LLukas22/scidocs huggingface dataset:
The RV coefficient and the congruence coefficient

The congruence coefficient was first introduced by Burt (1948) under the name of unadjusted correlation as a measure of the similarity of two factorial configurations. The name congruence coefficient was later tailored by Tucker (1951, see also Harman, 1976). The congruence coefficient is also sometimes called a monotonicity coefficient (Borg & Groenen, 1997, p. 203). The congruence coefficient takes values between −1 and +1. The RV coefficient was introduced by Escoufier (1973, see also Robert & Escoufier, 1976) as a measure of similarity between squared symmetric matrices (specifically: positive semi-definite matrices) and as a theoretical tool to analyze multivariate techniques. The RV coefficient is used in several statistical techniques such as STATIS and DISTATIS (see corresponding entries and Abdi, 2003). In order to compare rectangular matrices using the RV coefficient the first step is to transform them into square matrices. The RV 1In: Neil Salkind (Ed.) (2007). Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. Address correspondence to: Hervé Abdi Program in Cognition and Neurosciences, MS: Gr.4.1, The University of Texas at Dallas, Richardson, TX 75083–0688, USA E-mail: herve@utdallas.edu http://www.utd.edu/∼herve
Hypotrichosis with juvenile macular dystrophy is caused by a mutation in CDH3, encoding P-cadherin

Congenital hypotrichosis associated with juvenile macular dystrophy (HJMD; MIM601553) is an autosomal recessive disorder of unknown etiology, characterized by hair loss heralding progressive macular degeneration and early blindness. We used homozygosity mapping in four consanguineous families to localize the gene defective in HJMD to 16q22.1. This region contains CDH3, encoding P-cadherin, which is expressed in the retinal pigment epithelium and hair follicles. Mutation analysis shows in all families a common homozygous deletion in exon 8 of CDH3. These results establish the molecular etiology of HJMD and implicate for the first time a cadherin molecule in the pathogenesis of a human hair and retinal disorder.
Navigated transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a unique method for non-invasive brain imaging. The fundamental difference between TMS and other available non-invasive brain imaging techniques is that when a physiological response is evoked by stimulation of a cortical area, that specific cortical area is causally related to the response. With other imaging methods, it is only possible to detect and map a brain area that participates in a given task or reaction. TMS has been shown to be clinically accurate and effective in mapping cortical motor areas and applicable to the functional assessment of motor tracts following stroke, for example. Many hundreds of studies have been published indicating that repetitive TMS (rTMS) may also have multiple therapeutic applications. Techniques and protocols for individually targeting and dosing rTMS urgently need to be developed in order to ascertain the accuracy, repeatability and reproducibility required of TMS in clinical applications. We review the basic concepts behind navigated TMS and evaluate the currently accepted physical and physiological factors contributing to the accuracy and reproducibility of navigated TMS. The advantages of navigated TMS over functional MRI in motor cortex mapping are briefly discussed. Illustrative cases utilizing navigated TMS are shown in presurgical mapping of the motor cortex, in therapy for depression, and in the follow-up of recovery from stroke.
Mining , Pruning and Visualizing Frequent Patterns for Temporal Event Sequence Analysis

Integrating frequent pattern mining with interactive visualization for temporal event sequence analysis poses many interesting research questions and challenges. We review and reflect on some of these challenges based on our experiences working on event sequence data from two domains: web analytics and application logs. These challenges can be organized using a three-stage framework: pattern mining, pattern pruning and interactive visualization.
Cognitive Machine-Learning Algorithm for Cardiac Imaging: A Pilot Study for Differentiating Constrictive Pericarditis From Restrictive Cardiomyopathy.

BACKGROUND
Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy.


METHODS AND RESULTS
Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions.


CONCLUSIONS
This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience.
The Cycloid Permanent Magnetic Gear

This paper presents a new permanent-magnet gear based on the cycloid gearing principle, which normally is characterized by an extreme torque density and a very high gearing ratio. An initial design of the proposed magnetic gear was designed, analyzed, and optimized with an analytical model regarding torque density. The results were promising as compared to other high-performance magnetic-gear designs. A test model was constructed to verify the analytical model.
Veto players and the structure of budgets in advanced industrialized countries

This article tests expectations generated by the veto players theory with respect to the over time composition of budgets in a multidimensional policy space. The theory predicts that countries with many veto players (i.e., coalition governments, bicameral political systems, presidents with veto) will have difficulty altering the budget structures. In addition, countries that tend to make significant shifts in government composition will have commensurate modifications of the budget. Data collected from 19 advanced industrialized countries from 1973 to 1995 confirm these expectations, even when one introduces socioeconomic controls for budget adjustments like unemployment variations, size of retired population and types of government (minimum winning coalitions, minority or oversized governments). The methodological innovation of the article is the use of empirical indicators to operationalize the multidimensional policy spaces underlying the structure of budgets. The results are consistent with other analyses of macroeconomic outcomes like inflation, budget deficits and taxation that are changed at a slower pace by multiparty governments. The purpose of this article is to test empirically the expectations of the veto players theory in a multidimensional setting. The theory defines ‘veto players’ as individuals or institutions whose agreement is required for a change of the status quo. The basic prediction of the theory is that when the number of veto players and their ideological distances increase, policy stability also increases (only small departures from the status quo are possible) (Tsebelis 1995, 1999, 2000, 2002). The theory was designed for the study of unidimensional and multidimensional policy spaces. While no policy domain is strictly unidimensional, existing empirical tests have only focused on analyzing political economy issues in a single dimension. These studies have confirmed the veto players theory’s expectations (see Bawn (1999) on budgets; Hallerberg & Basinger (1998) on taxes; Tsebelis (1999) on labor legislation; Treisman (2000) on inflation; Franzese (1999) on budget deficits). This article is the first attempt to test whether the predictions of the veto players theory hold in multidimensional policy spaces. We will study a phenomenon that cannot be considered unidimensional: the ‘structure’ of budgets – that is, their percentage composition, and the change in this composition over © European Consortium for Political Research 2004 Published by Blackwell Publishing Ltd., 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA
Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale

Aim Research into social networking addiction has greatly increased over the last decade. However, the number of validated instruments assessing addiction to social networking sites (SNSs) remains few, and none have been validated in the Italian language. Consequently, this study tested the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale (BSMAS), as well as providing empirical data concerning the relationship between attachment styles and SNS addiction. Methods A total of 769 participants were recruited to this study. Confirmatory factor analysis (CFA) and multigroup analyses were applied to assess construct validity of the Italian version of the BSMAS. Reliability analyses comprised the average variance extracted, the standard error of measurement, and the factor determinacy coefficient. Results Indices obtained from the CFA showed the Italian version of the BSMAS to have an excellent fit of the model to the data, thus confirming the single-factor structure of the instrument. Measurement invariance was established at configural, metric, and strict invariances across age groups, and at configural and metric levels across gender groups. Internal consistency was supported by several indicators. In addition, the theoretical associations between SNS addiction and attachment styles were generally supported. Conclusion This study provides evidence that the Italian version of the BSMAS is a psychometrically robust tool that can be used in future Italian research into social networking addiction.
The Geographic Distribution of Big Five Personality Traits

JOURNAL OF CROSS-CULTURAL PSYCHOLOGY, Vol. 38 No. 2, March 2007 173-212 DOI: 10.1177/0022022106297299 © 2007 Sage Publications Lidia Alcalay, Pontificia Universidad Católica de Chile Lara Ault, University of Louisville Ivars Austers, University of Latvia Kevin L. Bennett, Penn State University, Beaver Gabriel Bianchi, Slovak Academy of Sciences Fredrick Boholst, University of San Carlos Mary Ann Borg Cunen, University of Malta Johan Braeckman, Ghent University Edwin G. Brainerd Jr., Clemson University Leo Gerard A. Caral, University of San Carlos Gabrielle Caron, Université of Laval Maria Martina Casullo, University of Buenos Aires Michael Cunningham, University of Louisville Ikuo Daibo, Osaka University Charlotte De Backer, Ghent University Eros DeSouza, Illinois State University Rolando Diaz-Loving, National Autonomous University of Mexico Gláucia Diniz, University of Brasilia Kevin Durkin, The University of Western Australia Marcela Echegaray, University of Lima Ekin Eremsoy, Bogaziçi Üniversitesi Harald A. Euler, University of Kassel Ruth Falzon, University of Malta Maryanne L. Fisher, York University Dolores Foley, University of Queensland Douglas P. Fry, Åbo Akademi University Sirpa Fry, Åbo Akademi University M. Arif Ghayur, Al-Akhawayn University Vijai N. Giri, Indian Institute of Technology Debra L. Golden, University of Hawaii–Manoa Karl Grammer, Ludwig-Boltzmann-Institute for Urban Ethology Liria Grimaldi, University of Catania Jamin Halberstadt, University of Otago Shamsul Haque, University of Dhaka Dora Herrera, University of Lima Janine Hertel, Technische Universität Chemnitz Heather Hoffmann, Knox College Danica Hooper, University of Queensland Zuzana Hradilekova, Comenius University Jasna Hudek-Kene-evi, University of Rijeka Jas Jaafar, University of Malaya Margarita Jankauskaite, Vilnius University Heidi Kabangu-Stahel, Centre d’Enseignement les Gazelles Igor Kardum, University of Rijeka Brigitte Khoury, American University of Beirut Hayrran Kwon, Kwangju Health College Kaia Laidra, University of Tartu Anton-Rupert Laireiter, University of Salzburg Dustin Lakerveld, University of Utrecht Ada Lampert, The Ruppin Institute Maryanne Lauri, University of Malta Marguerite Lavallée, Université of Laval Suk-Jae Lee, National Computerization Agency Luk Chung Leung, City University of Hong Kong Kenneth D. Locke, University of Idaho Vance Locke, University of Western Australia Ivan Luksik, Slovak Academy of Sciences Ishmael Magaisa, University of Zimbabwe Dalia Marcinkeviciene, Vilnius University André Mata, University of Lisbon Rui Mata, University of Lisbon Barry McCarthy, University of Central Lancashire Michael E. Mills, Loyola Marymount University Nhlanhla J. Mkhize, University of Natal João Moreira, University of Lisbon Sérgio Moreira, University of Lisbon Miguel Moya, University of Granada M. Munyae, University of Botswana Patricia Noller, University of Queensland Hmoud Olimat, University of Jordan Adrian Opre, Babes Bolyai University Alexia Panayiotou, University of Cyprus Nebojsa Petrovic, University of Belgrade Karolien Poels, Ghent University
Anomalous video event detection using spatiotemporal context

Compared to other anomalous video event detection approaches that analyze object trajectories only, we propose a context-aware method to detect anomalies. By tracking all moving objects in the video, three different levels of spatiotemporal contexts are considered, i.e., point anomaly of a video object, sequential anomaly of an object trajectory, and co-occurrence anomaly of multiple video objects. A hierarchical data mining approach is proposed. At each level, frequency based analysis is performed to automatically discover regular rules of normal events. Events deviating from these rules are identified as anomalies. The proposed method is computationally efficient and can infer complex rules. Experiments on real traffic video validate that the detected video anomalies are hazardous or illegal according to traffic ∗Corresponding author (phone: 224-392-2622, fax: 847-491-4455) Email addresses: fanjiang2008@u.northwestern.edu (Fan Jiang), jsyuan@ntu.edu.sg (Junsong Yuan), s-tsaftaris@northwestern.edu (Sotirios A. Tsaftaris), aggk@eecs.northwestern.edu (Aggelos K. Katsaggelos) Preprint submitted to Computer Vision and Image Understanding February 28, 2010 regulations.
The Mediatization of Society A Theory of the Media as Agents of Social and Cultural Change

Using mediatization as the key concept, this article presents a theory of the influence media exert on society and culture. After reviewing existing discussions of mediatization by Krotz (2007), Schulz (2004), Thompson (1995), and others, an institutional approach to the mediatization process is suggested. Mediatization is to be considered a double-sided process of high modernity in which the media on the one hand emerge as an independent institution with a logic of its own that other social institutions have to accommodate to. On the other hand, media simultaneously become an integrated part of other institutions like politics, work, family, and religion as more and more of these institutional activities are performed through both interactive and mass media. The logic of the media refers to the institutional and technological modus operandi of the media, including the ways in which media distribute material and symbolic resources and make use of formal and informal rules.
Evaluating TV Ad Campaigns Using Set-Top Box Data

Google has developed new metrics based on set-top box data for predicting the future audience retention of TV ads. This paper examines how to use these metrics to judge the effectiveness of TV ad campaigns. More specifically, we analyze how these metrics can inform future campaign targeting and placement goals. Introduction In recent years, there has been an explosion of interest in collecting and analyzing television set-top box (STB) data (also called “return path” data). As US television moves from analog to digital signals, digital set-top boxes are increasingly common in American homes. Where these STBs are attached to some sort of return path, this data can be aggregated and licensed to companies wishing to measure television viewership. For example, Google aggregates data, collected and anonymized by DISH Network L.L.C., describing the precise second-by-second tuning behavior from television set-top boxes in millions of US households. This data can be combined with detailed airing logs for thousands of daily TV ads to estimate second-by-second fluctuations in audience during TV commercials (Zigmond and Lanning, 2008). These data hold the promise of providing accurate measurement for much of the niche TV content that eludes current panel-based methods. But in addition to using these data for raw audience measurement, it is possible to make more qualitative judgments about the content – and specifically the advertising – on television. Google has developed a measure of audience retention based on STB data that can be used to predict future audience response for TV ads (Zigmond, 2009a and Zigmond et al, 2009b). This paper will look at how this new retention metric can be applied to measure the effectiveness of TV ad campaigns. Retention Scores Raw measures of audience tuning behavior during TV ads can be useful in evaluating TV ads. However, we have found that these metrics are highly influenced by extraneous factors such as the time-of-day, day-of-week, and the network on which the ads were aired. These are nuisance variables and make direct comparison of such measures very difficult. Rather than using these measures directly, we have developed a model for normalizing the scores relative to expected tuning behavior (Zigmond et al, 2009b). We do this by using a statistical model to estimate the “expected” tuning behavior during a given ad spot (based on known influencing factors like time-of-day, day-of-week, etc.), and subtract from this the observed tuning behavior during a specific ad airing. We then score ads or campaigns by looking at the percentage of airings in which this residual (ie, the expected minus the actual) exceeds the median. We call this quantity the “retention
VALIDITY CONVERGENT AND RELIABILITY TEST-RETEST OF THE REY AUDITORY VERBAL LEARNING TEST

Objective: This study provides evidence for the validity and reliability of the Rey Auditory Verbal Learning Test
Facial Component Extraction and Face Recognition with Support Vector Machines

A method for face recognition is proposed which uses a two-step approach: first a number of facial components are found, which are then glued together, and the resulting face vector is recognized as representing one of the possible persons. During the extraction step, a wavelet statistics subsystem provides the possible locations of eyes and mouth which are used by the Support Vector Machine (SVM) subsystem to extract facial components. The use of wavelet statistics subsystem speeds up the recognition process markedly. Both the feature detection SVMs and wavelet statistics are trained on a small number of actual images with features marked. Afterwards, a large number of face vectors are constructed, which are then classified with another set of SVM machines.
A General Design Rule to Manipulate Photocarrier Transport Path in Solar Cells and Its Realization by the Plasmonic-Electrical Effect

It is well known that transport paths of photocarriers (electrons and holes) before collected by electrodes strongly affect bulk recombination and thus electrical properties of solar cells, including open-circuit voltage and fill factor. For boosting device performance, a general design rule, tailored to arbitrary electron to hole mobility ratio, is proposed to decide the transport paths of photocarriers. Due to a unique ability to localize and concentrate light, plasmonics is explored to manipulate photocarrier transport through spatially redistributing light absorption at the active layer of devices. Without changing the active materials, we conceive a plasmonic-electrical concept, which tunes electrical properties of solar cells via the plasmon-modified optical field distribution, to realize the design rule. Incorporating spectrally and spatially configurable metallic nanostructures, thin-film solar cells are theoretically modelled and experimentally fabricated to validate the design rule and verify the plasmonic-tunable electrical properties. The general design rule, together with the plasmonic-electrical effect, contributes to the evolution of emerging photovoltaics.
Predicting retinal tears in posterior vitreous detachment.

OBJECTIVE
The purpose of this study is to determine whether patients with acute posterior vitreous detachment (PVD) who develop delayed retinal tears within the first 6 weeks after initial presentation have predictive characteristics.


DESIGN
Prospective cohort study.


PARTICIPANTS
All patients presenting to the Hotel Dieu Hospital Emergency Eye Clinic between September 2008 and July 2009 diagnosed with acute PVD were offered enrollment.


METHODS
At the initial visit, patients were given the previously validated Queen's University Posterior Vitreous Detachment Patient Diary to record their daily symptoms for 6 weeks. Two or 6 weeks later, patients were reexamined in detail, and their diaries were collected and analyzed. Exact logistic regression was used to establish characteristics predictive of delayed retinal tears.


RESULTS
In our study population of 99 patients, 2 developed delayed retinal tears. One had retinal hemorrhages and the other had a cloud-like floater at initial presentation. Vitreal or retinal hemorrhage, large number of floaters at initial presentation, and high floater frequency at initial presentation indicated a high risk of delayed retinal tear formation, yielding a median unbiased estimated odds ratio of 36.18 with p value 0.009. No other presenting risk factors or symptomatology followed daily over the first 6 weeks after acute PVD were predictive of delayed retinal tear formation.


CONCLUSIONS
PVD patients with retinal or vitreal hemorrhage, a significant number of floaters or a cloud like appearance to the floaters, or high floater frequency are at higher risk of developing delayed retinal tears.