Number of found documents: 1632
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Spatio-Spectral EEG Patterns in the Source-Reconstructed Space and Relation to Resting-State Networks: An EEG-fMRI Study
Jiříček, Stanislav; Koudelka, V.; Mantini, D.; Mareček, R.; Hlinka, Jaroslav
2022 - English
In this work, we present and evaluate a novel EEG-fMRI integration approach combining a spatio-spectral decomposition method and a reliable source localization technique. On the large 72 subjects resting- state hdEEG-fMRI data set we tested the stability of the proposed method in terms of both extracted spatio-spectral patterns(SSPs) as well as their correspondence to the BOLD signal. We also compared the proposed method with the spatio-spectral decomposition in the electrode space as well as well-known occipital alpha correlate in terms of the explained variance of BOLD signal. We showed that the proposed method is stable in terms of extracted patterns and where they correlate with the BOLD signal. Furthermore, we show that the proposed method explains a very similar level of the BOLD signal with the other methods and that the BOLD signal in areas of typical BOLD functional networks is explained significantly more than by a chance. Nevertheless, we didn’t observe a significant relation between our source-space SSPs and the BOLD ICs when spatio-temporally comparing them. Finally, we report several the most stable source space EEG-fMRI patterns together with their interpretation and comparison to the electrode space patterns. Keywords: EEG-fMRI Integration; EEG-informed fMRI; Spatio-spectral Decomposition; Electrical Source Imaging; Independent Component Analysis; Resting State Networks Available in digital repository of the ASCR
Spatio-Spectral EEG Patterns in the Source-Reconstructed Space and Relation to Resting-State Networks: An EEG-fMRI Study

In this work, we present and evaluate a novel EEG-fMRI integration approach combining a spatio-spectral decomposition method and a reliable source localization technique. On the large 72 subjects ...

Jiříček, Stanislav; Koudelka, V.; Mantini, D.; Mareček, R.; Hlinka, Jaroslav
Ústav informatiky, 2022

Tisková zpráva - měření tepelného komfortu
Geletič, Jan; Lehnert, M.
2022 - Czech
Fulltext is available at external website.
Tisková zpráva - měření tepelného komfortu

Geletič, Jan; Lehnert, M.
Ústav informatiky, 2022

Czech Gathering of Logicians 2022. Book of Abstracts
Haniková, Zuzana; Švejdar, V.; Wannenburg, Johann Joubert
2022 - English
Fulltext is available at external website.
Czech Gathering of Logicians 2022. Book of Abstracts

Haniková, Zuzana; Švejdar, V.; Wannenburg, Johann Joubert
Ústav informatiky, 2022

A Bootstrap Comparison of Robust Regression Estimators
Kalina, Jan; Janáček, P.
2022 - English
The ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more preferable alternatives. It has been repeatedly recommended to use the least squares together with a robust estimator, where the latter is understood as a diagnostic tool for the former. In other words, only if the robust estimator yields a very different result, the user should investigate the dataset closer and search for explanations. For this purpose, a hypothesis test of equality of the means of two alternative linear regression estimators is proposed here based on nonparametric bootstrap. The performance of the test is presented on three real economic datasets with small samples. Robust estimates turn out not to be significantly different from non-robust estimates in the selected datasets. Still, robust estimation is beneficial in these datasets and the experiments illustrate one of possible ways of exploiting the bootstrap methodology in regression modeling. The bootstrap test could be easily extended to nonlinear regression models. Keywords: linear regression; robust estimation; nonparametric bootstrap; bootstrap hypothesis testing Fulltext is available at external website.
A Bootstrap Comparison of Robust Regression Estimators

The ordinary least squares estimator in linear regression is well known to be highly vulnerable to the presence of outliers in the data and available robust statistical estimators represent more ...

Kalina, Jan; Janáček, P.
Ústav informatiky, 2022

A Measure of Variability WIthin Parametric Families of Continuous Distributions
Fabián, Zdeněk
2022 - English
A continuous probability measure on an open interval of the real line induces in it a unique geometry, "center of gravity" of which is the typical value of the distribution. In the paper is identified a score variance as a finite measure of variability of distributions with respect to the typical value and discussed its properties and methods of estimation. Itroducing a generalized Rao distance in the sample space one can appraise the precision of the estimate of the typical value. Keywords: scalar-valued score; score mean; score variance; distance in the sample space Available at various institutes of the ASCR
A Measure of Variability WIthin Parametric Families of Continuous Distributions

A continuous probability measure on an open interval of the real line induces in it a unique geometry, "center of gravity" of which is the typical value of the distribution. In the paper is identified ...

Fabián, Zdeněk
Ústav informatiky, 2022

Large Perimeter Objects Surrounded by a 1.5D Terrain
Keikha, Vahideh
2022 - English
Given is a 1.5D terrain T , i.e., an x-monotone polygonal chain in R2. Our objective is to approximate the largest area or perimeter convex polygon with at most k vertices inside T . For a constant k > 0, we design an FPTAS that efficiently approximates such polygons within a factor (1 − ǫ). For the special case of the´largest-perimeter contained triangle in T , we design an O(n log n) time exact algorithm that matches the same result for the area measure. Available in digital repository of the ASCR
Large Perimeter Objects Surrounded by a 1.5D Terrain

Given is a 1.5D terrain T , i.e., an x-monotone polygonal chain in R2. Our objective is to approximate the largest area or perimeter convex polygon with at most k vertices inside T . For a constant k ...

Keikha, Vahideh
Ústav informatiky, 2022

Recent Trends in Machine Learning with a Focus on Applications in Finance
Kalina, Jan; Neoral, Aleš
2022 - English
Machine learning methods penetrate to applications in the analysis of financial data, particularly to supervised learning tasks including regression or classification. Other approaches, such as reinforcement learning or automated machine learning, are not so well known in the context of finance yet. In this paper, we discuss the advantages of an automated data analysis, which is beneficial especially if a larger number of datasets should be analyzed under a time pressure. Important types of learning include reinforcement learning, automated machine learning, or metalearning. This paper overviews their principles and recalls some of their inspiring applications. We include a discussion of the importance of the concept of information and of the search for the most relevant information in the field of mathematical finance. We come to the conclusion that a statistical interpretation of the results of theautomatic machine learning remains crucial for a proper understanding of the knowledge acquired by the analysis of the given (financial) data. Keywords: statistical learning; automated machine learning; metalearning; financial data analysis; stock market investing Fulltext is available at external website.
Recent Trends in Machine Learning with a Focus on Applications in Finance

Machine learning methods penetrate to applications in the analysis of financial data, particularly to supervised learning tasks including regression or classification. Other approaches, such as ...

Kalina, Jan; Neoral, Aleš
Ústav informatiky, 2022

The 2020 Election In The United States: Beta Regression Versus Regression Quantiles
Kalina, Jan
2021 - English
The results of the presidential election in the United States in 2020 desire a detailed statistical analysis by advanced statistical tools, as they were much different from the majority of available prognoses as well as from the presented opinion polls. We perform regression modeling for explaining the election results by means of three demographic predictors for individual 50 states: weekly attendance at religious services, percentage of Afroamerican population, and population density. We compare the performance of beta regression with linear regression, while beta regression performs only slightly better in terms of predicting the response. Because the United States population is very heterogeneous and the regression models are heteroscedastic, we focus on regression quantiles in the linear regression model. Particularly, we develop an original quintile regression map, such graphical visualization allows to perform an interesting interpretation of the effect of the demographic predictors on the election outcome on the level of individual states. Keywords: elections results; electoral demography; quantile regression; heteroscedasticity; outliers Fulltext is available at external website.
The 2020 Election In The United States: Beta Regression Versus Regression Quantiles

The results of the presidential election in the United States in 2020 desire a detailed statistical analysis by advanced statistical tools, as they were much different from the majority of available ...

Kalina, Jan
Ústav informatiky, 2021

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
SVÍTEK, M., PŘIBYL, O., VOREL, J., GARLÍK, B., RESLER, J., KOZHEVNIKOV, S., KRČ, P., GELETIČ, J., DANIEL, M., DOSTÁL, R., JANČA, T., MYŠKA, V., ARALKINA, O., PEREIRA, A. M. City simulation software for modeling, planning, and strategic assessment of territorial city units. 1.1. Prague: CTU & ICS CAS, 2021. Technical Report. ABSTRACT: 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. Keywords: Smart city; City simulation; Energy resource-demand modelling; Environmental modelling; Synthetic population; Transport modelling Available on request at various institutes of the ASCR
City simulation software for modeling, planning, and strategic assessment of territorial city units

SVÍTEK, M., PŘIBYL, O., VOREL, J., GARLÍK, B., RESLER, J., KOZHEVNIKOV, S., KRČ, P., GELETIČ, J., DANIEL, M., DOSTÁL, R., JANČA, T., MYŠKA, V., ARALKINA, O., PEREIRA, A. M. City simulation software ...

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

Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix
Turčičová, Marie; Mandel, J.; Eben, Kryštof
2021 - English
We present an ensemble filter that provides a rigorous covariance regularization when the underlying random field is Gaussian Markov. We use a linear model for the precision matrix (inverse of covariance) and estimate its parameters together with the analysis mean by the Score Matching method. This procedure provides an explicit expression for parameter estimators. The resulting analysis step formula is the same as in the traditional ensemble Kalman filter. Keywords: Score matching; ensemble filter; Gaussian Markov random field; covariance modelling Available at various institutes of the ASCR
Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix

We present an ensemble filter that provides a rigorous covariance regularization when the underlying random field is Gaussian Markov. We use a linear model for the precision matrix (inverse of ...

Turčičová, Marie; Mandel, J.; Eben, Kryštof
Ústav informatiky, 2021

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