Synergy between the Parameter Estimation and a Design Variable Optimization for FRAP Experiments
Matonoha, Ctirad; Papáček, Š.
2015 - English Available in digital repository of the ASCR Synergy between the Parameter Estimation and a Design Variable Optimization for FRAP Experiments

Matonoha, Ctirad; Papáček, Š.
Ústav informatiky, 2015

Dynamic Contact Problems in Bone Neoplasm Analyses and the Primal-Dual Active Set (PDAS) Method
Nedoma, Jiří
2015 - English In the contribution growths of the neoplasms (benign and malignant tumors and cysts), located in a system of loaded bones, will be simulated. The main goal of the contribution is to present the useful methods and efficient algorithms for their solutions. Because the geometry of the system of loaded and possible fractured bones with enlarged neoplasms changes in time, the corresponding mathematical models of tumor’s and cyst’s evolutions lead to the coupled free boundary problems and the dynamic contact problems with or without friction. The discussed parts of these models will be based on the theory of dynamic contact problems without or with Tresca or Coulomb frictions in the visco-elastic rheology. The numerical solution of the problem with Coulomb friction is based on the semi-implicit scheme in time and the finite element method in space, where the Coulomb law of friction at every time level will be approximated by its value from the previous time level. The algorithm for the corresponding model of friction will be based on the discrete mortar formulation of the saddle point problem and the primal-dual active set algorithm. The algorithm for the Coulomb friction model will be based on the fixpoint algorithm, that will be an extension of the PDAS algorithm for the Tresca friction. In this algorithm the friction bound is iteratively modified using the normal component of the Lagrange multiplier. Thus the friction bound and the active and inactive sets are updated in every step of the iterative algorithm and at every time step corresponding to the semi-implicit scheme. Keywords: dynamic contact problems; mathematical models of neoplasms - tumors and cysts; Coulomb and Tresca frictions; variational formulation; semi-implicit scheme; FEM; mortar approximation; PDAS algorithm Fulltext is available at external website. Dynamic Contact Problems in Bone Neoplasm Analyses and the Primal-Dual Active Set (PDAS) Method

In the contribution growths of the neoplasms (benign and malignant tumors and cysts), located in a system of loaded bones, will be simulated. The main goal of the contribution is to present the useful ...

Nedoma, Jiří
Ústav informatiky, 2015

Homomorphic Coordinates of Dempster’s Semigroup
Daniel, Milan
2015 - English Coordinates of belief functions on two-element frame of discernment are defined using homomorphisms of Dempster’s semigroup (the algebra of belief functions with Dempster’s rule). Three systems of the coordinates (h-f, h-f0, and coordinates based on decomposition of belief functions) are analysed with a focus to their homomorphic properties. Further, ideas of generalisation of the investigated systems of coordinates to general finite frame of discernment are presented. Keywords: belief functions; Dempster-Shafer Theory; Dempster's semigroup; homomorphism; homomorphic coordinates Fulltext is available at external website. Homomorphic Coordinates of Dempster’s Semigroup

Coordinates of belief functions on two-element frame of discernment are defined using homomorphisms of Dempster’s semigroup (the algebra of belief functions with Dempster’s rule). Three systems of the ...

Daniel, Milan
Ústav informatiky, 2015

Representations of Boolean Functions by Perceptron Networks
Kůrková, Věra
2014 - English Limitations of capabilities of shallow perceptron networks are investigated. Lower bounds are derived for growth of numbers of units and sizes of output weights in networks representing Boolean functions of d variables. It is shown that for large d, almost any randomly chosen Boolean function cannot be tractably represented by shallow perceptron networks, i.e., each its representation requires a network with number of units or sizes of output weights depending on d exponentially Keywords: perceptron networks; model complexity; Boolean functions Available in digital repository of the ASCR Representations of Boolean Functions by Perceptron Networks

Limitations of capabilities of shallow perceptron networks are investigated. Lower bounds are derived for growth of numbers of units and sizes of output weights in networks representing Boolean ...

Kůrková, Věra
Ústav informatiky, 2014

Noise revealing in Golub-Kahan bidiagonalization as a mean of regularization in discrete inverse problems
Kubínová, Marie; Hnětynková, Iveta
2014 - English Keywords: ill-posed problems; regularization; Krylov subspace Available in a digital repository NRGL Noise revealing in Golub-Kahan bidiagonalization as a mean of regularization in discrete inverse problems

Kubínová, Marie; Hnětynková, Iveta
Ústav informatiky, 2014

On three equivalent methods for parameter estimation problem based on spatio-temporal FRAP data
Matonoha, Ctirad; Papáček, Š.
2014 - English Keywords: inverse problem formulation; Tikhonov regularizaton; least-squares problem Available in a digital repository NRGL On three equivalent methods for parameter estimation problem based on spatio-temporal FRAP data

Matonoha, Ctirad; Papáček, Š.
Ústav informatiky, 2014

Explaining Anomalies with Sapling Random Forests
Pevný, T.; Kopp, Martin
2014 - English The main objective of anomaly detection algorithms is finding samples deviating from the majority. Although a vast number of algorithms designed for this already exist, almost none of them explain, why a particular sample was labelled as an anomaly. To address this issue, we propose an algorithm called Explainer, which returns the explanation of sample’s differentness in disjunctive normal form (DNF), which is easy to understand by humans. Since Explainer treats anomaly detection algorithms as black-boxes, it can be applied in many domains to simplify investigation of anomalies. The core of Explainer is a set of specifically trained trees, which we call sapling random forests. Since their training is fast and memory efficient, the whole algorithm is lightweight and applicable to large databases, datastreams, and real-time problems. The correctness of Explainer is demonstrated on a wide range of synthetic and real world datasets. Keywords: anomaly explanation; decision trees; feature selection; random forest Available in digital repository of the ASCR Explaining Anomalies with Sapling Random Forests

The main objective of anomaly detection algorithms is finding samples deviating from the majority. Although a vast number of algorithms designed for this already exist, almost none of them explain, ...

Pevný, T.; Kopp, Martin
Ústav informatiky, 2014

Case Study in Approaches to the Classification of Audiovisual Recordings of Lectures and Conferences
Pulc, P.; Holeňa, Martin
2014 - English Several methods for classification of semistructured documents already exist, thus also classifications for individual modalities of multimedia content. However, every classifier can behave differently on different data modalities and can be differently appropriate for classification of the considered multimedia content as a whole. Because of that, relying on a single classifier or a static weighting of the classification of individual modalities is not adequate. The present paper describes a case study in searching for suitable classification methods, and in investigating appropriate methods for the aggregation of their results to determine a final class of a lecture or conference recording. Keywords: multimedial data; classification; ensembles of classifiers Available in digital repository of the ASCR Case Study in Approaches to the Classification of Audiovisual Recordings of Lectures and Conferences

Several methods for classification of semistructured documents already exist, thus also classifications for individual modalities of multimedia content. However, every classifier can behave ...

Pulc, P.; Holeňa, Martin
Ústav informatiky, 2014

Meta-Parameters of Kernel Methods and Their Optimization
Vidnerová, Petra; Neruda, Roman
2014 - English In this work we deal with the problem of metalearning for kernel based methods. Among the kernel methods we focus on the support vector machine (SVM), that have become a method of choice in a wide range of practical applications, and on the regularization network (RN) with a sound background in approximation theory. We discuss the role of kernel function in learning, and we explain several search methods for kernel function optimization, including grid search, genetic search and simulated annealing. The proposed methodology is demonstrated on experiments using benchmark data sets. Keywords: kernel methods; metalearning; computational intelligence Available in digital repository of the ASCR Meta-Parameters of Kernel Methods and Their Optimization

In this work we deal with the problem of metalearning for kernel based methods. Among the kernel methods we focus on the support vector machine (SVM), that have become a method of choice in a wide ...

Vidnerová, Petra; Neruda, Roman
Ústav informatiky, 2014

Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models
Chotard, A.; Holeňa, Martin
2014 - English Several recent publications investigated Markov-chain modelling of linear optimization by a (1,lambda)-ES, considering both unconstrained and linearly constrained optimization, and both constant and varying step size. All of them assume normality of the involved random steps. This is a very strong and specific assumption. The objective of our contribution is to show that in the constant step size case, valuable properties of the Markov chain can be obtained even for steps with substantially more general distributions. Several results that have been previously proved using the normality assumption are proved here in a more general way without that assumption. Finally, the decomposition of a multidimensional distribution into its marginals and the copula combining them is applied to the new distributional assumptions, particular attention being paid to distributions with Archimedean copulas. Keywords: evolution strategies; random steps; linear optimization; Markov chain models; Archimedean copulas Available in digital repository of the ASCR Important Markov-Chain Properties of (1,lambda)-ES Linear Optimization Models

Several recent publications investigated Markov-chain modelling of linear optimization by a (1,lambda)-ES, considering both unconstrained and linearly constrained optimization, and both constant and ...

Chotard, A.; Holeňa, Martin
Ústav informatiky, 2014