Search results for "Autor"
showing 10 items of 820 documents
Kopēšanas pakalpojums bibliotēkās - autortiesiskais aspekts
2015
Latvijas Republikas Autortiesību likumā ir iestrādāti punkti, kas ir saistīti ar taisnīgas reprogrāfiskās reproducēšanas īstenošanu, tomēr praksē atlīdzību izmaksāšana autoriem nenotiek. Pētījuma mērķis ir analizēt reprogrāfiskās reproducēšanas jautājumus regulējošo tiesisko bāzi un noskaidrot, kāpēc netiek īstenota Autortiesību likumā noteiktā atlīdzību izmaksāšana. Darba gaitā analizēti trīs starptautiskie dokumenti, Latvijas un četri citu valstu nacionālie autortiesību likumi, kā arī intervēti biedrības “LATREPRO” un Kultūras ministrijas pārstāvji. Pētījuma rezultāti parāda, ka problēma ar reprogrāfiskās reproducēšanas administrēšanu tiek risināta, jo biedrība “LATREPRO” ir saņēmusi atļa…
On Martial Law at 50 : Fact-Checking the Marcos Story, Countering the EDSA History
2022
To fact-check and counter the historical denialism of the Marcos family, there is need for a counterfactual history analysis of the failings of the 1986 EDSA People Power Revolution. nonPeerReviewed
The beer market and advertising expenditure
2009
PurposeThe purpose of this paper is to examine the impacts of advertising expenditure on brands' market shares, utilizing a novel four‐week advertising‐sales data from the highly competitive oligopolistic Finnish beer market in which price competition among the homogeneous larger‐type beer brands is not allowed during the period of the study.Design/methodology/approachCompetition is modelled using the Lanchester model. The impacts of advertising on market shares are estimated using the impulse‐response functions from vector autoregression, and the full information maximum likelihood and advertising elasticities.FindingsSome new insights into beer market dynamics are obtained. First, the imp…
Explicit Recursive and Adaptive Filtering in Reproducing Kernel Hilbert Spaces
2014
This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the $\gamma$ -filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and elect…
Robust estimation of partial directed coherence by the vector optimal parameter search algorithm
2009
We propose a method for the accurate estimation of Partial Directed Coherence (PDC) from multichannel time series. The method is based on multivariate vector autoregressive (MVAR) model identification performed through the recently proposed Vector Optimal Parameter Search (VOPS) algorithm. Using Monte Carlo simulations generated by different MVAR models, the proposed VOPS algorithm is compared with the traditional Vector Least Squares (VLS) identification method. We show that the VOPS provides more accurate PDC estimates than the VLS (either overall and single-arc errors) in presence of interactions with long delays and missing terms, and for noisy multichannel time series. ©2009 IEEE.
Smart load prediction analysis for distributed power network of Holiday Cabins in Norwegian rural area
2020
Abstract The Norwegian rural distributed power network is mainly designed for Holiday Cabins with limited electrical loading capacity. Load prediction analysis, within such type of network, is necessary for effective operation and to manage the increasing demand of new appliances (e. g. electric vehicles and heat pumps). In this paper, load prediction of a distributed power network (i.e. a typical Norwegian rural area power network of 125 cottages with 478 kW peak demand) is carried out using regression analysis techniques for establishing autocorrelations and correlations among weather parameters and occurrence time in the period of 2014–2018. In this study, the regression analysis for loa…
Parametric and nonparametric methods to generate time-varying surrogate data.
2009
We present both nonparametric and parametric approaches to generating time-varying surrogate data. Nonparametric and parametric approaches are based on the use of the short-time Fourier transform and a time-varying autoregressive model, respectively. Time-varying surrogate data (TVSD) can be used to determine the statistical significance of the linear and nonlinear coherence function estimates. Two advantages of the TVSD are that it keeps one from having to make an arbitrary decision about the significance of the coherence value, and it properly takes into account statistical significance levels, which may change with time. Our simulation examples and experimental results on blood pressure …
Nonlinear effects of respiration on the crosstalk between cardiovascular and cerebrovascular control systems.
2016
Cardiovascular and cerebrovascular regulatory systems are vital control mechanisms responsible for guaranteeing homeostasis and are affected by respiration. This work proposes the investigation of cardiovascular and cerebrovascular control systems and the nonlinear influences of respiration on both regulations through joint symbolic analysis (JSA), conditioned or unconditioned on respiration. Interactions between cardiovascular and cerebrovascular regulatory systems were evaluated as well by performing correlation analysis between JSA indexes describing the two control systems. Heart period, systolic and mean arterial pressure, mean cerebral blood flow velocity and respiration were acquired…
Therapeutic Drug Monitoring of Kidney Transplant Recipients Using Profiled Support Vector Machines
2007
This paper proposes a twofold approach for therapeutic drug monitoring (TDM) of kidney recipients using support vector machines (SVMs), for both predicting and detecting Cyclosporine A (CyA) blood concentrations. The final goal is to build useful, robust, and ultimately understandable models for individualizing the dosage of CyA. We compare SVMs with several neural network models, such as the multilayer perceptron (MLP), the Elman recurrent network, finite/infinite impulse response networks, and neural network ARMAX approaches. In addition, we present a profile-dependent SVM (PD-SVM), which incorporates a priori knowledge in both tasks. Models are compared numerically, statistically, and in…