24,017 research outputs found
Recommended from our members
EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI
A SOCIO-COGNITIVE BASIS FOR STRATEGIC GROUPS: COGNITIVE DISSONANCE IN SWINE GENETICS
Institutional and Behavioral Economics, Livestock Production/Industries,
Complete characterization of sink-strengths for 1D to 3D mobilities of defect clusters.II. Bridging between limiting cases with effective sink-strengths calculations
In a companion paper, we proposed new analytical expressions of cluster
sink-strengths (CSS) indispensable to any complete parameterization of rate
equations cluster dynamics accounting for reaction between defect clusters
populations having a 1D-mobility. In this second paper, we first establish
simulation setup rules for truly converged estimates of effective CSS by
Kinetic Monte-Carlo, and then we grid on a wide set of radii, rotation
energies, diffusion coefficients and concentrations of both reaction partners.
Symmetric roles of some parameters are used to infer a generic form for a
semi-analytical expression of CSS depending on all these interaction
parameters: it is composed of the various analytical limiting cases established
and fitted transition functions that allow a gradual switching between them.
The analysis of the residuals shows that the overall agreement is reasonably
good: it is only in the transition zones that discrepancies are located and
this is due to the asymmetry of the actual transition functions. The expression
can be easily extended to temperatures at least a few hundred degrees around
the reference. But further extending the CSS evaluations to much smaller
diffusion coefficients ratios, we see that the domain for 1D-1D mobility is
very extended: for a ratio the computed CSS is still not correctly
described by the 1D-CSS with respect to a fixed sink (1D-0), but rather by the
established 1D-1D expression. For our typical sets of conditions, it is only
when approaching a ratio of that the 1D-0 CSS starts to become more
relevant. This highlights the counter-intuitive fact that the growth kinetics
of moderately trapped 1D mobile loops, whose effective mobility is greatly
reduced, may not be described by 1D-0 kinetics but rather by appropriately
corrected 1D-1D CSS which have completely different order of magnitude and
kinetic orders.Comment: 21 pages, 12 figure
Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics
First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049
Categorization of interestingness measures for knowledge extraction
Finding interesting association rules is an important and active research
field in data mining. The algorithms of the Apriori family are based on two
rule extraction measures, support and confidence. Although these two measures
have the virtue of being algorithmically fast, they generate a prohibitive
number of rules most of which are redundant and irrelevant. It is therefore
necessary to use further measures which filter uninteresting rules. Many
synthesis studies were then realized on the interestingness measures according
to several points of view. Different reported studies have been carried out to
identify "good" properties of rule extraction measures and these properties
have been assessed on 61 measures. The purpose of this paper is twofold. First
to extend the number of the measures and properties to be studied, in addition
to the formalization of the properties proposed in the literature. Second, in
the light of this formal study, to categorize the studied measures. This paper
leads then to identify categories of measures in order to help the users to
efficiently select an appropriate measure by choosing one or more measure(s)
during the knowledge extraction process. The properties evaluation on the 61
measures has enabled us to identify 7 classes of measures, classes that we
obtained using two different clustering techniques.Comment: 34 pages, 4 figure
- …