Number of found documents: 833
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City simulation software for modeling, planning, and strategic assessment of territorial city units
Svítek, M.; Přibyl, O.; Vorel, J.; Garlík, B.; Resler, Jaroslav; Kozhevnikov, S.; Krč, Pavel; Geletič, Jan; Daniel, Milan; Dostál, R.; Janča, T.; Myška, V.; Aralkina, O.; Pereira, A. M.
2021 - English
The Smart Resilience City concept is a new vision of a city as a digital platform and eco-system of smart services where agents of people, things, documents, robots, and other entities can directly negotiate with each other on resource demand principals providing the best possible solution. It creates the smart environment making possible self-organization in sustainable or, when needed, resilient way of individuals, groups and the whole system objectives. Available on request at various institutes of the ASCR
City simulation software for modeling, planning, and strategic assessment of territorial city units

The Smart Resilience City concept is a new vision of a city as a digital platform and eco-system of smart services where agents of people, things, documents, robots, and other entities can directly ...

Svítek, M.; Přibyl, O.; Vorel, J.; Garlík, B.; Resler, Jaroslav; Kozhevnikov, S.; Krč, Pavel; Geletič, Jan; Daniel, Milan; Dostál, R.; Janča, T.; Myška, V.; Aralkina, O.; Pereira, A. M.
Ústav informatiky, 2021

Visual Images Segmentation based on Uniform Textures Extraction
Goltsev, A.; Gritsenko, V.; Húsek, Dušan
2020 - English
A new effective procedure for partial texture segmentation of visual images is proposed. The procedure segments any input image into a number of non-overlapping homogeneous ne-grained texture areas. The main advantages of the proposed procedure are as follows. It is completely unsupervised, that is, it processes the input image without any prior knowledge of either the type of textures or the number of texture segments in the image. In addition, the procedure segments arbitrary images of all types. This means that no changes to the procedure parameters are required to switch from one image type to another. Another major advantage of the procedure is that in most cases it extracts the uniform ne-grained texture segments present in the image, just as humans do. This result is supported by series of experiments that demonstrate the ability of the procedure to delineate uniform ne-grained texture segments over a wide range of images. At a minimum, image processing according to the proposed technique leads to a signficant reduction in the uncertainty of the internal structure of the analyzed image. Keywords: Texture feature; Texture window; Homogeneous ne-grained texture segment; Texture segment extraction; Texture segmentation Available at various institutes of the ASCR
Visual Images Segmentation based on Uniform Textures Extraction

A new effective procedure for partial texture segmentation of visual images is proposed. The procedure segments any input image into a number of non-overlapping homogeneous ne-grained texture areas. ...

Goltsev, A.; Gritsenko, V.; Húsek, Dušan
Ústav informatiky, 2020

The scalar-valued score functions of continuous probability distribution
Fabián, Zdeněk
2019 - English
In this report we give theoretical basis of probability theory of continuous random variables based on scalar valued score functions. We maintain consistently the following point of view: It is not the observed value, which is to be used in probabilistic and statistical considerations, but its 'treated form', the value of the scalar-valued score function of distribution of the assumed model. Actually, the opinion that an observed value of random variable should be 'treated' with respect to underlying model is one of main ideas of the inference based on likelihood in classical statistics. However, a vector nature of Fisher score functions of classical statistics does not enable a consistent use of this point of view. Instead, various inference functions are suggested and used in solutions of various statistical problems. Inference function of this report is the scalar-valued score function of distribution. Keywords: Shortcomings of probability theory; Scalar-valued score functions; Characteristics of continous random variables; Parametric estimation; Transformed distributions; Skew-symmetric distributions Available at various institutes of the ASCR
The scalar-valued score functions of continuous probability distribution

In this report we give theoretical basis of probability theory of continuous random variables based on scalar valued score functions. We maintain consistently the following point of view: It is not ...

Fabián, Zdeněk
Ústav informatiky, 2019

MAT TRIAD 2019: Book of Abstracts
Bok, J.; Hartman, David; Hladík, M.; Rozložník, Miroslav
2019 - English
This volume contains the Book of abstracts of the 8th International Conference on Matrix Analysis and its Applications, MAT TRIAD 2019. The MATTRIAD conferences represent a platform for researchers in a variety of aspects of matrix analysis and its interdisciplinary applications to meet and share interests and ideas. The conference topics include matrix and operator theory and computation, spectral problems, applications of linear algebra in statistics, statistical models, matrices and graphs as well as combinatorial matrix theory and others. The goal of this event is to encourage further growth of matrix analysis research including its possible extension to other fields and domains. Available on request at various institutes of the ASCR
MAT TRIAD 2019: Book of Abstracts

This volume contains the Book of abstracts of the 8th International Conference on Matrix Analysis and its Applications, MAT TRIAD 2019. The MATTRIAD conferences represent a platform for researchers ...

Bok, J.; Hartman, David; Hladík, M.; Rozložník, Miroslav
Ústav informatiky, 2019

A Hybrid Method for Nonlinear Least Squares that Uses Quasi-Newton Updates Applied to an Approximation of the Jacobian Matrix
Lukšan, Ladislav; Vlček, Jan
2019 - English
In this contribution, we propose a new hybrid method for minimization of nonlinear least squares. This method is based on quasi-Newton updates, applied to an approximation A of the Jacobian matrix J, such that AT f = JT f. This property allows us to solve a linear least squares problem, minimizing ∥Ad+f∥ instead of solving the normal equation ATAd+JT f = 0, where d ∈ Rn is the required direction vector. Computational experiments confirm the efficiency of the new method. Keywords: nonlinear least squares; hybrid methods; trust-region methods; quasi-Newton methods; numerical algorithms; numerical experiments Available at various institutes of the ASCR
A Hybrid Method for Nonlinear Least Squares that Uses Quasi-Newton Updates Applied to an Approximation of the Jacobian Matrix

In this contribution, we propose a new hybrid method for minimization of nonlinear least squares. This method is based on quasi-Newton updates, applied to an approximation A of the Jacobian matrix J, ...

Lukšan, Ladislav; Vlček, Jan
Ústav informatiky, 2019

Application of the Infinitely Many Times Repeated BNS Update and Conjugate Directions to Limited-Memory Optimization Methods
Vlček, Jan; Lukšan, Ladislav
2019 - English
To improve the performance of the L-BFGS method for large scale unconstrained optimization, repeating of some BFGS updates was proposed. Since this can be time consuming, the extra updates need to be selected carefully. We show that groups of these updates can be repeated infinitely many times under some conditions, without a noticeable increase of the computational time. The limit update is a block BFGS update. It can be obtained by solving of some Lyapunov matrix equation whose order can be decreased by application of vector corrections for conjugacy. Global convergence of the proposed algorithm is established for convex and sufficiently smooth functions. Numerical results indicate the efficiency of the new method. Keywords: unconstrained minimization; limited-memory variable metric methods; the repeated Byrd-Nocedal-Schnabel update; the Lyapunov matrix equation; the conjugate directions; global convergence; numerical results Available at various institutes of the ASCR
Application of the Infinitely Many Times Repeated BNS Update and Conjugate Directions to Limited-Memory Optimization Methods

To improve the performance of the L-BFGS method for large scale unconstrained optimization, repeating of some BFGS updates was proposed. Since this can be time consuming, the extra updates need to be ...

Vlček, Jan; Lukšan, Ladislav
Ústav informatiky, 2019

How to down-weight observations in robust regression: A metalearning study
Kalina, Jan; Pitra, Zbyněk
2018 - English
Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination. We focus on comparing the prediction performance of the least weighted squares estimator with various weighting schemes. A broader spectrum of classification methods is applied and a support vector machine turns out to yield the best results. While results of a leave-1-out cross validation are very different from results of autovalidation, we realize that metalearning is highly unstable and its results should be interpreted with care. We also focus on discussing all possible limitations of the metalearning methodology in general. Keywords: metalearning; robust statistics; linear regression; outliers Available on request at various institutes of the ASCR
How to down-weight observations in robust regression: A metalearning study

Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is ...

Kalina, Jan; Pitra, Zbyněk
Ústav informatiky, 2018

Stav předaných dat a úprava rozdělení vybraných měření 2018
Novák, Jakub; Jiřina, M.; Benešová, Michaela
2018 - Czech
Zpráva se týká stavu předávaných dat a úpravy rozdělení vybraných měření pro rok 2018 v rámci projektu TDD-ČR. Cílem je informovat o aktuálním stavu dat a navrhnout opatření pro zachování reprezentativity celého vzorku. Jsou uvedena kritéria filtrace dat a navrženy postupy pro přerozdělení vybraných měření. Keywords: typový diagram dodávky; TDD; spotřeba plynu; měřicí místa; kritéria; filtrace; náhrady Available on request at various institutes of the ASCR
Stav předaných dat a úprava rozdělení vybraných měření 2018

Zpráva se týká stavu předávaných dat a úpravy rozdělení vybraných měření pro rok 2018 v rámci projektu TDD-ČR. Cílem je informovat o aktuálním stavu dat a navrhnout opatření pro zachování ...

Novák, Jakub; Jiřina, M.; Benešová, Michaela
Ústav informatiky, 2018

Popis TDD modelu verze 3.9
Novák, Jakub; Jiřina, M.; Benešová, Michaela
2018 - Czech
Zpráva je závěrečnou roční zprávou pro rok 2018 v rámci Projektu TDD-ČR. Cílem je předat metodiky pro užití modelu jak provozovatelem distribuční soustavy, tak operátorem trhu a dále informovat o aktuálním stavu modelu. Jsou popsány předávané soubory včetně vzorového výpočtu na reálných datech a jejich obsah. Keywords: typový diagram dodávky; typový diagram dodávky; spotřeba plynu; TDD; popis modelu Available on request at various institutes of the ASCR
Popis TDD modelu verze 3.9

Zpráva je závěrečnou roční zprávou pro rok 2018 v rámci Projektu TDD-ČR. Cílem je předat metodiky pro užití modelu jak provozovatelem distribuční soustavy, tak operátorem trhu a dále informovat o ...

Novák, Jakub; Jiřina, M.; Benešová, Michaela
Ústav informatiky, 2018

The Computational Power of Neural Networks and Representations of Numbers in Non-Integer Bases.
Šíma, Jiří
2017 - English
We briefly survey the basic concepts and results concerning the computational power of neural networks which basically depends on the information content of weight parameters. In particular, recurrent neural networks with integer, rational, and arbitrary real weights are classified within the Chomsky and finer complexity hierarchies. Then we refine the analysis between integer and rational weights by investigating an intermediate model of integer-weight neural networks with an extra analog rational-weight neuron (1ANN). We show a representation theorem which characterizes the classification problems solvable by 1ANNs, by using so-called cut languages. Our analysis reveals an interesting link to an active research field on non-standard positional numeral systems with non-integer bases. Within this framework, we introduce a new concept of quasi-periodic numbers which is used to classify the computational power of 1ANNs within the Chomsky hierarchy. Keywords: neural network; Chomsky hierarchy; beta-expansion; cut language Available at various institutes of the ASCR
The Computational Power of Neural Networks and Representations of Numbers in Non-Integer Bases.

We briefly survey the basic concepts and results concerning the computational power of neural networks which basically depends on the information content of weight parameters. In particular, recurrent ...

Šíma, Jiří
Ústav informatiky, 2017

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