scholarly journals Vulnerability of DM watermarking of non-iid host signals to attacks utilising the statistics of independent components

2006 ◽  
Vol 153 (3) ◽  
pp. 127 ◽  
Author(s):  
P. Bas ◽  
J. Hurri
Keyword(s):  
Independent Components ◽  
Host Signals

Space creation, structural construction and force distribution

2013 ◽  
Vol 4 (1) ◽  
pp. 1-12
Author(s):  
G. Lámer
Keyword(s):  
The Other ◽  
Frame Structure ◽  
Force Distribution ◽  
Independent Components ◽  
Other Hand

Abstract The paper is an overview of issues related to the space creation of a building, possibilities of developing frame structure and connections of force distribution in the construction. In plane the force distribution can be compression, bending and tension. In space “enclosing” a geometric solid means space creation. In space as it is to be expected, the force distribution must be compression, bending and tension in two different directions at the same time. This can be really variant but in the case of surface or surface-like constructions generated by translations (and/or rotations) on one hand, there are some other surfaces, which cannot be generated by translations (and/or rotations), on the other hand, the dimension of the inside “forces” is not two but three (independent components of a two-by-two tensor either in the case of compression tension, or in the case of bending). By this, force distribution is more complicated in space than in plane.

scholarly journals Familial hypertriglyceridemia: an entity with distinguishable features from other causes of hypertriglyceridemia

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ivette Cruz-Bautista ◽  
Alicia Huerta-Chagoya ◽  
Hortensia Moreno-Macías ◽  
Rosario Rodríguez-Guillén ◽  
María Luisa Ordóñez-Sánchez ◽  
...  
Keyword(s):  
Apolipoprotein B ◽  
Rare Variants ◽  
Regulatory Proteins ◽  
Fibroblast Growth Factor 21 ◽  
Fibroblast Growth ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Apo B ◽  
Independent Components ◽  
Apolipoprotein A

Abstract Background Familial hypertriglyceridemia (FHTG) is a partially characterized primary dyslipidemia which is frequently confused with other forms hypertriglyceridemia. The aim of this work is to search for specific features that can help physicians recognize this disease. Methods This study included 84 FHTG cases, 728 subjects with common mild-to-moderate hypertriglyceridemia (CHTG) and 609 normotriglyceridemic controls. All subjects underwent genetic, clinical and biochemical assessments. A set of 53 single nucleotide polymorphisms (SNPs) previously associated with triglycerides levels, as well as 37 rare variants within the five main genes associated with hypertriglyceridemia (i.e. LPL, APOC2, APOA5, LMF1 and GPIHBP1) were analyzed. A panel of endocrine regulatory proteins associated with triglycerides homeostasis were compared between the FHTG and CHTG groups. Results Apolipoprotein B, fibroblast growth factor 21(FGF-21), angiopoietin-like proteins 3 (ANGPTL3) and apolipoprotein A-II concentrations, were independent components of a model to detect FHTG compared with CHTG (AUC 0.948, 95%CI 0.901–0.970, 98.5% sensitivity, 92.2% specificity, P < 0.001). The polygenic set of SNPs, accounted for 1.78% of the variance in triglyceride levels in FHTG and 6.73% in CHTG. Conclusions The clinical and genetic differences observed between FHTG and CHTG supports the notion that FHTG is a unique entity, distinguishable from other causes of hypertriglyceridemia by the higher concentrations of insulin, FGF-21, ANGPTL3, apo A-II and lower levels of apo B. We propose the inclusion of these parameters as useful markers for differentiating FHTG from other causes of hypertriglyceridemia.

scholarly journals Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

2021 ◽  
Author(s):  
Nestor Timonidis ◽  
Alberto Llera ◽  
Paul H. E. Tiesinga
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Structural Connectivity ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Synaptic Function ◽  
Reticular Nucleus ◽  
Sources Of Information ◽  
Independent Components ◽  
Brain Connectome

AbstractFinding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

scholarly journals Copula-Based Bayesian Reliability Analysis of a Product of a Probability and a Frequency Model for Parallel Systems When Components Are Dependent

2021 ◽  
Vol 11 (4) ◽  
pp. 1697
Author(s):  
Shi-Woei Lin ◽  
Tapiwa Blessing Matanhire ◽  
Yi-Ting Liu
Keyword(s):  
System Reliability ◽  
Classification Tree ◽  
Parallel Systems ◽  
Reliability Model ◽  
Key Factors ◽  
Frequency Model ◽  
Independent Components ◽  
Aggregate Analysis ◽  
Dependence Assumption ◽  
Disaggregate Analysis

While the dependence assumption among the components is naturally important in evaluating the reliability of a system, studies investigating the issues of aggregation errors in Bayesian reliability analyses have been focused mainly on systems with independent components. This study developed a copula-based Bayesian reliability model to formulate dependency between components of a parallel system and to estimate the failure rate of the system. In particular, we integrated Monte Carlo simulation and classification tree learning to identify key factors that affect the magnitude of errors in the estimation of posterior means of system reliability (for different Bayesian analysis approaches—aggregate analysis, disaggregate analysis, and simplified disaggregate analysis) to provide important guidelines for choosing the most appropriate approach for analyzing a model of products of a probability and a frequency for parallel systems with dependent components.

Analysis of R-out-of-N repairable systems: the case of phase-type distributions

2004 ◽  
Vol 36 (1) ◽  
pp. 116-138 ◽  
Author(s):  
Yonit Barron ◽  
Esther Frostig ◽  
Benny Levikson
Keyword(s):  
Repairable System ◽  
Repairable Systems ◽  
Regenerative Processes ◽  
Numerical Examples ◽  
Independent Components ◽  
Duality Results ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Two Systems ◽  
Repair Facility

An R-out-of-N repairable system, consisting of N independent components, is operating if at least R components are functioning. The system fails whenever the number of good components decreases from R to R-1. A failed component is sent to a repair facility. After a failed component has been repaired it is as good as new. Formulae for the availability of the system using Markov renewal and semi-regenerative processes are derived. We assume that either the repair times of the components are generally distributed and the components' lifetimes are phase-type distributed or vice versa. Some duality results between the two systems are obtained. Numerical examples are given for several distributions of lifetimes and of repair times.

scholarly journals Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
N. Vanello ◽  
E. Ricciardi ◽  
L. Landini
Keyword(s):  
Clustering Algorithm ◽  
A Priori ◽  
Similarity Measures ◽  
Real Data ◽  
Fmri Data ◽  
Order Selection ◽  
Functional Magnetic Resonance ◽  
Model Order Selection ◽  
Model Order ◽  
Independent Components

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data can be employed as an exploratory method. The lack in the ICA model of strong a priori assumptions about the signal or about the noise leads to difficult interpretations of the results. Moreover, the statistical independence of the components is only approximated. Residual dependencies among the components can reveal informative structure in the data. A major problem is related to model order selection, that is, the number of components to be extracted. Specifically, overestimation may lead to component splitting. In this work, a method based on hierarchical clustering of ICA applied to fMRI datasets is investigated. The clustering algorithm uses a metric based on the mutual information between the ICs. To estimate the similarity measure, a histogram-based technique and one based on kernel density estimation are tested on simulated datasets. Simulations results indicate that the method could be used to cluster components related to the same task and resulting from a splitting process occurring at different model orders. Different performances of the similarity measures were found and discussed. Preliminary results on real data are reported and show that the method can group task related and transiently task related components.

Feature Extraction Using Independent Components of Each Category

2005 ◽  
Vol 22 (2) ◽  
pp. 113-124 ◽  
Author(s):  
Manabu Kotani ◽  
Seiichi Ozawa
Keyword(s):  
Feature Extraction ◽  
Independent Components
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