Všechny publikace

This directory contains R scripts, data files, and analysis reports relatedto the analysis of adherence. FILE OVERVIEW DATA FILES* data.xlsx  Main subject-level dataset. * data-fitbit.xlsx  …

This directory contains R scripts, data files, and analysis reports relatedto the analysis of adherence. FILE OVERVIEW DATA FILES* data.xlsx  Main subject-level dataset. * data-fitbit.xlsx  Dataset with detailed Fitbit-derived measures. DATA LOADING AND PREPROCESSING* data.R  R script for loading and preprocessing the main dataset. * data-fitbit.R  R script for loading and preprocessing the detailed Fitbit dataset. DESCRIPTIVE AND BASELINE ANALYSES* summary.Rmd  R Markdown document providing a basic description of the datasets. * baseline.Rmd  R Markdown document with baseline characteristics and missing-value  analysis, including evaluation and imputation of missing values in SWL  (new variable SWLlm.predicted). * baseline-by-burst.Rmd  R Markdown document with baseline characteristics stratified by burst  number. MODELING AND VARIABLE IMPORTANCE* importance.Rmd  R Markdown analysis template for evaluating variable importance using  stepwise regression and random forest models. PROJECT CONFIGURATION AND BUILD SYSTEM* Makefile.R  Project definition for the rmake package; generates the GNU Makefile. * Makefile  File dependencies and compilation commands used by GNU Make to generate  all project results. * Rproject.Rproj  RStudio project file. ------------------------------------------------------------------------------REQUIREMENTS------------------------------------------------------------------------------ 1) INSTALL REQUIRED R PACKAGES Run the following commands in R:   install.packages(c(    "tidyverse", "knitr", "rmake", "caret", "randomForest",    "fastDummies", "broom", "devtools"  ))  devtools::install_github("beerda/hammer")  devtools::install_github("beerda/mbrtools") 2) GENERATE PROJECT ANALYSES Run the analysis pipeline using rmake:   make() This command generates all results defined in the project Makefile.------------------------------------------------------------------------------  

Abstract This paper presents a structured overview and novel insights into the universal approximation property offeedforward neural networks. We categorize existing results based on the characteristi…

Abstract This paper presents a structured overview and novel insights into the universal approximation property offeedforward neural networks. We categorize existing results based on the characteristics of activation functions— ranging from strictly monotonic to weakly monotonic and continuous almost everywhere — and examinetheir implications under architectural constraints such as bounded depth and width. Building on classical resultsby Cybenko [1], Hornik [2], and Maiorov [3], we introduce new activation functions that enable even simplerneural network architectures to retain universal approximation capabilities. Notably, we demonstrate thatsingle-layer networks with only two neurons and fixed weights can approximate any continuous univariatefunction, and that two-layer networks can extend this capability to multivariate functions. These findings refinethe known lower bounds of neural network complexity and offer constructive approaches that preserve strictmonotonicity, improving upon prior work that relied on relaxed monotonicity conditions. Our results contributeto the theoretical foundation of neural networks and open pathways for designing minimal yet expressivearchitectures.

Abstract In this paper, we explore the use of General Unary Hypotheses Automaton quantifiers and provide representations for their specific subclasses. Furthermore, we focus explicitly on implication…

Abstract In this paper, we explore the use of General Unary Hypotheses Automaton quantifiers and provide representations for their specific subclasses. Furthermore, we focus explicitly on implicational quantifiers for analyzing specific relational dependencies. We discuss their suitability in fuzzy modeling and demonstrate their integration with appropriate fuzzy rules to create a new class of weighted fuzzy rules. This study contributes to the advancement of fuzzy modeling and offers a framework for further research and practical applications.  

Abstract This paper investigates the use of fuzzy set theory in approximating solutions to differential equations under uncertainty. By defining lower and upper bounds, a robust framework is develope…

Abstract This paper investigates the use of fuzzy set theory in approximating solutions to differential equations under uncertainty. By defining lower and upper bounds, a robust framework is developed for modeling transitions between function extrema, extending classical methods to fuzzy initial value problems. The study demonstrates universal approximation properties and proposes numerical techniques that ensure stability and convergence. Applications highlight the practical utility of this approach in engineering and computational mathematics, solidifying fuzzy set theory as a powerful tool for addressing uncertainty in mathematical modeling.  

Abstract This paper introduces an enhanced framework for performing division operations on interactive fuzzy numbers characterized by complete correlation. Unlike traditional methods reliant on the i…

Abstract This paper introduces an enhanced framework for performing division operations on interactive fuzzy numbers characterized by complete correlation. Unlike traditional methods reliant on the independence assumption, we build on the sup-J extension framework to support correlated input fuzzy values. The proposed method establishes precise conditions under which the result aligns with, diverges from, or subsumes conventional divisions such as Zadeh’s and the generalized Hukuhara division. Additionally, we investigate invertibility conditions for the proposed division with respect to multiplication. These refinements offer valuable theoretical insights and have implications for models involving uncertainty, including difference equations.  

In this study, we present an analysis, driven by the Critical Discourse Studies, Frame Analysis, and Narrative Analysis, of how truancy is represented in the Czech media. Based on our findings, we ass…

In this study, we present an analysis, driven by the Critical Discourse Studies, Frame Analysis, and Narrative Analysis, of how truancy is represented in the Czech media. Based on our findings, we assert that Czech media employ three frames (gender bias; moralisation and individualisation; and repression/retribution) for representing truancy, effectively depicting it as a criminal problem created by irresponsible mothers (and children they neglected) who must be punished to address the issue. Employing the cultural and feminist criminology framework, as a combination that is scarcely used in studies, we argue that media (re)produce criminalisation of mothers by amplifying deeply rooted and routinised gendered cultural stereotypes. In this sense, we show that cultural criminology is useful not only for analysing adrenaline/“exciting” transgression, subcultures, or edgework, but, especially when combined with feminist criminology, also for analysing possibilities of criminalisation rooted in mainstream culture. Furthermore, our analysis shows that Czech media representation of truancy is damaging both for society and for addressing the issue, reducing it to a mere neoliberal individual choice of irresponsible mothers, even though truancy is a much more complex phenomenon with communal and structural levels and can be seen rather as a product of advanced marginality.

We observe an unknown function of d variables f(t), t ∈ [0, 1]^d, in the Gaussian white noise model of intensity ε > 0. We assume that the function f is regular and that it is a sum of…

We observe an unknown function of d variables f(t), t ∈ [0, 1]^d, in the Gaussian white noise model of intensity ε > 0. We assume that the function f is regular and that it is a sum of k-variate functions, where k varies from 1 to s (1 ≤ s ≤ d). These functions are unknown to us and only a few of them are nonzero. In this article, we address the problem of identifying the nonzero components of f in the case when d = d_ε → ∞ as ε → 0 and s is either fixed or s = s_ε → ∞, s = o(d) as ε → ∞. This may be viewed as a variable selection problem. We derive the conditions when exact variable selection in the model at hand is possible and provide a selection procedure that achieves this type of selection. The procedure is adaptive to a degree of model sparsity described by the sparsity parameter β ∈ (0, 1). We also derive conditions that make the exact variable selection impossible. Our results augment previous work in this area.

Abstract   Intermediate quantifiers are expressions of natural language, for example “most, almost all, many, a few” using which we quantify a number of some objects in a given univer…

Abstract   Intermediate quantifiers are expressions of natural language, for example “most, almost all, many, a few” using which we quantify a number of some objects in a given universe. We have shown in [23] that all valid syllogisms with intermediate  quantifiers are a consequence of only two algebraic inequalities and one equality. The result was obtained in the formalism  of Lukasiewicz fuzzy type theory whose truth values form a linearly ordered complete MV-algebra. In this paper we will  prove that the same holds if we replace MV-algebra by a much more general IEQ-algebra (involutive EQ-algebra).