L’intoxication tabagique, source d’oxydants, est un facteur de risque important de développement de l’emphysème pulmonaire et du pneumothorax spontané primitif. Les macrophages alvéolaires contribuent pour une large part à l’inflammation pulmonaire au cours de ces pathologies en produisant des métalloprotéases et des espèces réactives de l’oxygène à l’origine du déséquilibre des balances protéase/anti-protéase et oxydant/antioxydant. L'hème oxygénase-1 (HO-1), exprimée principalement par les macrophages, est une enzyme clé des défenses anti-oxydantes pulmonaires. Nous avons initialement étudié l’expression et la localisation cellulaire de l’HO-1 et de ses régulateurs potentiels (Nrf2, Keap1, Bach1 et HIF-1a) dans les macrophages alvéolaires au cours de l’emphysème pulmonaire post-tabagique et du pneumothorax spontané primitif. Les voies de régulation de l’expression de ces protéines ont été analysées in vitro sur des macrophages dérivés de la lignée THP-1 exposés ou non au condensat de fumée de cigarette et à l’hypoxieréoxygénation visant à mimer une partie des effets de l’atélectasie-réexpansion observée lors de la prise en charge thérapeutique des pneumothorax récidivants. Les travaux présentés dans cette thèse nous ont permis de mettre en évidence une altération de l’expression de la voie Nrf2/Keap1-Bach1 associée à une diminution de l’expression des enzymes anti-oxydantes, dont l’HO-1, dans les macrophages alvéolaires au cours de l’emphysème pulmonaire sévère post-tabagique, malgré un stress oxydant important. In vitro, ces altérations pourraient être liées à une activation spécifique des MAPKinases ERK1/2 et JNK par le condensat de fumée de cigarette. Nous avons également montré que la stimulation du système de l’HO-1 était probablement orchestrée par la voie du facteur HIF-1a, et non par celle de Nrf2, dans les macrophages alvéolaires au cours du pneumothorax spontané primitif récidivant du sujet fumeur. Ces résultats pourraient contribuer à une meilleure connaissance de la physiopathologie de l’emphysème pulmonaire et permettre d’envisager de nouvelles approches thérapeutiques basées sur la préservation et/ou la restauration de l’équilibre Nrf2/Keap1-Bach1. Nos travaux suggèrent également que la physiopathologie du pneumothorax spontané primitif est différente chez les patients fumeurs et non fumeurs. Le pneumothorax du sujet fumeur est associé à un stress oxydant pulmonaire et à une induction de l’HO-1 probablement orchestrée par HIF-1a. Ces résultats, confirmés in vitro, mettent en évidence une interaction potentielle entre le stress oxydant et l’hypoxie-réoxygénation / Chronic cigarette smoking, a source of oxidants, is an important risk factor for lung emphysema and primary spontaneous pneumothorax development. Alveolar macrophages are mainly involved in lung inflammation observed in these pathologies through the production of metalloproteases and reactive oxygen species resulting to protease/anti-protease and oxidant/anti-oxidant imbalances. Heme oxygenase-1 (HO-1), mainly expressed in macrophages, is a key enzyme in pulmonary anti-oxidant defences. Therefore, the first aim of our studies was to investigate the expression and cellular localisation of HO-1 and its potential regulators (Nrf2, Keap1, Bach1 and HIF-1a) in alveolar macrophages from smoking related lung emphysema and primary spontaneous pneumothorax. Regulation pathways involved in expression of these proteins were assessed in vitro in macrophage cell line THP-1 exposed or not to cigarette smoke condensate and with or without hypoxia-reoxygenation mimicking parts of events induced by atelectasia-reexpansion during recurrent pneumothorax constitution and treatment. In these studies, we showed an altered expression of Nrf2/Keap1- Bach1 pathway associated with a reduced expression of anti-oxidants enzymes, like HO-1, in alveolar macrophages from smoking related lung emphysema patients, despite an important oxidative stress. These alterations might be related to cigarette smoke condensate activated ERK1/2 and JNK MAPKinases as observed in THP-1 cells. Furthermore, we showed that HO- 1 system induction was mediated by HIF-1a instead of Nrf2 pathway in alveolar macrophages from smoking related recurrent primary spontaneous pneumothorax. These findings may contribute to a better knowledge of the pathophysiology of lung emphysema and could provide new therapeutic approaches based on preservation and/or restoration of Nrf2/Keap1-Bach1 equilibrium. Our results also suggest that the pathophysiology of primary spontaneous pneumothorax could be different in smokers and non smokers. Spontaneous pneumothorax in smokers is associated with lung oxidative stress and the orchestrated induction of HO-1 probably via HIF-1a. These results provide a new link between oxidative stress and hypoxia/reoxygenation

Proton MR spectroscopy is a non-invasive tool for measuring in vivo concentrations of several metabolites. The aim of this thesis was to test its applicability and reliability in neurosurgical praxis. In the first part of the study multiple MR spectroscopy methods were applied in a group of patients after surgery and oncologic treatment for high-grade glioma to test which method performed best in discriminating recurrent tumor from radionecrosis in the presence of a new enhancing lesion. The best diagnostic yield was achieved by comparison of choline, creatine and lactate between lesion and contralateral side (sensitivity 93.3%, specificity 78.6%). Creatine was significantly decreased in patients compared to controls. The inhibiting effect of ongoing oncologic treatment on cerebral and tumoral metabolism makes differential diagnosis trickier. Therefore, a diagnosis of radionecrosis assessed during ongoing radio- and chemotherapy should be confirmed after its completion. In the second part of the study MR spectroscopy data was compared with MR hippocampal volumetry and transcranial doppler examination in a cohort of patients with unilateral occlusion of the internal carotid artery. The N-acetylaspartate/choline ratio and hippocampal volume were significantly lower in both hemispheres of patients...

<div><div><div><p>Many complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.</p></div></div></div>

The aim of this work is to create a tool for automatic transcription of historical documents. The work is mainly focused on the recognition of texts from the period of modern times written using font Fraktur. The problem is solved with a newly designed recurrent convolutional neural networks and a Spatial Transformer Network. Part of the solution is also an implemented generator of artificial historical texts. Using this generator, an artificial data set is created on which the convolutional neural network for line recognition is trained. This network is then tested on real historical lines of text on which the network achieves up to 89.0 % of character accuracy. The contribution of this work is primarily the newly designed neural network for text line recognition and the implemented artificial text generator, with which it is possible to train the neural network to recognize real historical lines of text.

The goal of this thesis is to describe and build a system for neural machine translation. System is built with recurrent neural networks - encoder-decoder architecture in particular. The result is a nmt library used to conduct experiments with different model parameters. Results of the experiments are compared with system built with the statistical tool Moses.

Computer-Assisted Pronunciation Training (CAPT) is becoming more and more popular these days. However, the accuracy of existing CAPT systems is still quite low. Therefore, this diploma thesis focuses on improving existing methods for automatic pronunciation evaluation on the segmental level. The first part describes common techniques for this task. Afterwards, we proposed the system based on two approaches. Finally, performed experiments show significant improvement over the reference system.

Neuronové sítě vycházející z architektury WaveNet a sítě využívající rekurentní vrstvy jsou v současnosti používány jak pro syntézu lidské řeči, tak pro „black box“ modelování systémů pro úpravu akustického signálu – modulační efekty, nelineární zkreslovače apod. Úkolem studenta bude shrnout dosavadní poznatky o možnostech využití neuronových sítí při modelování akustických signálů. Student dále implementuje některý z modelů neuronových sítí v programovacím jazyce Python a využije jej pro natrénování a následnou simulaci libovolného efektu nebo systému pro úpravu akustického signálu. V rámci semestrální práce vypracujte teoretickou část práce, vytvořte zvukovou databázi pro trénování neuronové sítě a implementujte jednu ze struktur sítí pro modelování zvukového signálu. Neuronové sítě jsou v průběhu posledních let používány stále více, a to víceméně přes celé spektrum vědních oborů. Neuronové sítě založené na architektuře WaveNet a sítě využívající rekurentních vrstev se v současné době používají v celé řadě využití, zahrnující například syntézu lidské řeči, nebo napřklad při metodě "black-box" modelování akustických systémů, které upravují zvukový signál (modulačí efekty, nelineární zkreslovače, apod.). Tato akademická práce si dává za cíl poskytnout úvod do problematiky neuronových sítí, vysvětlit základní pojmy a mechanismy této problematiky. Popsat využití neuronových sítí v modelování akustických systémů a využít těchto poznatků k implementaci neuronových sítí za cílem modelování libovolného efektu nebo zařízení pro úpravu zvukového signálu.

This Master’s thesis deals with beat tracking systems, whose functionality is based on neural networks. It describes the structure of these systems and how the signal is processed in their individual blocks. Emphasis is then placed on recurrent and temporal convolutional networks, which by they nature can effectively detect tempo and beats in audio recordings. The selected methods, network architectures and their modifications are then implemented within a comprehensive detection system, which is further tested and evaluated through a cross-validation process on a genre-diverse data-set. The results show that the system, with proposed temporal convolutional network architecture, produces comparable results with foreign publications. For example, within the SMC dataset, it proved to be the most successful, on the contrary, in the case of other datasets it was slightly below the accuracy of state-of-the-art systems. In addition,the proposed network retains low computational complexity despite increased number of internal parameters.

Goal of this master thesis is to study harmonization based on knowledge of given melody and to design a system which will meaningfully automate this activity. In the work there is covered basics of music theory needed for this topic and previous other approaches to this problematic. There is also covered machine learning, neural networks and recurrent neural networks. In the end, there is outlined design of the system, how to make it work and how to use it. Three experiments were executed with the system. Harmonization of the melodies were unpleasant though. A possible cause might be relatively small used neural network of the system.

In this master’s thesis are discussed static properties of network traffic trace. There are also addressed the possibility of a predication with a focus on neural networks. Specifically, therefore recurrent neural networks. Training data were downloaded from freely accessible on the internet link. This is the captured packej of traffic of LAN network in 2001. They are not the most actual, but it is possible to use them to achieve the objective results of the work. Input data needed to be processed into acceptable form. In the Visual Studio 2005 was created program to aggregate the intensities of these data. The best combining appeared after 100 ms. This was achieved by the input vector, which was divided according to the needs of network training and testing part. The various types of networks operate with the same input data, thereby to make more objective results. In practical terms, it was necessary to verify the two principles. Principle of training and the principle of generalization. The first of the nominated designs require stoking training and verification training by using gradient and mean square error. The second one represents unknown designs application on neural network. It was monitored the response of network to these input data. It can be said that the best model seemed the Layer recurrent neural network (LRN). So, it was a solution developed in this direction, followed by searching the appropriate option of recurrent network and optimal configuration. Found a variant of topology is 10-10-1. It was used the Matlab 7.6, with an extension of Neural Network toolbox 6. The results are processed in the form of graphs and the final appreciation. All successful models and network topologies are on the enclosed CD. However, Neural Network toolbox reported some problems when importing networks. In creating this work wasn’t import of network functions practically used. The network can be imported, but the majority appear to be non-trannin. Unsuccessful models of networks are not presented in this master’s thesis, because it would be make a deterioration of clarity and orientation.