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Background: The psychometric properties of an online test are not necessarily identical to its paper and pencil original. The aim of this study is to test whether the factor structure of the Community Assessment of Psychic Experiences (CAPE) is measurement invariant with respect to online vs. paper and pencil assessment. Method: The factor structure of CAPE items assessed by paper and pencil (N = 796) was compared with the factor structure of CAPE items assessed by the Internet (N = 21,590) using formal tests for Measurement Invariance (MI). The effect size was calculated by estimating the Signed Item Difference in the Sample (SIDS) index and the Signed Test Difference in the Sample (STDS) for a hypothetical subject who scores 2 standard deviations above average on the latent dimensions. Results: The more restricted Metric Invariance model showed a significantly worse fit compared to the less restricted Configural Invariance model (χ2(23) = 152.75, p<0.001). However, the SIDS indices appear to be small, with an average of −0.11. A STDS of −4.80 indicates that Internet sample members who score 2 standard deviations above average would be expected to score 4.80 points lower on the CAPE total scale (ranging from 42 to 114 points) than would members of the Paper sample with the same latent trait score. Conclusions: Our findings did not support measurement invariance with respect to assessment method. Because of the small effect sizes, the measurement differences between the online assessed CAPE and its paper and pencil original can be neglected without major consequences for research purposes. However, a person with a high vulnerability for psychotic symptoms would score 4.80 points lower on the total scale if the CAPE is assessed online compared to paper and pencil assessment. Therefore, for clinical purposes, one should be cautious with online assessment of the CAPE. [ABSTRACT FROM AUTHOR]

Background: Finding duplicates is an important phase of systematic review. However, no consensus regarding the methods to find duplicates has been provided. This study aims to describe a pragmatic strategy of combining auto- and hand-searching duplicates in systematic review and to evaluate the prevalence and characteristics of duplicates. Methods and Findings: Literatures regarding portal vein thrombosis (PVT) and Budd-Chiari syndrome (BCS) were searched by the PubMed, EMBASE, and Cochrane library databases. Duplicates included one index paper and one or more redundant papers. They were divided into type-I (duplicates among different databases) and type-II (duplicate publications in different journals/issues) duplicates. For type-I duplicates, reference items were further compared between index and redundant papers. Of 10936 papers regarding PVT, 2399 and 1307 were identified as auto- and hand-searched duplicates, respectively. The prevalence of auto- and hand-searched redundant papers was 11.0% (1201/10936) and 6.1% (665/10936), respectively. They included 3431 type-I and 275 type-II duplicates. Of 11403 papers regarding BCS, 3275 and 2064 were identified as auto- and hand-searched duplicates, respectively. The prevalence of auto- and hand-searched redundant papers was 14.4% (1640/11403) and 9.1% (1039/11403), respectively. They included 5053 type-I and 286 type-II duplicates. Most of type-I duplicates were identified by auto-searching method (69.5%, 2385/3431 in PVT literatures; 64.6%, 3263/5053 in BCS literatures). Nearly all type-II duplicates were identified by hand-searching method (94.9%, 261/275 in PVT literatures; 95.8%, 274/286 in BCS literatures). Compared with those identified by auto-searching method, type-I duplicates identified by hand-searching method had a significantly higher prevalence of wrong items (47/2385 versus 498/1046, p<0.0001 in PVT literatures; 30/3263 versus 778/1790, p<0.0001 in BCS literatures). Most of wrong items originated from EMBASE database. Conclusion: Given the inadequacy of a single strategy of auto-searching method, a combined strategy of auto- and hand-searching methods should be employed to find duplicates in systematic review. [ABSTRACT FROM AUTHOR]

This paper identifies liquidity spillovers through different time scales based on a wavelet multiscaling method. We decompose daily data from U.S., British, Brazilian and Hong Kong stock markets indices in order to calculate the scale correlation between their illiquidities. The sample is divided in order to consider non-crisis, sub-prime crisis and Eurozone crisis. We find that there are changes in correlations of distinct scales and different periods. Association in finest scales is smaller than in coarse scales. There is a rise on associations in periods of crisis. In frequencies, there is predominance for significant distinctions involving the coarsest scale, while for crises periods there is predominance for distinctions on the finest scale. [ABSTRACT FROM AUTHOR]

Current metrics for estimating a scientist’s academic performance treat the author’s publications as if these were solely attributable to the author. However, this approach ignores the substantive contributions of co-authors, leading to misjudgments about the individual’s own scientific merits and consequently to misallocation of funding resources and academic positions. This problem is becoming the more urgent in the biomedical field where the number of collaborations is growing rapidly, making it increasingly harder to support the best scientists. Therefore, here we introduce a simple harmonic weighing algorithm for correcting citations and citation-based metrics such as the h-index for co-authorships. This weighing algorithm can account for both the nvumber of co-authors and the sequence of authors on a paper. We then derive a measure called the ‘profit (p)-index’, which estimates the contribution of co-authors to the work of a given author. By using samples of researchers from a renowned Dutch University hospital, Spinoza Prize laureates (the most prestigious Dutch science award), and Nobel Prize laureates in Physiology or Medicine, we show that the contribution of co-authors to the work of a particular author is generally substantial (i.e., about 80%) and that researchers’ relative rankings change materially when adjusted for the contributions of co-authors. Interestingly, although the top University hospital researchers had the highest h-indices, this appeared to be due to their significantly higher p-indices. Importantly, the ranking completely reversed when using the profit adjusted h-indices, with the Nobel laureates having the highest, the Spinoza Prize laureates having an intermediate, and the top University hospital researchers having the lowest profit adjusted h-indices, respectively, suggesting that exceptional researchers are characterized by a relatively high degree of scientific independency/originality. The concepts and methods introduced here may thus provide a more fair impression of a scientist’s autonomous academic performance. [ABSTRACT FROM AUTHOR]

In this paper evidence of anthropogenic influence over the warming of the 20th century is presented and the debate regarding the time-series properties of global temperatures is addressed in depth. The 20th century global temperature simulations produced for the Intergovernmental Panel on Climate Change’s Fourth Assessment Report and a set of the radiative forcing series used to drive them are analyzed using modern econometric techniques. Results show that both temperatures and radiative forcing series share similar time-series properties and a common nonlinear secular movement. This long-term co-movement is characterized by the existence of time-ordered breaks in the slope of their trend functions. The evidence presented in this paper suggests that while natural forcing factors may help explain the warming of the first part of the century, anthropogenic forcing has been its main driver since the 1970’s. In terms of Article 2 of the United Nations Framework Convention on Climate Change, significant anthropogenic interference with the climate system has already occurred and the current climate models are capable of accurately simulating the response of the climate system, even if it consists in a rapid or abrupt change, to changes in external forcing factors. This paper presents a new methodological approach for conducting time-series based attribution studies. [ABSTRACT FROM AUTHOR]

Recent studies of urban scaling show that important socioeconomic city characteristics such as wealth and innovation capacity exhibit a nonlinear, particularly a power law scaling with population size. These nonlinear effects are common to all cities, with similar power law exponents. These findings mean that the larger the city, the more disproportionally they are places of wealth and innovation. Local properties of cities cause a deviation from the expected behavior as predicted by the power law scaling. In this paper we demonstrate that universities show a similar behavior as cities in the distribution of the ‘gross university income’ in terms of total number of citations over ‘size’ in terms of total number of publications. Moreover, the power law exponents for university scaling are comparable to those for urban scaling. We find that deviations from the expected behavior can indeed be explained by specific local properties of universities, particularly the field-specific composition of a university, and its quality in terms of field-normalized citation impact. By studying both the set of the 500 largest universities worldwide and a specific subset of these 500 universities -the top-100 European universities- we are also able to distinguish between properties of universities with as well as without selection of one specific local property, the quality of a university in terms of its average field-normalized citation impact. It also reveals an interesting observation concerning the working of a crucial property in networked systems, preferential attachment. [ABSTRACT FROM AUTHOR]

Input-output analysis has been proven to be a powerful instrument for estimating embodied (direct plus indirect) energy usage through economic sectors. Using 9 economic input-output tables of years 1987, 1990, 1992, 1995, 1997, 2000, 2002, 2005, and 2007, this paper analyzes energy flows for the entire city of Beijing and its 30 economic sectors, respectively. Results show that the embodied energy consumption of Beijing increased from 38.85 million tonnes of coal equivalent (Mtce) to 206.2 Mtce over the past twenty years of rapid urbanization; the share of indirect energy consumption in total energy consumption increased from 48% to 76%, suggesting the transition of Beijing from a production-based and manufacturing-dominated economy to a consumption-based and service-dominated economy. Real estate development has shown to be a major driving factor of the growth in indirect energy consumption. The boom and bust of construction activities have been strongly correlated with the increase and decrease of system-side indirect energy consumption. Traditional heavy industries remain the most energy-intensive sectors in the economy. However, the transportation and service sectors have contributed most to the rapid increase in overall energy consumption. The analyses in this paper demonstrate that a system-wide approach such as that based on input-output model can be a useful tool for robust energy policy making. [ABSTRACT FROM AUTHOR]

The influence of the statistical properties of the network on the knowledge diffusion has been extensively studied. However, the structure evolution and the knowledge generation processes are always integrated simultaneously. By introducing the Cobb-Douglas production function and treating the knowledge growth as a cooperative production of knowledge, in this paper, we present two knowledge-generation dynamic evolving models based on different evolving mechanisms. The first model, named “HDPH model,” adopts the hyperedge growth and the hyperdegree preferential attachment mechanisms. The second model, named “KSPH model,” adopts the hyperedge growth and the knowledge stock preferential attachment mechanisms. We investigate the effect of the parameters on the total knowledge stock of the two models. The hyperdegree distribution of the HDPH model can be theoretically analyzed by the mean-field theory. The analytic result indicates that the hyperdegree distribution of the HDPH model obeys the power-law distribution and the exponent is . Furthermore, we present the distributions of the knowledge stock for different parameters . The findings indicate that our proposed models could be helpful for deeply understanding the scientific research cooperation. [ABSTRACT FROM AUTHOR]

Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus. [ABSTRACT FROM AUTHOR]

There has been an increasing interest in the geographic aspects of economic development, exemplified by P. Krugman’s logical analysis. We show in this paper that the geographic aspects of economic development can be modeled using multi-agent systems that incorporate multiple underlying factors. The extent of information sharing is assumed to be a driving force that leads to economic geographic heterogeneity across locations without geographic advantages or disadvantages. We propose an agent-based market model that considers a spectrum of different information-sharing mechanisms: no information sharing, information sharing among friends and pheromone-like information sharing. Finally, we build a unified model that accommodates all three of these information-sharing mechanisms based on the number of friends who can share information. We find that the no information-sharing model does not yield large economic zones, and more information sharing can give rise to a power-law distribution of market size that corresponds to the stylized fact of city size and firm size distributions. The simulations show that this model is robust. This paper provides an alternative approach to studying economic geographic development, and this model could be used as a test bed to validate the detailed assumptions that regulate real economic agglomeration. [ABSTRACT FROM AUTHOR]

We analyse the output-unemployment relationship using an approach based on cross-recurrence plots and quantitative recurrence analysis. We use post-war period quarterly U.S. data. The results obtained show the emergence of a complex and interesting relationship. [ABSTRACT FROM AUTHOR]

: The objective of this paper is to verify the hypotheses presented in the literature on the causal relationship between inflation and its uncertainty, for the newest EU countries. To ensure the robustness of the results, in the study four models for inflation uncertainty are estimated in parallel: ARCH (1), GARCH (1,1), EGARCH (1,1,1) and PARCH (1,1,1). The Granger method is used to test the causality between two variables. The working hypothesis is that groups of countries with a similar political and economic background in 1990 and are likely to be characterized by the same causal relationship between inflation and inflation uncertainty. Empirical results partially confirm this hypothesis. Jel Classification: C22, E31, E37. [ABSTRACT FROM AUTHOR]

In the business context, concern for the environment began to develop when pressure from the public administration and environmental awareness groups raised the specific requirements for companies. The Theory of Planned Behavior considers that people's conduct is determined by the intention of carrying out a certain behavior. Thus, the individual's intent is determined by three factors related to the desired outcome of the behavior: the Personal Attitude toward the Results, the Perceived Social Norms, and the Perceived Behavioral Control over the action. Therefore, the objectives of this paper are to clarify the attitudes of the managers of Canarian small and medium-sized companies about taking environmental measures, and try to demonstrate whether there is a relationship between the proposed factors and the intention to take these measures. [ABSTRACT FROM AUTHOR]

An understanding of how individuals shape and impact the evolution of society is vastly limited due to the unavailability of large-scale reliable datasets that can simultaneously capture information regarding individual movements and social interactions. We believe that the popular Indian film industry, “Bollywood”, can provide a social network apt for such a study. Bollywood provides massive amounts of real, unbiased data that spans more than 100 years, and hence this network has been used as a model for the present paper. The nodes which maintain a moderate degree or widely cooperate with the other nodes of the network tend to be more fit (measured as the success of the node in the industry) in comparison to the other nodes. The analysis carried forth in the current work, using a conjoined framework of complex network theory and random matrix theory, aims to quantify the elements that determine the fitness of an individual node and the factors that contribute to the robustness of a network. The authors of this paper believe that the method of study used in the current paper can be extended to study various other industries and organizations. [ABSTRACT FROM AUTHOR]

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model. [ABSTRACT FROM AUTHOR]

Common-pool resource (CPR) dilemmas distinguish themselves from general public good problems by encompassing both social and physical features. This paper examines how a physical mechanism, namely asymmetric payoff; and a social mechanism, reciprocity; simultaneously affect collective cooperation in theoretical water sharing interactions. We present an iterative N-person game theoretic model to investigate the joint effects of these two mechanisms in a linear fully connected river system under three information assumptions. From a simple evolutionary perspective, this paper quantitatively addresses the conditions for Nash Equilibrium in which collective cooperation might be established. The results suggest that direct reciprocity increases every actor’s motivation to contribute to the collective good of the river system. Meanwhile, various upstream and downstream actors manifest individual disparities as a result of the direct reciprocity and asymmetric payoff mechanisms. More specifically, the downstream actors are less willing to cooperate unless there is a high probability that long-term interactions are ensured; however, a greater level of asymmetries is likely to increase upstream actors’ incentives to cooperate even though the interactions could quickly end. The upstream actors also display weak sensitivity to an increase in the total number of actors, which generally results in a reduction in the other actors’ motivation for cooperation. It is also shown that the indirect reciprocity mechanism relaxes the overall conditions for cooperative Nash Equilibrium. [ABSTRACT FROM AUTHOR]

The huge regional disparity in government health expenditures (GHE) is a major policy concern in China. This paper addresses whether provincial GHE converges in China from 1997 to 2009 using the economic convergence framework based on neoclassical economic growth theory. Our empirical investigation provides compelling evidence of long-term convergence in provincial GHE within China, but not in short-term. Policy implications of these empirical results are discussed. [ABSTRACT FROM AUTHOR]

This paper extends classical work on economics of doping into a multi-player game setting. Apart from being among the first papers formally formulating and analysing a multi-player doping situation, we find interesting results related to different types of Nash-equilibria (NE). Based mainly on analytic results, we claim at least two different NE structures linked to the choice of prize functions. Linear prize functions provide NEs characterised by either everyone or nobody taking drugs, while non-linear prize functions lead to qualitatively different NEs with significantly more complex predictive characteristics. [ABSTRACT FROM AUTHOR]

The ASJP (Automated Similarity Judgment Program) described an automated, lexical similarity-based method for dating the world’s language groups using 52 archaeological, epigraphic and historical calibration date points. The present paper describes a new automated dating method, based on phonotactic diversity. Unlike ASJP, our method does not require any information on the internal classification of a language group. Also, the method can use all the available word lists for a language and its dialects eschewing the debate on ‘language’ vs. ‘dialect’. We further combine these dates and provide a new baseline which, to our knowledge, is the best one. We make a systematic comparison of our method, ASJP’s dating procedure, and combined dates. We predict time depths for world’s language families and sub-families using this new baseline. Finally, we explain our results in the model of language change given by Nettle. [ABSTRACT FROM AUTHOR]

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such “less can be more” feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

: Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures. [ABSTRACT FROM AUTHOR]

Social networking services (e.g., Twitter, Facebook) are now major sources of World Wide Web (called “Web”) dynamics, together with Web search services (e.g., Google). These two types of Web services mutually influence each other but generate different dynamics. In this paper, we distinguish two modes of Web dynamics: the reactive mode and the default mode. It is assumed that Twitter messages (called “tweets”) and Google search queries react to significant social movements and events, but they also demonstrate signs of becoming self-activated, thereby forming a baseline Web activity. We define the former as the reactive mode and the latter as the default mode of the Web. In this paper, we investigate these reactive and default modes of the Web's dynamics using transfer entropy (TE). The amount of information transferred between a time series of 1,000 frequent keywords in Twitter and the same keywords in Google queries is investigated across an 11-month time period. Study of the information flow on Google and Twitter revealed that information is generally transferred from Twitter to Google, indicating that Twitter time series have some preceding information about Google time series. We also studied the information flow among different Twitter keywords time series by taking keywords as nodes and flow directions as edges of a network. An analysis of this network revealed that frequent keywords tend to become an information source and infrequent keywords tend to become sink for other keywords. Based on these findings, we hypothesize that frequent keywords form the Web's default mode, which becomes an information source for infrequent keywords that generally form the Web's reactive mode. We also found that the Web consists of different time resolutions with respect to TE among Twitter keywords, which will be another focal point of this paper. [ABSTRACT FROM AUTHOR]

Sample size calculations are an important part of research to balance the use of resources and to avoid undue harm to participants. Effect sizes are an integral part of these calculations and meaningful values are often unknown to the researcher. General recommendations for effect sizes have been proposed for several commonly used statistical procedures. For the analysis of tables, recommendations have been given for the correlation coefficient for binary data; however, it is well known that suffers from poor statistical properties. The odds ratio is not problematic, although recommendations based on objective reasoning do not exist. This paper proposes odds ratio recommendations that are anchored to for fixed marginal probabilities. It will further be demonstrated that the marginal assumptions can be relaxed resulting in more general results. [ABSTRACT FROM AUTHOR]

The population migration algorithm (PMA) is a simulation of a population of the intelligent algorithm. Given the prematurity and low precision of PMA, this paper introduces a local search mechanism of the leap-frog algorithm and crossover operator to improve the PMA search speed and global convergence properties. The typical test function verifies the improved algorithm through its performance. Compared with the improved population migration and other intelligential algorithms, the result shows that the convergence rate of the improved PMA is very high and its convergence is proved. [ABSTRACT FROM AUTHOR]

Recent theories from complexity science argue that complex dynamics are ubiquitous in social and economic systems. These claims emerge from the analysis of individually simple agents whose collective behavior is surprisingly complicated. However, economists have argued that iterated reasoning–what you think I think you think–will suppress complex dynamics by stabilizing or accelerating convergence to Nash equilibrium. We report stable and efficient periodic behavior in human groups playing the Mod Game, a multi-player game similar to Rock-Paper-Scissors. The game rewards subjects for thinking exactly one step ahead of others in their group. Groups that play this game exhibit cycles that are inconsistent with any fixed-point solution concept. These cycles are driven by a “hopping” behavior that is consistent with other accounts of iterated reasoning: agents are constrained to about two steps of iterated reasoning and learn an additional one-half step with each session. If higher-order reasoning can be complicit in complex emergent dynamics, then cyclic and chaotic patterns may be endogenous features of real-world social and economic systems. [ABSTRACT FROM AUTHOR]

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model. [ABSTRACT FROM AUTHOR]

Background: Large scale surveys are the main source of data pertaining to all the social and demographic indicators, hence its quality is also of great concern. In this paper, we discuss the indicators used to examine the quality of data. We focus on age misreporting, incompleteness and inconsistency of information; and skipping of questions on reproductive and sexual health related issues. In order to observe the practical consequences of errors in a survey; the District Level Household and Facility Survey (DLHS-3) is used as an example dataset. Methods: Whipple's and Myer's indices are used to identify age misreporting. Age displacements are identified by estimating downward and upward transfers for women from bordering age groups of the eligible age range. Skipping pattern is examined by recording the responses to the questions which precede the sections on birth history, immunization, and reproductive and sexual health. Results: The study observed errors in age reporting, in all the states, but the extent of misreporting differs by state and individual characteristics. Illiteracy, rural residence and poor economic condition are the major factors that lead to age misreporting. Female were excluded from the eligible age group, to reduce the duration of interview. The study further observed that respondents tend to skip questions on HIV/RTI and other questions which follow a set of questions. Conclusion: The study concludes that age misreporting, inconsistency and incomplete response are three sources of error that need to be considered carefully before drawing conclusions from any survey. DLHS-3 also suffers from age misreporting, particularly for female in the reproductive ages. In view of the coverage of the survey, it may not be possible to control age misreporting completely, but some extra effort to probe a better answer may help in improving the quality of data in the survey. [ABSTRACT FROM AUTHOR]

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks. [ABSTRACT FROM AUTHOR]

Economic (business) cycles are some of the most noted features of market economies, also ranked among the most serious of economic problems. Despite long historical persistence, the nature and the origin of business cycles remain controversial. In this paper we investigate the problem of the nature of business cycles from the positions of the market systems viewed as complex systems of many interacting market agents. We show that the development of cyclic instabilities in these settings can be traced down to just two fundamental factors – the competition of market agents for market shares in the settings of an open market, and the depression of market caused by accumulation of durable overproduced commodities on the market. These findings present the problem of business cycles in a new light as a systemic property of efficient market systems emerging directly from the free market competition itself, and existing in market economies at a very fundamental level. [ABSTRACT FROM AUTHOR]

Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation. [ABSTRACT FROM AUTHOR]

The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes. [ABSTRACT FROM AUTHOR]

The aim of this study was to contribute to knowledge about classroom assessment by identifying profiles of teachers’ assessment of their students’ understanding of mathematics. For carrying out this study we used data of a nationwide teacher survey (N = 960) in the Netherlands. The data were collected by an online questionnaire. Through exploratory factor analyses the underlying structure of what is measured by this questionnaire was uncovered as consisting of five factors: Goal centeredness of assessment, Authentic nature of assessment, Perceived usefulness of assessment, Diversity of assessment problem format, and Allocated importance of assessing skills and knowledge. By using a latent class analysis four different assessment profiles of teachers were identified: Enthusiastic assessors, Mainstream assessors, Non-enthusiastic assessors, and Alternative assessors. The findings suggest that teachers with particular assessment profiles have qualitatively different assessment practices. The paper concludes with discussing theoretical implications of these assessment profiles and indications these profiles can offer both for designing material for professional development in classroom assessment and for evaluating changes in teachers’ classroom assessment practice. [ABSTRACT FROM AUTHOR]

Two-way selection is a common phenomenon in nature and society. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytical solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller group's people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two-way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system. [ABSTRACT FROM AUTHOR]

There are many different evaluation problems that involve several groups (societies, firms or institutions) whose members can be classified into ordered categories, pursuant to their characteristics or their achievements. This paper addresses these types of problems and provides an evaluation criterion based on the distribution of the agents across categories. The starting point is that of dominance relations in pair-wise comparisons. We say that group i dominates group j when the expected category of a member of i is higher than the expected category of a member of j. We introduce the notion of relative advantage of a group to extend this principle to multi-group comparisons and show that there is a unique evaluation function that ranks all groups consistently in terms of this criterion. This function associates to each evaluation problem the (unique) dominant eigenvector of a matrix whose entries describe the dominance relations between groups in pair-wise comparisons. The working of the model is illustrated by means of three different applications. [ABSTRACT FROM AUTHOR]

With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF algorithms interactively learn a user’s preferences to bridge the “semantic gap” between low-level computerized visual features and high-level human semantic perception and thus improve retrieval performance. However, most existing RF algorithms perform in the original high-dimensional feature space and ignore the manifold structure of the low-level visual features of images. In this paper, we propose a new method, termed dual-force ISOMAP (DFISOMAP), for content-based medical image retrieval. Under the assumption that medical images lie on a low-dimensional manifold embedded in a high-dimensional ambient space, DFISOMAP operates in the following three stages. First, the geometric structure of positive examples in the learned low-dimensional embedding is preserved according to the isometric feature mapping (ISOMAP) criterion. To precisely model the geometric structure, a reconstruction error constraint is also added. Second, the average distance between positive and negative examples is maximized to separate them; this margin maximization acts as a force that pushes negative examples far away from positive examples. Finally, the similarity propagation technique is utilized to provide negative examples with another force that will pull them back into the negative sample set. We evaluate the proposed method on a subset of the IRMA medical image dataset with a RF-based medical image retrieval framework. Experimental results show that DFISOMAP outperforms popular approaches for content-based medical image retrieval in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks. [ABSTRACT FROM AUTHOR]

We consider the recently introduced edge-based compartmental models (EBCM) for the spread of susceptible-infected-recovered (SIR) diseases in networks. These models differ from standard infectious disease models by focusing on the status of a random partner in the population, rather than a random individual. This change in focus leads to simple analytic models for the spread of SIR diseases in random networks with heterogeneous degree. In this paper we extend this approach to handle deviations of the disease or population from the simplistic assumptions of earlier work. We allow the population to have structure due to effects such as demographic features or multiple types of risk behavior. We allow the disease to have more complicated natural history. Although we introduce these modifications in the static network context, it is straightforward to incorporate them into dynamic network models. We also consider serosorting, which requires using dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here. Our goal is twofold: to provide a number of examples generalizing the EBCM method for various different population or disease structures and to provide insight into how to derive such a model under new sets of assumptions. [ABSTRACT FROM AUTHOR]

In second language acquisition research, the critical period hypothesis (cph) holds that the function between learners' age and their susceptibility to second language input is non-linear. This paper revisits the indistinctness found in the literature with regard to this hypothesis's scope and predictions. Even when its scope is clearly delineated and its predictions are spelt out, however, empirical studies–with few exceptions–use analytical (statistical) tools that are irrelevant with respect to the predictions made. This paper discusses statistical fallacies common in cph research and illustrates an alternative analytical method (piecewise regression) by means of a reanalysis of two datasets from a 2010 paper purporting to have found cross-linguistic evidence in favour of the cph. This reanalysis reveals that the specific age patterns predicted by the cph are not cross-linguistically robust. Applying the principle of parsimony, it is concluded that age patterns in second language acquisition are not governed by a critical period. To conclude, this paper highlights the role of confirmation bias in the scientific enterprise and appeals to second language acquisition researchers to reanalyse their old datasets using the methods discussed in this paper. The data and R commands that were used for the reanalysis are provided as supplementary materials. [ABSTRACT FROM AUTHOR]

Pedestrian movements are the consequence of several complex and stochastic facts. The modelling of pedestrian movements and the ability to predict the travel time are useful for evaluating the performance of a pedestrian facility. However, only a few studies can be found that incorporate the design of the facility, local pedestrian body dimensions, the delay experienced by the pedestrians, and level of service to the pedestrian movements. In this paper, a queuing based analytical model is developed as a function of relevant determinants and functional factors to predict the travel time on pedestrian facilities. The model can be used to assess the overall serving rate or performance of a facility layout and correlate it to the level of service that is possible to provide the pedestrians. It has also the ability to provide a clear suggestion on the designing and sizing of pedestrian facilities. The model is empirically validated and is found to be a robust tool to understand how well a particular walking facility makes possible comfort and convenient pedestrian movements. The sensitivity analysis is also performed to see the impact of some crucial parameters of the developed model on the performance of pedestrian facilities. [ABSTRACT FROM AUTHOR]

In many fields of research, so-called ‘multiblock’ data are collected, i.e., data containing multivariate observations that are nested within higher-level research units (e.g., inhabitants of different countries). Each higher-level unit (e.g., country) then corresponds to a ‘data block’. For such data, it may be interesting to investigate the extent to which the correlation structure of the variables differs between the data blocks. More specifically, when capturing the correlation structure by means of component analysis, one may want to explore which components are common across all data blocks and which components differ across the data blocks. This paper presents a common and cluster-specific simultaneous component method which clusters the data blocks according to their correlation structure and allows for common and cluster-specific components. Model estimation and model selection procedures are described and simulation results validate their performance. Also, the method is applied to data from cross-cultural values research to illustrate its empirical value. [ABSTRACT FROM AUTHOR]

Background: Citation analysis has become an important tool for research performance assessment in the medical sciences. However, different areas of medical research may have considerably different citation practices, even within the same medical field. Because of this, it is unclear to what extent citation-based bibliometric indicators allow for valid comparisons between research units active in different areas of medical research. Methodology: A visualization methodology is introduced that reveals differences in citation practices between medical research areas. The methodology extracts terms from the titles and abstracts of a large collection of publications and uses these terms to visualize the structure of a medical field and to indicate how research areas within this field differ from each other in their average citation impact. Results: Visualizations are provided for 32 medical fields, defined based on journal subject categories in the Web of Science database. The analysis focuses on three fields: Cardiac & cardiovascular systems, Clinical neurology, and Surgery. In each of these fields, there turn out to be large differences in citation practices between research areas. Low-impact research areas tend to focus on clinical intervention research, while high-impact research areas are often more oriented on basic and diagnostic research. Conclusions: Popular bibliometric indicators, such as the h-index and the impact factor, do not correct for differences in citation practices between medical fields. These indicators therefore cannot be used to make accurate between-field comparisons. More sophisticated bibliometric indicators do correct for field differences but still fail to take into account within-field heterogeneity in citation practices. As a consequence, the citation impact of clinical intervention research may be substantially underestimated in comparison with basic and diagnostic research. [ABSTRACT FROM AUTHOR]

In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and within-subject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed. [ABSTRACT FROM AUTHOR]

Traveler's dilemma (TD) is one of social dilemmas which has been well studied in the economics community, but it is attracted little attention in the physics community. The TD game is a two-person game. Each player can select an integer value between and () as a pure strategy. If both of them select the same value, the payoff to them will be that value. If the players select different values, say and (), then the payoff to the player who chooses the small value will be and the payoff to the other player will be . We term the player who selects a large value as the cooperator, and the one who chooses a small value as the defector. The reason is that if both of them select large values, it will result in a large total payoff. The Nash equilibrium of the TD game is to choose the smallest value . However, in previous behavioral studies, players in TD game typically select values that are much larger than , and the average selected value exhibits an inverse relationship with . To explain such anomalous behavior, in this paper, we study the evolution of cooperation in spatial traveler's dilemma game where the players are located on a square lattice and each player plays TD games with his neighbors. Players in our model can adopt their neighbors' strategies following two standard models of spatial game dynamics. Monte-Carlo simulation is applied to our model, and the results show that the cooperation level of the system, which is proportional to the average value of the strategies, decreases with increasing until is greater than the critical value where cooperation vanishes. Our findings indicate that spatial reciprocity promotes the evolution of cooperation in TD game and the spatial TD game model can interpret the anomalous behavior observed in previous behavioral experiments. [ABSTRACT FROM AUTHOR]

The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for this problem, its NP-hardness still represents a computational challenge. Some of the best computational results obtained for this problem are consequences of hybridizing the various known heuristics. Automatically revising the space constituted by combining these techniques to find the most adequate combination has received less attention. In this paper, we propose exploring the heuristics space for the vertex coloring problem using evolutionary algorithms. We automatically generate three new algorithms by combining elementary heuristics. To evaluate the new algorithms, a computational experiment was performed that allowed comparing them numerically with existing heuristics. The obtained algorithms present an average 29.97% relative error, while four other heuristics selected from the literature present a 59.73% error, considering 29 of the more difficult instances in the DIMACS benchmark. [ABSTRACT FROM AUTHOR]

Neoclassical noncooperative game theory is based on a simple, yet powerful synthesis of mathematical and logical concepts: unconditional and immutable preference orderings and individual rationality. Although this structure has proven useful for characterizing competitive multi-player behavior, its applicability to scenarios involving complex social relationships is problematic. In this paper we directly address this limitation by the introduction of a conditional preference structure that permits players to modulate their preference orderings as functions of the preferences of other players. Embedding this expanded preference structure in a formal and graphical framework provides a systematic approach for characterizing a complex society. The result is an influence network that allows conditional preferences to propagate through the community, resulting in an emergent social model which characterizes all of the social relationships that exist and which leads to solution concepts that account for both group and individual interests. The Ultimatum game is presented as an example of how social influence can be modeled with conditional preferences. [ABSTRACT FROM AUTHOR]

In this paper I investigate the evolution of cooperation in the prisoner's dilemma when individuals change their strategies subject to performance evaluation of their neighbours over variable time horizons. In the monochrome setting, in which all agents per default share the same performance evaluation rule, weighing past events strongly dramatically enhances the prevalence of cooperators. For co-evolutionary models, in which evaluation time horizons and strategies can co-evolve, I demonstrate that cooperation naturally associates with long-term evaluation of others while defection is typically paired with very short time horizons. Moreover, considering the continuous spectrum in between enhanced and discounted weights of past performance, cooperation is optimally supported when cooperators neither give enhanced weight to past nor more recent events, but simply average payoffs. Payoff averaging is also found to emerge as the dominant strategy for cooperators in co-evolutionary models, thus proposing a natural route to the evolution of cooperation in viscous populations. [ABSTRACT FROM AUTHOR]