62,086 research outputs found
Estimation of extended mixed models using latent classes and latent processes: the R package lcmm
The R package lcmm provides a series of functions to estimate statistical
models based on linear mixed model theory. It includes the estimation of mixed
models and latent class mixed models for Gaussian longitudinal outcomes (hlme),
curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear
multivariate outcomes (multlcmm), as well as joint latent class mixed models
(Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a
time-to-event that can be possibly left-truncated right-censored and defined in
a competing setting. Maximum likelihood esimators are obtained using a modified
Marquardt algorithm with strict convergence criteria based on the parameters
and likelihood stability, and on the negativity of the second derivatives. The
package also provides various post-fit functions including goodness-of-fit
analyses, classification, plots, predicted trajectories, individual dynamic
prediction of the event and predictive accuracy assessment. This paper
constitutes a companion paper to the package by introducing each family of
models, the estimation technique, some implementation details and giving
examples through a dataset on cognitive aging
Kinematic parameters and membership probabilities of open clusters in the Bordeaux PM2000 catalogue
We derive lists of proper-motions and kinematic membership probabilities for
49 open clusters and possible open clusters in the zone of the Bordeaux PM2000
proper motion catalogue (). We test
different parametrisations of the proper motion and position distribution
functions and select the most successful one. In the light of those results, we
analyse some objects individually. The segregation between cluster and field
member stars, and the assignment of membership probabilities, is accomplished
by applying a new and fully automated method based on both parametrisations of
the proper motion and position distribution functions, and genetic algorithm
optimization heuristics associated with a derivative-based hill climbing
algorithm for the likelihood optimization. We present a catalogue comprising
kinematic parameters and associated membership probability lists for 49 open
clusters and possible open clusters in the Bordeaux PM2000 catalogue region. We
note that this is the first determination of proper motions for five open
clusters. We confirm the non-existence of two kinematic populations in the
region of 15 previously suspected non-existent objects.Comment: 14 pages, 6 figures, 4 tables. Accepted for publication in Astronomy
& Astrophysic
Methods For Fuzzy Demand Assessment For IT Specialties
The rapid development of information technologies and their penetration into various spheres of human activity cause a sharply increased demand for IT specialists, in many countries of the world far exceeding the supply on them. High rates of technological transformation contribute to the diversification of the IT segment of the labor market, on the one hand, stimulate the disappearance of some and the emergence of new IT specialties, on the other. This creates a discrepancy between the structure of IT-related education and the labor market demand for IT specialists of the required profile and determines the relevance of developing methods for assessing the demand for IT specialties.This article is devoted to the study and solution of the problem of identifying the demand for IT specialties in the absence of accurate and complete information about the situation in the IT market segment. For the assessment of IT specialties and their ranking by the degree of demand in the labor market, the tasks of making individual and group decisions in the context of fuzzy initial information are formulated and solved. The methodological basis of the tasks posed is multi-criteria decision support methods based on fuzzy relations of expert preferences.The proposed approach as a mathematical tool for minimizing the structural imbalance of supply and demand for IT specialties is one of the components of the system of intellectual management of the labor market of IT specialists. The latter is designed to support the adoption of scientifically based management decisions to eliminate the mismatch of supply and demand in the IT segment of the labor market in professional, quantitative and qualitative sections
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Fuzzy geometry, entropy, and image information
Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Many automatically analyzable scientific questions are well-posed and offer a
variety of information about the expected outcome a priori. Although often
being neglected, this prior knowledge can be systematically exploited to make
automated analysis operations sensitive to a desired phenomenon or to evaluate
extracted content with respect to this prior knowledge. For instance, the
performance of processing operators can be greatly enhanced by a more focused
detection strategy and the direct information about the ambiguity inherent in
the extracted data. We present a new concept for the estimation and propagation
of uncertainty involved in image analysis operators. This allows using simple
processing operators that are suitable for analyzing large-scale 3D+t
microscopy images without compromising the result quality. On the foundation of
fuzzy set theory, we transform available prior knowledge into a mathematical
representation and extensively use it enhance the result quality of various
processing operators. All presented concepts are illustrated on a typical
bioimage analysis pipeline comprised of seed point detection, segmentation,
multiview fusion and tracking. Furthermore, the functionality of the proposed
approach is validated on a comprehensive simulated 3D+t benchmark data set that
mimics embryonic development and on large-scale light-sheet microscopy data of
a zebrafish embryo. The general concept introduced in this contribution
represents a new approach to efficiently exploit prior knowledge to improve the
result quality of image analysis pipelines. Especially, the automated analysis
of terabyte-scale microscopy data will benefit from sophisticated and efficient
algorithms that enable a quantitative and fast readout. The generality of the
concept, however, makes it also applicable to practically any other field with
processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure
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