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Effect of chromatic component on function of antipredatory warning signals
Truhlářová, Marie ; Exnerová, Alice (advisor) ; Pipek, Pavel (referee)
Warning coloration is used by prey to signal its unprofitability to potential predators. Warning colours may have different effects on various cognitive processes of predators (innate avoidance, avoidance learning, memory and generalization). Typical colours regarded as aposematic are red, orange and yellow. Red colour is considered to be the most effective signal and has a significant effect on avoidance learning, memory and generalization. Orange also represents an effective warning signal, though it has been studied less. Yellow is effective aposematic stimulus but it has frequently been found less effective compared to red and orange. Warning functions of white, blue, violet and ultraviolet colours were studied less frequently and their role in aposematism is not yet clear. Iridescent coloration might also be an effective warning signal affecting avoidance learning, memory and generalization. In this thesis I present a summary of information with regard to different warning colours and their effect on cognitive processes of predators. Key words: aposematism, warning coloration, cognitive processes, red, orange, yellow, iridescence, avoidance learning, innate avoidance, memory, generalization
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Smoothness of Functions Learned by Neural Networks
Volhejn, Václav ; Musil, Tomáš (advisor) ; Straka, Milan (referee)
Modern neural networks can easily fit their training set perfectly. Surprisingly, they generalize well despite being "overfit" in this way, defying the bias-variance trade-off. A prevalent explanation is that stochastic gradient descent has an implicit bias which leads it to learn functions that are simple, and these simple functions generalize well. However, the specifics of this implicit bias are not well understood. In this work, we explore the hypothesis that SGD is implicitly biased towards learning functions that are smooth. We propose several measures to formalize the intuitive notion of smoothness, and conduct experiments to determine whether these measures are implicitly being optimized for. We exclude the possibility that smoothness measures based on first derivatives (the gradient) are being implicitly optimized for. Measures based on second derivatives (the Hessian), on the other hand, show promising results. 1
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Generalization of LOD2 building models using the aggregation method
Měchurová, Kristýna ; Brůha, Lukáš (advisor) ; Pokorný, Tomáš (referee)
Generalization of LOD2 building models using the aggregation method Abstract The thesis proposes and implements a method of 3D building models aggregation. The procedure achieves global optima by the means of mathematic optimization. Buildings are aggregated according to similarity characteristics typical for LOD2, e. g. roof type. Aggregation process is driven by minimalization of volume changes and of the aggregate count. The optimization problem was implemented as a Python script with optional parameters to meet custom demands of a wide range of users. Input data models of buildings are created by the method of procedural modelling. Its outcome is further restructured into form of continuous blocks. Finally, the visualization procedure is designed and implemented to illustrate the results of optimized aggregation of 3D building models. Keywords: 3D GIS, generalization, aggregation, mathematical optimization, procedural modelling
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Simplification of buildings based on typification
Gottstein, Otomar ; Bayer, Tomáš (advisor) ; Jindrák, Přemysl (referee)
Simplification of buildings based on typification Abstract The diploma thesis is focused on cartographic generalization. Its main aim was to develop a new method of simplification of buildings based on typification for areas with lower density of buildings (rural or mountain areas). The proposed method was designed for large scale maps (1 : 25 000 and 1 : 50 000). The presented generalization algorithm is based on preferential selection of buildings to be drawn on the map according to their civic importance, area and location towards roads, railways and streams. It respects cartographic rules used for this type of generalization. The algorithm was implemented in the Python programming language using the Shapely and Fiona libraries for a purpose of its proper testing. The ZABAGED and DATA50 data were chosen as suitable test data. Among other things, this thesis also introduces the evaluation method of typification results, which uses Voronoi diagram. Achieved results are presented on maps of fifteen villages with different spatial structure in both targeted scales. Keywords: digital cartography, generalization, typification, building, simplification
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Generalization of Road Network in Topographic Maps
Vojtíšková, Zuzana ; Lysák, Jakub (advisor) ; Jindrák, Přemysl (referee)
Generalization of Road Network in Topographic Map Abstract The diploma thesis presents automated selection of the elements of path network. The review deals with this term and describes its position in map generalization process; the ways of path thinning apllied in the main Czech cartography institutions are reviewed too. Next part of the thesis describes the data and the tools that are applied in the proposed method. The main part introduces the suggested method of selecting elements of path network which was implemented on the test data. Keywords: map generalization, path network, path thinning, graph theory, ZABAGED, ArcGIS, Python, NetworkX
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National Stereotypes: the realtion between Czechs and Spanish
RYBÁKOVÁ, Lenka
The aim of this bachelor thesis is to present the issue of national stereotypes which is nowadays coming to the fore across different fields of science. Within its theoretical part this topic is initially analysed in general terms and the author focuses on the characteristics of the concept of the term stereotype as well as particular aspects which clarify the creation, modification and reduction of national stereotypes. Special attention is paid to the mechanisms of transmission of the stereotypes which form a key element of the following research. These mechanisms together with a set of selected stereotypes about the Spanish people and their culture create a link between both parts of the thesis. The analysis centres around the changing views of the Czechs towrds Spaniards as a result of their increasing participation in international exchanges within Spain such as the Erasmus+ programme. This study also aims to discover the ways in which stereotypes are transmitted that have the most frequent impact on the selected sample of the respondents. The part of this thesis is a résumé in Spanish.
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Artificial Neural Networks and Their Usage For Knowledge Extraction
Petříčková, Zuzana ; Mrázová, Iveta (advisor) ; Procházka, Aleš (referee) ; Andrejková, Gabriela (referee)
Title: Artificial Neural Networks and Their Usage For Knowledge Extraction Author: RNDr. Zuzana Petříčková Department: Department of Theoretical Computer Science and Mathema- tical Logic Supervisor: doc. RNDr. Iveta Mrázová, CSc., Department of Theoretical Computer Science and Mathematical Logic Abstract: The model of multi/layered feed/forward neural networks is well known for its ability to generalize well and to find complex non/linear dependencies in the data. On the other hand, it tends to create complex internal structures, especially for large data sets. Efficient solutions to demanding tasks currently dealt with require fast training, adequate generalization and a transparent and simple network structure. In this thesis, we propose a general framework for training of BP/networks. It is based on the fast and robust scaled conjugate gradient technique. This classical training algorithm is enhanced with analytical or approximative sensitivity inhibition during training and enforcement of a transparent in- ternal knowledge representation. Redundant hidden and input neurons are pruned based on internal representation and sensitivity analysis. The performance of the developed framework has been tested on various types of data with promising results. The framework provides a fast training algorithm,...
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Food and host specialization in Aculeata (Hymenoptera)
Hochová, Veronika ; Policarová, Jana (advisor) ; Černá, Kateřina (referee)
Aculeata is a group of insects, its representatives vary significantly in the use of resources which are necessary for its survival and reproduction. Herbivores which live on parts of plants, carnivores which hunt other insects and omnivores are included in this group. There are also parasitic species such as cleptoparazites, brood parasites or parasitoids classified in Aculeata. Particular groups of Aculeata are adapted to resourcing, these adaptations exist in adult and immature stages simultaneously. Adaptation to the kind of source can lead to adjustment of mouthpart for easier prey hunting or nectar collecting, corbicula and special hair intended for collecting pollen or oils, a sting used for incapacitate the host or a sting for defense. Aculeata varies to such an extent how they are specialized in food and the host. Aculeata incorporates closely specialized species together with generalized species. The known information about the specialization Aculeata on food and host is summarized in this thesis.
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Discrimination and generalization of prey in lizards (Squamata: Sauria)
Vohralík, Martin ; Gregorovičová, Martina (advisor) ; Schořálková, Tereza (referee)
The ability to find and recognize palatable prey is fundamental for survival of any organism. Here we are discussing different ways of recognition of such a prey in order Squamata and the ways they learn this discrimination. Lizards are well known for their ability to analyse chemical cues brought from their tongue to the Jacobson's organ, which is completely separate from the nassal cavity in Squamata. However, the leading sense used for discrimination in Squamata can also be vision or other forms of chemoreception. Dominance of one sense can be assesed from morphology of tongue and abundance of taste buds or ecological strategy used for hunting a prey. Once the predator learns which cue to discriminate, it can generalise similar cues. Powered by TCPDF (www.tcpdf.org)
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