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Signal complexity evaluation in the processing of functional magnetic resonance imaging
Vyhnánek, Jan ; Boldyš, Jiří (advisor) ; Dvořák, Jiří (referee)
Functional magnetic resonance imaging has been recently the most common tool for examining the neural activity in human and animals. The goal of a typical data-mining challenge is the localisation of brain areas activated during a cognitive task which is usually performed using a linear model or correlation methods. For this purpose several authors have proposed the use of methods evaluating signal complexity which could possibly overcome some of the shortcomings of the standards methods due to their independence on a priori knowledge of data characteristics. This work explains possibilities of using such methods including aspects of their configuration and it proposes an evaluation of performance of the methods applied on simulated data following expected biological characteristics. The results of the evaluation of performance showed little advantage of these methods over the standard ones in cases when the standard methods were possible to apply. However, some of the methods evaluating signal complexity were found useful for determining the regularity of signals which is a feature that cannot be assessed by the standard methods. Optimal parameters of the methods evaluating signal regularity were determined on simulated data and finally the methods were applied on the data examining emotional processing of...
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Slabá řešení pro třídu nelineárních integrodiferenciálních rovnic
Soukup, Ivan ; Bárta, Tomáš (advisor) ; Kaplický, Petr (referee)
Title: Weak solutions for a class of nonlinear integrodifferential equations Author: Ivan Soukup Department: Department of mathematical analysis Supervisor: RNDr. Tomáš Bárta, Ph.D. Supervisor's e-mail address: tomas.barta@mff.cuni.cz Abstract: The work investigates a system of evolutionary nonlinear partial integrodifferential equations in three dimensional space. In particular it stud- ies an existence of a solution to the system introduced in [1] with Dirichlet boundary condition and initial condition u0. We adopt the scheme of the proof from [9] and try to avoid the complications rising from the integral term. The procedure consists of an approximation of the convective term and an ap- proximation of the potentials of both nonlinearities using a quadratic function, proving the existence of the approximative solution and then returning to the original problem via regularity of the approximative solution and properties of the nonlinearities. The aim is to improve the results of the paper [1]. 1
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Stochastic Evolution Equations
Čoupek, Petr ; Maslowski, Bohdan (advisor) ; Garrido-Atienza, María J. (referee) ; Hlubinka, Daniel (referee)
Stochastic Evolution Equations Petr Čoupek Doctoral Thesis Abstract Linear stochastic evolution equations with additive regular Volterra noise are studied in the thesis. Regular Volterra processes need not be Gaussian, Markov or semimartingales, but they admit a certain covariance structure instead. Particular examples cover the fractional Brownian motion of H > 1/2 and, in the non-Gaussian case, the Rosenblatt process. The solution is considered in the mild form, which is given by the variation of constants formula, and takes values either in a separable Hilbert space or the space Lp(D, µ) for large p. In the Hilbert-space setting, existence, space-time regularity and large-time behaviour of the solutions are studied. In the Lp setting, existence and regularity is studied, and in concrete cases of stochastic partial differential equations, the solution is shown to be a space-time continuous random field.
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Stochastic Evolution Equations
Čoupek, Petr ; Maslowski, Bohdan (advisor)
Stochastic Evolution Equations Petr Čoupek Doctoral Thesis Abstract Linear stochastic evolution equations with additive regular Volterra noise are studied in the thesis. Regular Volterra processes need not be Gaussian, Markov or semimartingales, but they admit a certain covariance structure instead. Particular examples cover the fractional Brownian motion of H > 1/2 and, in the non-Gaussian case, the Rosenblatt process. The solution is considered in the mild form, which is given by the variation of constants formula, and takes values either in a separable Hilbert space or the space Lp(D, µ) for large p. In the Hilbert-space setting, existence, space-time regularity and large-time behaviour of the solutions are studied. In the Lp setting, existence and regularity is studied, and in concrete cases of stochastic partial differential equations, the solution is shown to be a space-time continuous random field.
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Stochastic Evolution Equations
Čoupek, Petr ; Maslowski, Bohdan (advisor)
Stochastic Evolution Equations Petr Čoupek Doctoral Thesis Abstract Linear stochastic evolution equations with additive regular Volterra noise are studied in the thesis. Regular Volterra processes need not be Gaussian, Markov or semimartingales, but they admit a certain covariance structure instead. Particular examples cover the fractional Brownian motion of H > 1/2 and, in the non-Gaussian case, the Rosenblatt process. The solution is considered in the mild form, which is given by the variation of constants formula, and takes values either in a separable Hilbert space or the space Lp(D, µ) for large p. In the Hilbert-space setting, existence, space-time regularity and large-time behaviour of the solutions are studied. In the Lp setting, existence and regularity is studied, and in concrete cases of stochastic partial differential equations, the solution is shown to be a space-time continuous random field.
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Stochastic Evolution Equations
Čoupek, Petr ; Maslowski, Bohdan (advisor) ; Garrido-Atienza, María J. (referee) ; Hlubinka, Daniel (referee)
Stochastic Evolution Equations Petr Čoupek Doctoral Thesis Abstract Linear stochastic evolution equations with additive regular Volterra noise are studied in the thesis. Regular Volterra processes need not be Gaussian, Markov or semimartingales, but they admit a certain covariance structure instead. Particular examples cover the fractional Brownian motion of H > 1/2 and, in the non-Gaussian case, the Rosenblatt process. The solution is considered in the mild form, which is given by the variation of constants formula, and takes values either in a separable Hilbert space or the space Lp(D, µ) for large p. In the Hilbert-space setting, existence, space-time regularity and large-time behaviour of the solutions are studied. In the Lp setting, existence and regularity is studied, and in concrete cases of stochastic partial differential equations, the solution is shown to be a space-time continuous random field.
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Sobolev-type Spaces on Metric Measure Spaces
Malý, Lukáš ; Pick, Luboš (advisor) ; Malý, Jan (referee) ; Shanmugalingam, Nages (referee)
Title: Sobolev-Type Spaces on Metric Measure Spaces Author: RNDr. Lukáš Malý Department: Department of Mathematical Analysis Supervisor: Prof. RNDr. Luboš Pick, CSc., DSc., Department of Mathematical Analysis Abstract: is thesis focuses on function spaces related to rst-order analysis in abstract metric measure spaces. In metric spaces, we can replace distributional gra- dients, whose de nition depends on the linear structure of Rn , by upper gradients that control the functions' behavior along all recti able curves. is gives rise to the so-called Newtonian spaces. e summability condition, considered in the thesis, is expressed using a general Banach function lattice quasi-norm and so an extensive framework is built. Sobolev-type spaces (mainly based on the Lp norm) on metric spaces, and Newtonian spaces in particular, have been under intensive study since the mid- s. Standard toolbox for the theory is set up in this general setting and Newto- nian spaces are proven complete. Summability of an upper gradient of a function is shown to guarantee the function's absolute continuity on almost all curves. Ex- istence of a unique minimal weak upper gradient is established. Regularization of Newtonian functions via Lipschitz truncations is discussed in doubling Poincaré spaces using weak boundedness of maximal...
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