A teoria de sistemas semidinâmicos impulsivos é um capítulo importante e moderno da teoria de sistemas dinâmicos topológicos. Sistemas impulsivos descrevem processos de evolução que sofrem variações de estado de curta duração e que podem ser consideradas instantâneas. Os sistemas impulsivos admitem vários fenômenos interessantes às vezes, por causa da sua irregularidade, e às vezes por causa da sua regularidade. Para muitos fenômenos naturais, os modelos determinísticos mais realistas são frequentemente descritos por sistemas que envolvem impulsos. Esta teoria vem sendo desenvolvida continuamente. O presente trabalho apresenta resultados originais sobre a teoria de conjuntos minimais, movimentos recorrentes, movimentos quase periódicos e fracamente quase periódicos, teoria de estabilidade de Lyapunov, teoria da quase estabilidade de Zhukovskij e, finalmente, a construção de trajetórias negativas para sistemas semidinâmicos com impulsos. Os resultados novos apresentados neste trabalho estão contidos em três artigos, dos quais dois já foram aceitos para publicação. Veja [13], [14] e [15] / The theory of impulsive semidynamical systems is an important and modern chapter of the theory of topological dynamical systems. Impulsive systems describe the evolution of process whose continuous dynamics are interrupted by abrupt changes of state. This kind of systems admits various interesting phenomena sometimes, because of their irregularity, and sometimes because of their regularity. In many natural phenomena, the real deterministic models are often described by systems which involve impulses. This theory has been developed continuously. This work presents original results involving the theory of minimal sets, recurrent motions, almost periodic and weakly almost periodic motions, the study of Lyapunov stability and Zhukovshij Quasi stability and the construction of negative trajectories for impulsive semidynamical systems. The new results presented in this work are contained in three papers namely [13], [14] and [15]

Accurate long-term energy consumption forecasting of industrial entities is of interest to distribution companies as it can potentially help reduce their churn and offer support in decision making when hedging. This thesis work presents different methods to forecast the energy consumption for industrial entities over a long time prediction horizon of 1 year. Notably, it includes experimentations with two variants of the Recurrent Neural Networks, namely Gated Recurrent Unit (GRU) and Long-Short-Term-Memory (LSTM). Their performance is compared against traditional approaches namely Multiple Linear Regression (MLR) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Further on, the investigation focuses on tailoring the Recurrent Neural Network model to improve the performance. The experiments focus on the impact of different model architectures. Secondly, it focuses on testing the effect of time-related feature selection as an additional input to the Recurrent Neural Network (RNN) networks. Specifically, it explored how traditional methods such as Exploratory Data Analysis, Autocorrelation, and Partial Autocorrelation Functions Plots can contribute to the performance of RNN model. The current work shows through an empirical study on three industrial datasets that GRU architecture is a powerful method for the long-term forecasting task which outperforms LSTM on certain scenarios. In comparison to the MLR model, the RNN achieved a reduction in the RMSE between 5% up to to 10%. The most important findings include: (i) GRU architecture outperforms LSTM on industrial energy consumption datasets when compared against a lower number of hidden units. Also, GRU outperforms LSTM on certain datasets, regardless of the choice units number; (ii) RNN variants yield a better accuracy than statistical or regression models; (iii) using ACF and PACF as dicovery tools in the feature selection process is unconclusive and unefficient when aiming for a general model; (iv) using deterministic features (such as day of the year, day of the month) has limited effects on improving the deep learning model’s performance. / Noggranna långsiktiga energiprognosprognoser för industriella enheter är av intresse för distributionsföretag eftersom det potentiellt kan bidra till att minska deras churn och erbjuda stöd i beslutsfattandet vid säkring. Detta avhandlingsarbete presenterar olika metoder för att prognostisera energiförbrukningen för industriella enheter under en lång tids förutsägelsehorisont på 1 år. I synnerhet inkluderar det experiment med två varianter av de återkommande neurala nätverken, nämligen GRU och LSTM. Deras prestanda jämförs med traditionella metoder, nämligen MLR och SARIMA. Vidare fokuserar undersökningen på att skräddarsy modellen för återkommande neurala nätverk för att förbättra prestanda. Experimenten fokuserar på effekterna av olika modellarkitekturer. För det andra fokuserar den på att testa effekten av tidsrelaterat funktionsval som en extra ingång till RNN -nätverk. Specifikt undersökte den hur traditionella metoder som Exploratory Data Analysis, Autocorrelation och Partial Autocorrelation Funtions Plots kan bidra till prestanda för RNN -modellen. Det aktuella arbetet visar genom en empirisk studie av tre industriella datamängder att GRU -arkitektur är en kraftfull metod för den långsiktiga prognosuppgiften som överträffar ac LSTM på vissa scenarier. Jämfört med MLR -modellen uppnådde RNN en minskning av RMSE mellan 5 % upp till 10 %. De viktigaste resultaten inkluderar: (i) GRU -arkitekturen överträffar LSTM på datauppsättningar för industriell energiförbrukning jämfört med ett lägre antal dolda enheter. GRU överträffar också LSTM på vissa datauppsättningar, oavsett antalet valenheter; (ii) RNN -varianter ger bättre noggrannhet än statistiska modeller eller regressionsmodeller; (iii) att använda ACF och PACF som verktyg för upptäckt i funktionsvalsprocessen är otydligt och ineffektivt när man siktar på en allmän modell; (iv) att använda deterministiska funktioner (t.ex. årets dag, månadsdagen) har begränsade effekter på att förbättra djupinlärningsmodellens prestanda.

可轉換公司債為複合式證券，除了具有債券性質外，並給予持有者於債券流通期間內行使轉換為股票之權利。以存活分析方法探討可轉債之研究尚屬少見，本論文乃以台灣上市櫃公司發行之5年期可轉債為研究資料，先整理出與公司經營有關的變數，再分別以Cox模式與再發事件之兩種邊際模型(marginal model)：A-G (Anderson-Gill) 模式、PWP-TT (Prentice-Williams-Petersen)模式為研究分析方法，探討可轉債之流通時間及大量交易時間的問題。本論文並將可轉債分類為債券類型、混合類型和權益類型，且由於不同類型可轉債之流通時間有所差異，因此以其為分層條件加入模式中進行分析。研究結果發現，資產總額、總負債率、TCRI評等及董監持股率等變數，具有顯著解釋可轉債流通時間的能力，可見公司財務負債狀況與穩定性與流通期間有關；而最高差價(當月最高股價與轉換價之相對差價)、長期負債率、總負債率及股價報酬率等變數，則可顯著解釋大量交易的發生時間，表示公司財務負債狀況與股價利潤差與大量交易發生之快慢有關，其中資產總額、最高差價、TCRI評等及股價報酬率之係數均顯著為正，長期負債率、總負債率及董監持股率之係數則顯著為負。由於平均表現之存活曲線與經驗存活曲線相當接近，以Kolmogorov-Smirnov檢定多無顯著差異，顯示這些模式有不錯的配適能力；至於對個別公司估計出之存活曲線，則或有與經驗存活曲線相差較多的現象，顯示所建立的模式可對個別公司提供可轉債即將結束流通或發生大量交易之預警。 / Convertible bonds are hybrid securities that possess the properties of bonds and the right to convert bonds into shocks. Few articles employed survival analysis to analyze the characteristics of convertible bonds. To investigate the effects of the issuer’s financial information to the duration of circulation and the timing of the massive trading about convertible bonds, Taiwan’s 5-year convertible bonds were collected, and three methods of survival analysis were employed：Cox model、A-G (Anderson-Gill) model and PWP-TT(Prentice-Williams-Petersen) model. We classified convertible bonds as debt-like, equity-like, and hedge-like, and then make the classification as a stratification condition later. In summary, total Assets, total debt ratio, TCRI, and the proportion of holding share in supervisors and directors are significant variables on circulation period of convertible bonds. Apparently, the extent of debt and financial stability of issuers have significant effects on circulation period; the difference between stock price and conversion price, long-term debt ratio, total debt ratio and stock return rate contribute significantly on the timing of massive trading of convertible bonds. While the extent of debt and the return of stock hasten the hazard of the timing of massive trading. Furthermore, there are no significant differences between the survival curves evaluated at the average performance levels and the corresponding empirical survival curves, according to the results of Kolmogorov-Smirnov test. However, the differences between individual survival probabilities and overall empirical survival probabilities might be large, which indicates that the models incorporate companies’ performance overtime may provide a warning message for the termination of circulation or the timing of massive trading for a particular convertible bond.

Despite years of research, females continue to have a higher incidence of non-contact ACL injuries. One of the major findings of this research is that males and females perform certain tasks, such as, cutting, landing, and single-leg squatting, differently. In particular, females tend to move the knee into a more valgus position; a motion putting the ACL at risk for injury. Yet the underlying spinal control mechanisms modulating this motion are unknown. Additionally, the mechanisms regulating the ability to rapidly initiate and produce maximal torque are also unknown. Therefore, the purpose was to: 1) determine if the sexes modulate spinal control differently, 2) examine the contributions of spinal control mechanisms to valgus knee motion, and 3) identify contributions of spinal control to the ability to rapidly produce force. The spinal control variables were the first derivative of the Hoffmann (H)-reflex, the first derivative of extrinsic pre-synaptic inhibition (EPI), the first derivative of intrinsic pre-synaptic inhibition (IPI), recurrent inhibition (RI), and V-waves. To assess the neuromuscular system’s ability to rapidly activate, rate of torque development (RTD) and electromechanical delay (EMD) were measured. Lastly, valgus motion was determined by the frontal plane projection angle (FPPA). The results reveal males and females do modulate spinal control differently; specifically males had an increased RTD, which is the slope of the torque-time curve, and increased RI, which is a post-synaptic regulator of torque output. However, the spinal control mechanisms did not significantly contribute to FPPA at the knee. EMD which is the time lag from muscle activity to torque production was significantly predicted by the spinal control mechanisms. Specifically, EPI, a modulator of afferent inflow from peripheral and descending sources, IPI, a regulator of Ia afferent inflow, and sex significantly contributed to EMD. Lastly, the spinal control mechanisms significantly contributed to RTD. Specifically, IPI, sex, and V-waves, a measure of supraspinal drive, all significantly contributed to RTD. / Graduation date: 2009

The object of this thesis is the study of constrained measurement systems of signals having low-dimensional structure using analytic tools from Compressed Sensing (CS). Realistic measurement systems usually have architectural constraints that make them differ from their idealized, well-studied counterparts. Nonetheless, these measurement systems can exploit structure in the signals that they measure. Signals considered in this research have low-dimensional structure and can be broken down into two types: static or dynamic. Static signals are either sparse in a specified basis or lying on a low-dimensional manifold (called manifold-modeled signals). Dynamic signals, exemplified as states of a dynamical system, either lie on a low-dimensional manifold or have converged onto a low-dimensional attractor. In CS, the Restricted Isometry Property (RIP) of a measurement system ensures that distances between all signals of a certain sparsity are preserved. This stable embedding ensures that sparse signals can be distinguished one from another by their measurements and therefore be robustly recovered. Moreover, signal-processing and data-inference algorithms can be performed directly on the measurements instead of requiring a prior signal recovery step. Taking inspiration from the RIP, this research analyzes conditions on realistic, constrained measurement systems (of the signals described above) such that they are stable embeddings of the signals that they measure. Specifically, this thesis focuses on four different types of measurement systems. First, we study the concentration of measure and the RIP of random block diagonal matrices that represent measurement systems constrained to make local measurements. Second, we study the stable embedding of manifold-modeled signals by existing CS matrices. The third part of this thesis deals with measurement systems of dynamical systems that produce time series observations. While Takens' embedding result ensures that this time series output can be an embedding of the dynamical systems' states, our research establishes that a stronger stable embedding result is possible under certain conditions. The final part of this thesis is the application of CS ideas to the study of the short-term memory of neural networks. In particular, we show that the nodes of a recurrent neural network can be a stable embedding of sparse input sequences.

In this essay I analyze the way my collection of poetry relates to the concept cyclic. What is cyclic and what different interpretations are there concerning the concept? Which other related concepts are there regarding things that seem to reoccur regularly? How does the cyclical, the circular, relate to the linear in regard, for example, to the perception of time? Life often creates the sense that different phenomena and experiences repeat themselves. Different processes are conducted according to regular or irregular cyclical phases, situations seem familiar and events can be relived.          Starting from my writing project Sinnligt kviller, a collection of poems, I discuss these questions in themselves and the impact they have had on my collection. I describe the phenomenon cyclic as a result of the emotions and moods this term creates in a more or less decisive way.          The intention with this paper is to show how Sinnligt kviller offers different perspectives of and reflections upon the various feelings provoked by the cyclic in our lives, feelings that are given my own voice in the literary text under study.

處理多重事件或再發事件之事件發生時間的資料時，常會以Cox模式為基礎而予以延伸，其中較適合再發事件的模式為：A-G模式、GT-UR 模式、PWP-CP 模式及PWP-GT模式。這些模式又可按照是否以發生次數為分層變數，而分為未分層模式(包含A-G模式、 GT-UR 模式)，及分層模式(包含PWP-CP模式、 PWP-GT 模式)。 本論文將以改良的Cox延伸模式，包括對變異數進行修正或加入事故傾向(或隨機效果)，探討公務人員升等的快慢與哪些變數有關。變異數修正方式利用穩健標準誤以解決事件之再發時間之間的相依問題；事故傾向模式則主要是以隨機效果代表無法觀察到的個體間之異質性，且同一個體的各次發生時間共享相同的異質性，並假定異質性服從某種特定分配。對於各種Cox的延伸模式，我們可比較採用穩健變異數與否對估計及推論結果的差異，以及事故傾向加入前後，估計及推論結果與模式配適上的差異。 由本論文對公務人員升等資料的分析可發現，採用變異數修正方法時，未分層的模式有較小的變異數估計值，所以顯著的變數較多，包括性別、官等、教育程度及年齡；分層模式中顯著變數則只有官等及教育程度。若假定事故傾向服從對數Gamma分配，並加入於上述四種模式中，則顯著的變數與未加入事故傾向時一致，且各模式之下均無法拒絕所有人的事故傾向同為0的假設。這種現象或許是因為我們無法取得教育程度與公務人員考試及格種類之歷史資料，也有可能是因為公務人員升等的體制健全，且法規制定嚴謹，運作也有正常的模式可循所致。

Ziel der vorliegenden Arbeit ist die retrospektive Beurteilung der Sicherheit und Effektivität der Leberteilresektion bei der Behandlung von Lebermetastasen des kolorektalen Karzinoms sowie der Re-Resektion bei Patienten mit Rezidivlebermetastasen. Weiterhin soll das operative Vorgehen bei synchronen Lebermetastasen hinsichtlich simultaner Resektionsverfahren und zweizeitigen Vorgehens untersucht werden. Insgesamt wurden die Ergebnisse von 660 Patienten ausgewertet, die zwischen 1988 und 2004 mit 685 Leberteilresektionen behandelt wurden. Unter diesen waren 75 Patienten, die eine Re-Resektion erhielten sowie 202 Patienten, bei denen die Lebermetastasen synchron auftraten. Neben der Analyse der postoperativen Letalität und postoperativen Komplikationen sollen prognostische Faktoren für das Langzeitüberleben und das Auftreten von Tumorrezidiven nach Leberteilresektion identifiziert werden. Da sich die Studienpopulation aus einem Zeitraum von über 15 Jahren rekrutiert, sollen außerdem verschiedene Zeitabschnitte vergleichend analysiert werden. Die Leberteilresektion ist derzeit die einzige potentiell kurative Therapie bei kolorektalen Lebermetastasen. Als prognostisch günstige Parameter in der multivariaten Analyse zeigten sich die Radikalität des Eingriffes, die Anzahl der Metastasen, vorhandene ligamentäre Lymph-knotenmetastasen sowie das Jahr der Resektion. Auch bei Rezidiven kolorektaler Lebermetastasen ist das chirurgische Vorgehen derzeit die einzige kurative Intervention. Re-Resektionen weisen ein vergleichbares operatives Risiko und vergleichbare Langzeitüberlebensraten auf wie Erstresektionen. Als einziger prognostischer Parameter für das Langzeitüberleben erwies sich in der multivariaten Analyse die Radikalität des Eingriffes. Bei synchronen Lebermetastasen sind die wichtigsten Kriterien, um eine simultane Resektion durchzuführen, die Berücksichtigung des Alters sowie des Resektionsausmaßes. Simultane Resektionen sind bei synchronen kolorektalen Lebermetastasen dann so sicher und effizient durchführbar wie Resektionen im zweizeitigen Vorgehen.

Η παρούσα διατριβή πραγματεύεται την αναγνώριση ομιλητή σε πραγματικές συνθήκες. Τα κύρια σημεία της εργασίας είναι: (1) αξιολόγηση διαφόρων προσεγγίσεων εξαγωγής χαρακτηριστικών παραμέτρων ομιλίας, (2) μείωση της ισχύος της περιβαλλοντικής επίδρασης στην απόδοση της αναγνώρισης ομιλητή, και (3) μελέτη τεχνικών κατηγοριοποίησης, εναλλακτικών προς τις υπάρχουσες. Συγκεκριμένα, στο (1), προτείνεται μια νέα δομή εξαγωγής παραμέτρων ομιλίας βασισμένη σε πακέτα κυματομορφών, κατάλληλα σχεδιασμένη για αναγνώριση ομιλητή. Εξάγεται με ένα αντικειμενικό τρόπο σε σχέση με την απόδοση αναγνώρισης ομιλητή, σε αντίθεση με την MFCC προσέγγιση, που βασίζεται στην προσέγγιση της αντίληψης της ανθρώπινης ακοής. Έπειτα, στο (2), δίνεται μια δομή για την εξαγωγή παραμέτρων βασισμένη στα MFCC, ανεκτική στο θόρυβο, για την βελτίωση της απόδοσης της αναγνώρισης ομιλητή σε πραγματικό περιβάλλον. Συνοπτικά, μια τεχνική μείωσης του θορύβου βασισμένη σε μοντέλο προσαρμοσμένη στο πρόβλημα της επιβεβαίωσης ομιλητή ενσωματώνεται απευθείας στη δομή υπολογισμού των MFCC. Αυτή η προσέγγιση επέδειξε σημαντικό πλεονέκτημα σε πραγματικό και ταχέως μεταβαλλόμενο περιβάλλον. Τέλος, στο (3), εισάγονται δύο νέοι κατηγοριοποιητές που αναφέρονται ως Locally Recurrent Probabilistic Neural Network (LR PNN), και Generalized Locally Recurrent Probabilistic Neural Network (GLR PNN). Είναι υβρίδια μεταξύ των Recurrent Neural Network (RNN) και Probabilistic Neural Network (PNN) και συνδυάζουν τα πλεονεκτήματα των γεννετικών και διαφορικών προσσεγγίσεων κατηγοριοποίησης. Επιπλέον, τα νέα αυτά νευρωνικά δίκτυα είναι ευαίσθητα σε παροδικές και ειδικές συσχετίσεις μεταξύ διαδοχικών εισόδων, και έτσι, είναι κατάλληλα για να αξιοποιήσουν την συσχέτιση παραμέτρων ομιλίας μεταξύ πλαισίων ομιλίας. Κατά την εξαγωγή των πειραμάτων, διαφάνηκε ότι οι αρχιτεκτονικές LR PNN και GLR PNN παρέχουν καλύτερη απόδοση, σε σχέση με τα αυθεντικά PNN. / This dissertation dials with speaker recognition in real-world conditions. The main accent falls on: (1) evaluation of various speech feature extraction approaches, (2) reduction of the impact of environmental interferences on the speaker recognition performance, and (3) studying alternative to the present state-of-the-art classification techniques. Specifically, within (1), a novel wavelet packet-based speech features extraction scheme fine-tuned for speaker recognition is proposed. It is derived in an objective manner with respect to the speaker recognition performance, in contrast to the state-of-the-art MFCC scheme, which is based on approximation of human auditory perception. Next, within (2), an advanced noise-robust feature extraction scheme based on MFCC is offered for improving the speaker recognition performance in real-world environments. In brief, a model-based noise reduction technique adapted for the specifics of the speaker verification task is incorporated directly into the MFCC computation scheme. This approach demonstrated significant advantage in real-world fast-varying environments. Finally, within (3), two novel classifiers referred to as Locally Recurrent Probabilistic Neural Network (LR PNN), and Generalized Locally Recurrent Probabilistic Neural Network (GLR PNN) are introduced. They are hybrids between Recurrent Neural Network (RNN) and Probabilistic Neural Network (PNN) and combine the virtues of the generative and discriminative classification approaches. Moreover, these novel neural networks are sensitive to temporal and special correlations among consecutive inputs, and therefore, are capable to exploit the inter-frame correlations among speech features derived for successive speech frames. In the experimentations, it was demonstrated that the LR PNN and GLR PNN architectures provide benefit in terms of performance, when compared to the original PNN.

No description available.