to the impact of agro-tourism activities on the environment, enhancing an eco-friendly approach on agro-tourism activities by increasing the quality reputation of the agro-tourism products and services. Taking into account the impact on the environment, agrotourism can play an important role by protecting and conserving it.

  1. Ranking Highlights in Personal Videos by Analyzing Edited Videos.

    Science.gov (United States)

    Sun, Min; Farhadi, Ali; Chen, Tseng-Hung; Seitz, Steve

    2016-11-01

    We present a fully automatic system for ranking domain-specific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a targeted domain such as "surfing," our system mines the YouTube database to find pairs of raw and their corresponding edited videos. Leveraging the assumption that an edited video is more likely to contain highlights than the trimmed parts of the raw video, we obtain pair-wise ranking constraints to train our model. The learning task is challenging due to the amount of noise and variation in the mined data. Hence, a latent loss function is incorporated to mitigate the issues caused by the noise. We efficiently learn the latent model on a large number of videos (about 870 min in total) using a novel EM-like procedure. Our latent ranking model outperforms its classification counterpart and is fairly competitive compared with a fully supervised ranking system that requires labels from Amazon Mechanical Turk. We further show that a state-of-the-art audio feature mel-frequency cepstral coefficients is inferior to a state-of-the-art visual feature. By combining both audio-visual features, we obtain the best performance in dog activity, surfing, skating, and viral video domains. Finally, we show that impressive highlights can be detected without additional human supervision for seven domains (i.e., skating, surfing, skiing, gymnastics, parkour, dog activity, and viral video) in unconstrained personal videos.

  2. Multiple hypothesis tracking based extraction of airway trees from CT data

    DEFF Research Database (Denmark)

    Raghavendra, Selvan; Petersen, Jens; de Bruijne, Marleen

    Segmentation of airway trees from CT scans of lungs has important clinical applications, in relation to the diagnosis of chronic obstructive pulmonary disease (COPD). Here we present a method based on multiple hypothesis tracking (MHT) and template matching, originally devised for vessel...... segmentation, to extract airway trees. Idealized tubular templates are constructed and ranked using scores assigned based on the image data. Several such regularly spaced hypotheses are used in constructing a hypothesis tree, which is then traversed to obtain improved segmentation results....

  3. Linear Subspace Ranking Hashing for Cross-Modal Retrieval.

    Science.gov (United States)

    Li, Kai; Qi, Guo-Jun; Ye, Jun; Hua, Kien A

    2017-09-01

    Hashing has attracted a great deal of research in recent years due to its effectiveness for the retrieval and indexing of large-scale high-dimensional multimedia data. In this paper, we propose a novel ranking-based hashing framework that maps data from different modalities into a common Hamming space where the cross-modal similarity can be measured using Hamming distance. Unlike existing cross-modal hashing algorithms where the learned hash functions are binary space partitioning functions, such as the sign and threshold function, the proposed hashing scheme takes advantage of a new class of hash functions closely related to rank correlation measures which are known to be scale-invariant, numerically stable, and highly nonlinear. Specifically, we jointly learn two groups of linear subspaces, one for each modality, so that features' ranking orders in different linear subspaces maximally preserve the cross-modal similarities. We show that the ranking-based hash function has a natural probabilistic approximation which transforms the original highly discontinuous optimization problem into one that can be efficiently solved using simple gradient descent algorithms. The proposed hashing framework is also flexible in the sense that the optimization procedures are not tied up to any specific form of loss function, which is typical for existing cross-modal hashing methods, but rather we can flexibly accommodate different loss functions with minimal changes to the learning steps. We demonstrate through extensive experiments on four widely-used real-world multimodal datasets that the proposed cross-modal hashing method can achieve competitive performance against several state-of-the-arts with only moderate training and testing time.

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

    Science.gov (United States)

    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.

  5. Text mixing shapes the anatomy of rank-frequency distributions

    Science.gov (United States)

    Williams, Jake Ryland; Bagrow, James P.; Danforth, Christopher M.; Dodds, Peter Sheridan

    2015-05-01

    Natural languages are full of rules and exceptions. One of the most famous quantitative rules is Zipf's law, which states that the frequency of occurrence of a word is approximately inversely proportional to its rank. Though this "law" of ranks has been found to hold across disparate texts and forms of data, analyses of increasingly large corpora since the late 1990s have revealed the existence of two scaling regimes. These regimes have thus far been explained by a hypothesis suggesting a separability of languages into core and noncore lexica. Here we present and defend an alternative hypothesis that the two scaling regimes result from the act of aggregating texts. We observe that text mixing leads to an effective decay of word introduction, which we show provides accurate predictions of the location and severity of breaks in scaling. Upon examining large corpora from 10 languages in the Project Gutenberg eBooks collection, we find emphatic empirical support for the universality of our claim.

  6. Methodology for ranking restoration options

    International Nuclear Information System (INIS)

    Hedemann Jensen, Per

    1999-04-01

    The work described in this report has been performed as a part of the RESTRAT Project FI4P-CT95-0021a (PL 950128) co-funded by the Nuclear Fission Safety Programme of the European Commission. The RESTRAT project has the overall objective of developing generic methodologies for ranking restoration techniques as a function of contamination and site characteristics. The project includes analyses of existing remediation methodologies and contaminated sites, and is structured in the following steps: characterisation of relevant contaminated sites; identification and characterisation of relevant restoration techniques; assessment of the radiological impact; development and application of a selection methodology for restoration options; formulation of generic conclusions and development of a manual. The project is intended to apply to situations in which sites with nuclear installations have been contaminated with radioactive materials as a result of the operation of these installations. The areas considered for remedial measures include contaminated land areas, rivers and sediments in rivers, lakes, and sea areas. Five contaminated European sites have been studied. Various remedial measures have been envisaged with respect to the optimisation of the protection of the populations being exposed to the radionuclides at the sites. Cost-benefit analysis and multi-attribute utility analysis have been applied for optimisation. Health, economic and social attributes have been included and weighting factors for the different attributes have been determined by the use of scaling constants. (au)

  7. Questioning the social intelligence hypothesis.

    Science.gov (United States)

    Holekamp, Kay E

    2007-02-01

    The social intelligence hypothesis posits that complex cognition and enlarged "executive brains" evolved in response to challenges that are associated with social complexity. This hypothesis has been well supported, but some recent data are inconsistent with its predictions. It is becoming increasingly clear that multiple selective agents, and non-selective constraints, must have acted to shape cognitive abilities in humans and other animals. The task now is to develop a larger theoretical framework that takes into account both inter-specific differences and similarities in cognition. This new framework should facilitate consideration of how selection pressures that are associated with sociality interact with those that are imposed by non-social forms of environmental complexity, and how both types of functional demands interact with phylogenetic and developmental constraints.

  8. Citation graph based ranking in Invenio

    CERN Document Server

    Marian, Ludmila; Rajman, Martin; Vesely, Martin

    2010-01-01

    Invenio is the web-based integrated digital library system developed at CERN. Within this framework, we present four types of ranking models based on the citation graph that complement the simple approach based on citation counts: time-dependent citation counts, a relevancy ranking which extends the PageRank model, a time-dependent ranking which combines the freshness of citations with PageRank and a ranking that takes into consideration the external citations. We present our analysis and results obtained on two main data sets: Inspire and CERN Document Server. Our main contributions are: (i) a study of the currently available ranking methods based on the citation graph; (ii) the development of new ranking methods that correct some of the identified limitations of the current methods such as treating all citations of equal importance, not taking time into account or considering the citation graph complete; (iii) a detailed study of the key parameters for these ranking methods. (The original publication is ava...

  9. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  10. Ranking Entities in Networks via Lefschetz Duality

    DEFF Research Database (Denmark)

    Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Poulsen, Bjarne

    2014-01-01

    then be ranked according to how essential their positions are in the network by considering the effect of their respective absences. Defining a ranking of a network which takes the individual position of each entity into account has the purpose of assigning different roles to the entities, e.g. agents......, in the network. In this paper it is shown that the topology of a given network induces a ranking of the entities in the network. Further, it is demonstrated how to calculate this ranking and thus how to identify weak sub-networks in any given network....

  11. Whiplash and the compensation hypothesis.

    Science.gov (United States)

    Spearing, Natalie M; Connelly, Luke B

    2011-12-01

    Review article. To explain why the evidence that compensation-related factors lead to worse health outcomes is not compelling, either in general, or in the specific case of whiplash. There is a common view that compensation-related factors lead to worse health outcomes ("the compensation hypothesis"), despite the presence of important, and unresolved sources of bias. The empirical evidence on this question has ramifications for the design of compensation schemes. Using studies on whiplash, this article outlines the methodological problems that impede attempts to confirm or refute the compensation hypothesis. Compensation studies are prone to measurement bias, reverse causation bias, and selection bias. Errors in measurement are largely due to the latent nature of whiplash injuries and health itself, a lack of clarity over the unit of measurement (specific factors, or "compensation"), and a lack of appreciation for the heterogeneous qualities of compensation-related factors and schemes. There has been a failure to acknowledge and empirically address reverse causation bias, or the likelihood that poor health influences the decision to pursue compensation: it is unclear if compensation is a cause or a consequence of poor health, or both. Finally, unresolved selection bias (and hence, confounding) is evident in longitudinal studies and natural experiments. In both cases, between-group differences have not been addressed convincingly. The nature of the relationship between compensation-related factors and health is unclear. Current approaches to testing the compensation hypothesis are prone to several important sources of bias, which compromise the validity of their results. Methods that explicitly test the hypothesis and establish whether or not a causal relationship exists between compensation factors and prolonged whiplash symptoms are needed in future studies.

  12. Hypothesis Sampling Systems among Preoperational and Concrete Operational Kindergarten Children

    Science.gov (United States)

    Gholson, Barry; And Others

    1976-01-01

    Preoperational and concrete operational kindergarten children received stimulus differentiation training, either with or without feedback, and then a series of discrimination learning problems in which a blank trial probe was used to detect a child's hypothesis after each feedback trial. Piagetian stage theory requires elaboration to account…

  13. Analysis of some methods for reduced rank Gaussian process regression

    DEFF Research Database (Denmark)

    Quinonero-Candela, J.; Rasmussen, Carl Edward

    2005-01-01

    While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent...... proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank...... Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning...

  14. Ranking scientific publications: the effect of nonlinearity

    Science.gov (United States)

    Yao, Liyang; Wei, Tian; Zeng, An; Fan, Ying; di, Zengru

    2014-10-01

    Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.

  15. Ranking scientific publications: the effect of nonlinearity.

    Science.gov (United States)

    Yao, Liyang; Wei, Tian; Zeng, An; Fan, Ying; Di, Zengru

    2014-10-17

    Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.

  16. A Rational Method for Ranking Engineering Programs.

    Science.gov (United States)

    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)

  17. Adaptive distributional extensions to DFR ranking

    DEFF Research Database (Denmark)

    Petersen, Casper; Simonsen, Jakob Grue; Järvelin, Kalervo

    2016-01-01

    -fitting distribution. We call this model Adaptive Distributional Ranking (ADR) because it adapts the ranking to the statistics of the specific dataset being processed each time. Experiments on TREC data show ADR to outperform DFR models (and their extensions) and be comparable in performance to a query likelihood...

  18. Contests with rank-order spillovers

    NARCIS (Netherlands)

    M.R. Baye (Michael); D. Kovenock (Dan); C.G. de Vries (Casper)

    2012-01-01

    textabstractThis paper presents a unified framework for characterizing symmetric equilibrium in simultaneous move, two-player, rank-order contests with complete information, in which each player's strategy generates direct or indirect affine "spillover" effects that depend on the rank-order of her

  19. Classification of rank 2 cluster varieties

    DEFF Research Database (Denmark)

    Mandel, Travis

    We classify rank 2 cluster varieties (those whose corresponding skew-form has rank 2) according to the deformation type of a generic fiber U of their X-spaces, as defined by Fock and Goncharov. Our approach is based on the work of Gross, Hacking, and Keel for cluster varieties and log Calabi...

  20. Using centrality to rank web snippets

    NARCIS (Netherlands)

    Jijkoun, V.; de Rijke, M.; Peters, C.; Jijkoun, V.; Mandl, T.; Müller, H.; Oard, D.W.; Peñas, A.; Petras, V.; Santos, D.

    2008-01-01

    We describe our participation in the WebCLEF 2007 task, targeted at snippet retrieval from web data. Our system ranks snippets based on a simple similarity-based centrality, inspired by the web page ranking algorithms. We experimented with retrieval units (sentences and paragraphs) and with the

  1. Mining Feedback in Ranking and Recommendation Systems

    Science.gov (United States)

    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…

  2. Entity Ranking using Wikipedia as a Pivot

    NARCIS (Netherlands)

    R. Kaptein; P. Serdyukov; A.P. de Vries (Arjen); J. Kamps

    2010-01-01

    htmlabstractIn this paper we investigate the task of Entity Ranking on the Web. Searchers looking for entities are arguably better served by presenting a ranked list of entities directly, rather than a list of web pages with relevant but also potentially redundant information about

  3. Entity ranking using Wikipedia as a pivot

    NARCIS (Netherlands)

    Kaptein, R.; Serdyukov, P.; de Vries, A.; Kamps, J.; Huang, X.J.; Jones, G.; Koudas, N.; Wu, X.; Collins-Thompson, K.

    2010-01-01

    In this paper we investigate the task of Entity Ranking on the Web. Searchers looking for entities are arguably better served by presenting a ranked list of entities directly, rather than a list of web pages with relevant but also potentially redundant information about these entities. Since

  4. Rank 2 fusion rings are complete intersections

    DEFF Research Database (Denmark)

    Andersen, Troels Bak

    We give a non-constructive proof that fusion rings attached to a simple complex Lie algebra of rank 2 are complete intersections.......We give a non-constructive proof that fusion rings attached to a simple complex Lie algebra of rank 2 are complete intersections....

  5. A Ranking Method for Evaluating Constructed Responses

    Science.gov (United States)

    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…

  6. Ranking Music Data by Relevance and Importance

    DEFF Research Database (Denmark)

    Ruxanda, Maria Magdalena; Nanopoulos, Alexandros; Jensen, Christian Søndergaard

    2008-01-01

    Due to the rapidly increasing availability of audio files on the Web, it is relevant to augment search engines with advanced audio search functionality. In this context, the ranking of the retrieved music is an important issue. This paper proposes a music ranking method capable of flexibly fusing...

  7. Ranking of Unwarranted Variations in Healthcare Treatments

    NARCIS (Netherlands)

    Moes, Herry; Brekelmans, Ruud; Hamers, Herbert; Hasaart, F.

    2017-01-01

    In this paper, we introduce a framework designed to identify and rank possible unwarranted variation of treatments in healthcare. The innovative aspect of this framework is a ranking procedure that aims to identify healthcare institutions where unwarranted variation is most severe, and diagnosis

  8. The Rankings Game: Who's Playing Whom?

    Science.gov (United States)

    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…

  9. Dynamic collective entity representations for entity ranking

    NARCIS (Netherlands)

    Graus, D.; Tsagkias, M.; Weerkamp, W.; Meij, E.; de Rijke, M.

    2016-01-01

    Entity ranking, i.e., successfully positioning a relevant entity at the top of the ranking for a given query, is inherently difficult due to the potential mismatch between the entity's description in a knowledge base, and the way people refer to the entity when searching for it. To counter this

  10. Comparing classical and quantum PageRanks

    Science.gov (United States)

    Loke, T.; Tang, J. W.; Rodriguez, J.; Small, M.; Wang, J. B.

    2017-01-01

    Following recent developments in quantum PageRanking, we present a comparative analysis of discrete-time and continuous-time quantum-walk-based PageRank algorithms. Relative to classical PageRank and to different extents, the quantum measures better highlight secondary hubs and resolve ranking degeneracy among peripheral nodes for all networks we studied in this paper. For the discrete-time case, we investigated the periodic nature of the walker's probability distribution for a wide range of networks and found that the dominant period does not grow with the size of these networks. Based on this observation, we introduce a new quantum measure using the maximum probabilities of the associated walker during the first couple of periods. This is particularly important, since it leads to a quantum PageRanking scheme that is scalable with respect to network size.

  11. Universal emergence of PageRank

    Energy Technology Data Exchange (ETDEWEB)

    Frahm, K M; Georgeot, B; Shepelyansky, D L, E-mail: frahm@irsamc.ups-tlse.fr, E-mail: georgeot@irsamc.ups-tlse.fr, E-mail: dima@irsamc.ups-tlse.fr [Laboratoire de Physique Theorique du CNRS, IRSAMC, Universite de Toulouse, UPS, 31062 Toulouse (France)

    2011-11-18

    The PageRank algorithm enables us to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter {alpha} Element-Of ]0, 1[. Using extensive numerical simulations of large web networks, with a special accent on British University networks, we determine numerically and analytically the universal features of the PageRank vector at its emergence when {alpha} {yields} 1. The whole network can be divided into a core part and a group of invariant subspaces. For {alpha} {yields} 1, PageRank converges to a universal power-law distribution on the invariant subspaces whose size distribution also follows a universal power law. The convergence of PageRank at {alpha} {yields} 1 is controlled by eigenvalues of the core part of the Google matrix, which are extremely close to unity, leading to large relaxation times as, for example, in spin glasses. (paper)

  12. Universal emergence of PageRank

    International Nuclear Information System (INIS)

    Frahm, K M; Georgeot, B; Shepelyansky, D L

    2011-01-01

    The PageRank algorithm enables us to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter α ∈ ]0, 1[. Using extensive numerical simulations of large web networks, with a special accent on British University networks, we determine numerically and analytically the universal features of the PageRank vector at its emergence when α → 1. The whole network can be divided into a core part and a group of invariant subspaces. For α → 1, PageRank converges to a universal power-law distribution on the invariant subspaces whose size distribution also follows a universal power law. The convergence of PageRank at α → 1 is controlled by eigenvalues of the core part of the Google matrix, which are extremely close to unity, leading to large relaxation times as, for example, in spin glasses. (paper)

  13. A Molecular–Structure Hypothesis

    Directory of Open Access Journals (Sweden)

    Jan C. A. Boeyens

    2010-11-01

    Full Text Available The self-similar symmetry that occurs between atomic nuclei, biological growth structures, the solar system, globular clusters and spiral galaxies suggests that a similar pattern should characterize atomic and molecular structures. This possibility is explored in terms of the current molecular structure-hypothesis and its extension into four-dimensional space-time. It is concluded that a quantum molecule only has structure in four dimensions and that classical (Newtonian structure, which occurs in three dimensions, cannot be simulated by quantum-chemical computation.

  14. Antiaging therapy: a prospective hypothesis

    Directory of Open Access Journals (Sweden)

    Shahidi Bonjar MR

    2015-01-01

    Full Text Available Mohammad Rashid Shahidi Bonjar,1 Leyla Shahidi Bonjar2 1School of Dentistry, Kerman University of Medical Sciences, Kerman Iran; 2Department of Pharmacology, College of Pharmacy, Kerman University of Medical Sciences, Kerman, Iran Abstract: This hypothesis proposes a new prospective approach to slow the aging process in older humans. The hypothesis could lead to developing new treatments for age-related illnesses and help humans to live longer. This hypothesis has no previous documentation in scientific media and has no protocol. Scientists have presented evidence that systemic aging is influenced by peculiar molecules in the blood. Researchers at Albert Einstein College of Medicine, New York, and Harvard University in Cambridge discovered elevated titer of aging-related molecules (ARMs in blood, which trigger cascade of aging process in mice; they also indicated that the process can be reduced or even reversed. By inhibiting the production of ARMs, they could reduce age-related cognitive and physical declines. The present hypothesis offers a new approach to translate these findings into medical treatment: extracorporeal adjustment of ARMs would lead to slower rates of aging. A prospective “antiaging blood filtration column” (AABFC is a nanotechnological device that would fulfill the central role in this approach. An AABFC would set a near-youth homeostatic titer of ARMs in the blood. In this regard, the AABFC immobilizes ARMs from the blood while blood passes through the column. The AABFC harbors antibodies against ARMs. ARM antibodies would be conjugated irreversibly to ARMs on contact surfaces of the reaction platforms inside the AABFC till near-youth homeostasis is attained. The treatment is performed with the aid of a blood-circulating pump. Similar to a renal dialysis machine, blood would circulate from the body to the AABFC and from there back to the body in a closed circuit until ARMs were sufficiently depleted from the blood. The

  15. PageRank and rank-reversal dependence on the damping factor

    Science.gov (United States)

    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.

  16. PageRank and rank-reversal dependence on the damping factor.

    Science.gov (United States)

    Son, S-W; Christensen, C; Grassberger, P; Paczuski, M

    2012-12-01

    PageRank (PR) is an algorithm originally developed by Google to evaluate the importance of web pages. Considering how deeply rooted Google's PR algorithm is to gathering relevant information or to the success of modern businesses, the question of rank stability and choice of the damping factor (a parameter in the algorithm) is clearly important. We investigate PR as a function of the damping factor d on a network obtained from a domain of the World Wide Web, finding that rank reversal happens frequently over a broad range of PR (and of d). We use three different correlation measures, Pearson, Spearman, and Kendall, to study rank reversal as d changes, and we show that the correlation of PR vectors drops rapidly as d changes from its frequently cited value, d_{0}=0.85. Rank reversal is also observed by measuring the Spearman and Kendall rank correlation, which evaluate relative ranks rather than absolute PR. Rank reversal happens not only in directed networks containing rank sinks but also in a single strongly connected component, which by definition does not contain any sinks. We relate rank reversals to rank pockets and bottlenecks in the directed network structure. For the network studied, the relative rank is more stable by our measures around d=0.65 than at d=d_{0}.

  17. A tilting approach to ranking influence

    KAUST Repository

    Genton, Marc G.

    2014-12-01

    We suggest a new approach, which is applicable for general statistics computed from random samples of univariate or vector-valued or functional data, to assessing the influence that individual data have on the value of a statistic, and to ranking the data in terms of that influence. Our method is based on, first, perturbing the value of the statistic by ‘tilting’, or reweighting, each data value, where the total amount of tilt is constrained to be the least possible, subject to achieving a given small perturbation of the statistic, and, then, taking the ranking of the influence of data values to be that which corresponds to ranking the changes in data weights. It is shown, both theoretically and numerically, that this ranking does not depend on the size of the perturbation, provided that the perturbation is sufficiently small. That simple result leads directly to an elegant geometric interpretation of the ranks; they are the ranks of the lengths of projections of the weights onto a ‘line’ determined by the first empirical principal component function in a generalized measure of covariance. To illustrate the generality of the method we introduce and explore it in the case of functional data, where (for example) it leads to generalized boxplots. The method has the advantage of providing an interpretable ranking that depends on the statistic under consideration. For example, the ranking of data, in terms of their influence on the value of a statistic, is different for a measure of location and for a measure of scale. This is as it should be; a ranking of data in terms of their influence should depend on the manner in which the data are used. Additionally, the ranking recognizes, rather than ignores, sign, and in particular can identify left- and right-hand ‘tails’ of the distribution of a random function or vector.

  18. Is PMI the Hypothesis or the Null Hypothesis?

    Science.gov (United States)

    Tarone, Aaron M; Sanford, Michelle R

    2017-09-01

    Over the past several decades, there have been several strident exchanges regarding whether forensic entomologists estimate the postmortem interval (PMI), minimum PMI, or something else. During that time, there has been a proliferation of terminology reflecting this concern regarding "what we do." This has been a frustrating conversation for some in the community because much of this debate appears to be centered on what assumptions are acknowledged directly and which are embedded within a list of assumptions (or ignored altogether) in the literature and in case reports. An additional component of the conversation centers on a concern that moving away from the use of certain terminology like PMI acknowledges limitations and problems that would make the application of entomology appear less useful in court-a problem for lawyers, but one that should not be problematic for scientists in the forensic entomology community, as uncertainty is part of science that should and can be presented effectively in the courtroom (e.g., population genetic concepts in forensics). Unfortunately, a consequence of the way this conversation is conducted is that even as all involved in the debate acknowledge the concerns of their colleagues, parties continue to talk past one another advocating their preferred terminology. Progress will not be made until the community recognizes that all of the terms under consideration take the form of null hypothesis statements and that thinking about "what we do" as a null hypothesis has useful legal and scientific ramifications that transcend arguments over the usage of preferred terminology. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  19. The Stoichiometric Divisome: A Hypothesis

    Directory of Open Access Journals (Sweden)

    Waldemar eVollmer

    2015-05-01

    Full Text Available Dividing Escherichia coli cells simultaneously constrict the inner membrane, peptidoglycan layer and outer membrane to synthesize the new poles of the daughter cells. For this, more than 30 proteins localize to mid-cell where they form a large, ring-like assembly, the divisome, facilitating division. Although the precise function of most divisome proteins is unknown, it became apparent in recent years that dynamic protein-protein interactions are essential for divisome assembly and function. However, little is known about the nature of the interactions involved and the stoichiometry of the proteins within the divisome. A recent study (Li et al., 2014 used ribosome profiling to measure the absolute protein synthesis rates in E. coli. Interestingly, they observed that most proteins which participate in known multiprotein complexes are synthesized proportional to their stoichiometry. Based on this principle we present a hypothesis for the stoichiometry of the core of the divisome, taking into account known protein-protein interactions. From this hypothesis we infer a possible mechanism for PG synthesis during division.

  20. First rank symptoms for schizophrenia.

    Science.gov (United States)

    Soares-Weiser, Karla; Maayan, Nicola; Bergman, Hanna; Davenport, Clare; Kirkham, Amanda J; Grabowski, Sarah; Adams, Clive E

    2015-01-25

    Early and accurate diagnosis and treatment of schizophrenia may have long-term advantages for the patient; the longer psychosis goes untreated the more severe the repercussions for relapse and recovery. If the correct diagnosis is not schizophrenia, but another psychotic disorder with some symptoms similar to schizophrenia, appropriate treatment might be delayed, with possible severe repercussions for the person involved and their family. There is widespread uncertainty about the diagnostic accuracy of First Rank Symptoms (FRS); we examined whether they are a useful diagnostic tool to differentiate schizophrenia from other psychotic disorders. To determine the diagnostic accuracy of one or multiple FRS for diagnosing schizophrenia, verified by clinical history and examination by a qualified professional (e.g. psychiatrists, nurses, social workers), with or without the use of operational criteria and checklists, in people thought to have non-organic psychotic symptoms. We conducted searches in MEDLINE, EMBASE, and PsycInfo using OvidSP in April, June, July 2011 and December 2012. We also searched MEDION in December 2013. We selected studies that consecutively enrolled or randomly selected adults and adolescents with symptoms of psychosis, and assessed the diagnostic accuracy of FRS for schizophrenia compared to history and clinical examination performed by a qualified professional, which may or may not involve the use of symptom checklists or based on operational criteria such as ICD and DSM. Two review authors independently screened all references for inclusion. Risk of bias in included studies were assessed using the QUADAS-2 instrument. We recorded the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) for constructing a 2 x 2 table for each study or derived 2 x 2 data from reported summary statistics such as sensitivity, specificity, and/or likelihood ratios. We included 21 studies with a total of 6253 participants

  1. Adiabatic quantum algorithm for search engine ranking.

    Science.gov (United States)

    Garnerone, Silvano; Zanardi, Paolo; Lidar, Daniel A

    2012-06-08

    We propose an adiabatic quantum algorithm for generating a quantum pure state encoding of the PageRank vector, the most widely used tool in ranking the relative importance of internet pages. We present extensive numerical simulations which provide evidence that this algorithm can prepare the quantum PageRank state in a time which, on average, scales polylogarithmically in the number of web pages. We argue that the main topological feature of the underlying web graph allowing for such a scaling is the out-degree distribution. The top-ranked log(n) entries of the quantum PageRank state can then be estimated with a polynomial quantum speed-up. Moreover, the quantum PageRank state can be used in "q-sampling" protocols for testing properties of distributions, which require exponentially fewer measurements than all classical schemes designed for the same task. This can be used to decide whether to run a classical update of the PageRank.

  2. Ranking Adverse Drug Reactions With Crowdsourcing

    KAUST Repository

    Gottlieb, Assaf

    2015-03-23

    Background: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. Results: There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. Conclusions: ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.

  3. Ranking adverse drug reactions with crowdsourcing.

    Science.gov (United States)

    Gottlieb, Assaf; Hoehndorf, Robert; Dumontier, Michel; Altman, Russ B

    2015-03-23

    There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. The intent of the study was to rank ADRs according to severity. We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.

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

    Science.gov (United States)

    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.

  5. Augmenting the Deliberative Method for Ranking Risks.

    Science.gov (United States)

    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.

  6. Assessing the Readability of Medical Documents: A Ranking Approach.

    Science.gov (United States)

    Zheng, Jiaping; Yu, Hong

    2018-03-23

    The use of electronic health record (EHR) systems with patient engagement capabilities, including viewing, downloading, and transmitting health information, has recently grown tremendously. However, using these resources to engage patients in managing their own health remains challenging due to the complex and technical nature of the EHR narratives. Our objective was to develop a machine learning-based system to assess readability levels of complex documents such as EHR notes. We collected difficulty ratings of EHR notes and Wikipedia articles using crowdsourcing from 90 readers. We built a supervised model to assess readability based on relative orders of text difficulty using both surface text features and word embeddings. We evaluated system performance using the Kendall coefficient of concordance against human ratings. Our system achieved significantly higher concordance (.734) with human annotators than did a baseline using the Flesch-Kincaid Grade Level, a widely adopted readability formula (.531). The improvement was also consistent across different disease topics. This method's concordance with an individual human user's ratings was also higher than the concordance between different human annotators (.658). We explored methods to automatically assess the readability levels of clinical narratives. Our ranking-based system using simple textual features and easy-to-learn word embeddings outperformed a widely used readability formula. Our ranking-based method can predict relative difficulties of medical documents. It is not constrained to a predefined set of readability levels, a common design in many machine learning-based systems. Furthermore, the feature set does not rely on complex processing of the documents. One potential application of our readability ranking is personalization, allowing patients to better accommodate their own background knowledge. ©Jiaping Zheng, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.03.2018.

  7. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    show that the problem of deciding whether a non trivial community exists is NP complete. Nevertheless, experiments show that a very simple greedy approach can identify members of a community in the Danish part of the web graph with time complexity only dependent on the size of the found community...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  8. A Universal Rank-Size Law

    Science.gov (United States)

    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

  9. Consumer preference in ranking walking function utilizing the walking index for spinal cord injury II.

    Science.gov (United States)

    Patrick, M; Ditunno, P; Ditunno, J F; Marino, R J; Scivoletto, G; Lam, T; Loffree, J; Tamburella, F; Leiby, B

    2011-12-01

    Blinded rank ordering. To determine consumer preference in walking function utilizing the walking Index for spinal cord injury II (WISCI II) in individuals with spinal cord injury (SCI)from the Canada, the Italy and the United States of America. In all, 42 consumers with incomplete SCI (25 cervical, 12 thoracic, 5 lumbar) from Canada (12/42), Italy (14/42) and the United States of America (16/42) ranked the 20 levels of the WISCI II scale by their individual preference for walking. Subjects were blinded to the original ranking of the WISCI II scale by clinical scientists. Photographs of each WISCI II level used in a previous pilot study were randomly shuffled and rank ordered. Percentile, conjoint/cluster and graphic analyses were performed. All three analyses illustrated consumer ranking followed a bimodal distribution. Ranking for two levels with physical assistance and two levels with a walker were bimodal with a difference of five to six ranks between consumer subgroups (quartile analysis). The larger cluster (N=20) showed preference for walking with assistance over the smaller cluster (N=12), whose preference was walking without assistance and more devices. In all, 64% (27/42) of consumers ranked WISCI II level with no devices or braces and 1 person assistance higher than multiple levels of the WISCI II requiring no assistance. These results were unexpected, as the hypothesis was that consumers would rank independent walking higher than walking with assistance. Consumer preference for walking function should be considered in addition to objective measures in designing SCI trials that use significant improvement in walking function as an outcome measure.

  10. The equilibrium-point hypothesis--past, present and future.

    Science.gov (United States)

    Feldman, Anatol G; Levin, Mindy F

    2009-01-01

    This chapter is a brief account of fundamentals of the equilibrium-point hypothesis or more adequately called the threshold control theory (TCT). It also compares the TCT with other approaches to motor control. The basic notions of the TCT are reviewed with a major focus on solutions to the problems of multi-muscle and multi-degrees of freedom redundancy. The TCT incorporates cognitive aspects by explaining how neurons recognize that internal (neural) and external (environmental) events match each other. These aspects as well as how motor learning occurs are subjects of further development of the TCT hypothesis.

  11. Biostatistics series module 2: Overview of hypothesis testing

    Directory of Open Access Journals (Sweden)

    Avijit Hazra

    2016-01-01

    Full Text Available Hypothesis testing (or statistical inference is one of the major applications of biostatistics. Much of medical research begins with a research question that can be framed as a hypothesis. Inferential statistics begins with a null hypothesis that reflects the conservative position of no change or no difference in comparison to baseline or between groups. Usually, the researcher has reason to believe that there is some effect or some difference which is the alternative hypothesis. The researcher therefore proceeds to study samples and measure outcomes in the hope of generating evidence strong enough for the statistician to be able to reject the null hypothesis. The concept of the P value is almost universally used in hypothesis testing. It denotes the probability of obtaining by chance a result at least as extreme as that observed, even when the null hypothesis is true and no real difference exists. Usually, if P is < 0.05 the null hypothesis is rejected and sample results are deemed statistically significant. With the increasing availability of computers and access to specialized statistical software, the drudgery involved in statistical calculations is now a thing of the past, once the learning curve of the software has been traversed. The life sciences researcher is therefore free to devote oneself to optimally designing the study, carefully selecting the hypothesis tests to be applied, and taking care in conducting the study well. Unfortunately, selecting the right test seems difficult initially. Thinking of the research hypothesis as addressing one of five generic research questions helps in selection of the right hypothesis test. In addition, it is important to be clear about the nature of the variables (e.g., numerical vs. categorical; parametric vs. nonparametric and the number of groups or data sets being compared (e.g., two or more than two at a time. The same research question may be explored by more than one type of hypothesis test

  12. The Stress Acceleration Hypothesis of Nightmares

    Directory of Open Access Journals (Sweden)

    Tore Nielsen

    2017-06-01

    Full Text Available Adverse childhood experiences can deleteriously affect future physical and mental health, increasing risk for many illnesses, including psychiatric problems, sleep disorders, and, according to the present hypothesis, idiopathic nightmares. Much like post-traumatic nightmares, which are triggered by trauma and lead to recurrent emotional dreaming about the trauma, idiopathic nightmares are hypothesized to originate in early adverse experiences that lead in later life to the expression of early memories and emotions in dream content. Accordingly, the objectives of this paper are to (1 review existing literature on sleep, dreaming and nightmares in relation to early adverse experiences, drawing upon both empirical studies of dreaming and nightmares and books and chapters by recognized nightmare experts and (2 propose a new approach to explaining nightmares that is based upon the Stress Acceleration Hypothesis of mental illness. The latter stipulates that susceptibility to mental illness is increased by adversity occurring during a developmentally sensitive window for emotional maturation—the infantile amnesia period—that ends around age 3½. Early adversity accelerates the neural and behavioral maturation of emotional systems governing the expression, learning, and extinction of fear memories and may afford short-term adaptive value. But it also engenders long-term dysfunctional consequences including an increased risk for nightmares. Two mechanisms are proposed: (1 disruption of infantile amnesia allows normally forgotten early childhood memories to influence later emotions, cognitions and behavior, including the common expression of threats in nightmares; (2 alterations of normal emotion regulation processes of both waking and sleep lead to increased fear sensitivity and less effective fear extinction. These changes influence an affect network previously hypothesized to regulate fear extinction during REM sleep, disruption of which leads to

  13. Scalable Faceted Ranking in Tagging Systems

    Science.gov (United States)

    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.

  14. Evaluation of treatment effects by ranking

    DEFF Research Database (Denmark)

    Halekoh, U; Kristensen, K

    2008-01-01

    In crop experiments measurements are often made by a judge evaluating the crops' conditions after treatment. In the present paper an analysis is proposed for experiments where plots of crops treated differently are mutually ranked. In the experimental layout the crops are treated on consecutive...... plots usually placed side by side in one or more rows. In the proposed method a judge ranks several neighbouring plots, say three, by ranking them from best to worst. For the next observation the judge moves on by no more than two plots, such that up to two plots will be re-evaluated again...... in a comparison with the new plot(s). Data from studies using this set-up were analysed by a Thurstonian random utility model, which assumed that the judge's rankings were obtained by comparing latent continuous utilities or treatment effects. For the latent utilities a variance component model was considered...

  15. Superfund Hazard Ranking System Training Course

    Science.gov (United States)

    The Hazard Ranking System (HRS) training course is a four and ½ day, intermediate-level course designed for personnel who are required to compile, draft, and review preliminary assessments (PAs), site inspections (SIs), and HRS documentation records/packag

  16. Who's bigger? where historical figures really rank

    CERN Document Server

    Skiena, Steven

    2014-01-01

    Is Hitler bigger than Napoleon? Washington bigger than Lincoln? Picasso bigger than Einstein? Quantitative analysts are rapidly finding homes in social and cultural domains, from finance to politics. What about history? In this fascinating book, Steve Skiena and Charles Ward bring quantitative analysis to bear on ranking and comparing historical reputations. They evaluate each person by aggregating the traces of millions of opinions, just as Google ranks webpages. The book includes a technical discussion for readers interested in the details of the methods, but no mathematical or computational background is necessary to understand the rankings or conclusions. Along the way, the authors present the rankings of more than one thousand of history's most significant people in science, politics, entertainment, and all areas of human endeavor. Anyone interested in history or biography can see where their favorite figures place in the grand scheme of things.

  17. Ranking Forestry Investments With Parametric Linear Programming

    Science.gov (United States)

    Paul A. Murphy

    1976-01-01

    Parametric linear programming is introduced as a technique for ranking forestry investments under multiple constraints; it combines the advantages of simple tanking and linear programming as capital budgeting tools.

  18. Enhancing collaborative filtering by user interest expansion via personalized ranking.

    Science.gov (United States)

    Liu, Qi; Chen, Enhong; Xiong, Hui; Ding, Chris H Q; Chen, Jian

    2012-02-01

    Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.

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

    Science.gov (United States)

    González-Galván, María del Carmen; Mosqueda-Taylor, Adalberto; Bologna-Molina, Ronell; Setien-Olarra, Amaia; Marichalar-Mendia, Xabier; Aguirre-Urizar, José-Manuel

    2018-01-01

    Background Odontogenic myxoma (OM) is a benign intraosseous neoplasm that exhibits local aggressiveness and high recurrence rates. Osteoclastogenesis is an important phenomenon in the tumor growth of maxillary neoplasms. RANK (Receptor Activator of Nuclear Factor κappa B) is the signaling receptor of RANK-L (Receptor activator of nuclear factor kappa-Β ligand) that activates the osteoclasts. OPG (osteoprotegerin) is a decoy receptor for RANK-L that inhibits pro-osteoclastogenesis. The RANK / RANKL / OPG system participates in the regulation of osteolytic activity under normal conditions, and its alteration has been associated with greater bone destruction, and also with tumor growth. Objectives To analyze the immunohistochemical expression of OPG, RANK and RANK-L proteins in odontogenic myxomas (OMs) and their relationship with the tumor size. Material and Methods Eighteen OMs, 4 small ( 3cm) and 18 dental follicles (DF) that were included as control were studied by means of standard immunohistochemical procedure with RANK, RANKL and OPG antibodies. For the evaluation, 5 fields (40x) of representative areas of OM and DF were selected where the expression of each antibody was determined. Descriptive and comparative statistical analyses were performed with the obtained data. Results There are significant differences in the expression of RANK in OM samples as compared to DF (p = 0.022) and among the OMSs and OMLs (p = 0.032). Also a strong association is recognized in the expression of RANK-L and OPG in OM samples. Conclusions Activation of the RANK / RANK-L / OPG triad seems to be involved in the mechanisms of bone balance and destruction, as well as associated with tumor growth in odontogenic myxomas. Key words:Odontogenic myxoma, dental follicle, RANK, RANK-L, OPG, osteoclastogenesis. PMID:29680857

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

    Science.gov (United States)

    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.

  1. Memory in astrocytes: a hypothesis

    Directory of Open Access Journals (Sweden)

    Caudle Robert M

    2006-01-01

    Full Text Available Abstract Background Recent work has indicated an increasingly complex role for astrocytes in the central nervous system. Astrocytes are now known to exchange information with neurons at synaptic junctions and to alter the information processing capabilities of the neurons. As an extension of this trend a hypothesis was proposed that astrocytes function to store information. To explore this idea the ion channels in biological membranes were compared to models known as cellular automata. These comparisons were made to test the hypothesis that ion channels in the membranes of astrocytes form a dynamic information storage device. Results Two dimensional cellular automata were found to behave similarly to ion channels in a membrane when they function at the boundary between order and chaos. The length of time information is stored in this class of cellular automata is exponentially related to the number of units. Therefore the length of time biological ion channels store information was plotted versus the estimated number of ion channels in the tissue. This analysis indicates that there is an exponential relationship between memory and the number of ion channels. Extrapolation of this relationship to the estimated number of ion channels in the astrocytes of a human brain indicates that memory can be stored in this system for an entire life span. Interestingly, this information is not affixed to any physical structure, but is stored as an organization of the activity of the ion channels. Further analysis of two dimensional cellular automata also demonstrates that these systems have both associative and temporal memory capabilities. Conclusion It is concluded that astrocytes may serve as a dynamic information sink for neurons. The memory in the astrocytes is stored by organizing the activity of ion channels and is not associated with a physical location such as a synapse. In order for this form of memory to be of significant duration it is necessary

  2. Rank distributions: A panoramic macroscopic outlook

    Science.gov (United States)

    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.

  3. Fair ranking of researchers and research teams.

    Science.gov (United States)

    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).

  4. Variable screening and ranking using sampling-based sensitivity measures

    International Nuclear Information System (INIS)

    Wu, Y-T.; Mohanty, Sitakanta

    2006-01-01

    This paper presents a methodology for screening insignificant random variables and ranking significant important random variables using sensitivity measures including two cumulative distribution function (CDF)-based and two mean-response based measures. The methodology features (1) using random samples to compute sensitivities and (2) using acceptance limits, derived from the test-of-hypothesis, to classify significant and insignificant random variables. Because no approximation is needed in either the form of the performance functions or the type of continuous distribution functions representing input variables, the sampling-based approach can handle highly nonlinear functions with non-normal variables. The main characteristics and effectiveness of the sampling-based sensitivity measures are investigated using both simple and complex examples. Because the number of samples needed does not depend on the number of variables, the methodology appears to be particularly suitable for problems with large, complex models that have large numbers of random variables but relatively few numbers of significant random variables

  5. Robust and distributed hypothesis testing

    CERN Document Server

    Gül, Gökhan

    2017-01-01

    This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the boo...

  6. The venom optimization hypothesis revisited.

    Science.gov (United States)

    Morgenstern, David; King, Glenn F

    2013-03-01

    Animal venoms are complex chemical mixtures that typically contain hundreds of proteins and non-proteinaceous compounds, resulting in a potent weapon for prey immobilization and predator deterrence. However, because venoms are protein-rich, they come with a high metabolic price tag. The metabolic cost of venom is sufficiently high to result in secondary loss of venom whenever its use becomes non-essential to survival of the animal. The high metabolic cost of venom leads to the prediction that venomous animals may have evolved strategies for minimizing venom expenditure. Indeed, various behaviors have been identified that appear consistent with frugality of venom use. This has led to formulation of the "venom optimization hypothesis" (Wigger et al. (2002) Toxicon 40, 749-752), also known as "venom metering", which postulates that venom is metabolically expensive and therefore used frugally through behavioral control. Here, we review the available data concerning economy of venom use by animals with either ancient or more recently evolved venom systems. We conclude that the convergent nature of the evidence in multiple taxa strongly suggests the existence of evolutionary pressures favoring frugal use of venom. However, there remains an unresolved dichotomy between this economy of venom use and the lavish biochemical complexity of venom, which includes a high degree of functional redundancy. We discuss the evidence for biochemical optimization of venom as a means of resolving this conundrum. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Alien abduction: a medical hypothesis.

    Science.gov (United States)

    Forrest, David V

    2008-01-01

    In response to a new psychological study of persons who believe they have been abducted by space aliens that found that sleep paralysis, a history of being hypnotized, and preoccupation with the paranormal and extraterrestrial were predisposing experiences, I noted that many of the frequently reported particulars of the abduction experience bear more than a passing resemblance to medical-surgical procedures and propose that experience with these may also be contributory. There is the altered state of consciousness, uniformly colored figures with prominent eyes, in a high-tech room under a round bright saucerlike object; there is nakedness, pain and a loss of control while the body's boundaries are being probed; and yet the figures are thought benevolent. No medical-surgical history was apparently taken in the above mentioned study, but psychological laboratory work evaluated false memory formation. I discuss problems in assessing intraoperative awareness and ways in which the medical hypothesis could be elaborated and tested. If physicians are causing this syndrome in a percentage of patients, we should know about it; and persons who feel they have been abducted should be encouraged to inform their surgeons and anesthesiologists without challenging their beliefs.

  8. The oxidative hypothesis of senescence

    Directory of Open Access Journals (Sweden)

    Gilca M

    2007-01-01

    Full Text Available The oxidative hypothesis of senescence, since its origin in 1956, has garnered significant evidence and growing support among scientists for the notion that free radicals play an important role in ageing, either as "damaging" molecules or as signaling molecules. Age-increasing oxidative injuries induced by free radicals, higher susceptibility to oxidative stress in short-lived organisms, genetic manipulations that alter both oxidative resistance and longevity and the anti-ageing effect of caloric restriction and intermittent fasting are a few examples of accepted scientific facts that support the oxidative theory of senescence. Though not completely understood due to the complex "network" of redox regulatory systems, the implication of oxidative stress in the ageing process is now well documented. Moreover, it is compatible with other current ageing theories (e.g., those implicating the mitochondrial damage/mitochondrial-lysosomal axis, stress-induced premature senescence, biological "garbage" accumulation, etc. This review is intended to summarize and critically discuss the redox mechanisms involved during the ageing process: sources of oxidant agents in ageing (mitochondrial -electron transport chain, nitric oxide synthase reaction- and non-mitochondrial- Fenton reaction, microsomal cytochrome P450 enzymes, peroxisomal β -oxidation and respiratory burst of phagocytic cells, antioxidant changes in ageing (enzymatic- superoxide dismutase, glutathione-reductase, glutathion peroxidase, catalase- and non-enzymatic glutathione, ascorbate, urate, bilirubine, melatonin, tocopherols, carotenoids, ubiquinol, alteration of oxidative damage repairing mechanisms and the role of free radicals as signaling molecules in ageing.

  9. Intentional Learning Vs Incidental Learning

    OpenAIRE

    Shahbaz Ahmed

    2017-01-01

    This study is conducted to demonstrate the knowledge of intentional learning and incidental learning. Hypothesis of this experiment is intentional learning is better than incidental learning, participants were demonstrated and were asked to learn the 10 non sense syllables in a specific sequence from the colored cards in the end they were asked to recall the background color of each card instead of non-sense syllables. Independent variables of the experiment are the colored cards containing n...

  10. PageRank as a method to rank biomedical literature by importance.

    Science.gov (United States)

    Yates, Elliot J; Dixon, Louise C

    2015-01-01

    Optimal ranking of literature importance is vital in overcoming article overload. Existing ranking methods are typically based on raw citation counts, giving a sum of 'inbound' links with no consideration of citation importance. PageRank, an algorithm originally developed for ranking webpages at the search engine, Google, could potentially be adapted to bibliometrics to quantify the relative importance weightings of a citation network. This article seeks to validate such an approach on the freely available, PubMed Central open access subset (PMC-OAS) of biomedical literature. On-demand cloud computing infrastructure was used to extract a citation network from over 600,000 full-text PMC-OAS articles. PageRanks and citation counts were calculated for each node in this network. PageRank is highly correlated with citation count (R = 0.905, P PageRank can be trivially computed on commodity cluster hardware and is linearly correlated with citation count. Given its putative benefits in quantifying relative importance, we suggest it may enrich the citation network, thereby overcoming the existing inadequacy of citation counts alone. We thus suggest PageRank as a feasible supplement to, or replacement of, existing bibliometric ranking methods.

  11. RANK/RANK-Ligand/OPG: Ein neuer Therapieansatz in der Osteoporosebehandlung

    Directory of Open Access Journals (Sweden)

    Preisinger E

    2007-01-01

    Full Text Available Die Erforschung der Kopplungsmechanismen zur Osteoklastogenese, Knochenresorption und Remodellierung eröffnete neue mögliche Therapieansätze in der Behandlung der Osteoporose. Eine Schlüsselrolle beim Knochenabbau spielt der RANK- ("receptor activator of nuclear factor (NF- κB"- Ligand (RANKL. Durch die Bindung von RANKL an den Rezeptor RANK wird die Knochenresorption eingeleitet. OPG (Osteoprotegerin sowie der für den klinischen Gebrauch entwickelte humane monoklonale Antikörper (IgG2 Denosumab blockieren die Bindung von RANK-Ligand an RANK und verhindern den Knochenabbau.

  12. Country-specific determinants of world university rankings

    OpenAIRE

    Pietrucha, Jacek

    2017-01-01

    This paper examines country-specific factors that affect the three most influential world university rankings (the Academic Ranking of World Universities, the QS World University Ranking, and the Times Higher Education World University Ranking). We run a cross sectional regression that covers 42–71 countries (depending on the ranking and data availability). We show that the position of universities from a country in the ranking is determined by the following country-specific variables: econom...

  13. Bootstrap Determination of the Co-integration Rank in Heteroskedastic VAR Models

    DEFF Research Database (Denmark)

    Cavaliere, Giuseppe; Rahbek, Anders; Taylor, A.M.Robert

    In a recent paper Cavaliere et al. (2012) develop bootstrap implementations of the (pseudo-) likelihood ratio [PLR] co-integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates...... of the underlying VAR model which obtain under the reduced rank null hypothesis. They propose methods based on an i.i.d. bootstrap re-sampling scheme and establish the validity of their proposed bootstrap procedures in the context of a co-integrated VAR model with i.i.d. innovations. In this paper we investigate...... the properties of their bootstrap procedures, together with analogous procedures based on a wild bootstrap re-sampling scheme, when time-varying behaviour is present in either the conditional or unconditional variance of the innovations. We show that the bootstrap PLR tests are asymptotically correctly sized and...

  14. Bootstrap Determination of the Co-Integration Rank in Heteroskedastic VAR Models

    DEFF Research Database (Denmark)

    Cavaliere, G.; Rahbek, Anders; Taylor, A.M.R.

    2014-01-01

    In a recent paper Cavaliere et al. (2012) develop bootstrap implementations of the (pseudo-) likelihood ratio (PLR) co-integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates...... of the underlying vector autoregressive (VAR) model which obtain under the reduced rank null hypothesis. They propose methods based on an independent and individual distributed (i.i.d.) bootstrap resampling scheme and establish the validity of their proposed bootstrap procedures in the context of a co......-integrated VAR model with i.i.d. innovations. In this paper we investigate the properties of their bootstrap procedures, together with analogous procedures based on a wild bootstrap resampling scheme, when time-varying behavior is present in either the conditional or unconditional variance of the innovations. We...

  15. Bootstrap Determination of the Co-Integration Rank in Heteroskedastic VAR Models

    DEFF Research Database (Denmark)

    Cavaliere, Giuseppe; Rahbek, Anders; Taylor, A. M. Robert

    In a recent paper Cavaliere et al. (2012) develop bootstrap implementations of the (pseudo-) likelihood ratio [PLR] co-integration rank test and associated sequential rank determination procedure of Johansen (1996). The bootstrap samples are constructed using the restricted parameter estimates...... of the underlying VAR model which obtain under the reduced rank null hypothesis. They propose methods based on an i.i.d. bootstrap re-sampling scheme and establish the validity of their proposed bootstrap procedures in the context of a co-integrated VAR model with i.i.d. innovations. In this paper we investigate...... the properties of their bootstrap procedures, together with analogous procedures based on a wild bootstrap re-sampling scheme, when time-varying behaviour is present in either the conditional or unconditional variance of the innovations. We show that the bootstrap PLR tests are asymptotically correctly sized and...

  16. Dopamine and reward: the anhedonia hypothesis 30 years on.

    Science.gov (United States)

    Wise, Roy A

    2008-10-01

    The anhedonia hypothesis--that brain dopamine plays a critical role in the subjective pleasure associated with positive rewards--was intended to draw the attention of psychiatrists to the growing evidence that dopamine plays a critical role in the objective reinforcement and incentive motivation associated with food and water, brain stimulation reward, and psychomotor stimulant and opiate reward. The hypothesis called to attention the apparent paradox that neuroleptics, drugs used to treat a condition involving anhedonia (schizophrenia), attenuated in laboratory animals the positive reinforcement that we normally associate with pleasure. The hypothesis held only brief interest for psychiatrists, who pointed out that the animal studies reflected acute actions of neuroleptics whereas the treatment of schizophrenia appears to result from neuroadaptations to chronic neuroleptic administration, and that it is the positive symptoms of schizophrenia that neuroleptics alleviate, rather than the negative symptoms that include anhedonia. Perhaps for these reasons, the hypothesis has had minimal impact in the psychiatric literature. Despite its limited heuristic value for the understanding of schizophrenia, however, the anhedonia hypothesis has had major impact on biological theories of reinforcement, motivation, and addiction. Brain dopamine plays a very important role in reinforcement of response habits, conditioned preferences, and synaptic plasticity in cellular models of learning and memory. The notion that dopamine plays a dominant role in reinforcement is fundamental to the psychomotor stimulant theory of addiction, to most neuroadaptation theories of addiction, and to current theories of conditioned reinforcement and reward prediction. Properly understood, it is also fundamental to recent theories of incentive motivation.

  17. Global network centrality of university rankings

    Science.gov (United States)

    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.

  18. Diversity rankings among bacterial lineages in soil.

    Science.gov (United States)

    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.

  19. Social class rank, essentialism, and punitive judgment.

    Science.gov (United States)

    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.

  20. RANK und RANKL - Vom Knochen zum Mammakarzinom

    Directory of Open Access Journals (Sweden)

    Sigl V

    2012-01-01

    Full Text Available RANK („Receptor Activator of NF-κB“ und sein Ligand RANKL sind Schlüsselmoleküle im Knochenmetabolismus und spielen eine essenzielle Rolle in der Entstehung von pathologischen Knochenveränderungen. Die Deregulation des RANK/RANKL-Systems ist zum Beispiel ein Hauptgrund für das Auftreten von postmenopausaler Osteoporose bei Frauen. Eine weitere wesentliche Funktion von RANK und RANKL liegt in der Entwicklung von milchsekretierenden Drüsen während der Schwangerschaft. Dabei regulieren Sexualhormone, wie zum Beispiel Progesteron, die Expression von RANKL und induzieren dadurch die Proliferation von epithelialen Zellen der Brust. Seit Längerem war schon bekannt, dass RANK und RANKL in der Metastasenbildung von Brustkrebszellen im Knochengewebe beteiligt sind. Wir konnten nun das RANK/RANKLSystem auch als essenziellen Mechanismus in der Entstehung von hormonellem Brustkrebs identifizieren. In diesem Beitrag werden wir daher den neuesten Erkenntnissen besondere Aufmerksamkeit schenken und diese kritisch in Bezug auf Brustkrebsentwicklung betrachten.

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

    Science.gov (United States)

    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.

  2. Resolution of ranking hierarchies in directed networks

    Science.gov (United States)

    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

  3. Ranking beta sheet topologies of proteins

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; Helles, Glennie; Winter, Pawel

    2010-01-01

    One of the challenges of protein structure prediction is to identify long-range interactions between amino acids. To reliably predict such interactions, we enumerate, score and rank all beta-topologies (partitions of beta-strands into sheets, orderings of strands within sheets and orientations...... of paired strands) of a given protein. We show that the beta-topology corresponding to the native structure is, with high probability, among the top-ranked. Since full enumeration is very time-consuming, we also suggest a method to deal with proteins with many beta-strands. The results reported...... in this paper are highly relevant for ab initio protein structure prediction methods based on decoy generation. The top-ranked beta-topologies can be used to find initial conformations from which conformational searches can be started. They can also be used to filter decoys by removing those with poorly...

  4. Data envelopment analysis of randomized ranks

    Directory of Open Access Journals (Sweden)

    Sant'Anna Annibal P.

    2002-01-01

    Full Text Available Probabilities and odds, derived from vectors of ranks, are here compared as measures of efficiency of decision-making units (DMUs. These measures are computed with the goal of providing preliminary information before starting a Data Envelopment Analysis (DEA or the application of any other evaluation or composition of preferences methodology. Preferences, quality and productivity evaluations are usually measured with errors or subject to influence of other random disturbances. Reducing evaluations to ranks and treating the ranks as estimates of location parameters of random variables, we are able to compute the probability of each DMU being classified as the best according to the consumption of each input and the production of each output. Employing the probabilities of being the best as efficiency measures, we stretch distances between the most efficient units. We combine these partial probabilities in a global efficiency score determined in terms of proximity to the efficiency frontier.

  5. Ranking spreaders by decomposing complex networks

    International Nuclear Information System (INIS)

    Zeng, An; Zhang, Cheng-Jun

    2013-01-01

    Ranking the nodes' ability of spreading in networks is crucial for designing efficient strategies to hinder spreading in the case of diseases or accelerate spreading in the case of information dissemination. In the well-known k-shell method, nodes are ranked only according to the links between the remaining nodes (residual links) while the links connecting to the removed nodes (exhausted links) are entirely ignored. In this Letter, we propose a mixed degree decomposition (MDD) procedure in which both the residual degree and the exhausted degree are considered. By simulating the epidemic spreading process on real networks, we show that the MDD method can outperform the k-shell and degree methods in ranking spreaders.

  6. RankProdIt: A web-interactive Rank Products analysis tool

    Directory of Open Access Journals (Sweden)

    Laing Emma

    2010-08-01

    Full Text Available Abstract Background The first objective of a DNA microarray experiment is typically to generate a list of genes or probes that are found to be differentially expressed or represented (in the case of comparative genomic hybridizations and/or copy number variation between two conditions or strains. Rank Products analysis comprises a robust algorithm for deriving such lists from microarray experiments that comprise small numbers of replicates, for example, less than the number required for the commonly used t-test. Currently, users wishing to apply Rank Products analysis to their own microarray data sets have been restricted to the use of command line-based software which can limit its usage within the biological community. Findings Here we have developed a web interface to existing Rank Products analysis tools allowing users to quickly process their data in an intuitive and step-wise manner to obtain the respective Rank Product or Rank Sum, probability of false prediction and p-values in a downloadable file. Conclusions The online interactive Rank Products analysis tool RankProdIt, for analysis of any data set containing measurements for multiple replicated conditions, is available at: http://strep-microarray.sbs.surrey.ac.uk/RankProducts

  7. Rank-based Tests of the Cointegrating Rank in Semiparametric Error Correction Models

    NARCIS (Netherlands)

    Hallin, M.; van den Akker, R.; Werker, B.J.M.

    2012-01-01

    Abstract: This paper introduces rank-based tests for the cointegrating rank in an Error Correction Model with i.i.d. elliptical innovations. The tests are asymptotically distribution-free, and their validity does not depend on the actual distribution of the innovations. This result holds despite the

  8. Cointegration rank testing under conditional heteroskedasticity

    DEFF Research Database (Denmark)

    Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, Robert M.

    2010-01-01

    We analyze the properties of the conventional Gaussian-based cointegrating rank tests of Johansen (1996, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models) in the case where the vector of series under test is driven by globally stationary, conditionally heteroskedastic......, relative to tests based on the asymptotic critical values or the i.i.d. bootstrap, the wild bootstrap rank tests perform very well in small samples under a variety of conditionally heteroskedastic innovation processes. An empirical application to the term structure of interest rates is given....

  9. Ranking health between countries in international comparisons

    DEFF Research Database (Denmark)

    Brønnum-Hansen, Henrik

    2014-01-01

    Cross-national comparisons and ranking of summary measures of population health sometimes give rise to inconsistent and diverging conclusions. In order to minimise confusion, international comparative studies ought to be based on well-harmonised data with common standards of definitions and docum......Cross-national comparisons and ranking of summary measures of population health sometimes give rise to inconsistent and diverging conclusions. In order to minimise confusion, international comparative studies ought to be based on well-harmonised data with common standards of definitions...

  10. Compressed Sensing with Rank Deficient Dictionaries

    DEFF Research Database (Denmark)

    Hansen, Thomas Lundgaard; Johansen, Daniel Højrup; Jørgensen, Peter Bjørn

    2012-01-01

    In compressed sensing it is generally assumed that the dictionary matrix constitutes a (possibly overcomplete) basis of the signal space. In this paper we consider dictionaries that do not span the signal space, i.e. rank deficient dictionaries. We show that in this case the signal-to-noise ratio...... (SNR) in the compressed samples can be increased by selecting the rows of the measurement matrix from the column space of the dictionary. As an example application of compressed sensing with a rank deficient dictionary, we present a case study of compressed sensing applied to the Coarse Acquisition (C...

  11. Ranking mutual funds using Sortino method

    Directory of Open Access Journals (Sweden)

    Khosro Faghani Makrani

    2014-04-01

    Full Text Available One of the primary concerns on most business activities is to determine an efficient method for ranking mutual funds. This paper performs an empirical investigation to rank 42 mutual funds listed on Tehran Stock Exchange using Sortino method over the period 2011-2012. The results of survey have been compared with market return and the results have confirmed that there were some positive and meaningful relationships between Sortino return and market return. In addition, there were some positive and meaningful relationship between two Sortino methods.

  12. Research of Subgraph Estimation Page Rank Algorithm for Web Page Rank

    Directory of Open Access Journals (Sweden)

    LI Lan-yin

    2017-04-01

    Full Text Available The traditional PageRank algorithm can not efficiently perform large data Webpage scheduling problem. This paper proposes an accelerated algorithm named topK-Rank,which is based on PageRank on the MapReduce platform. It can find top k nodes efficiently for a given graph without sacrificing accuracy. In order to identify top k nodes,topK-Rank algorithm prunes unnecessary nodes and edges in each iteration to dynamically construct subgraphs,and iteratively estimates lower/upper bounds of PageRank scores through subgraphs. Theoretical analysis shows that this method guarantees result exactness. Experiments show that topK-Rank algorithm can find k nodes much faster than the existing approaches.

  13. Blocking in Category Learning

    OpenAIRE

    Bott, Lewis; Hoffman, Aaron B.; Murphy, Gregory L.

    2007-01-01

    Many theories of category learning assume that learning is driven by a need to minimize classification error. When there is no classification error, therefore, learning of individual features should be negligible. We tested this hypothesis by conducting three category learning experiments adapted from an associative learning blocking paradigm. Contrary to an error-driven account of learning, participants learned a wide range of information when they learned about categories, and blocking effe...

  14. Validity of Linder Hypothesis in Bric Countries

    Directory of Open Access Journals (Sweden)

    Rana Atabay

    2016-03-01

    Full Text Available In this study, the theory of similarity in preferences (Linder hypothesis has been introduced and trade in BRIC countries has been examined whether the trade between these countries was valid for this hypothesis. Using the data for the period 1996 – 2010, the study applies to panel data analysis in order to provide evidence regarding the empirical validity of the Linder hypothesis for BRIC countries’ international trade. Empirical findings show that the trade between BRIC countries is in support of Linder hypothesis.

  15. Subject Gateway Sites and Search Engine Ranking.

    Science.gov (United States)

    Thelwall, Mike

    2002-01-01

    Discusses subject gateway sites and commercial search engines for the Web and presents an explanation of Google's PageRank algorithm. The principle question addressed is the conditions under which a gateway site will increase the likelihood that a target page is found in search engines. (LRW)

  16. Rank reduction of correlation matrices by majorization

    NARCIS (Netherlands)

    R. Pietersz (Raoul); P.J.F. Groenen (Patrick)

    2004-01-01

    textabstractIn this paper a novel method is developed for the problem of finding a low-rank correlation matrix nearest to a given correlation matrix. The method is based on majorization and therefore it is globally convergent. The method is computationally efficient, is straightforward to implement,

  17. Ranking related entities: components and analyses

    NARCIS (Netherlands)

    Bron, M.; Balog, K.; de Rijke, M.

    2010-01-01

    Related entity finding is the task of returning a ranked list of homepages of relevant entities of a specified type that need to engage in a given relationship with a given source entity. We propose a framework for addressing this task and perform a detailed analysis of four core components;

  18. Ranking Very Many Typed Entities on Wikipedia

    NARCIS (Netherlands)

    Zaragoza, Hugo; Rode, H.; Mika, Peter; Atserias, Jordi; Ciaramita, Massimiliano; Attardi, Guiseppe

    2007-01-01

    We discuss the problem of ranking very many entities of different types. In particular we deal with a heterogeneous set of types, some being very generic and some very specific. We discuss two approaches for this problem: i) exploiting the entity containment graph and ii) using a Web search engine

  19. On the Dirac groups of rank n

    International Nuclear Information System (INIS)

    Ferreira, P.L.; Alcaras, J.A.C.

    1980-01-01

    The group theoretical properties of the Dirac groups of rank n are discussed together with the properties and construction of their IR's. The cases n even and n odd show distinct features. Furthermore, for n odd, the cases n=4K+1 and n=4K+3 exhibit some different properties too. (Author) [pt

  20. On rank 2 Seiberg-Witten equations

    International Nuclear Information System (INIS)

    Massamba, F.; Thompson, G.

    2004-02-01

    We introduce and study a set of rank 2 Seiberg-Witten equations. We show that the moduli space of solutions is a compact, orientational and smooth manifold. For minimal surfaces of general type we are able to determine the basic classes. (author)

  1. A tilting approach to ranking influence

    KAUST Repository

    Genton, Marc G.; Hall, Peter

    2014-01-01

    We suggest a new approach, which is applicable for general statistics computed from random samples of univariate or vector-valued or functional data, to assessing the influence that individual data have on the value of a statistic, and to ranking

  2. Texture Repairing by Unified Low Rank Optimization

    Institute of Scientific and Technical Information of China (English)

    Xiao Liang; Xiang Ren; Zhengdong Zhang; Yi Ma

    2016-01-01

    In this paper, we show how to harness both low-rank and sparse structures in regular or near-regular textures for image completion. Our method is based on a unified formulation for both random and contiguous corruption. In addition to the low rank property of texture, the algorithm also uses the sparse assumption of the natural image: because the natural image is piecewise smooth, it is sparse in certain transformed domain (such as Fourier or wavelet transform). We combine low-rank and sparsity properties of the texture image together in the proposed algorithm. Our algorithm based on convex optimization can automatically and correctly repair the global structure of a corrupted texture, even without precise information about the regions to be completed. This algorithm integrates texture rectification and repairing into one optimization problem. Through extensive simulations, we show our method can complete and repair textures corrupted by errors with both random and contiguous supports better than existing low-rank matrix recovery methods. Our method demonstrates significant advantage over local patch based texture synthesis techniques in dealing with large corruption, non-uniform texture, and large perspective deformation.

  3. Semantic association ranking schemes for information retrieval ...

    Indian Academy of Sciences (India)

    retrieval applications using term association graph representation ... Department of Computer Science and Engineering, Government College of ... Introduction ... leads to poor precision, e.g., model, python, and chip. ...... The approaches proposed in this paper focuses on the query-centric re-ranking of search results.

  4. Efficient Rank Reduction of Correlation Matrices

    NARCIS (Netherlands)

    I. Grubisic (Igor); R. Pietersz (Raoul)

    2005-01-01

    textabstractGeometric optimisation algorithms are developed that efficiently find the nearest low-rank correlation matrix. We show, in numerical tests, that our methods compare favourably to the existing methods in the literature. The connection with the Lagrange multiplier method is established,

  5. Zero forcing parameters and minimum rank problems

    NARCIS (Netherlands)

    Barioli, F.; Barrett, W.; Fallat, S.M.; Hall, H.T.; Hogben, L.; Shader, B.L.; Driessche, van den P.; Holst, van der H.

    2010-01-01

    The zero forcing number Z(G), which is the minimum number of vertices in a zero forcing set of a graph G, is used to study the maximum nullity/minimum rank of the family of symmetric matrices described by G. It is shown that for a connected graph of order at least two, no vertex is in every zero

  6. A note on ranking assignments using reoptimization

    DEFF Research Database (Denmark)

    Pedersen, Christian Roed; Nielsen, L.R.; Andersen, K.A.

    2005-01-01

    We consider the problem of ranking assignments according to cost in the classical linear assignment problem. An algorithm partitioning the set of possible assignments, as suggested by Murty, is presented where, for each partition, the optimal assignment is calculated using a new reoptimization...

  7. Language Games: University Responses to Ranking Metrics

    Science.gov (United States)

    Heffernan, Troy A.; Heffernan, Amanda

    2018-01-01

    League tables of universities that measure performance in various ways are now commonplace, with numerous bodies providing their own rankings of how institutions throughout the world are seen to be performing on a range of metrics. This paper uses Lyotard's notion of language games to theorise that universities are regaining some power over being…

  8. Ranking Thinning Potential of Lodgepole Pine Stands

    OpenAIRE

    United States Department of Agriculture, Forest Service

    1987-01-01

    This paper presents models for predicting edge-response of dominant and codominant trees to clearing. Procedures are given for converting predictions to a thinning response index, for ranking stands for thinning priority. Data requirements, sampling suggestions, examples of application, and suggestions for management use are included to facilitate use as a field guide.

  9. Primate Innovation: Sex, Age and Social Rank

    NARCIS (Netherlands)

    Reader, S.M.; Laland, K.N.

    2001-01-01

    Analysis of an exhaustive survey of primate behavior collated from the published literature revealed significant variation in rates of innovation among individuals of different sex, age and social rank. We searched approximately 1,000 articles in four primatology journals, together with other

  10. Biomechanics Scholar Citations across Academic Ranks

    Directory of Open Access Journals (Sweden)

    Knudson Duane

    2015-11-01

    Full Text Available Study aim: citations to the publications of a scholar have been used as a measure of the quality or influence of their research record. A world-wide descriptive study of the citations to the publications of biomechanics scholars of various academic ranks was conducted.

  11. An algorithm for ranking assignments using reoptimization

    DEFF Research Database (Denmark)

    Pedersen, Christian Roed; Nielsen, Lars Relund; Andersen, Kim Allan

    2008-01-01

    We consider the problem of ranking assignments according to cost in the classical linear assignment problem. An algorithm partitioning the set of possible assignments, as suggested by Murty, is presented where, for each partition, the optimal assignment is calculated using a new reoptimization...... technique. Computational results for the new algorithm are presented...

  12. Comparing survival curves using rank tests

    NARCIS (Netherlands)

    Albers, Willem/Wim

    1990-01-01

    Survival times of patients can be compared using rank tests in various experimental setups, including the two-sample case and the case of paired data. Attention is focussed on two frequently occurring complications in medical applications: censoring and tail alternatives. A review is given of the

  13. A generalization of Friedman's rank statistic

    NARCIS (Netherlands)

    Kroon, de J.; Laan, van der P.

    1983-01-01

    In this paper a very natural generalization of the two·way analysis of variance rank statistic of FRIEDMAN is given. The general distribution-free test procedure based on this statistic for the effect of J treatments in a random block design can be applied in general two-way layouts without

  14. Probabilistic relation between In-Degree and PageRank

    NARCIS (Netherlands)

    Litvak, Nelli; Scheinhardt, Willem R.W.; Volkovich, Y.

    2008-01-01

    This paper presents a novel stochastic model that explains the relation between power laws of In-Degree and PageRank. PageRank is a popularity measure designed by Google to rank Web pages. We model the relation between PageRank and In-Degree through a stochastic equation, which is inspired by the

  15. Generalized reduced rank tests using the singular value decomposition

    NARCIS (Netherlands)

    Kleibergen, F.R.; Paap, R.

    2002-01-01

    We propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables for the LDU

  16. Nominal versus Attained Weights in Universitas 21 Ranking

    Science.gov (United States)

    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…

  17. The effect of new links on Google PageRank

    NARCIS (Netherlands)

    Avrachenkov, Konstatin; Litvak, Nelli

    2004-01-01

    PageRank is one of the principle criteria according to which Google ranks Web pages. PageRank can be interpreted as a frequency of visiting a Web page by a random surfer and thus it reflects the popularity of a Web page. We study the effect of newly created links on Google PageRank. We discuss to

  18. Generalized Reduced Rank Tests using the Singular Value Decomposition

    NARCIS (Netherlands)

    F.R. Kleibergen (Frank); R. Paap (Richard)

    2003-01-01

    textabstractWe propose a novel statistic to test the rank of a matrix. The rank statistic overcomes deficiencies of existing rank statistics, like: necessity of a Kronecker covariance matrix for the canonical correlation rank statistic of Anderson (1951), sensitivity to the ordering of the variables

  19. VaRank: a simple and powerful tool for ranking genetic variants

    Directory of Open Access Journals (Sweden)

    Véronique Geoffroy

    2015-03-01

    Full Text Available Background. Most genetic disorders are caused by single nucleotide variations (SNVs or small insertion/deletions (indels. High throughput sequencing has broadened the catalogue of human variation, including common polymorphisms, rare variations or disease causing mutations. However, identifying one variation among hundreds or thousands of others is still a complex task for biologists, geneticists and clinicians.Results. We have developed VaRank, a command-line tool for the ranking of genetic variants detected by high-throughput sequencing. VaRank scores and prioritizes variants annotated either by Alamut Batch or SnpEff. A barcode allows users to quickly view the presence/absence of variants (with homozygote/heterozygote status in analyzed samples. VaRank supports the commonly used VCF input format for variants analysis thus allowing it to be easily integrated into NGS bioinformatics analysis pipelines. VaRank has been successfully applied to disease-gene identification as well as to molecular diagnostics setup for several hundred patients.Conclusions. VaRank is implemented in Tcl/Tk, a scripting language which is platform-independent but has been tested only on Unix environment. The source code is available under the GNU GPL, and together with sample data and detailed documentation can be downloaded from http://www.lbgi.fr/VaRank/.

  20. Model of Decision Making through Consensus in Ranking Case

    Science.gov (United States)

    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).

  1. Selection and ranking of patient video cases in paediatric neurology in relation to learner levels.

    Science.gov (United States)

    Balslev, Thomas; Muijtjens, Arno M M; Maarbjerg, Sabine Frølich; de Grave, Willem

    2018-05-01

    Teaching and learning with patient video cases may add authenticity, enhance diagnostic accuracy and improve chances of early diagnosis. The aim of this study is firstly to identify selection criteria for key Patient video cases (PVCs), secondly to identify trends in relevance of PVCs for learner levels and thirdly, to rank PVCs for learner levels. Based on a literature review, we identified criteria for key PVCs for use in paediatric neurology. We then performed a multi-round Delphi analysis to obtain agreement between 28 expert clinician teachers concerning key PVCs for four learner levels. We identified two major criteria: key PVCs should demonstrate key movements, and these movements should be subtle and/or difficult to note. The expert clinician teachers subsequently assessed a list of 14 topics for key PVCs. We found a clear, increasing trend in relevance scores, from medical students to young residents to experienced residents and specialists. For medical students and residents, epileptic spasms, Down syndrome, developmental delay, cerebral palsy and absence epilepsy were highly ranked. For specialists, conditions like chorea, focal seizures or eye movement disorders topped the ranking list, although ranking was less clear for this group of advanced learners. Key PVCs should demonstrate movements that are difficult to note for learners. Ranked lists of key PVCs for teaching and learning at different learner levels are now available and may help institutions build validated local libraries of PVCs. Copyright © 2017 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

  2. Multitask Classification Hypothesis Space With Improved Generalization Bounds.

    Science.gov (United States)

    Li, Cong; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2015-07-01

    This paper presents a pair of hypothesis spaces (HSs) of vector-valued functions intended to be used in the context of multitask classification. While both are parameterized on the elements of reproducing kernel Hilbert spaces and impose a feature mapping that is common to all tasks, one of them assumes this mapping as fixed, while the more general one learns the mapping via multiple kernel learning. For these new HSs, empirical Rademacher complexity-based generalization bounds are derived, and are shown to be tighter than the bound of a particular HS, which has appeared recently in the literature, leading to improved performance. As a matter of fact, the latter HS is shown to be a special case of ours. Based on an equivalence to Group-Lasso type HSs, the proposed HSs are utilized toward corresponding support vector machine-based formulations. Finally, experimental results on multitask learning problems underline the quality of the derived bounds and validate this paper's analysis.

  3. Differential invariants for higher-rank tensors. A progress report

    International Nuclear Information System (INIS)

    Tapial, V.

    2004-07-01

    We outline the construction of differential invariants for higher-rank tensors. In section 2 we outline the general method for the construction of differential invariants. A first result is that the simplest tensor differential invariant contains derivatives of the same order as the rank of the tensor. In section 3 we review the construction for the first-rank tensors (vectors) and second-rank tensors (metrics). In section 4 we outline the same construction for higher-rank tensors. (author)

  4. Beyond Low Rank: A Data-Adaptive Tensor Completion Method

    OpenAIRE

    Zhang, Lei; Wei, Wei; Shi, Qinfeng; Shen, Chunhua; Hengel, Anton van den; Zhang, Yanning

    2017-01-01

    Low rank tensor representation underpins much of recent progress in tensor completion. In real applications, however, this approach is confronted with two challenging problems, namely (1) tensor rank determination; (2) handling real tensor data which only approximately fulfils the low-rank requirement. To address these two issues, we develop a data-adaptive tensor completion model which explicitly represents both the low-rank and non-low-rank structures in a latent tensor. Representing the no...

  5. Hypothesis Testing in the Real World

    Science.gov (United States)

    Miller, Jeff

    2017-01-01

    Critics of null hypothesis significance testing suggest that (a) its basic logic is invalid and (b) it addresses a question that is of no interest. In contrast to (a), I argue that the underlying logic of hypothesis testing is actually extremely straightforward and compelling. To substantiate that, I present examples showing that hypothesis…

  6. Error probabilities in default Bayesian hypothesis testing

    NARCIS (Netherlands)

    Gu, Xin; Hoijtink, Herbert; Mulder, J,

    2016-01-01

    This paper investigates the classical type I and type II error probabilities of default Bayes factors for a Bayesian t test. Default Bayes factors quantify the relative evidence between the null hypothesis and the unrestricted alternative hypothesis without needing to specify prior distributions for

  7. Reassessing the Trade-off Hypothesis

    DEFF Research Database (Denmark)

    Rosas, Guillermo; Manzetti, Luigi

    2015-01-01

    Do economic conditions drive voters to punish politicians that tolerate corruption? Previous scholarly work contends that citizens in young democracies support corrupt governments that are capable of promoting good economic outcomes, the so-called trade-off hypothesis. We test this hypothesis based...

  8. RELIABLE COGNITIVE DIMENSIONAL DOCUMENT RANKING BY WEIGHTED STANDARD CAUCHY DISTRIBUTION

    Directory of Open Access Journals (Sweden)

    S Florence Vijila

    2017-04-01

    Full Text Available Categorization of cognitively uniform and consistent documents such as University question papers are in demand by e-learners. Literature indicates that Standard Cauchy distribution and the derived values are extensively used for checking uniformity and consistency of documents. The paper attempts to apply this technique for categorizing question papers according to four selective cognitive dimensions. For this purpose cognitive dimensional keyword sets of these four categories (also termed as portrayal concepts are assumed and an automatic procedure is developed to quantify these dimensions in question papers. The categorization is relatively accurate when checked with manual methods. Hence simple and well established term frequency / inverse document frequency ‘tf/ IDF’ technique is considered for automating the categorization process. After the documents categorization, standard Cauchy formula is applied to rank order the documents that have the least differences among Cauchy value, (according to Cauchy theorem so as obtain consistent and uniform documents in an order or ranked. For the purpose of experiments and social survey, seven question papers (documents have been designed with various consistencies. To validate this proposed technique social survey is administered on selective samples of e-learners of Tamil Nadu, India. Results are encouraging and conclusions drawn out of the experiments will be useful to researchers of concept mining and categorizing documents according to concepts. Findings have also contributed utility value to e-learning system designers.

  9. Ranking insurance firms using AHP and Factor Analysis

    Directory of Open Access Journals (Sweden)

    Mohammad Khodaei Valahzaghard

    2013-03-01

    Full Text Available Insurance industry includes a significant part of economy and it is important to learn more about the capabilities of different firms, which are active in this industry. In this paper, we present an empirical study to rank the insurance firms using analytical hierarchy process as well as factor analysis. The study considers four criteria including capital adequacy, quality of earning, quality of cash flow and quality of firms’ assets. The results of the implementation of factor analysis (FA have been verified using Kaiser-Meyer-Olkin (KMO=0.573 and Bartlett's Chi-Square (443.267 P-value=0.000 tests. According to the results FA, the first important factor, capital adequacy, represents 21.557% of total variance, the second factor, quality of income, represents 20.958% of total variance. In addition, the third factor, quality of cash flow, represents 19.417% of total variance and the last factor, quality of assets, represents 18.641% of total variance. The study has also used analytical hierarchy process (AHP to rank insurance firms. The results of our survey indicate that capital adequacy (0.559 is accounted as the most important factor followed by quality of income (0.235, quality of cash flow (0.144 and quality of assets (0.061. The results of AHP are consistent with the results of FA, which somewhat validates the overall study.

  10. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm

  11. Correlates of androgens in wild male Barbary macaques: Testing the challenge hypothesis.

    Science.gov (United States)

    Rincon, Alan V; Maréchal, Laëtitia; Semple, Stuart; Majolo, Bonaventura; MacLarnon, Ann

    2017-10-01

    Investigating causes and consequences of variation in hormonal expression is a key focus in behavioral ecology. Many studies have explored patterns of secretion of the androgen testosterone in male vertebrates, using the challenge hypothesis (Wingfield, Hegner, Dufty, & Ball, 1990; The American Naturalist, 136(6), 829-846) as a theoretical framework. Rather than the classic association of testosterone with male sexual behavior, this hypothesis predicts that high levels of testosterone are associated with male-male reproductive competition but also inhibit paternal care. The hypothesis was originally developed for birds, and subsequently tested in other vertebrate taxa, including primates. Such studies have explored the link between testosterone and reproductive aggression as well as other measures of mating competition, or between testosterone and aspects of male behavior related to the presence of infants. Very few studies have simultaneously investigated the links between testosterone and male aggression, other aspects of mating competition and infant-related behavior. We tested predictions derived from the challenge hypothesis in wild male Barbary macaques (Macaca sylvanus), a species with marked breeding seasonality and high levels of male-infant affiliation, providing a powerful test of this theoretical framework. Over 11 months, 251 hr of behavioral observations and 296 fecal samples were collected from seven adult males in the Middle Atlas Mountains, Morocco. Fecal androgen levels rose before the onset of the mating season, during a period of rank instability, and were positively related to group mating activity across the mating season. Androgen levels were unrelated to rates of male-male aggression in any period, but higher ranked males had higher levels in both the mating season and in the period of rank instability. Lower androgen levels were associated with increased rates of male-infant grooming during the mating and unstable periods. Our results

  12. Weighted Low-Rank Approximation of Matrices and Background Modeling

    KAUST Repository

    Dutta, Aritra

    2018-04-15

    We primarily study a special a weighted low-rank approximation of matrices and then apply it to solve the background modeling problem. We propose two algorithms for this purpose: one operates in the batch mode on the entire data and the other one operates in the batch-incremental mode on the data and naturally captures more background variations and computationally more effective. Moreover, we propose a robust technique that learns the background frame indices from the data and does not require any training frames. We demonstrate through extensive experiments that by inserting a simple weight in the Frobenius norm, it can be made robust to the outliers similar to the $\\\\ell_1$ norm. Our methods match or outperform several state-of-the-art online and batch background modeling methods in virtually all quantitative and qualitative measures.

  13. Weighted Low-Rank Approximation of Matrices and Background Modeling

    KAUST Repository

    Dutta, Aritra; Li, Xin; Richtarik, Peter

    2018-01-01

    We primarily study a special a weighted low-rank approximation of matrices and then apply it to solve the background modeling problem. We propose two algorithms for this purpose: one operates in the batch mode on the entire data and the other one operates in the batch-incremental mode on the data and naturally captures more background variations and computationally more effective. Moreover, we propose a robust technique that learns the background frame indices from the data and does not require any training frames. We demonstrate through extensive experiments that by inserting a simple weight in the Frobenius norm, it can be made robust to the outliers similar to the $\\ell_1$ norm. Our methods match or outperform several state-of-the-art online and batch background modeling methods in virtually all quantitative and qualitative measures.

  14. Robust subspace estimation using low-rank optimization theory and applications

    CERN Document Server

    Oreifej, Omar

    2014-01-01

    Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book,?the authors?discuss fundame

  15. Fourth-rank gravity. A progress report

    International Nuclear Information System (INIS)

    Tapia, V.

    1992-04-01

    We consider the consequences of describing the metric properties of space-time through a quartic line element. The associated ''metric'' is a fourth-rank tensor. After developing some fundamentals for such geometry, we construct a field theory for the gravitational field. This theory coincides with General Relativity in the vacuum case. Departures from General Relativity are obtained only in the presence of matter. We develop a simple cosmological model which is not in contradiction with the observed value Ω approx. 0.2-0.3 for the energy density parameter. A further application concerns conformal field theory. We are able to prove that a conformal field theory possesses an infinite-dimensional symmetry group only if the dimension of space-time is equal to the rank of the metric. In this case we are able to construct an integrable conformal field theory in four dimensions. The model is renormalisable by power counting. (author). 9 refs

  16. Ranking oil sands bitumen recovery techniques

    Energy Technology Data Exchange (ETDEWEB)

    Lam, A.; Nobes, D.S.; Lipsett, M.G. [Alberta Univ., Edmonton, AB (Canada). Dept. of Mechanical Engineering

    2009-07-01

    The preference ranking organization method (PROMETHEE) was used to assess and rank 3 techniques for in situ bitumen recovery: (1) steam assisted gravity drainage; (2) vapour extraction (VAPEX); and (3) toe-to-heel air injection (THAI). The study used a business scenario where management-type indicators included potential production rates; estimated overall operating costs; energy consumption; facilities requirement; recovery efficiency; and energy loss. Amounts of carbon dioxide (CO{sub 2}) emissions were also considered, as well as the production depth, formation thickness, and API gravity of the produced bitumen. The study showed that THAI recovery methods had the most beneficial criteria weighting of the 3 processes, while SAGD was the least favourable choice. However, SAGD processes are the most widely used of the 3 processes, while THAI has only been demonstrated on a limited scale. It was concluded that the maturity of a technology should be weighted more heavily when using the PROMETHEE method. 8 refs., 2 tabs.

  17. Social Media Impact on Website Ranking

    OpenAIRE

    Vaghela, Dushyant

    2014-01-01

    Internet is fast becoming critically important to commerce, industry and individuals. Search Engine (SE) is the most vital component for communication network and also used for discover information for users or people. Search engine optimization (SEO) is the process that is mostly used to increasing traffic from free, organic or natural listings on search engines and also helps to increase website ranking. It includes techniques like link building, directory submission, classified submission ...

  18. On Locally Most Powerful Sequential Rank Tests

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2017-01-01

    Roč. 36, č. 1 (2017), s. 111-125 ISSN 0747-4946 R&D Projects: GA ČR GA17-07384S Grant - others:Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985807 Keywords : nonparametric test s * sequential ranks * stopping variable Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.339, year: 2016

  19. Probabilistic real-time contingency ranking method

    International Nuclear Information System (INIS)

    Mijuskovic, N.A.; Stojnic, D.

    2000-01-01

    This paper describes a real-time contingency method based on a probabilistic index-expected energy not supplied. This way it is possible to take into account the stochastic nature of the electric power system equipment outages. This approach enables more comprehensive ranking of contingencies and it is possible to form reliability cost values that can form the basis for hourly spot price calculations. The electric power system of Serbia is used as an example for the method proposed. (author)

  20. Returns to Tenure: Time or Rank?

    DEFF Research Database (Denmark)

    Buhai, Ioan Sebastian

    -specific investment, efficiency-wages or adverse-selection models. However, rent extracting arguments as suggested by the theory of internal labor markets, indicate that the relative position of the worker in the seniority hierarchy of the firm, her 'seniority rank', may also explain part of the observed returns...... relative to their peer workers), as predicted by theories on unionized and insider-outsider markets....

  1. Efficient Low Rank Tensor Ring Completion

    OpenAIRE

    Wang, Wenqi; Aggarwal, Vaneet; Aeron, Shuchin

    2017-01-01

    Using the matrix product state (MPS) representation of the recently proposed tensor ring decompositions, in this paper we propose a tensor completion algorithm, which is an alternating minimization algorithm that alternates over the factors in the MPS representation. This development is motivated in part by the success of matrix completion algorithms that alternate over the (low-rank) factors. In this paper, we propose a spectral initialization for the tensor ring completion algorithm and ana...

  2. Citation ranking versus peer evaluation of senior faculty research performance

    DEFF Research Database (Denmark)

    Meho, Lokman I.; Sonnenwald, Diane H.

    2000-01-01

    The purpose of this study is to analyze the relationship between citation ranking and peer evaluation in assessing senior faculty research performance. Other studies typically derive their peer evaluation data directly from referees, often in the form of ranking. This study uses two additional...... indicator of research performance of senior faculty members? Citation data, book reviews, and peer ranking were compiled and examined for faculty members specializing in Kurdish studies. Analysis shows that normalized citation ranking and citation content analysis data yield identical ranking results....... Analysis also shows that normalized citation ranking and citation content analysis, book reviews, and peer ranking perform similarly (i.e., are highly correlated) for high-ranked and low-ranked senior scholars. Additional evaluation methods and measures that take into account the context and content...

  3. Association between Metabolic Syndrome and Job Rank.

    Science.gov (United States)

    Mehrdad, Ramin; Pouryaghoub, Gholamreza; Moradi, Mahboubeh

    2018-01-01

    The occupation of the people can influence the development of metabolic syndrome. To determine the association between metabolic syndrome and its determinants with the job rank in workers of a large car factory in Iran. 3989 male workers at a large car manufacturing company were invited to participate in this cross-sectional study. Demographic and anthropometric data of the participants, including age, height, weight, and abdominal circumference were measured. Blood samples were taken to measure lipid profile and blood glucose level. Metabolic syndrome was diagnosed in each participant based on ATPIII 2001 criteria. The workers were categorized based on their job rank into 3 groups of (1) office workers, (2) workers with physical exertion, and (3) workers with chemical exposure. The study characteristics, particularly the frequency of metabolic syndrome and its determinants were compared among the study groups. The prevalence of metabolic syndrome in our study was 7.7% (95% CI 6.9 to 8.5). HDL levels were significantly lower in those who had chemical exposure (p=0.045). Diastolic blood pressure was significantly higher in those who had mechanical exertion (p=0.026). The frequency of metabolic syndrome in the office workers, workers with physical exertion, and workers with chemical exposure was 7.3%, 7.9%, and 7.8%, respectively (p=0.836). Seemingly, there is no association between metabolic syndrome and job rank.

  4. Rank-dependant factorization of entanglement evolution

    International Nuclear Information System (INIS)

    Siomau, Michael

    2016-01-01

    Highlights: • In some cases the complex entanglement evolution can be factorized on simple terms. • We suggest factorization equations for multiqubit entanglement evolution. • The factorization is solely defined by the rank of the final state density matrices. • The factorization is independent on the local noisy channels and initial pure states. - Abstract: The description of the entanglement evolution of a complex quantum system can be significantly simplified due to the symmetries of the initial state and the quantum channels, which simultaneously affect parts of the system. Using concurrence as the entanglement measure, we study the entanglement evolution of few qubit systems, when each of the qubits is affected by a local unital channel independently on the others. We found that for low-rank density matrices of the final quantum state, such complex entanglement dynamics can be completely described by a combination of independent factors representing the evolution of entanglement of the initial state, when just one of the qubits is affected by a local channel. We suggest necessary conditions for the rank of the density matrices to represent the entanglement evolution through the factors. Our finding is supported with analytical examples and numerical simulations.

  5. Fourth-rank gravity and cosmology

    International Nuclear Information System (INIS)

    Marrakchi, A.L.; Tapia, V.

    1992-07-01

    We consider the consequences of describing the metric properties of space-time through a quartic line element. The associated ''metric'' is a fourth-rank tensor G μυλπ . In order to recover a Riemannian behaviour of the geometry it is necessary to have G μυλπ = g (μυ g λπ) . We construct a theory for the gravitational field based on the fourth-rank metric G μυλπ . In the absence of matter the fourth-rank metric becomes separable and the theory coincides with General Relativity. In the presence of matter we can maintain Riemmanianicity, but now gravitation couples, as compared to General Relativity, in a different way to matter. We develop a simple cosmological model based on a FRW metric with matter described by a perfect fluid. For the present time the field equations are compatible with k OBS = O and Ω OBS t CLAS approx. 10 20 t PLANCK approx. 10 -23 s. Our final and most important result is the fact that the entropy is an increasing function of time. When interpreted at the light of General Relativity the treatment is shown to be almost equivalent to that of the standard model of cosmology combined with the inflationary scenario. (author). 16 refs, 1 fig

  6. Ranking agility factors affecting hospitals in Iran

    Directory of Open Access Journals (Sweden)

    M. Abdi Talarposht

    2017-04-01

    Full Text Available Background: Agility is an effective response to the changing and unpredictable environment and using these changes as opportunities for organizational improvement. Objective: The aim of the present study was to rank the factors affecting agile supply chain of hospitals of Iran. Methods: This applied study was conducted by cross sectional-descriptive method at some point of 2015 for one year. The research population included managers, administrators, faculty members and experts were selected hospitals. A total of 260 people were selected as sample from the health centers. The construct validity of the questionnaire was approved by confirmatory factor analysis test and its reliability was approved by Cronbach's alpha (α=0.97. All data were analyzed by Kolmogorov-Smirnov, Chi-square and Friedman tests. Findings: The development of staff skills, the use of information technology, the integration of processes, appropriate planning, and customer satisfaction and product quality had a significant impact on the agility of public hospitals of Iran (P<0.001. New product introductions had earned the highest ranking and the development of staff skills earned the lowest ranking. Conclusion: The new product introduction, market responsiveness and sensitivity, reduce costs, and the integration of organizational processes, ratings better to have acquired agility hospitals in Iran. Therefore, planners and officials of hospitals have to, through the promotion quality and variety of services customer-oriented, providing a basis for investing in the hospital and etc to apply for agility supply chain public hospitals of Iran.

  7. Estimation of rank correlation for clustered data.

    Science.gov (United States)

    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.

  8. Ranking environmental liabilities at a petroleum refinery

    International Nuclear Information System (INIS)

    Lupo, M.

    1995-01-01

    A new computer model is available to allow the management of a petroleum refinery to prioritize environmental action and construct a holistic approach to remediation. A large refinery may have numerous solid waste management units regulated by the Resource Conservation and Recovery Act (RCRA), as well as process units that emit hazardous chemicals into the environment. These sources can impact several environmental media, potentially including the air, the soil, the groundwater, the unsaturated zone water, and surface water. The number of chemicals of concern may be large. The new model is able to rank the sources by considering the impact of each chemical in each medium from each source in terms of concentration, release rate, and a weighted index based on toxicity. In addition to environmental impact, the sources can be ranked in three other ways: (1) by cost to remediate, (2) by environmental risk reduction caused by the remediation in terms of the decreases in release rate, concentration, and weighted index, and (3) by cost-benefit, which is the environmental risk reduction for each source divided by the cost of the remedy. Ranking each unit in the refinery allows management to use its limited environmental resources in a pro-active strategic manner that produces long-term results, rather than in reactive, narrowly focused, costly, regulatory-driven campaigns that produce only short-term results

  9. Iris Template Protection Based on Local Ranking

    Directory of Open Access Journals (Sweden)

    Dongdong Zhao

    2018-01-01

    Full Text Available Biometrics have been widely studied in recent years, and they are increasingly employed in real-world applications. Meanwhile, a number of potential threats to the privacy of biometric data arise. Iris template protection demands that the privacy of iris data should be protected when performing iris recognition. According to the international standard ISO/IEC 24745, iris template protection should satisfy the irreversibility, revocability, and unlinkability. However, existing works about iris template protection demonstrate that it is difficult to satisfy the three privacy requirements simultaneously while supporting effective iris recognition. In this paper, we propose an iris template protection method based on local ranking. Specifically, the iris data are first XORed (Exclusive OR operation with an application-specific string; next, we divide the results into blocks and then partition the blocks into groups. The blocks in each group are ranked according to their decimal values, and original blocks are transformed to their rank values for storage. We also extend the basic method to support the shifting strategy and masking strategy, which are two important strategies for iris recognition. We demonstrate that the proposed method satisfies the irreversibility, revocability, and unlinkability. Experimental results on typical iris datasets (i.e., CASIA-IrisV3-Interval, CASIA-IrisV4-Lamp, UBIRIS-V1-S1, and MMU-V1 show that the proposed method could maintain the recognition performance while protecting the privacy of iris data.

  10. Drug-target interaction prediction: A Bayesian ranking approach.

    Science.gov (United States)

    Peska, Ladislav; Buza, Krisztian; Koller, Júlia

    2017-12-01

    In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

  11. A critique of statistical hypothesis testing in clinical research

    Directory of Open Access Journals (Sweden)

    Somik Raha

    2011-01-01

    Full Text Available Many have documented the difficulty of using the current paradigm of Randomized Controlled Trials (RCTs to test and validate the effectiveness of alternative medical systems such as Ayurveda. This paper critiques the applicability of RCTs for all clinical knowledge-seeking endeavors, of which Ayurveda research is a part. This is done by examining statistical hypothesis testing, the underlying foundation of RCTs, from a practical and philosophical perspective. In the philosophical critique, the two main worldviews of probability are that of the Bayesian and the frequentist. The frequentist worldview is a special case of the Bayesian worldview requiring the unrealistic assumptions of knowing nothing about the universe and believing that all observations are unrelated to each other. Many have claimed that the first belief is necessary for science, and this claim is debunked by comparing variations in learning with different prior beliefs. Moving beyond the Bayesian and frequentist worldviews, the notion of hypothesis testing itself is challenged on the grounds that a hypothesis is an unclear distinction, and assigning a probability on an unclear distinction is an exercise that does not lead to clarity of action. This critique is of the theory itself and not any particular application of statistical hypothesis testing. A decision-making frame is proposed as a way of both addressing this critique and transcending ideological debates on probability. An example of a Bayesian decision-making approach is shown as an alternative to statistical hypothesis testing, utilizing data from a past clinical trial that studied the effect of Aspirin on heart attacks in a sample population of doctors. As a big reason for the prevalence of RCTs in academia is legislation requiring it, the ethics of legislating the use of statistical methods for clinical research is also examined.

  12. Country-specific determinants of world university rankings.

    Science.gov (United States)

    Pietrucha, Jacek

    2018-01-01

    This paper examines country-specific factors that affect the three most influential world university rankings (the Academic Ranking of World Universities, the QS World University Ranking, and the Times Higher Education World University Ranking). We run a cross sectional regression that covers 42-71 countries (depending on the ranking and data availability). We show that the position of universities from a country in the ranking is determined by the following country-specific variables: economic potential of the country, research and development expenditure, long-term political stability (freedom from war, occupation, coups and major changes in the political system), and institutional variables, including government effectiveness.

  13. Knockouts of high-ranking males have limited impact on baboon social networks.

    Science.gov (United States)

    Franz, Mathias; Altmann, Jeanne; Alberts, Susan C

    Social network structures can crucially impact complex social processes such as collective behaviour or the transmission of information and diseases. However, currently it is poorly understood how social networks change over time. Previous studies on primates suggest that `knockouts' (due to death or dispersal) of high-ranking individuals might be important drivers for structural changes in animal social networks. Here we test this hypothesis using long-term data on a natural population of baboons, examining the effects of 29 natural knockouts of alpha or beta males on adult female social networks. We investigated whether and how knockouts affected (1) changes in grooming and association rates among adult females, and (2) changes in mean degree and global clustering coefficient in these networks. The only significant effect that we found was a decrease in mean degree in grooming networks in the first month after knockouts, but this decrease was rather small, and grooming networks rebounded to baseline levels by the second month after knockouts. Taken together our results indicate that the removal of high-ranking males has only limited or no lasting effects on social networks of adult female baboons. This finding calls into question the hypothesis that the removal of high-ranking individuals has a destabilizing effect on social network structures in social animals.

  14. From animal cruelty to serial murder: applying the graduation hypothesis.

    Science.gov (United States)

    Wright, Jeremy; Hensley, Christopher

    2003-02-01

    Although serial murder has been recorded for centuries, limited academic attention has been given to this important topic. Scholars have attempted to examine the causality and motivations behind the rare phenomenon of serial murder. However, scant research exists which delves into the childhood characteristics of serial murderers. Using social learning theory, some of these studies present supporting evidence for a link between childhood animal cruelty and adult aggression toward humans. Based on five case studies of serial murderers, we contribute to the existing literature by exploring the possible link between childhood cruelty toward animals and serial murder with the application of the graduation hypothesis.

  15. Implications of the Bohm-Aharonov hypothesis

    International Nuclear Information System (INIS)

    Ghirardi, G.C.; Rimini, A.; Weber, T.

    1976-01-01

    It is proved that the Bohm-Aharonov hypothesis concerning largerly separated subsystems of composite quantum systems implies that it is impossible to express the dynamical evolution in terms of the density operator

  16. Multi-agent sequential hypothesis testing

    KAUST Repository

    Kim, Kwang-Ki K.; Shamma, Jeff S.

    2014-01-01

    incorporate costs of taking private/public measurements, costs of time-difference and disagreement in actions of agents, and costs of false declaration/choices in the sequential hypothesis testing. The corresponding sequential decision processes have well

  17. The (not so) Immortal Strand Hypothesis

    OpenAIRE

    Tomasetti, Cristian; Bozic, Ivana

    2015-01-01

    Background: Non-random segregation of DNA strands during stem cell replication has been proposed as a mechanism to minimize accumulated genetic errors in stem cells of rapidly dividing tissues. According to this hypothesis, an “immortal” DNA strand is passed to the stem cell daughter and not the more differentiated cell, keeping the stem cell lineage replication error-free. After it was introduced, experimental evidence both in favor and against the hypothesis has been presented. Principal...

  18. Ranking U-Sapiens 2010-2

    Directory of Open Access Journals (Sweden)

    Carlos-Roberto Peña-Barrera

    2011-08-01

    Full Text Available Los principales objetivos de esta investigación son los siguientes: (1 que la comunidad científica nacional e internacional y la sociedad en general co-nozcan los resultados del Ranking U-Sapiens Colombia 2010_2, el cual clasifica a cada institución de educación superior colombiana según puntaje, posición y cuartil; (2 destacar los movimientos más importantes al comparar los resultados del ranking 2010_1 con los del 2010_2; (3 publicar las respuestas de algunos actores de la academia nacional con respecto a la dinámica de la investigación en el país; (4 reconocer algunas instituciones, medios de comunicación e investigadores que se han interesado a modo de reflexión, referenciación o citación por esta investigación; y (5 dar a conocer el «Sello Ranking U-Sapiens Colombia» para las IES clasificadas. El alcance de este estudio en cuanto a actores abordó todas y cada una de las IES nacionales (aunque solo algunas lograran entrar al ranking y en cuanto a tiempo, un periodo referido al primer semestre de 2010 con respecto a: (1 los resultados 2010-1 de revistas indexadas en Publindex, (2 los programas de maestrías y doctorados activos durante 2010-1 según el Ministerio de Educación Nacional, y (3 los resultados de grupos de investigación clasificados para 2010 según Colciencias. El método empleado para esta investigación es el mismo que para el ranking 2010_1, salvo por una especificación aún más detallada en uno de los pasos del modelo (las variables α, β, γ; es completamente cuantitativo y los datos de las variables que fundamentan sus resultados provienen de Colciencias y el Ministerio de Educación Nacional; y en esta ocasión se darán a conocer los resultados por variable para 2010_1 y 2010_2. Los resultados más relevantes son estos: (1 entraron 8 IES al ranking y salieron 3; (2 las 3 primeras IES son públicas; (3 en total hay 6 instituciones universitarias en el ranking; (4 7 de las 10 primeras IES son

  19. Experiential Learning of the Efficient Market Hypothesis: Two Trading Games

    Science.gov (United States)

    Park, Andreas

    2010-01-01

    In goods markets, an equilibrium price balances demand and supply. In a financial market, an equilibrium price also aggregates people's information to reveal the true value of a financial security. Although the underlying idea of informationally efficient markets is one of the centerpieces of capital market theory, students often have difficulties…

  20. Sex Role Learning: A Test of the Selective Attention Hypothesis.

    Science.gov (United States)

    Bryan, Janice Westlund; Luria, Zella

    This paper reports three studies designed to determine whether children show selective attention and/or differential memory to slide pictures of same-sex vs. opposite-sex models and activities. Attention was measured using a feedback EEG procedure, which measured the presence or absence of alpha rhythms in the subjects' brains during presentation…

  1. Emotions in Reading: Disgust, Empathy and the Contextual Learning Hypothesis

    Science.gov (United States)

    Silva, Catarina; Montant, Marie; Ponz, Aurelie; Ziegler, Johannes C.

    2012-01-01

    Emotion effects in reading have typically been investigated by manipulating words' emotional valence and arousal in lexical decision. The standard finding is that valence and arousal can have both facilitatory and inhibitory effects, which is hard to reconcile with current theories of emotion processing in reading. Here, we contrasted these…

  2. Adaptive designs for the one-sample log-rank test.

    Science.gov (United States)

    Schmidt, Rene; Faldum, Andreas; Kwiecien, Robert

    2017-09-22

    Traditional designs in phase IIa cancer trials are single-arm designs with a binary outcome, for example, tumor response. In some settings, however, a time-to-event endpoint might appear more appropriate, particularly in the presence of loss to follow-up. Then the one-sample log-rank test might be the method of choice. It allows to compare the survival curve of the patients under treatment to a prespecified reference survival curve. The reference curve usually represents the expected survival under standard of the care. In this work, convergence of the one-sample log-rank statistic to Brownian motion is proven using Rebolledo's martingale central limit theorem while accounting for staggered entry times of the patients. On this basis, a confirmatory adaptive one-sample log-rank test is proposed where provision is made for data dependent sample size reassessment. The focus is to apply the inverse normal method. This is done in two different directions. The first strategy exploits the independent increments property of the one-sample log-rank statistic. The second strategy is based on the patient-wise separation principle. It is shown by simulation that the proposed adaptive test might help to rescue an underpowered trial and at the same time lowers the average sample number (ASN) under the null hypothesis as compared to a single-stage fixed sample design. © 2017, The International Biometric Society.

  3. Ranking the Online Documents Based on Relative Credibility Measures

    Directory of Open Access Journals (Sweden)

    Ahmad Dahlan

    2013-09-01

    Full Text Available Information searching is the most popular activity in Internet. Usually the search engine provides the search results ranked by the relevance. However, for a certain purpose that concerns with information credibility, particularly citing information for scientific works, another approach of ranking the search engine results is required. This paper presents a study on developing a new ranking method based on the credibility of information. The method is built up upon two well-known algorithms, PageRank and Citation Analysis. The result of the experiment that used Spearman Rank Correlation Coefficient to compare the proposed rank (generated by the method with the standard rank (generated manually by a group of experts showed that the average Spearman 0 < rS < critical value. It means that the correlation was proven but it was not significant. Hence the proposed rank does not satisfy the standard but the performance could be improved.

  4. Ranking the Online Documents Based on Relative Credibility Measures

    Directory of Open Access Journals (Sweden)

    Ahmad Dahlan

    2009-05-01

    Full Text Available Information searching is the most popular activity in Internet. Usually the search engine provides the search results ranked by the relevance. However, for a certain purpose that concerns with information credibility, particularly citing information for scientific works, another approach of ranking the search engine results is required. This paper presents a study on developing a new ranking method based on the credibility of information. The method is built up upon two well-known algorithms, PageRank and Citation Analysis. The result of the experiment that used Spearman Rank Correlation Coefficient to compare the proposed rank (generated by the method with the standard rank (generated manually by a group of experts showed that the average Spearman 0 < rS < critical value. It means that the correlation was proven but it was not significant. Hence the proposed rank does not satisfy the standard but the performance could be improved.

  5. Algebraic and computational aspects of real tensor ranks

    CERN Document Server

    Sakata, Toshio; Miyazaki, Mitsuhiro

    2016-01-01

    This book provides comprehensive summaries of theoretical (algebraic) and computational aspects of tensor ranks, maximal ranks, and typical ranks, over the real number field. Although tensor ranks have been often argued in the complex number field, it should be emphasized that this book treats real tensor ranks, which have direct applications in statistics. The book provides several interesting ideas, including determinant polynomials, determinantal ideals, absolutely nonsingular tensors, absolutely full column rank tensors, and their connection to bilinear maps and Hurwitz-Radon numbers. In addition to reviews of methods to determine real tensor ranks in details, global theories such as the Jacobian method are also reviewed in details. The book includes as well an accessible and comprehensive introduction of mathematical backgrounds, with basics of positive polynomials and calculations by using the Groebner basis. Furthermore, this book provides insights into numerical methods of finding tensor ranks through...

  6. Next Generation Nuclear Plant Phenomena Identification and Ranking Tables (PIRTs) Volume 5: Graphite PIRTs

    International Nuclear Information System (INIS)

    Burchell, Timothy D.; Bratton, Rob; Marsden, Barry; Srinivasan, Makuteswara; Penfield, Scott; Mitchell, Mark; Windes, Will

    2008-01-01

    Here we report the outcome of the application of the Nuclear Regulatory Commission (NRC) Phenomena Identification and Ranking Table (PIRT) process to the issue of nuclear-grade graphite for the moderator and structural components of a next generation nuclear plant (NGNP), considering both routine (normal operation) and postulated accident conditions for the NGNP. The NGNP is assumed to be a modular high-temperature gas-cooled reactor (HTGR), either a gas-turbine modular helium reactor (GTMHR) version (a prismatic-core modular reactor (PMR)] or a pebble-bed modular reactor (PBMR) version (a pebble bed reactor (PBR)] design, with either a direct- or indirect-cycle gas turbine (Brayton cycle) system for electric power production, and an indirect-cycle component for hydrogen production. NGNP design options with a high-pressure steam generator (Rankine cycle) in the primary loop are not considered in this PIRT. This graphite PIRT was conducted in parallel with four other NRC PIRT activities, taking advantage of the relationships and overlaps in subject matter. The graphite PIRT panel identified numerous phenomena, five of which were ranked high importance-low knowledge. A further nine were ranked with high importance and medium knowledge rank. Two phenomena were ranked with medium importance and low knowledge, and a further 14 were ranked medium importance and medium knowledge rank. The last 12 phenomena were ranked with low importance and high knowledge rank (or similar combinations suggesting they have low priority). The ranking/scoring rationale for the reported graphite phenomena is discussed. Much has been learned about the behavior of graphite in reactor environments in the 60-plus years since the first graphite rectors went into service. The extensive list of references in the Bibliography is plainly testament to this fact. Our current knowledge base is well developed. Although data are lacking for the specific grades being considered for Generation IV (Gen IV

  7. On Locally Most Powerful Sequential Rank Tests

    Czech Academy of Sciences Publication Activity Database

    Kalina, Jan

    2017-01-01

    Roč. 36, č. 1 (2017), s. 111-125 ISSN 0747-4946 R&D Projects: GA ČR GA17-07384S Grant - others:Nadační fond na podporu vědy(CZ) Neuron Institutional support: RVO:67985556 Keywords : nonparametric test s * sequential ranks * stopping variable Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.339, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/kalina-0474065.pdf

  8. Motion in fourth-rank gravity

    International Nuclear Information System (INIS)

    Tapia, V.

    1992-04-01

    Recently we have explored the consequences of describing the metric properties of our universe through a quartic line element. In this geometry the natural object is a fourth-rank metric, i.e., a tensor with four indices. Based on this geometry we constructed a simple field theory for the gravitational field. The field equations coincide with the Einstein field equations in the vacuum case. This fact, however, does not guarantee the observational equivalence of both theories since one must still verify that, as a consequence of the field equations, test particles move along geodesics. This letter is aimed at establishing this result. (author). 7 refs

  9. Classical impurities associated to high rank algebras

    Energy Technology Data Exchange (ETDEWEB)

    Doikou, Anastasia, E-mail: A.Doikou@hw.ac.uk [Department of Mathematics, Heriot–Watt University, EH14 4AS, Edinburgh (United Kingdom); Department of Computer Engineering and Informatics, University of Patras, Patras GR-26500 (Greece)

    2014-07-15

    Classical integrable impurities associated with high rank (gl{sub N}) algebras are investigated. A particular prototype, i.e. the vector non-linear Schrödinger (NLS) model, is chosen as an example. A systematic construction of local integrals of motion as well as the time components of the corresponding Lax pairs is presented based on the underlying classical algebra. Suitable gluing conditions compatible with integrability are also extracted. The defect contribution is also examined in the case where non-trivial integrable conditions are implemented. It turns out that the integrable boundaries may drastically alter the bulk behavior, and in particular the defect contribution.

  10. Low-rank driving in quantum systems

    International Nuclear Information System (INIS)

    Burkey, R.S.

    1989-01-01

    A new property of quantum systems called low-rank driving is introduced. Numerous simplifications in the solution of the time-dependent Schroedinger equation are pointed out for systems having this property. These simplifications are in the areas of finding eigenvalues, taking the Laplace transform, converting Schroedinger's equation to an integral form, discretizing the continuum, generalizing the Weisskopf-Wigner approximation, band-diagonalizing the Hamiltonian, finding new exact solutions to Schroedinger's equation, and so forth. The principal physical application considered is the phenomenon of coherent populations-trapping in continuum-continuum interactions

  11. Classical impurities associated to high rank algebras

    International Nuclear Information System (INIS)

    Doikou, Anastasia

    2014-01-01

    Classical integrable impurities associated with high rank (gl N ) algebras are investigated. A particular prototype, i.e. the vector non-linear Schrödinger (NLS) model, is chosen as an example. A systematic construction of local integrals of motion as well as the time components of the corresponding Lax pairs is presented based on the underlying classical algebra. Suitable gluing conditions compatible with integrability are also extracted. The defect contribution is also examined in the case where non-trivial integrable conditions are implemented. It turns out that the integrable boundaries may drastically alter the bulk behavior, and in particular the defect contribution

  12. An improved rank based disease prediction using web navigation patterns on bio-medical databases

    Directory of Open Access Journals (Sweden)

    P. Dhanalakshmi

    2017-12-01

    Full Text Available Applying machine learning techniques to on-line biomedical databases is a challenging task, as this data is collected from large number of sources and it is multi-dimensional. Also retrieval of relevant document from large repository such as gene document takes more processing time and an increased false positive rate. Generally, the extraction of biomedical document is based on the stream of prior observations of gene parameters taken at different time periods. Traditional web usage models such as Markov, Bayesian and Clustering models are sensitive to analyze the user navigation patterns and session identification in online biomedical database. Moreover, most of the document ranking models on biomedical database are sensitive to sparsity and outliers. In this paper, a novel user recommendation system was implemented to predict the top ranked biomedical documents using the disease type, gene entities and user navigation patterns. In this recommendation system, dynamic session identification, dynamic user identification and document ranking techniques were used to extract the highly relevant disease documents on the online PubMed repository. To verify the performance of the proposed model, the true positive rate and runtime of the model was compared with that of traditional static models such as Bayesian and Fuzzy rank. Experimental results show that the performance of the proposed ranking model is better than the traditional models.

  13. Two Ranking Methods of Single Valued Triangular Neutrosophic Numbers to Rank and Evaluate Information Systems Quality

    Directory of Open Access Journals (Sweden)

    Samah Ibrahim Abdel Aal

    2018-03-01

    Full Text Available The concept of neutrosophic can provide a generalization of fuzzy set and intuitionistic fuzzy set that make it is the best fit in representing indeterminacy and uncertainty. Single Valued Triangular Numbers (SVTrN-numbers is a special case of neutrosophic set that can handle ill-known quantity very difficult problems. This work intended to introduce a framework with two types of ranking methods. The results indicated that each ranking method has its own advantage. In this perspective, the weighted value and ambiguity based method gives more attention to uncertainty in ranking and evaluating ISQ as well as it takes into account cut sets of SVTrN numbers that can reflect the information on Truth-membership-membership degree, false membership-membership degree and Indeterminacy-membership degree. The value index and ambiguity index method can reflect the decision maker's subjectivity attitude to the SVTrN- numbers.

  14. Content-based image retrieval with ontological ranking

    Science.gov (United States)

    Tsai, Shen-Fu; Tsai, Min-Hsuan; Huang, Thomas S.

    2010-02-01

    Images are a much more powerful medium of expression than text, as the adage says: "One picture is worth a thousand words." It is because compared with text consisting of an array of words, an image has more degrees of freedom and therefore a more complicated structure. However, the less limited structure of images presents researchers in the computer vision community a tough task of teaching machines to understand and organize images, especially when a limit number of learning examples and background knowledge are given. The advance of internet and web technology in the past decade has changed the way human gain knowledge. People, hence, can exchange knowledge with others by discussing and contributing information on the web. As a result, the web pages in the internet have become a living and growing source of information. One is therefore tempted to wonder whether machines can learn from the web knowledge base as well. Indeed, it is possible to make computer learn from the internet and provide human with more meaningful knowledge. In this work, we explore this novel possibility on image understanding applied to semantic image search. We exploit web resources to obtain links from images to keywords and a semantic ontology constituting human's general knowledge. The former maps visual content to related text in contrast to the traditional way of associating images with surrounding text; the latter provides relations between concepts for machines to understand to what extent and in what sense an image is close to the image search query. With the aid of these two tools, the resulting image search system is thus content-based and moreover, organized. The returned images are ranked and organized such that semantically similar images are grouped together and given a rank based on the semantic closeness to the input query. The novelty of the system is twofold: first, images are retrieved not only based on text cues but their actual contents as well; second, the grouping

  15. RANKING RELATIONS USING ANALOGIES IN BIOLOGICAL AND INFORMATION NETWORKS1

    Science.gov (United States)

    Silva, Ricardo; Heller, Katherine; Ghahramani, Zoubin; Airoldi, Edoardo M.

    2013-01-01

    Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to relational learning which, given a set of pairs of objects S = {A(1) : B(1), A(2) : B(2), …, A(N) : B(N)}, measures how well other pairs A : B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of interest. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relationships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interactions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided. PMID:24587838

  16. The BiPublishers ranking: Main results and methodological problems when constructing rankings of academic publishers

    Directory of Open Access Journals (Sweden)

    Torres-Salinas, Daniel

    2015-12-01

    Full Text Available We present the results of the Bibliometric Indicators for Publishers project (also known as BiPublishers. This project represents the first attempt to systematically develop bibliometric publisher rankings. The data for this project was derived from the Book Citation Index and the study time period was 2009-2013. We have developed 42 rankings: 4 by fields and 38 by disciplines. We display six indicators for publishers divided into three types: output, impact and publisher’s profile. The aim is to capture different characteristics of the research performance of publishers. 254 publishers were processed and classified according to publisher type: commercial publishers and university presses. We present the main publishers by field and then discuss the principal challenges presented when developing this type of tool. The BiPublishers ranking is an on-going project which aims to develop and explore new data sources and indicators to better capture and define the research impact of publishers.Presentamos los resultados del proyecto Bibliometric Indicators for Publishers (BiPublishers. Es el primer proyecto que desarrolla de manera sistemática rankings bibliométricos de editoriales. La fuente de datos empleada es el Book Citation Index y el periodo de análisis 2009-2013. Se presentan 42 rankings: 4 por áreas y 38 por disciplinas. Mostramos seis indicadores por editorial divididos según su tipología: producción, impacto y características editoriales. Se procesaron 254 editoriales y se clasificaron según el tipo: comerciales y universitarias. Se presentan las principales editoriales por áreas. Después, se discuten los principales retos a superar en el desarrollo de este tipo de herramientas. El ranking Bipublishers es un proyecto en desarrollo que persigue analizar y explorar nuevas fuentes de datos e indicadores para captar y definir el impacto de las editoriales académicas.

  17. Generalized PageRank on Directed Configuration Networks

    NARCIS (Netherlands)

    Chen, Ningyuan; Litvak, Nelli; Olvera-Cravioto, Mariana

    2017-01-01

    Note: formula is not displayed correctly. This paper studies the distribution of a family of rankings, which includes Google’s PageRank, on a directed configuration model. In particular, it is shown that the distribution of the rank of a randomly chosen node in the graph converges in distribution to

  18. PageRank in scale-free random graphs

    NARCIS (Netherlands)

    Chen, Ningyuan; Litvak, Nelli; Olvera-Cravioto, Mariana; Bonata, Anthony; Chung, Fan; Pralat, Paweł

    2014-01-01

    We analyze the distribution of PageRank on a directed configuration model and show that as the size of the graph grows to infinity, the PageRank of a randomly chosen node can be closely approximated by the PageRank of the root node of an appropriately constructed tree. This tree approximation is in

  19. Ranking Quality in Higher Education: Guiding or Misleading?

    Science.gov (United States)

    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…

  20. Revisiting the Relationship between Institutional Rank and Student Engagement

    Science.gov (United States)

    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.…

  1. Academic Ranking--From Its Genesis to Its International Expansion

    Science.gov (United States)

    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…

  2. 7 CFR 1491.6 - Ranking considerations and proposal selection.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 10 2010-01-01 2010-01-01 false Ranking considerations and proposal selection. 1491.6... PROGRAM General Provisions § 1491.6 Ranking considerations and proposal selection. (a) Before the State.... The national ranking criteria will be established by the Chief and the State criteria will be...

  3. 46 CFR 282.11 - Ranking of flags.

    Science.gov (United States)

    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...

  4. 10 CFR 455.131 - State ranking of grant applications.

    Science.gov (United States)

    2010-01-01

    ... 10 Energy 3 2010-01-01 2010-01-01 false State ranking of grant applications. 455.131 Section 455... State ranking of grant applications. (a) Except as provided by § 455.92 of this part, all eligible... audit or energy use evaluation pursuant to § 455.20(k). Each State shall develop separate rankings for...

  5. Control by Numbers: New Managerialism and Ranking in Higher Education

    Science.gov (United States)

    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…

  6. Paired comparisons analysis: an axiomatic approach to ranking methods

    NARCIS (Netherlands)

    Gonzalez-Diaz, J.; Hendrickx, Ruud; Lohmann, E.R.M.A.

    2014-01-01

    In this paper we present an axiomatic analysis of several ranking methods for general tournaments. We find that the ranking method obtained by applying maximum likelihood to the (Zermelo-)Bradley-Terry model, the most common method in statistics and psychology, is one of the ranking methods that

  7. Extracting Rankings for Spatial Keyword Queries from GPS Data

    DEFF Research Database (Denmark)

    Keles, Ilkcan; Jensen, Christian Søndergaard; Saltenis, Simonas

    2018-01-01

    Studies suggest that many search engine queries have local intent. We consider the evaluation of ranking functions important for such queries. The key challenge is to be able to determine the “best” ranking for a query, as this enables evaluation of the results of ranking functions. We propose...

  8. Tutorial: Calculating Percentile Rank and Percentile Norms Using SPSS

    Science.gov (United States)

    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…

  9. Variation in rank abundance replicate samples and impact of clustering

    NARCIS (Netherlands)

    Neuteboom, J.H.; Struik, P.C.

    2005-01-01

    Calculating a single-sample rank abundance curve by using the negative-binomial distribution provides a way to investigate the variability within rank abundance replicate samples and yields a measure of the degree of heterogeneity of the sampled community. The calculation of the single-sample rank

  10. A Fast Algorithm for Generating Permutation Distribution of Ranks in ...

    African Journals Online (AJOL)

    ... function of the distribution of the ranks. This further gives insight into the permutation distribution of a rank statistics. The algorithm is implemented with the aid of the computer algebra system Mathematica. Key words: Combinatorics, generating function, permutation distribution, rank statistics, partitions, computer algebra.

  11. Rank hypocrisies the insult of the REF

    CERN Document Server

    Sayer, Derek

    2015-01-01

    "The REF is right out of Havel's and Kundera's Eastern Europe: a state-administered exercise to rank academic research like hotel chains dependent on the active collaboration of the UK professoriate. In crystalline text steeped in cold rage, Sayer takes aim at the REF's central claim, that it is a legitimate process of expert peer review. He critiques university and national-level REF processes against actual practices of scholarly review as found in academic journals, university presses, and North American tenure procedures. His analysis is damning. If the REF fails as scholarly review, how can academics and universities continue to participate? And how can government use its rankings as a basis for public policy?" - Tarak Barkawi, Reader in the Department of International Relations, London School of Economics "Many academics across the world have come to see the REF as an arrogant attempt to raise national research standards that has resulted in a variety of self-inflicted wounds to UK higher education. Der...

  12. Demographic Ranking of the Baltic Sea States

    Directory of Open Access Journals (Sweden)

    Sluka N.

    2014-06-01

    Full Text Available The relevance of the study lies in the acute need to modernise the tools for a more accurate and comparable reflection of the demographic reality of spatial objects of different scales. This article aims to test the methods of “demographic rankings” developed by Yermakov and Shmakov. The method is based on the principles of indirect standardisation of the major demographic coefficients relative to the age structure.The article describes the first attempt to apply the method to the analysis of birth and mortality rates in 1995 and 2010 for 140 countries against the global average, and for the Baltic Sea states against the European average. The grouping of countries and the analysis of changes over the given period confirmed a number of demographic development trends and the persistence of wide territorial disparities in major indicators. The authors identify opposite trends in ranking based on the standardised birth (country consolidation at the level of averaged values and mortality (polarisation rates. The features of demographic process development in the Baltic regions states are described against the global and European background. The study confirmed the validity of the demographic ranking method, which can be instrumental in solving not only scientific but also practical tasks, including those in the field of demographic and social policy.

  13. Multiple hypothesis tracking for the cyber domain

    Science.gov (United States)

    Schwoegler, Stefan; Blackman, Sam; Holsopple, Jared; Hirsch, Michael J.

    2011-09-01

    This paper discusses how methods used for conventional multiple hypothesis tracking (MHT) can be extended to domain-agnostic tracking of entities from non-kinematic constraints such as those imposed by cyber attacks in a potentially dense false alarm background. MHT is widely recognized as the premier method to avoid corrupting tracks with spurious data in the kinematic domain but it has not been extensively applied to other problem domains. The traditional approach is to tightly couple track maintenance (prediction, gating, filtering, probabilistic pruning, and target confirmation) with hypothesis management (clustering, incompatibility maintenance, hypothesis formation, and Nassociation pruning). However, by separating the domain specific track maintenance portion from the domain agnostic hypothesis management piece, we can begin to apply the wealth of knowledge gained from ground and air tracking solutions to the cyber (and other) domains. These realizations led to the creation of Raytheon's Multiple Hypothesis Extensible Tracking Architecture (MHETA). In this paper, we showcase MHETA for the cyber domain, plugging in a well established method, CUBRC's INFormation Engine for Real-time Decision making, (INFERD), for the association portion of the MHT. The result is a CyberMHT. We demonstrate the power of MHETA-INFERD using simulated data. Using metrics from both the tracking and cyber domains, we show that while no tracker is perfect, by applying MHETA-INFERD, advanced nonkinematic tracks can be captured in an automated way, perform better than non-MHT approaches, and decrease analyst response time to cyber threats.

  14. Aminoglycoside antibiotics and autism: a speculative hypothesis

    Directory of Open Access Journals (Sweden)

    Manev Hari

    2001-10-01

    Full Text Available Abstract Background Recently, it has been suspected that there is a relationship between therapy with some antibiotics and the onset of autism; but even more curious, some children benefited transiently from a subsequent treatment with a different antibiotic. Here, we speculate how aminoglycoside antibiotics might be associated with autism. Presentation We hypothesize that aminoglycoside antibiotics could a trigger the autism syndrome in susceptible infants by causing the stop codon readthrough, i.e., a misreading of the genetic code of a hypothetical critical gene, and/or b improve autism symptoms by correcting the premature stop codon mutation in a hypothetical polymorphic gene linked to autism. Testing Investigate, retrospectively, whether a link exists between aminoglycoside use (which is not extensive in children and the onset of autism symptoms (hypothesis "a", or between amino glycoside use and improvement of these symptoms (hypothesis "b". Whereas a prospective study to test hypothesis "a" is not ethically justifiable, a study could be designed to test hypothesis "b". Implications It should be stressed that at this stage no direct evidence supports our speculative hypothesis and that its main purpose is to initiate development of new ideas that, eventually, would improve our understanding of the pathobiology of autism.

  15. Testing competing forms of the Milankovitch hypothesis

    DEFF Research Database (Denmark)

    Kaufmann, Robert K.; Juselius, Katarina

    2016-01-01

    We test competing forms of the Milankovitch hypothesis by estimating the coefficients and diagnostic statistics for a cointegrated vector autoregressive model that includes 10 climate variables and four exogenous variables for solar insolation. The estimates are consistent with the physical...... ice volume and solar insolation. The estimated adjustment dynamics show that solar insolation affects an array of climate variables other than ice volume, each at a unique rate. This implies that previous efforts to test the strong form of the Milankovitch hypothesis by examining the relationship...... that the latter is consistent with a weak form of the Milankovitch hypothesis and that it should be restated as follows: Internal climate dynamics impose perturbations on glacial cycles that are driven by solar insolation. Our results show that these perturbations are likely caused by slow adjustment between land...

  16. Rejecting the equilibrium-point hypothesis.

    Science.gov (United States)

    Gottlieb, G L

    1998-01-01

    The lambda version of the equilibrium-point (EP) hypothesis as developed by Feldman and colleagues has been widely used and cited with insufficient critical understanding. This article offers a small antidote to that lack. First, the hypothesis implicitly, unrealistically assumes identical transformations of lambda into muscle tension for antagonist muscles. Without that assumption, its definitions of command variables R, C, and lambda are incompatible and an EP is not defined exclusively by R nor is it unaffected by C. Second, the model assumes unrealistic and unphysiological parameters for the damping properties of the muscles and reflexes. Finally, the theory lacks rules for two of its three command variables. A theory of movement should offer insight into why we make movements the way we do and why we activate muscles in particular patterns. The EP hypothesis offers no unique ideas that are helpful in addressing either of these questions.

  17. The linear hypothesis and radiation carcinogenesis

    International Nuclear Information System (INIS)

    Roberts, P.B.

    1981-10-01

    An assumption central to most estimations of the carcinogenic potential of low levels of ionising radiation is that the risk always increases in direct proportion to the dose received. This assumption (the linear hypothesis) has been both strongly defended and attacked on several counts. It appears unlikely that conclusive, direct evidence on the validity of the hypothesis will be forthcoming. We review the major indirect arguments used in the debate. All of them are subject to objections that can seriously weaken their case. In the present situation, retention of the linear hypothesis as the basis of extrapolations from high to low dose levels can lead to excessive fears, over-regulation and unnecessarily expensive protection measures. To offset these possibilities, support is given to suggestions urging a cut-off dose, probably some fraction of natural background, below which risks can be deemed acceptable

  18. Rayleigh's hypothesis and the geometrical optics limit.

    Science.gov (United States)

    Elfouhaily, Tanos; Hahn, Thomas

    2006-09-22

    The Rayleigh hypothesis (RH) is often invoked in the theoretical and numerical treatment of rough surface scattering in order to decouple the analytical form of the scattered field. The hypothesis stipulates that the scattered field away from the surface can be extended down onto the rough surface even though it is formed by solely up-going waves. Traditionally this hypothesis is systematically used to derive the Volterra series under the small perturbation method which is equivalent to the low-frequency limit. In this Letter we demonstrate that the RH also carries the high-frequency or the geometrical optics limit, at least to first order. This finding has never been explicitly derived in the literature. Our result comforts the idea that the RH might be an exact solution under some constraints in the general case of random rough surfaces and not only in the case of small-slope deterministic periodic gratings.

  19. Learning

    Directory of Open Access Journals (Sweden)

    Mohsen Laabidi

    2014-01-01

    Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

  20. Exact distributions of two-sample rank statistics and block rank statistics using computer algebra

    NARCIS (Netherlands)

    Wiel, van de M.A.

    1998-01-01

    We derive generating functions for various rank statistics and we use computer algebra to compute the exact null distribution of these statistics. We present various techniques for reducing time and memory space used by the computations. We use the results to write Mathematica notebooks for

  1. Low ranks make the difference : How achievement goals and ranking information affect cooperation intentions

    NARCIS (Netherlands)

    Poortvliet, P. Marijn; Janssen, Onne; Van Yperen, N.W.; Van de Vliert, E.

    This investigation tested the joint effect of achievement goals and ranking information on information exchange intentions with a commensurate exchange partner. Results showed that individuals with performance goals were less inclined to cooperate with an exchange partner when they had low or high

  2. Critical influences on succession management for non-commissioned ranks within Victoria police

    OpenAIRE

    Owbridge, Lynne Michelle

    2017-01-01

    This thesis examines issues affecting succession management within noncommissioned ranks (NCRs) of policing services. Using Victoria Police (VicPol) as a case study, this research explored the organisation's capacity to build individual and organisational capability through adequate recruitment of new police, professional learning, and retaining talent within NCRs. A grounded theory approach was used to explore VicPol's recruitment, foundation training, transfer, promotion, ...

  3. Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

    OpenAIRE

    Zhao, Liang; Liao, Siyu; Wang, Yanzhi; Li, Zhe; Tang, Jian; Pan, Victor; Yuan, Bo

    2017-01-01

    Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computational complexity while retaining high accuracy. We formally study LDR matrices in deep learning. First, we prove the universal approximation property of LDR neural networks with a ...

  4. Inhibition of osteoclastogenesis by RNA interference targeting RANK

    Directory of Open Access Journals (Sweden)

    Ma Ruofan

    2012-08-01

    Full Text Available Abstract Background Osteoclasts and osteoblasts regulate bone resorption and formation to allow bone remodeling and homeostasis. The balance between bone resorption and formation is disturbed by abnormal recruitment of osteoclasts. Osteoclast differentiation is dependent on the receptor activator of nuclear factor NF-kappa B (RANK ligand (RANKL as well as the macrophage colony-stimulating factor (M-CSF. The RANKL/RANK system and RANK signaling induce osteoclast formation mediated by various cytokines. The RANK/RANKL pathway has been primarily implicated in metabolic, degenerative and neoplastic bone disorders or osteolysis. The central role of RANK/RANKL interaction in osteoclastogenesis makes RANK an attractive target for potential therapies in treatment of osteolysis. The purpose of this study was to assess the effect of inhibition of RANK expression in mouse bone marrow macrophages on osteoclast differentiation and bone resorption. Methods Three pairs of short hairpin RNAs (shRNA targeting RANK were designed and synthesized. The optimal shRNA was selected among three pairs of shRNAs by RANK expression analyzed by Western blot and Real-time PCR. We investigated suppression of osteoclastogenesis of mouse bone marrow macrophages (BMMs using the optimal shRNA by targeting RANK. Results Among the three shRANKs examined, shRANK-3 significantly suppressed [88.3%] the RANK expression (p Conclusions These findings suggest that retrovirus-mediated shRNA targeting RANK inhibits osteoclast differentiation and osteolysis. It may appear an attractive target for preventing osteolysis in humans with a potential clinical application.

  5. Support vector methods for survival analysis: a comparison between ranking and regression approaches.

    Science.gov (United States)

    Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

    2011-10-01

    To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods

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

    Science.gov (United States)

    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

  7. Fuzzy-set based contingency ranking

    International Nuclear Information System (INIS)

    Hsu, Y.Y.; Kuo, H.C.

    1992-01-01

    In this paper, a new approach based on fuzzy set theory is developed for contingency ranking of Taiwan power system. To examine whether a power system can remain in a secure and reliable operating state under contingency conditions, those contingency cases that will result in loss-of-load, loss-of generation, or islanding are first identified. Then 1P-1Q iteration of fast decoupled load flow is preformed to estimate post-contingent quantities (line flows, bus voltages) for other contingency cases. Based on system operators' past experience, each post-contingent quantity is assigned a degree of severity according to the potential damage that could be imposed on the power system by the quantity, should the contingency occurs. An approach based on fuzzy set theory is developed to deal with the imprecision of linguistic terms

  8. Motif discovery in ranked lists of sequences

    DEFF Research Database (Denmark)

    Nielsen, Morten Muhlig; Tataru, Paula; Madsen, Tobias

    2016-01-01

    Motif analysis has long been an important method to characterize biological functionality and the current growth of sequencing-based genomics experiments further extends its potential. These diverse experiments often generate sequence lists ranked by some functional property. There is therefore...... advantage of the regular expression feature, including enrichments for combinations of different microRNA seed sites. The method is implemented and made publicly available as an R package and supports high parallelization on multi-core machinery....... a growing need for motif analysis methods that can exploit this coupled data structure and be tailored for specific biological questions. Here, we present an exploratory motif analysis tool, Regmex (REGular expression Motif EXplorer), which offers several methods to evaluate the correlation of motifs...

  9. Ranked retrieval of Computational Biology models.

    Science.gov (United States)

    Henkel, Ron; Endler, Lukas; Peters, Andre; Le Novère, Nicolas; Waltemath, Dagmar

    2010-08-11

    The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind. Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models. The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.

  10. On the generalized gravi-magnetic hypothesis

    International Nuclear Information System (INIS)

    Massa, C.

    1989-01-01

    According to a generalization of the gravi-magnetic hypothesis (GMH) any neutral mass moving in a curvilinear path with respect to an inertial frame creates a magnetic field, dependent on the curvature radius of the path. A simple astrophysical consequence of the generalized GMH is suggested considering the special cases of binary pulsars and binary neutron stars

  11. Remarks about the hypothesis of limiting fragmentation

    International Nuclear Information System (INIS)

    Chou, T.T.; Yang, C.N.

    1987-01-01

    Remarks are made about the hypothesis of limiting fragmentation. In particular, the concept of favored and disfavored fragment distribution is introduced. Also, a sum rule is proved leading to a useful quantity called energy-fragmentation fraction. (author). 11 refs, 1 fig., 2 tabs

  12. Multiple hypothesis clustering in radar plot extraction

    NARCIS (Netherlands)

    Huizing, A.G.; Theil, A.; Dorp, Ph. van; Ligthart, L.P.

    1995-01-01

    False plots and plots with inaccurate range and Doppler estimates may severely degrade the performance of tracking algorithms in radar systems. This paper describes how a multiple hypothesis clustering technique can be applied to mitigate the problems involved in plot extraction. The measures of

  13. The (not so immortal strand hypothesis

    Directory of Open Access Journals (Sweden)

    Cristian Tomasetti

    2015-03-01

    Significance: Utilizing an approach that is fundamentally different from previous efforts to confirm or refute the immortal strand hypothesis, we provide evidence against non-random segregation of DNA during stem cell replication. Our results strongly suggest that parental DNA is passed randomly to stem cell daughters and provides new insight into the mechanism of DNA replication in stem cells.

  14. A Developmental Study of the Infrahumanization Hypothesis

    Science.gov (United States)

    Martin, John; Bennett, Mark; Murray, Wayne S.

    2008-01-01

    Intergroup attitudes in children were examined based on Leyen's "infrahumanization hypothesis". This suggests that some uniquely human emotions, such as shame and guilt (secondary emotions), are reserved for the in-group, whilst other emotions that are not uniquely human and shared with animals, such as anger and pleasure (primary…

  15. Morbidity and Infant Development: A Hypothesis.

    Science.gov (United States)

    Pollitt, Ernesto

    1983-01-01

    Results of a study conducted in 14 villages of Sui Lin Township, Taiwan, suggest the hypothesis that, under conditions of extreme economic impoverishment and among children within populations where energy protein malnutrition is endemic, there is an inverse relationship between incidence of morbidity in infancy and measures of motor and mental…

  16. Diagnostic Hypothesis Generation and Human Judgment

    Science.gov (United States)

    Thomas, Rick P.; Dougherty, Michael R.; Sprenger, Amber M.; Harbison, J. Isaiah

    2008-01-01

    Diagnostic hypothesis-generation processes are ubiquitous in human reasoning. For example, clinicians generate disease hypotheses to explain symptoms and help guide treatment, auditors generate hypotheses for identifying sources of accounting errors, and laypeople generate hypotheses to explain patterns of information (i.e., data) in the…

  17. Multi-hypothesis distributed stereo video coding

    DEFF Research Database (Denmark)

    Salmistraro, Matteo; Zamarin, Marco; Forchhammer, Søren

    2013-01-01

    for stereo sequences, exploiting an interpolated intra-view SI and two inter-view SIs. The quality of the SI has a major impact on the DVC Rate-Distortion (RD) performance. As the inter-view SIs individually present lower RD performance compared with the intra-view SI, we propose multi-hypothesis decoding...

  18. [Resonance hypothesis of heart rate variability origin].

    Science.gov (United States)

    Sheĭkh-Zade, Iu R; Mukhambetaliev, G Kh; Cherednik, I L

    2009-09-01

    A hypothesis is advanced of the heart rate variability being subjected to beat-to-beat regulation of cardiac cycle duration in order to ensure the resonance interaction between respiratory and own fluctuation of the arterial system volume for minimization of power expenses of cardiorespiratory system. Myogenic, parasympathetic and sympathetic machanisms of heart rate variability are described.

  19. In Defense of Chi's Ontological Incompatibility Hypothesis

    Science.gov (United States)

    Slotta, James D.

    2011-01-01

    This article responds to an article by A. Gupta, D. Hammer, and E. F. Redish (2010) that asserts that M. T. H. Chi's (1992, 2005) hypothesis of an "ontological commitment" in conceptual development is fundamentally flawed. In this article, I argue that Chi's theoretical perspective is still very much intact and that the critique offered by Gupta…

  20. Vacuum counterexamples to the cosmic censorship hypothesis

    International Nuclear Information System (INIS)

    Miller, B.D.

    1981-01-01

    In cylindrically symmetric vacuum spacetimes it is possible to specify nonsingular initial conditions such that timelike singularities will (necessarily) evolve from these conditions. Examples are given; the spacetimes are somewhat analogous to one of the spherically symmetric counterexamples to the cosmic censorship hypothesis

  1. A novel hypothesis splitting method implementation for multi-hypothesis filters

    DEFF Research Database (Denmark)

    Bayramoglu, Enis; Ravn, Ole; Andersen, Nils Axel

    2013-01-01

    The paper presents a multi-hypothesis filter library featuring a novel method for splitting Gaussians into ones with smaller variances. The library is written in C++ for high performance and the source code is open and free1. The multi-hypothesis filters commonly approximate the distribution tran...

  2. The Income Inequality Hypothesis Revisited : Assessing the Hypothesis Using Four Methodological Approaches

    NARCIS (Netherlands)

    Kragten, N.; Rözer, J.

    The income inequality hypothesis states that income inequality has a negative effect on individual’s health, partially because it reduces social trust. This article aims to critically assess the income inequality hypothesis by comparing several analytical strategies, namely OLS regression,

  3. Einstein's Revolutionary Light-Quantum Hypothesis

    Science.gov (United States)

    Stuewer, Roger H.

    2005-05-01

    The paper in which Albert Einstein proposed his light-quantum hypothesis was the only one of his great papers of 1905 that he himself termed ``revolutionary.'' Contrary to widespread belief, Einstein did not propose his light-quantum hypothesis ``to explain the photoelectric effect.'' Instead, he based his argument for light quanta on the statistical interpretation of the second law of thermodynamics, with the photoelectric effect being only one of three phenomena that he offered as possible experimental support for it. I will discuss Einstein's light-quantum hypothesis of 1905 and his introduction of the wave-particle duality in 1909 and then turn to the reception of his work on light quanta by his contemporaries. We will examine the reasons that prominent physicists advanced to reject Einstein's light-quantum hypothesis in succeeding years. Those physicists included Robert A. Millikan, even though he provided convincing experimental proof of the validity of Einstein's equation of the photoelectric effect in 1915. The turning point came after Arthur Holly Compton discovered the Compton effect in late 1922, but even then Compton's discovery was contested both on experimental and on theoretical grounds. Niels Bohr, in particular, had never accepted the reality of light quanta and now, in 1924, proposed a theory, the Bohr-Kramers-Slater theory, which assumed that energy and momentum were conserved only statistically in microscopic interactions. Only after that theory was disproved experimentally in 1925 was Einstein's revolutionary light-quantum hypothesis generally accepted by physicists---a full two decades after Einstein had proposed it.

  4. A Dopamine Hypothesis of Autism Spectrum Disorder.

    Science.gov (United States)

    Pavăl, Denis

    2017-01-01

    Autism spectrum disorder (ASD) comprises a group of neurodevelopmental disorders characterized by social deficits and stereotyped behaviors. While several theories have emerged, the pathogenesis of ASD remains unknown. Although studies report dopamine signaling abnormalities in autistic patients, a coherent dopamine hypothesis which could link neurobiology to behavior in ASD is currently lacking. In this paper, we present such a hypothesis by proposing that autistic behavior arises from dysfunctions in the midbrain dopaminergic system. We hypothesize that a dysfunction of the mesocorticolimbic circuit leads to social deficits, while a dysfunction of the nigrostriatal circuit leads to stereotyped behaviors. Furthermore, we discuss 2 key predictions of our hypothesis, with emphasis on clinical and therapeutic aspects. First, we argue that dopaminergic dysfunctions in the same circuits should associate with autistic-like behavior in nonautistic subjects. Concerning this, we discuss the case of PANDAS (pediatric autoimmune neuropsychiatric disorder associated with streptococcal infections) which displays behaviors similar to those of ASD, presumed to arise from dopaminergic dysfunctions. Second, we argue that providing dopamine modulators to autistic subjects should lead to a behavioral improvement. Regarding this, we present clinical studies of dopamine antagonists which seem to have improving effects on autistic behavior. Furthermore, we explore the means of testing our hypothesis by using neuroreceptor imaging, which could provide comprehensive evidence for dopamine signaling dysfunctions in autistic subjects. Lastly, we discuss the limitations of our hypothesis. Along these lines, we aim to provide a dopaminergic model of ASD which might lead to a better understanding of the ASD pathogenesis. © 2017 S. Karger AG, Basel.

  5. A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text

    Science.gov (United States)

    Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia

    2013-01-01

    Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008

  6. An R package for analyzing and modeling ranking data.

    Science.gov (United States)

    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

  7. Cognitive differences between orang-utan species: a test of the cultural intelligence hypothesis.

    Science.gov (United States)

    Forss, Sofia I F; Willems, Erik; Call, Josep; van Schaik, Carel P

    2016-07-28

    Cultural species can - or even prefer to - learn their skills from conspecifics. According to the cultural intelligence hypothesis, selection on underlying mechanisms not only improves this social learning ability but also the asocial (individual) learning ability. Thus, species with systematically richer opportunities to socially acquire knowledge and skills should over time evolve to become more intelligent. We experimentally compared the problem-solving ability of Sumatran orang-utans (Pongo abelii), which are sociable in the wild, with that of the closely related, but more solitary Bornean orang-utans (P. pygmaeus), under the homogeneous environmental conditions provided by zoos. Our results revealed that Sumatrans showed superior innate problem-solving skills to Borneans, and also showed greater inhibition and a more cautious and less rough exploration style. This pattern is consistent with the cultural intelligence hypothesis, which predicts that the more sociable of two sister species experienced stronger selection on cognitive mechanisms underlying learning.

  8. Automatic vowels selection and ranking in Russian enciphered texts

    Directory of Open Access Journals (Sweden)

    Yuri I. Petrenko

    2018-01-01

    Full Text Available This work was developed while teaching students the cryptanalysis. The course includes the study of statistics of (Russian encrypted texts. The purpose of training is to learn how to extract redundant information of the text and to descript the cryptogram without a password. One of the most comfortable methods for learning is a simple substitution and similar encryption systems, which are presented in most courses of cryptography. This paper presents a method of automatic separation of vowels and consonants in Russian texts, which releases some of the redundancy of the cipher text. In addition, this method greatly facilitates the task of descript some other symmetric ciphers which may be reduced to simple substitution.The aim of this work is to develop and implement a method for the automatic selection of vowels in Russian texts, enciphered by a simple substitution and similar encryption systems.According to the theory of Shannon, for unambiguous decoding of the text you want the redundancy of the text that exceeds the entropy of the password. After the separation of vowels and consonants redundancy of the text increases to one bit per symbol, this allows you to open shorter encrypted texts. Moreover, the separation of vowels and consonants greatly simplifies the cryptanalysis of some ciphers. For instance, cryptanalysis of the most famous encryption method - method of simple substitution-requires selection of one of N! possible passwords (where N is the number of letters in the alphabet. For the Russian language it is 33! or nearly 2 to 123rd degrees of options. After the separation of vowels and consonants you will need a selection of 10!*23!, or nearly 2 to 96th degrees of options. The number of combinations is reduced to one hundred million times, that makes the cryptanalysis much easier. The program that implements this method first creates a matrix of the probabilities of bigrams of the text.For this matrix Markov criterion calculated

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

    Science.gov (United States)

    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

  10. Using incomplete citation data for MEDLINE results ranking.

    Science.gov (United States)

    Herskovic, Jorge R; Bernstam, Elmer V

    2005-01-01

    Information overload is a significant problem for modern medicine. Searching MEDLINE for common topics often retrieves more relevant documents than users can review. Therefore, we must identify documents that are not only relevant, but also important. Our system ranks articles using citation counts and the PageRank algorithm, incorporating data from the Science Citation Index. However, citation data is usually incomplete. Therefore, we explore the relationship between the quantity of citation information available to the system and the quality of the result ranking. Specifically, we test the ability of citation count and PageRank to identify "important articles" as defined by experts from large result sets with decreasing citation information. We found that PageRank performs better than simple citation counts, but both algorithms are surprisingly robust to information loss. We conclude that even an incomplete citation database is likely to be effective for importance ranking.

  11. Co-integration Rank Testing under Conditional Heteroskedasticity

    DEFF Research Database (Denmark)

    Cavaliere, Guiseppe; Rahbæk, Anders; Taylor, A.M. Robert

    null distributions of the rank statistics coincide with those derived by previous authors who assume either i.i.d. or (strict and covariance) stationary martingale difference innovations. We then propose wild bootstrap implementations of the co-integrating rank tests and demonstrate that the associated...... bootstrap rank statistics replicate the first-order asymptotic null distributions of the rank statistics. We show the same is also true of the corresponding rank tests based on the i.i.d. bootstrap of Swensen (2006). The wild bootstrap, however, has the important property that, unlike the i.i.d. bootstrap......, it preserves in the re-sampled data the pattern of heteroskedasticity present in the original shocks. Consistent with this, numerical evidence sug- gests that, relative to tests based on the asymptotic critical values or the i.i.d. bootstrap, the wild bootstrap rank tests perform very well in small samples un...

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

    Science.gov (United States)

    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.

  13. Tests of the Giant Impact Hypothesis

    Science.gov (United States)

    Jones, J. H.

    1998-01-01

    The giant impact hypothesis has gained popularity as a means of explaining a volatile-depleted Moon that still has a chemical affinity to the Earth. As Taylor's Axiom decrees, the best models of lunar origin are testable, but this is difficult with the giant impact model. The energy associated with the impact would be sufficient to totally melt and partially vaporize the Earth. And this means that there should he no geological vestige of Barber times. Accordingly, it is important to devise tests that may be used to evaluate the giant impact hypothesis. Three such tests are discussed here. None of these is supportive of the giant impact model, but neither do they disprove it.

  14. The discovered preference hypothesis - an empirical test

    DEFF Research Database (Denmark)

    Lundhede, Thomas; Ladenburg, Jacob; Olsen, Søren Bøye

    Using stated preference methods for valuation of non-market goods is known to be vulnerable to a range of biases. Some authors claim that these so-called anomalies in effect render the methods useless for the purpose. However, the Discovered Preference Hypothesis, as put forth by Plott [31], offers...... an nterpretation and explanation of biases which entails that the stated preference methods need not to be completely written off. In this paper we conduct a test for the validity and relevance of the DPH interpretation of biases. In a choice experiment concerning preferences for protection of Danish nature areas...... as respondents evaluate more and more choice sets. This finding supports the Discovered Preference Hypothesis interpretation and explanation of starting point bias....

  15. The Hypothesis-Driven Physical Examination.

    Science.gov (United States)

    Garibaldi, Brian T; Olson, Andrew P J

    2018-05-01

    The physical examination remains a vital part of the clinical encounter. However, physical examination skills have declined in recent years, in part because of decreased time at the bedside. Many clinicians question the relevance of physical examinations in the age of technology. A hypothesis-driven approach to teaching and practicing the physical examination emphasizes the performance of maneuvers that can alter the likelihood of disease. Likelihood ratios are diagnostic weights that allow clinicians to estimate the post-probability of disease. This hypothesis-driven approach to the physical examination increases its value and efficiency, while preserving its cultural role in the patient-physician relationship. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. MOLIERE: Automatic Biomedical Hypothesis Generation System.

    Science.gov (United States)

    Sybrandt, Justin; Shtutman, Michael; Safro, Ilya

    2017-08-01

    Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network, and demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community.

  17. The Method of Hypothesis in Plato's Philosophy

    Directory of Open Access Journals (Sweden)

    Malihe Aboie Mehrizi

    2016-09-01

    Full Text Available The article deals with the examination of method of hypothesis in Plato's philosophy. This method, respectively, will be examined in three dialogues of Meno, Phaedon and Republic in which it is explicitly indicated. It will be shown the process of change of Plato’s attitude towards the position and usage of the method of hypothesis in his realm of philosophy. In Meno, considering the geometry, Plato attempts to introduce a method that can be used in the realm of philosophy. But, ultimately in Republic, Plato’s special attention to the method and its importance in the philosophical investigations, leads him to revise it. Here, finally Plato introduces the particular method of philosophy, i.e., the dialectic

  18. Debates—Hypothesis testing in hydrology: Introduction

    Science.gov (United States)

    Blöschl, Günter

    2017-03-01

    This paper introduces the papers in the "Debates—Hypothesis testing in hydrology" series. The four articles in the series discuss whether and how the process of testing hypotheses leads to progress in hydrology. Repeated experiments with controlled boundary conditions are rarely feasible in hydrology. Research is therefore not easily aligned with the classical scientific method of testing hypotheses. Hypotheses in hydrology are often enshrined in computer models which are tested against observed data. Testability may be limited due to model complexity and data uncertainty. All four articles suggest that hypothesis testing has contributed to progress in hydrology and is needed in the future. However, the procedure is usually not as systematic as the philosophy of science suggests. A greater emphasis on a creative reasoning process on the basis of clues and explorative analyses is therefore needed.

  19. Hypothesis testing of scientific Monte Carlo calculations

    Science.gov (United States)

    Wallerberger, Markus; Gull, Emanuel

    2017-11-01

    The steadily increasing size of scientific Monte Carlo simulations and the desire for robust, correct, and reproducible results necessitates rigorous testing procedures for scientific simulations in order to detect numerical problems and programming bugs. However, the testing paradigms developed for deterministic algorithms have proven to be ill suited for stochastic algorithms. In this paper we demonstrate explicitly how the technique of statistical hypothesis testing, which is in wide use in other fields of science, can be used to devise automatic and reliable tests for Monte Carlo methods, and we show that these tests are able to detect some of the common problems encountered in stochastic scientific simulations. We argue that hypothesis testing should become part of the standard testing toolkit for scientific simulations.

  20. Exploring heterogeneous market hypothesis using realized volatility

    Science.gov (United States)

    Chin, Wen Cheong; Isa, Zaidi; Mohd Nor, Abu Hassan Shaari

    2013-04-01

    This study investigates the heterogeneous market hypothesis using high frequency data. The cascaded heterogeneous trading activities with different time durations are modelled by the heterogeneous autoregressive framework. The empirical study indicated the presence of long memory behaviour and predictability elements in the financial time series which supported heterogeneous market hypothesis. Besides the common sum-of-square intraday realized volatility, we also advocated two power variation realized volatilities in forecast evaluation and risk measurement in order to overcome the possible abrupt jumps during the credit crisis. Finally, the empirical results are used in determining the market risk using the value-at-risk approach. The findings of this study have implications for informationally market efficiency analysis, portfolio strategies and risk managements.

  1. Distant Supervision for Relation Extraction with Ranking-Based Methods

    Directory of Open Access Journals (Sweden)

    Yang Xiang

    2016-05-01

    Full Text Available Relation extraction has benefited from distant supervision in recent years with the development of natural language processing techniques and data explosion. However, distant supervision is still greatly limited by the quality of training data, due to its natural motivation for greatly reducing the heavy cost of data annotation. In this paper, we construct an architecture called MIML-sort (Multi-instance Multi-label Learning with Sorting Strategies, which is built on the famous MIML framework. Based on MIML-sort, we propose three ranking-based methods for sample selection with which we identify relation extractors from a subset of the training data. Experiments are set up on the KBP (Knowledge Base Propagation corpus, one of the benchmark datasets for distant supervision, which is large and noisy. Compared with previous work, the proposed methods produce considerably better results. Furthermore, the three methods together achieve the best F1 on the official testing set, with an optimal enhancement of F1 from 27.3% to 29.98%.

  2. Water Taxation and the Double Dividend Hypothesis

    OpenAIRE

    Nicholas Kilimani

    2014-01-01

    The double dividend hypothesis contends that environmental taxes have the potential to yield multiple benefits for the economy. However, empirical evidence of the potential impacts of environmental taxation in developing countries is still limited. This paper seeks to contribute to the literature by exploring the impact of a water tax in a developing country context, with Uganda as a case study. Policy makers in Uganda are exploring ways of raising revenue by taxing environmental goods such a...

  3. [Working memory, phonological awareness and spelling hypothesis].

    Science.gov (United States)

    Gindri, Gigiane; Keske-Soares, Márcia; Mota, Helena Bolli

    2007-01-01

    Working memory, phonological awareness and spelling hypothesis. To verify the relationship between working memory, phonological awareness and spelling hypothesis in pre-school children and first graders. Participants of this study were 90 students, belonging to state schools, who presented typical linguistic development. Forty students were preschoolers, with the average age of six and 50 students were first graders, with the average age of seven. Participants were submitted to an evaluation of the working memory abilities based on the Working Memory Model (Baddeley, 2000), involving phonological loop. Phonological loop was evaluated using the Auditory Sequential Test, subtest 5 of Illinois Test of Psycholinguistic Abilities (ITPA), Brazilian version (Bogossian & Santos, 1977), and the Meaningless Words Memory Test (Kessler, 1997). Phonological awareness abilities were investigated using the Phonological Awareness: Instrument of Sequential Assessment (CONFIAS - Moojen et al., 2003), involving syllabic and phonemic awareness tasks. Writing was characterized according to Ferreiro & Teberosky (1999). Preschoolers presented the ability of repeating sequences of 4.80 digits and 4.30 syllables. Regarding phonological awareness, the performance in the syllabic level was of 19.68 and in the phonemic level was of 8.58. Most of the preschoolers demonstrated to have a pre-syllabic writing hypothesis. First graders repeated, in average, sequences of 5.06 digits and 4.56 syllables. These children presented a phonological awareness of 31.12 in the syllabic level and of 16.18 in the phonemic level, and demonstrated to have an alphabetic writing hypothesis. The performance of working memory, phonological awareness and spelling level are inter-related, as well as being related to chronological age, development and scholarity.

  4. Privacy on Hypothesis Testing in Smart Grids

    OpenAIRE

    Li, Zuxing; Oechtering, Tobias

    2015-01-01

    In this paper, we study the problem of privacy information leakage in a smart grid. The privacy risk is assumed to be caused by an unauthorized binary hypothesis testing of the consumer's behaviour based on the smart meter readings of energy supplies from the energy provider. Another energy supplies are produced by an alternative energy source. A controller equipped with an energy storage device manages the energy inflows to satisfy the energy demand of the consumer. We study the optimal ener...

  5. Box-particle probability hypothesis density filtering

    OpenAIRE

    Schikora, M.; Gning, A.; Mihaylova, L.; Cremers, D.; Koch, W.

    2014-01-01

    This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small number of box-p...

  6. Quantum effects and hypothesis of cosmic censorship

    International Nuclear Information System (INIS)

    Parnovskij, S.L.

    1989-01-01

    It is shown that filamentary characteristics with linear mass of less than 10 25 g/cm distort slightly the space-time at distances, exceeding Planck ones. Their formation doesn't change vacuum energy and doesn't lead to strong quantum radiation. Therefore, the problem of their occurrence can be considered within the framework of classical collapse. Quantum effects can be ignored when considering the problem of validity of cosmic censorship hypothesis

  7. A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

    Science.gov (United States)

    Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

    2012-04-01

    We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

  8. Discovering author impact: A PageRank perspective

    OpenAIRE

    Yan, Erjia; Ding, Ying

    2010-01-01

    This article provides an alternative perspective for measuring author impact by applying PageRank algorithm to a coauthorship network. A weighted PageRank algorithm considering citation and coauthorship network topology is proposed. We test this algorithm under different damping factors by evaluating author impact in the informetrics research community. In addition, we also compare this weighted PageRank with the h-index, citation, and program committee (PC) membership of the International So...

  9. Convolutional Codes with Maximum Column Sum Rank for Network Streaming

    OpenAIRE

    Mahmood, Rafid; Badr, Ahmed; Khisti, Ashish

    2015-01-01

    The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank, that parallels column Hamming distance when streaming over a network with link failures. We prove rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction invol...

  10. Ranking agricultural, environmental and natural resource economics journals: A note

    OpenAIRE

    Halkos, George; Tzeremes, Nickolaos

    2012-01-01

    This paper by applying Data Envelopment Analysis (DEA) ranks for the first time Economics journals in the field of Agricultural, Environmental and Natural Resource. Specifically, by using one composite input and one composite output the paper ranks 32 journals. In addition for the first time three different quality ranking reports have been incorporated to the DEA modelling problem in order to classify the journals into four categories (‘A’ to ‘D’). The results reveal that the journals with t...

  11. Asympotic efficiency of signed - rank symmetry tests under skew alternatives.

    OpenAIRE

    Alessandra Durio; Yakov Nikitin

    2002-01-01

    The efficiency of some known tests for symmetry such as the sign test, the Wilcoxon signed-rank test or more general linear signed rank tests was studied mainly under the classical alternatives of location. However it is interesting to compare the efficiencies of these tests under asymmetric alternatives like the so-called skew alternative proposed in Azzalini (1985). We find and compare local Bahadur efficiencies of linear signed-rank statistics for skew alternatives and discuss also the con...

  12. Reduced Rank Adaptive Filtering in Impulsive Noise Environments

    KAUST Repository

    Soury, Hamza

    2014-01-06

    An impulsive noise environment is used in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction. The minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each algorithm is discussed.

  13. Reduced Rank Adaptive Filtering in Impulsive Noise Environments

    KAUST Repository

    Soury, Hamza; Abed-Meraim, Karim; Alouini, Mohamed-Slim

    2014-01-01

    An impulsive noise environment is used in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction. The minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each algorithm is discussed.

  14. A Citation-Based Ranking of Strategic Management Journals

    OpenAIRE

    Azar, Ofer H.; Brock, David M.

    2007-01-01

    Rankings of strategy journals are important for authors, readers, and promotion and tenure committees. We present several rankings, based either on the number of articles that cited the journal or the per-article impact. Our analyses cover various periods between 1991 and 2006, for most of which the Strategic Management Journal was in first place and Journal of Economics & Management Strategy (JEMS) second, although JEMS ranked first in certain instances. Long Range Planning and Technology An...

  15. The (not so) immortal strand hypothesis.

    Science.gov (United States)

    Tomasetti, Cristian; Bozic, Ivana

    2015-03-01

    Non-random segregation of DNA strands during stem cell replication has been proposed as a mechanism to minimize accumulated genetic errors in stem cells of rapidly dividing tissues. According to this hypothesis, an "immortal" DNA strand is passed to the stem cell daughter and not the more differentiated cell, keeping the stem cell lineage replication error-free. After it was introduced, experimental evidence both in favor and against the hypothesis has been presented. Using a novel methodology that utilizes cancer sequencing data we are able to estimate the rate of accumulation of mutations in healthy stem cells of the colon, blood and head and neck tissues. We find that in these tissues mutations in stem cells accumulate at rates strikingly similar to those expected without the protection from the immortal strand mechanism. Utilizing an approach that is fundamentally different from previous efforts to confirm or refute the immortal strand hypothesis, we provide evidence against non-random segregation of DNA during stem cell replication. Our results strongly suggest that parental DNA is passed randomly to stem cell daughters and provides new insight into the mechanism of DNA replication in stem cells. Copyright © 2015. Published by Elsevier B.V.

  16. A test of the orthographic recoding hypothesis

    Science.gov (United States)

    Gaygen, Daniel E.

    2003-04-01

    The Orthographic Recoding Hypothesis [D. E. Gaygen and P. A. Luce, Percept. Psychophys. 60, 465-483 (1998)] was tested. According to this hypothesis, listeners recognize spoken words heard for the first time by mapping them onto stored representations of the orthographic forms of the words. Listeners have a stable orthographic representation of words, but no phonological representation, when those words have been read frequently but never heard or spoken. Such may be the case for low frequency words such as jargon. Three experiments using visually and auditorily presented nonword stimuli tested this hypothesis. The first two experiments were explicit tests of memory (old-new tests) for words presented visually. In the first experiment, the recognition of auditorily presented nonwords was facilitated when they previously appeared on a visually presented list. The second experiment was similar, but included a concurrent articulation task during a visual word list presentation, thus preventing covert rehearsal of the nonwords. The results were similar to the first experiment. The third experiment was an indirect test of memory (auditory lexical decision task) for visually presented nonwords. Auditorily presented nonwords were identified as nonwords significantly more slowly if they had previously appeared on the visually presented list accompanied by a concurrent articulation task.

  17. Consumer health information seeking as hypothesis testing.

    Science.gov (United States)

    Keselman, Alla; Browne, Allen C; Kaufman, David R

    2008-01-01

    Despite the proliferation of consumer health sites, lay individuals often experience difficulty finding health information online. The present study attempts to understand users' information seeking difficulties by drawing on a hypothesis testing explanatory framework. It also addresses the role of user competencies and their interaction with internet resources. Twenty participants were interviewed about their understanding of a hypothetical scenario about a family member suffering from stable angina and then searched MedlinePlus consumer health information portal for information on the problem presented in the scenario. Participants' understanding of heart disease was analyzed via semantic analysis. Thematic coding was used to describe information seeking trajectories in terms of three key strategies: verification of the primary hypothesis, narrowing search within the general hypothesis area and bottom-up search. Compared to an expert model, participants' understanding of heart disease involved different key concepts, which were also differently grouped and defined. This understanding provided the framework for search-guiding hypotheses and results interpretation. Incorrect or imprecise domain knowledge led individuals to search for information on irrelevant sites, often seeking out data to confirm their incorrect initial hypotheses. Online search skills enhanced search efficiency, but did not eliminate these difficulties. Regardless of their web experience and general search skills, lay individuals may experience difficulty with health information searches. These difficulties may be related to formulating and evaluating hypotheses that are rooted in their domain knowledge. Informatics can provide support at the levels of health information portals, individual websites, and consumer education tools.

  18. Connectivity ranking of heterogeneous random conductivity models

    Science.gov (United States)

    Rizzo, C. B.; de Barros, F.

    2017-12-01

    To overcome the challenges associated with hydrogeological data scarcity, the hydraulic conductivity (K) field is often represented by a spatial random process. The state-of-the-art provides several methods to generate 2D or 3D random K-fields, such as the classic multi-Gaussian fields or non-Gaussian fields, training image-based fields and object-based fields. We provide a systematic comparison of these models based on their connectivity. We use the minimum hydraulic resistance as a connectivity measure, which it has been found to be strictly correlated with early time arrival of dissolved contaminants. A computationally efficient graph-based algorithm is employed, allowing a stochastic treatment of the minimum hydraulic resistance through a Monte-Carlo approach and therefore enabling the computation of its uncertainty. The results show the impact of geostatistical parameters on the connectivity for each group of random fields, being able to rank the fields according to their minimum hydraulic resistance.

  19. Multirelational Social Recommendations via Multigraph Ranking.

    Science.gov (United States)

    Mao, Mingsong; Lu, Jie; Zhang, Guangquan; Zhang, Jinlong

    2017-12-01

    Recommender systems aim to identify relevant items for particular users in large-scale online applications. The historical rating data of users is a valuable input resource for many recommendation models such as collaborative filtering (CF), but these models are known to suffer from the rating sparsity problem when the users or items under consideration have insufficient rating records. With the continued growth of online social networks, the increased user-to-user relationships are reported to be helpful and can alleviate the CF rating sparsity problem. Although researchers have developed a range of social network-based recommender systems, there is no unified model to handle multirelational social networks. To address this challenge, this paper represents different user relationships in a multigraph and develops a multigraph ranking model to identify and recommend the nearest neighbors of particular users in high-order environments. We conduct empirical experiments on two real-world datasets: 1) Epinions and 2) Last.fm, and the comprehensive comparison with other approaches demonstrates that our model improves recommendation performance in terms of both recommendation coverage and accuracy, especially when the rating data are sparse.

  20. Improving CBIR Systems Using Automated Ranking

    Directory of Open Access Journals (Sweden)

    B. D. Reljin

    2012-11-01

    Full Text Available The most common way of searching images on the Internet and in private collections is based on a similarity measuring of a series of text words that are assigned to each image with users query series. This method imposes strong constraints (the number of words to describe the image, the time necessary to thoroughly describe the subjective experience of images, the level of details in the picture, language barrier, etc., and is therefore very inefficient. Modern researches in this area are focused on the contentbased searching images (CBIR. In this way, all described disadvantages are overcome and the quality of searching results is improved. This paper presents a solution for CBIR systems where the search procedure is enhanced using sophisticated extraction and ranking of extracted images. The searching procedure is based on extraction and preprocessing of a large number of low level image features. Thus, when the user defines a query image, the proposed algorithm based on artificial intelligence, shows to the user a group of images which are most similar to a query image by content. The proposed algorithm is iterative, so the user can direct the searching procedure to an expected outcome and get a set of images that are more similar to the query one.