There are many existing models of automata working on two-dimensional inputs (pictures), though very little work has been done on the subject of learning of these automata. In this thesis, we introduce a new model called two-dimensional limited context restarting automaton. Our model works similarly as the two-dimensional restarting tiling automaton, yet we show that it is equally powerful as the two-dimensional sgraffito automaton. We propose an algorithm for learning of such automata from positive and negative samples of pictures. The algorithm is implemented and subsequently tested with several basic picture languages. Powered by TCPDF (www.tcpdf.org)

Abstract The metal halide lamp is one of high-intensity discharge lamps. It has many advantages such as good color rendering, high efficacy and a variety of color presentations. However, the problems of acoustic resonance, a long transition period of cold starting, and an extremely high ignition voltage for hot restarting should be overcome. The investigation of the thesis is focused on the electrical characteristics of the metal halide lamp operating at high-frequency. Included are the acoustic resonance, starting transient and steady state operation. Various ballast circuits incorporating with ignitors are designed to drive several 70-W metal halide lamps. The operating frequency ranges with acoustic resonance, the ignition voltages for both cold starting and hot restarting, the transition period, the dimming performance, and the lamp equivalent resistance during the lamp life are measured and analyzed. Based on the investigated results, a useful guideline can be provided for the operation and design of the electronic ballast for metal halide lamps. Key words : Metal halide lamp, electronic ballast, acoustic resonance, hot restarting.

Title: Testing the Learning of Restarting Automata using Genetic Algorithm Author: Bc. Lenka Kovářová Department: Department of Software and Computer Science Education Supervisor: RNDr. František Mráz, CSc. Abstract: Restarting automaton is a theoretical model of device recognizing a formal language. The construction of various versions of restarting automata can be a hard work. Many different methods of learning such automata have been developed till now. Among them are also methods for learning target restarting automaton from a finite set of positive and negative samples using genetic algorithms. In this work, we propose a method for improving learning of restarting automata by means of evolutionary algorithms. The improvement consists in inserting new rules of a special form enabling adaption of the learning algorithm to the particular language. Furthermore, there is proposed a system for testing of learning algorithms for restarting automata supporting especially learning by evolutionary algorithms. A part of the work is a program for learning restarting automata using the proposed new method with a subsequent testing of discovered automata and its evaluation in a graphic form mainly. Keywords: machine learning, grammatical inference, restarting automata, genetic algorithms

We study the inference of models of the analysis by reduction that forms an important tool for parsing natural language sentences. We prove that the inference of such models from positive and negative samples is NP-hard when requiring a small model. On the other hand, if only positive samples are considered, the problem is effectively solvable. We propose a new model of the analysis by reduction (the so-called single k-reversible restarting automaton) and propose a method for inferring it from positive samples of analyses by reduction. The power of the model lies between growing context-sensitive languages and context-sensitive languages. Benchmarks using targets based on grammars have several drawbacks. Therefore we propose a benchmark working with targets based on random automata, that can be used to evaluate inference algorithms. This benchmark is then used to evaluate our inference method. 1

Patients with mechanical heart valves who present with intracranial haemorrhage are initially treated by reversing their coagulopathy. However, these patients will ultimately require that their oral anticoagulant be restarted. The time at which oral anticoagulants are restarted is critical since restarting too early may increase the risk of recurrent bleeding, while withholding anticoagulants increases the patient’s risk of thromboembolic events. The ideal time to restart patients on their oral anticoagulant medication is defined as the time at which all these risks are minimized. This thesis includes a systematic review and meta-analysis of the literature. The main outcomes were recurrent haematoma, valve thrombosis, stroke and peripheral emboli. Results were stratified by types of intracranial haemorrhage. We also conducted a survey to gain insight into current practices of neurosurgeons and thrombosis experts in Canada and USA when they are faced with deciding on anticoagulant restart times in patients with ICH. Results were stratified by type of intracranial bleed and participants’ characteristics and demographics. The systematic review identified that the ideal time for restarting anticoagulant therapy in patients following an ICH is unknown. Meta-analysis was limited by the heterogeneity of the studies. The survey results indicated that physicians had a wide range of practice and that their practice was dependent on the patient’s clinical features, but many physicians would restart oral anticoagulants between 4 and 14 days after the haemorrhage. For this reason we have proposed a multi centre cohort study to investigate the safety and efficacy of restarting patients on anticoagulation therapy between day 5 and 9 post haemorrhage. A full study protocol is presented in this thesis.

Epidemic models help researchers understand and predict the nature of a potential epidemic. This study analyzes and improves network evolution technology that evolves contact networks so that simulated epidemics on the network mimic a specified epidemic pattern. The evolutionary algorithm incorporates the novel recentering-restarting algorithm, which is adopted into the optimizer to allow for efficient search of the space of networks. It also implements the toggle-delete representation which allows for broader search of solution space. Then, a diffusion character based method is used for analyzing the contact networks. A comparison of simulated epidemics that result from changing patient zero for a single contact network is performed. It is found that the location of patient zero is important for the behaviour of an epidemic. The social fabric representation is invented and then tested for parameter choices. The response to vaccination strategies (including ring vaccination) is then tested by incorporating them into the epidemic simulations. / Ontario Graduate Scholarship (OGS), Natural Sciences and Engineering Research Council of Canada (NSERC)

Patients with mechanical heart valves who present with intracranial haemorrhage are initially treated by reversing their coagulopathy. However, these patients will ultimately require that their oral anticoagulant be restarted. The time at which oral anticoagulants are restarted is critical since restarting too early may increase the risk of recurrent bleeding, while withholding anticoagulants increases the patient’s risk of thromboembolic events. The ideal time to restart patients on their oral anticoagulant medication is defined as the time at which all these risks are minimized. This thesis includes a systematic review and meta-analysis of the literature. The main outcomes were recurrent haematoma, valve thrombosis, stroke and peripheral emboli. Results were stratified by types of intracranial haemorrhage. We also conducted a survey to gain insight into current practices of neurosurgeons and thrombosis experts in Canada and USA when they are faced with deciding on anticoagulant restart times in patients with ICH. Results were stratified by type of intracranial bleed and participants’ characteristics and demographics. The systematic review identified that the ideal time for restarting anticoagulant therapy in patients following an ICH is unknown. Meta-analysis was limited by the heterogeneity of the studies. The survey results indicated that physicians had a wide range of practice and that their practice was dependent on the patient’s clinical features, but many physicians would restart oral anticoagulants between 4 and 14 days after the haemorrhage. For this reason we have proposed a multi centre cohort study to investigate the safety and efficacy of restarting patients on anticoagulation therapy between day 5 and 9 post haemorrhage. A full study protocol is presented in this thesis.

Restarting automata were introduced as a model for analysis by reduction which is a linguistically motivated method for checking correctness of a sentence. The thesis studies locally restricted models of restarting automata which (to the contrary of general restarting automata) can modify the input tape based only on a limited context. The investigation of such restricted models is easier than in the case of general restarting automata. Moreover, these models are effectively learnable from positive samples of reductions and their instructions are human readable. Powered by TCPDF (www.tcpdf.org)

Original results in computational and combinatorial theory of reset words in transition systems, road coloring in directed graphs, and discontinuous input processing in formal languages are presented, including strong lower bounds on subset synchronization thresholds, lower bounds on descriptive power of jumping finite automata, and corresponding complexity classifications.

Patients with mechanical heart valves who present with intracranial haemorrhage are initially treated by reversing their coagulopathy. However, these patients will ultimately require that their oral anticoagulant be restarted. The time at which oral anticoagulants are restarted is critical since restarting too early may increase the risk of recurrent bleeding, while withholding anticoagulants increases the patient’s risk of thromboembolic events. The ideal time to restart patients on their oral anticoagulant medication is defined as the time at which all these risks are minimized. This thesis includes a systematic review and meta-analysis of the literature. The main outcomes were recurrent haematoma, valve thrombosis, stroke and peripheral emboli. Results were stratified by types of intracranial haemorrhage. We also conducted a survey to gain insight into current practices of neurosurgeons and thrombosis experts in Canada and USA when they are faced with deciding on anticoagulant restart times in patients with ICH. Results were stratified by type of intracranial bleed and participants’ characteristics and demographics. The systematic review identified that the ideal time for restarting anticoagulant therapy in patients following an ICH is unknown. Meta-analysis was limited by the heterogeneity of the studies. The survey results indicated that physicians had a wide range of practice and that their practice was dependent on the patient’s clinical features, but many physicians would restart oral anticoagulants between 4 and 14 days after the haemorrhage. For this reason we have proposed a multi centre cohort study to investigate the safety and efficacy of restarting patients on anticoagulation therapy between day 5 and 9 post haemorrhage. A full study protocol is presented in this thesis.