The burden of labour taxation in Croatia, Slovenia and Slovakia in the period 2011–2017

This paper analyses the developments in labor income taxation in Croatia, Slovenia and Slovakia during the period 2011–2017. While the systems of social insurance contributions in these countries were relatively stable, their personal income taxes have undergone more important changes. Using tax-benefit microsimulation models, we compute average and marginal tax rates for the sample units and assess the impact of tax-benefit systems on income distributio

Censorship in the Internet

Tato diplomová práce se zabývá problematikou cenzury na internetu. Konkrétně popisuje technické prostředky pro cenzuru a způsoby jejího ověřování na již existujících projektech. Dále jsou zde uvedeny nejrůznější alternativy obcházení cenzury, kterými je umožněn přístup k zablokovaným informacím prostřednictvím internetu. Nakonec je popsán systém, který umožní praktické ověření cenzury v Čínské lidové republice. Výsledky získané tímto systémem jsou důkladně popsány a diskutovány.This master's thesis deals with the issue of censorship in the internet. Specifically, it describes technical means for censorship and methods of checking on existing projects. There are also presented various alternatives of circumvention censorship, which allow to access blocked information via the internet. Finally, it describes a system that allows practical verification of censorship in the People's republic of China. The results obtained with this system are thoroughly described and discussed.

Visualisation of Discretized Fields over Tetrahedra

Import 04/07/2011Účelem této bakalářské práce je vytvořit software s přívětivým grafickým rozhraním (GUI), který zobrazuje prostorová pole. Tyto 3D pole jsou zadány konstantními funkcemi nad danou diskretizací do čtyřstěnů. Pole vizualizujeme v řezech čtyřstěnů danou rovinou. Barvy řezů odpovídají funkci. Výsledná obarvená mapa n řezů je vizualizace prostorové diskretizace.The purpose of this bachelor thesis is to develop software with a friendly graphical user interface (GUI), which displays three dimensional fields. These 3D fields are specified by constant functions over the discretization into tetrahedra. The field is displayed in cuts of tetrahedra and a given plane. Colors of cuts correspond to the function. The final colored map of n cuts is visualization of the three dimensional discretization.470 - Katedra aplikované matematikydobř

Pancreatic exocrine insufficiency after bariatric surgery

Morbid obesity is a lifelong disease, and all patients require complementary follow-up including nutritional surveillance by a multidisciplinary team after bariatric procedures. Pancreatic exocrine insufficiency (PEI) refers to an insufficient secretion of pancreatic enzymes and/or sodium bicarbonate. PEI is a known multifactorial complication after upper gastrointestinal surgery, and might constitute an important clinical problem due to the large number of bariatric surgical procedures in the world. Symptoms of PEI often overlap with sequelae of gastric bypass, making the diagnosis difficult. Steatorrhea, weight loss, maldigestion and malabsorption are pathognomonic for both clinical conditions. Altered anatomy after bypass surgery can make the diagnostic process even more difficult. Fecal elastase-1 (FE1) is a useful diagnostic test. PEI should be considered in all patients after bariatric surgery with prolonged gastrointestinal complaints that are suggestive of maldigestion and/or malabsorption. Appropriate pancreatic enzyme replacement therapy should be part of the treatment algorithm in patients with confirmed PEI or symptoms suggestive of this complication

Investigation of toothed shaft from the view of modal parameters

The paper deals with the investigation of toothed shaft from the view of modal analysis, which is a base of dynamic analysis. The gear mechanism is newly designed based on new principle, at which the motion and power are transferred from one input shaft to two output shafts in one direction. Considering that structures vibrate in special shapes when excited at their resonant frequencies, by understanding the mode shapes, all the possible types of vibration can be predicted. The paper aimed to create experimental and computational model of the shaft with subsequent verification of selected theories, to compare results obtained by numerical and experimental modal analysis and to assess the dynamic characteristics of the component with respect to its operating conditions. There are described conditions of both numerical and experimental modal analysis in the paper. The measurements have shown that the values of natural frequencies along with the natural shapes are adequate and comparable within both of the investigation methods. Moreover, from the results it is clear that output shaft should not be subjected to resonance. On the other hand, by means of modal analysis, the number of teeth was specified, that can be risky at the dynamic load of the gear, if the gear ratio changes. © 2019, Strojarski Facultet. All rights reserved.Ministry of Education, Science, Research and Sport of Slovak Republic [KEGA 007TUKE-4/2018, VEGA 1/0795/19, APVV-17-0380

Machine Learning Meets The Herbrand Universe

The appearance of strong CDCL-based propositional (SAT) solvers has greatly advanced several areas of automated reasoning (AR). One of the directions in AR is thus to apply SAT solvers to expressive formalisms such as first-order logic, for which large corpora of general mathematical problems exist today. This is possible due to Herbrand's theorem, which allows reduction of first-order problems to propositional problems by instantiation. The core challenge is choosing the right instances from the typically infinite Herbrand universe. In this work, we develop the first machine learning system targeting this task, addressing its combinatorial and invariance properties. In particular, we develop a GNN2RNN architecture based on an invariant graph neural network (GNN) that learns from problems and their solutions independently of symbol names (addressing the abundance of skolems), combined with a recurrent neural network (RNN) that proposes for each clause its instantiations. The architecture is then trained on a corpus of mathematical problems and their instantiation-based proofs, and its performance is evaluated in several ways. We show that the trained system achieves high accuracy in predicting the right instances, and that it is capable of solving many problems by educated guessing when combined with a ground solver. To our knowledge, this is the first convincing use of machine learning in synthesizing relevant elements from arbitrary Herbrand universes.Comment: 8 pages, 10 figure

Income redistribution through taxes and social benefits: the case of Slovenia and Croatia

The article analyses the redistributive effect attained by personal income tax, social security contributions and social benefits in Slovenia and Croatia. The redistributive effect is decomposed first to reveal progressivity and horizontal inequity effects, and further to show contributions of different tax and benefit instruments. Even though both countries started from the same socioeconomic background two decades ago, the current results reveal divergence that is a consequence of diverse development during this period. The results indicate that Croatia experienced significantly higher pre-fiscal income inequality and lower redistributive effect than Slovenia. Horizontal inequity effects, though, were higher in Slovenia than in Croatia. In both countries, the means-tested social benefits exerted an over-proportionate influence on vertical effects, suggesting a strong impact of the welfare state on income position of their residents, but also induced a large amount of horizontal inequity. In Slovenia, the non-means-tested benefits slightly increased income inequality

The Role of Entropy in Guiding a Connection Prover

In this work we study how to learn good algorithms for selecting reasoning steps in theorem proving. We explore this in the connection tableau calculus implemented by leanCoP where the partial tableau provides a clean and compact notion of a state to which a limited number of inferences can be applied. We start by incorporating a state-of-the-art learning algorithm -- a graph neural network (GNN) -- into the plCoP theorem prover. Then we use it to observe the system's behaviour in a reinforcement learning setting, i.e., when learning inference guidance from successful Monte-Carlo tree searches on many problems. Despite its better pattern matching capability, the GNN initially performs worse than a simpler previously used learning algorithm. We observe that the simpler algorithm is less confident, i.e., its recommendations have higher entropy. This leads us to explore how the entropy of the inference selection implemented via the neural network influences the proof search. This is related to research in human decision-making under uncertainty, and in particular the probability matching theory. Our main result shows that a proper entropy regularisation, i.e., training the GNN not to be overconfident, greatly improves plCoP's performance on a large mathematical corpus