A semi-supervised approach to visualizing and manipulating overlapping communities

When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure. © 2013 IEEE

Adaptive visualization of research communities

Adaptive visualization approaches attempt to tune the content and the topology of information visualization to various user characteristics. While adapting visualization to user cognitive traits, goals, or knowledge has been relatively well explored, some other user characteristics have received no attention. This paper presents a methodology to adapt a traditional cluster-based visualization of communities to user individual model of community organization. This class of user-adapted visualization is not only achievable, but expected due to real world situation where users cannot be segmented into heterogeneous communities since many users have affinity to more than one group. An interactive clustering and visualization approach presented in the paper allows the user communicate their personal mental models of overlapping communities to the clustering algorithm itself and obtain a community visualization image that more realistically fits their prospects

Variable selection by searching for good subsets

Machine learning and statistical models are increasingly used in a prediction context and in the process of building these models the question of which variables to include often arises. Over the last 50 years a number of procedures have been proposed, especially in the statistical literature. In this paper a newvariable selection procedure is introduced for linear models. A subset of variables is defined here to be “good at margin λ” if it has two properties, namely (i) its associated criterion of fit will be improved in relative terms by less than λ if any variable is added to it, and (ii) its criterion of fit will deteriorate in relative terms by at least λ if any variable inside it, is dropped from it. Thus, such a subset contains all variables that are individually important and none that are unimportant at a given margin λ ≥ 0. This paper discusses calculation of such λ-good subsets. The “good” approach extends readily to generalised linear and many other models by using an appropriate criterion of performance. The approach is illustrated on an artificial data set and a number of real data sets

A framework for normal mean variance mixture innovations with application to GARCH modelling

GARCH models are useful to estimate the volatility of financial return series. Historically the innovation distribution of a GARCH model was assumed to be standard normal but recent research emphasizes the need for more general distributions allowing both asymmetry (skewness) and kurtosis in the innovation distribution to obtain better fitting models. A number of authors have proposed models which are special cases of the class of normal mean variance mixtures. We introduce a general framework within which this class of innovation distributions may be discussed. This entails writing the innovation term as a standardised combination of two variables, namely a normally distributed term and a mixing variable, each with its own interpretation. We list the existing models that fit into this framework and compare the corresponding innovation distributions, finding that they tend to be quite similar. This is confirmed by an empirical illustration which fits the models to the monthly excess returns series of the US stocks. The illustration finds further support for the ICAPM model of Merton, thus supporting recent results of Lanne and Saikonnen (2006)

Деградация человеческого потенциала как фактор латентной составляющей деятельности высшей школы Украины

Рассмотрены проблемы тенизации и коррупционности функционирования украин-ской высшей школы на фоне вектора развития показателей потенциала населения страны.Розглянуті проблеми тінізації і коррупційності функціонування української вищої школи на тлі вектору розвитку показників потенціалу населення країни

Spin Stiffness in the Hubbard model

The spin stiffness $\rho_{\rm s}$ of the repulsive Hubbard model that occurs in the hydrodynamic theory of antiferromagnetic spin waves is shown to be the same as the thermodynamically defined stiffness involved in twisting the order parameter. New expressions for $\rho_{\rm s}$ are derived, which enable easier interpretation, and connections with superconducting weight and gauge invariance are discussed.Comment: 21 Pages LaTeX2e, to be published in Journal of Physics

From numbers to meaningful change:Minimal important change by using PROMIS in a cohort of fracture patients

Introduction: use of the Patient-Reported Outcomes measurement Information System (PROMIS®) is slowly increasing in patients with a fracture. Yet, minimal important change of PROMIS in patients with fractures has been addressed in a very limited number of studies. As the minimal important change (MIC) is important to interpret PROMIS-scores, the goal is to estimate the MIC for PROMIS physical function (PF), PROMIS pain interference (PI) and PROMIS ability to participate in social roles and activities (APSRA) in patients with a fracture. Secondly, the smallest detectable change was determined. Materials and methods: A longitudinal cohort study on patients ≥ 18 years receiving surgical or non-surgical care for fractures was conducted. Patients completed PROMIS PF V1.1, PROMIS PI V1.1 and PROMIS APSRA V2.0. For follow-up, patients completed three additional anchor questions evaluating patient-reported improvement on a seven point rating scale. The predictive modeling method was used to estimate the MIC value of all three PROMIS questionnaires. Results: Hundred patients with a mean age of 55.4 ± 12.6 years were included of which sixty (60%) were female. Seventy-two (72%) patients were recovering from a surgical procedure. PROMIS-CAT T-scores of all PROMIS measures showed significant correlations with their anchor questions. The predictive modeling method showed a MIC value of +2.4 (n = 98) for PROMIS PF, -2.9 (n = 96) for PROMIS PI and +3.2 (n = 91) for PROMIS APSRA. Conclusion: By using the anchor based predictive modeling method, PROMIS MIC-values for improvement of respectively +2.4 points on a T-score metric for PROMIS-PF, -2.9 for PROMIS-PI and +3.2 for PROMIS APSRA give the impression of being meaningful to patients. These values can be used in clinical practice for managing patient expectations; to inform on treatment results; and to assess if patients experience significant change. This in order to encourage patient centered care.</p

Heat Capacity and Magnetic Phase Diagram of the Low-Dimensional Antiferromagnet Y2_2BaCuO5_5

A study by specific heat of a polycrystalline sample of the low-dimensional magnetic system Y$_2$BaCuO$_5$ is presented. Magnetic fields up to 14 T are applied and permit to extract the ($T$,$H$) phase diagram. Below $\mu_0H^*\simeq2$ T, the N\'eel temperature, associated with a three-dimensional antiferromagnetic long-range ordering, is constant and equals $T_N=15.6$ K. Above $H^*$, $T_N$ increases linearly with $H$ and a field-induced increase of the entropy at $T_N$ is related to the presence of an isosbestic point at $T_X\simeq20$ K, where all the specific heat curves cross. A comparison is made between Y$_2$BaCuO$_5$ and the quasi-two-dimensional magnetic systems BaNi$_{2}$V$_{2}$O$_{8}$, Sr$_2$CuO$_2$Cl$_2$, and Pr$_2$CuO$_4$, for which very similar phase diagrams have been reported. An effective field-induced magnetic anisotropy is proposed to explain these phase diagrams.Comment: 14 pages, 7 figure