Local Trigonometric Methods for Time Series Smoothing.

The thesis is concerned with local trigonometric regression methods. The aim was to develop a method for extraction of cyclical components in time series. The main results of the thesis are the following. First, a generalization of the filter proposed by Christiano and Fitzgerald is furnished for the smoothing of ARIMA(p,d,q) process. Second, a local trigonometric filter is built, with its statistical properties. Third, they are discussed the convergence properties of trigonometric estimators, and the problem of choosing the order of the model. A large scale simulation experiment has been designed in order to assess the performance of the proposed models and methods. The results show that local trigonometric regression may be a useful tool for periodic time series analysis.

Seismic tomographic full-waveform inversion for the Vrancea sinking lithosphere structure using the adjoint method.

The Vrancea region, at the south-eastern bend of the Carpathian Mountains in Romania, represents one of the most puzzling seismically active zones of Europe. Beside some shallow seismicity spread across the whole Romanian territory, Vrancea is the place of an intense seismicity with the presence of a cluster of intermediate-depth foci placed in a narrow nearly vertical volume. Although large-scale mantle seismic tomographic studies have revealed the presence of a narrow, almost vertical, high-velocity body in the upper mantle, the nature and the geodynamic of this deep intra-continental seismicity is still questioned. High-resolution seismic tomography could help to reveal more details in the subcrustal structure of Vrancea. Recent developments in computational seismology as well as the availability of parallel computing now allow to potentially retrieve more information out of seismic waveforms and to reach such high-resolution models. This study was aimed to evaluate the application of a full waveform inversion tomography at regional scale for the Vrancea lithosphere using data from the 1999 six months temporary local network CALIXTO. Starting from a detailed 3D Vp, Vs and density model, built on classical travel-time tomography together with gravity data, I evaluated the improvements obtained with the full waveform inversion approach. The latter proved to be highly problem dependent and highly computational expensive. The model retrieved after the first two iterations does not show large variations with respect to the initial model but remains in agreement with previous tomographic models. It presents a well-defined downgoing slab shape high velocity anomaly, composed of a N-S horizontal anomaly in the depths between 40 and 70km linked to a nearly vertical NE-SW anomaly from 70 to 180km.

Pricing in stochastic-local volatility models with default

In recent years is becoming increasingly important to handle credit risk. Credit risk is the risk associated with the possibility of bankruptcy. More precisely, if a derivative provides for a payment at cert time T but before that time the counterparty defaults, at maturity the payment cannot be effectively performed, so the owner of the contract loses it entirely or a part of it. It means that the payoff of the derivative, and consequently its price, depends on the underlying of the basic derivative and on the risk of bankruptcy of the counterparty. To value and to hedge credit risk in a consistent way, one needs to develop a quantitative model. We have studied analytical approximation formulas and numerical methods such as Monte Carlo method in order to calculate the price of a bond. We have illustrated how to obtain fast and accurate pricing approximations by expanding the drift and diffusion as a Taylor series and we have compared the second and third order approximation of the Bond and Call price with an accurate Monte Carlo simulation. We have analysed JDCEV model with constant or stochastic interest rate. We have provided numerical examples that illustrate the effectiveness and versatility of our methods. We have used Wolfram Mathematica and Matlab.

Entwicklung und Anwendung von mobilen Messstrategien und Analysemethoden zur Untersuchung urbanen Aerosols

Seit Anbeginn der Menschheitsgeschichte beeinflussen die Menschen ihre Umwelt. Durch anthropogene Emissionen ändert sich die Zusammensetzung der Atmosphäre, was einen zunehmenden Einfluss unter anderem auf die Atmosphärenchemie, die Gesundheit von Mensch, Flora und Fauna und das Klima hat. Die steigende Anzahl riesiger, wachsender Metropolen geht einher mit einer räumlichen Konzentration der Emission von Luftschadstoffen, was vor allem einen Einfluss auf die Luftqualität der windabwärts gelegenen ruralen Regionen hat. In dieser Doktorarbeit wurde im Rahmen des MEGAPOLI-Projektes die Abluftfahne der Megastadt Paris unter Anwendung des mobilen Aerosolforschungslabors MoLa untersucht. Dieses ist mit modernen, zeitlich hochauflösenden Instrumenten zur Messung der chemischen Zusammensetzung und Größenverteilung der Aerosolpartikel sowie einiger Spurengase ausgestattet. Es wurden mobile Messstrategien entwickelt und angewendet, die besonders geeignet zur Charakterisierung urbaner Emissionen sind. Querschnittsmessfahrten durch die Abluftfahne und atmosphärische Hintergrundluftmassen erlaubten sowohl die Bestimmung der Struktur und Homogenität der Abluftfahne als auch die Berechnung des Beitrags der urbanen Emissionen zur Gesamtbelastung der Atmosphäre. Quasi-Lagrange’sche Radialmessfahrten dienten der Erkundung der räumlichen Erstreckung der Abluftfahne sowie auftretender Transformationsprozesse der advehierten Luftschadstoffe. In Kombination mit Modellierungen konnte die Struktur der Abluftfahne vertieft untersucht werden. Flexible stationäre Messungen ergänzten den Datensatz und ließen zudem Vergleichsmessungen mit anderen Messstationen zu. Die Daten einer ortsfesten Messstation wurden zusätzlich verwendet, um die Alterung des organischen Partikelanteils zu beschreiben. Die Analyse der mobilen Messdaten erforderte die Entwicklung einer neuen Methode zur Bereinigung des Datensatzes von lokalen Störeinflüssen. Des Weiteren wurden die Möglichkeiten, Grenzen und Fehler bei der Anwendung komplexer Analyseprogramme zur Berechnung des O/C-Verhältnisses der Partikel sowie der Klassifizierung der Aerosolorganik untersucht. Eine Validierung verschiedener Methoden zur Bestimmung der Luftmassenherkunft war für die Auswertung ebenfalls notwendig. Die detaillierte Untersuchung der Abluftfahne von Paris ergab, dass diese sich anhand der Erhöhung der Konzentrationen von Indikatoren für unprozessierte Luftverschmutzung im Vergleich zu Hintergrundwerten identifizieren lässt. Ihre eher homogene Struktur kann zumeist durch eine Gauß-Form im Querschnitt mit einem exponentiellen Abfall der unprozessierten Schadstoffkonzentrationen mit zunehmender Distanz zur Stadt beschrieben werden. Hierfür ist hauptsächlich die turbulente Vermischung mit Umgebungsluftmassen verantwortlich. Es konnte nachgewiesen werden, dass in der advehierten Abluftfahne eine deutliche Oxidation der Aerosolorganik im Sommer stattfindet; im Winter hingegen ließ sich dieser Prozess während der durchgeführten Messungen nicht beobachten. In beiden Jahreszeiten setzt sich die Abluftfahne hauptsächlich aus Ruß und organischen Partikelkomponenten im PM1-Größenbereich zusammen, wobei die Quellen Verkehr und Kochen sowie zusätzlich Heizen in der kalten Jahreszeit dominieren. Die PM1-Partikelmasse erhöhte sich durch die urbanen Emissionen im Vergleich zum Hintergrundwert im Sommer in der Abluftfahne im Mittel um 30% und im Winter um 10%. Besonders starke Erhöhungen ließen sich für Polyaromaten beobachten, wo im Sommer eine mittlere Zunahme von 194% und im Winter von 131% vorlag. Jahreszeitliche Unterschiede waren ebenso in der Größenverteilung der Partikel der Abluftfahne zu finden, wo im Winter im Gegensatz zum Sommer keine zusätzlichen nukleierten kleinen Partikel, sondern nur durch Kondensation und Koagulation angewachsene Partikel zwischen etwa 10nm und 200nm auftraten. Die Spurengaskonzentrationen unterschieden sich ebenfalls, da chemische Reaktionen temperatur- und mitunter strahlungsabhängig sind. Weitere Anwendungsmöglichkeiten des MoLa wurden bei einer Überführungsfahrt von Deutschland an die spanische Atlantikküste demonstriert, woraus eine Kartierung der Luftqualität entlang der Fahrtroute resultierte. Es zeigte sich, dass hauptsächlich urbane Ballungszentren von unprozessierten Luftschadstoffen betroffen sind, advehierte gealterte Substanzen jedoch jede Region beeinflussen können. Die Untersuchung der Luftqualität an Standorten mit unterschiedlicher Exposition bezüglich anthropogener Quellen erweiterte diese Aussage um einen Einblick in die Variation der Luftqualität, abhängig unter anderem von der Wetterlage und der Nähe zu Emissionsquellen. Damit konnte gezeigt werden, dass sich die entwickelten Messstrategien und Analysemethoden nicht nur zur Untersuchung der Abluftfahne einer Großstadt, sondern auch auf verschiedene andere wissenschaftliche und umweltmesstechnische Fragestellungen anwenden lassen.

Learning Methods and Algorithms for Semantic Text Classification across Multiple Domains

Information is nowadays a key resource: machine learning and data mining techniques have been developed to extract high-level information from great amounts of data. As most data comes in form of unstructured text in natural languages, research on text mining is currently very active and dealing with practical problems. Among these, text categorization deals with the automatic organization of large quantities of documents in priorly defined taxonomies of topic categories, possibly arranged in large hierarchies. In commonly proposed machine learning approaches, classifiers are automatically trained from pre-labeled documents: they can perform very accurate classification, but often require a consistent training set and notable computational effort. Methods for cross-domain text categorization have been proposed, allowing to leverage a set of labeled documents of one domain to classify those of another one. Most methods use advanced statistical techniques, usually involving tuning of parameters. A first contribution presented here is a method based on nearest centroid classification, where profiles of categories are generated from the known domain and then iteratively adapted to the unknown one. Despite being conceptually simple and having easily tuned parameters, this method achieves state-of-the-art accuracy in most benchmark datasets with fast running times. A second, deeper contribution involves the design of a domain-independent model to distinguish the degree and type of relatedness between arbitrary documents and topics, inferred from the different types of semantic relationships between respective representative words, identified by specific search algorithms. The application of this model is tested on both flat and hierarchical text categorization, where it potentially allows the efficient addition of new categories during classification. Results show that classification accuracy still requires improvements, but models generated from one domain are shown to be effectively able to be reused in a different one.

Stochastic local volatility model for fx markets

Questa tesi verte sullo studio di un modello a volatilità stocastica e locale, utilizzato per valutare opzioni esotiche nei mercati dei cambio. La difficoltà nell'implementare un modello di tal tipo risiede nella calibrazione della leverage surface e uno degli scopi principali di questo lavoro è quello di mostrarne la procedura.

An inter-comparison between a VIS/IR and a MW satellite-based methods for cloud detection and classification

This thesis is aimed to assess similarities and mismatches between the outputs from two independent methods for the cloud cover quantification and classification based on quite different physical basis. One of them is the SAFNWC software package designed to process radiance data acquired by the SEVIRI sensor in the VIS/IR. The other is the MWCC algorithm, which uses the brightness temperatures acquired by the AMSU-B and MHS sensors in their channels centered in the MW water vapour absorption band. At a first stage their cloud detection capability has been tested, by comparing the Cloud Masks they produced. These showed a good agreement between two methods, although some critical situations stand out. The MWCC, in effect, fails to reveal clouds which according to SAFNWC are fractional, cirrus, very low and high opaque clouds. In the second stage of the inter-comparison the pixels classified as cloudy according to both softwares have been. The overall observed tendency of the MWCC method, is an overestimation of the lower cloud classes. Viceversa, the more the cloud top height grows up, the more the MWCC not reveal a certain cloud portion, rather detected by means of the SAFNWC tool. This is what also emerges from a series of tests carried out by using the cloud top height information in order to evaluate the height ranges in which each MWCC category is defined. Therefore, although the involved methods intend to provide the same kind of information, in reality they return quite different details on the same atmospheric column. The SAFNWC retrieval being very sensitive to the top temperature of a cloud, brings the actual level reached by this. The MWCC, by exploiting the capability of the microwaves, is able to give an information about the levels that are located more deeply within the atmospheric column.

Feature-invariant image registration method for quantification of surgical outcomes in patients with craniosynostosis: a preliminary study

Craniosynostosis consists of a premature fusion of the sutures in an infant skull that restricts skull and brain growth. During the last decades, there has been a rapid increase of fundamentally diverse surgical treatment methods. At present, the surgical outcome has been assessed using global variables such as cephalic index, head circumference, and intracranial volume. However, these variables have failed in describing the local deformations and morphological changes that may have a role in the neurologic disorders observed in the patients. This report describes a rigid image registration-based method to evaluate outcomes of craniosynostosis surgical treatments, local quantification of head growth, and indirect intracranial volume change measurements. The developed semiautomatic analysis method was applied to computed tomography data sets of a 5-month-old boy with sagittal craniosynostosis who underwent expansion of the posterior skull with cranioplasty. Quantification of the local changes between pre- and postoperative images was quantified by mapping the minimum distance of individual points from the preoperative to the postoperative surface meshes, and indirect intracranial volume changes were estimated. The proposed methodology can provide the surgeon a tool for the quantitative evaluation of surgical procedures and detection of abnormalities of the infant skull and its development.

A Flow Trace Generator using Graph-based Traffic Classification Techniques

We propose a novel methodology to generate realistic network flow traces to enable systematic evaluation of network monitoring systems in various traffic conditions. Our technique uses a graph-based approach to model the communication structure observed in real-world traces and to extract traffic templates. By combining extracted and user-defined traffic templates, realistic network flow traces that comprise normal traffic and customized conditions are generated in a scalable manner. A proof-of-concept implementation demonstrates the utility and simplicity of our method to produce a variety of evaluation scenarios. We show that the extraction of templates from real-world traffic leads to a manageable number of templates that still enable accurate re-creation of the original communication properties on the network flow level.

Modeling of Magic Water Clusters(H20)2 and (H20)21H+ with the PM3 Quantum-Mechanical Method

The PM3 quantum-mechanical method is able to model the magic water clusters (H20),, and (H20)&+. Results indicate that the H30+ ion is tightly bound within the (H20),, cluster by multiple hydrogen bonds, causing deformation to the symmetric (HzO),, pentagonal dodecahedron structure. The structures, energetics, and hydrogen bond patterns of six local minima (H20)21H+ clusters are presented.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.

Intratumoral budding is preoperative biopsies predicts local and distant metastasis in colorectal cancer patients

Objective: In 2011, the term “intratumoral budding, ITB” was used to describe the presence of tumor buds within the main tumor body and was correlated to worse clinical outcome in colorectal cancer patients. Here, we further elucidate the potential clinical role of ITB in pre-operative biopsies using pan-cytokeratin stained tissues and a quantitative scoring system. Method: 139 pre-operative biopsies from patients with colorectal cancer underwent immunohistochemistry for pancytokeratin (AE1/AE3). ITB were counted in the area of densest budding (40×) and classified as high-grade when >10 buds/HPF were observed based on receiver operating characteristic (ROC) curve analysis. Results: High-grade ITB occurred in 26.6 % of cases and was associated with right-sided tumor location (p=0.0356), more advanced pT (p=0.0198) and pN (p<0.0001) classifications, distant metastasis (p=0.0164), higher tumor grade (p=0.0037) and lymphatic invasion (p=0.0445). The specificity and positive predictive value for lymph node metastasis was 86.7 % and 75.6 %, respectively. Disease-free survival was significantly worse in patients with high-grade ITB (5-year survival=25 %) in comparison to patients with low-grade ITB (5-year survival=55 %) (p=0.0157). Conclusion: The assessment of ITB in pre-operative biopsies is predictive of local and distant metastasis in corresponding resections and should be considered in daily management of colorectal cancer patients.

Sonographic visualization and ultrasound-guided block of the third occipital nerve: prospective for a new method to diagnose C2-C3 zygapophysial joint pain

BACKGROUND: Chronic neck pain after whiplash injury is caused by cervical zygapophysial joints in 50% of patients. Diagnostic blocks of nerves supplying the joints are performed using fluoroscopy. The authors' hypothesis was that the third occipital nerve can be visualized and blocked with use of an ultrasound-guided technique. METHODS: In 14 volunteers, the authors placed a needle ultrasound-guided to the third occipital nerve on both sides of the neck. They punctured caudal and perpendicular to the 14-MHz transducer. In 11 volunteers, 0.9 ml of either local anesthetic or normal saline was applied in a randomized, double-blind, crossover manner. Anesthesia was controlled in the corresponding skin area by pinprick and cold testing. The position of the needle was controlled by fluoroscopy. RESULTS: The third occipital nerve could be visualized in all subjects and showed a median diameter of 2.0 mm. Anesthesia was missing after local anesthetic in only one case. There was neither anesthesia nor hyposensitivity after any of the saline injections. The C2-C3 joint, in a transversal plane visualized as a convex density, was identified correctly by ultrasound in 27 of 28 cases, and 23 needles were placed correctly into the target zone. CONCLUSIONS: The third occipital nerve can be visualized and blocked with use of an ultrasound-guided technique. The needles were positioned accurately in 82% of cases as confirmed by fluoroscopy; the nerve was blocked in 90% of cases. Because ultrasound is the only available technique today to visualize this nerve, it seems to be a promising new method for block guidance instead of fluoroscopy.

Antimicrobial therapy using a local drug delivery system (Arestin) in the treatment of peri-implantitis. I: Microbiological outcomes

OBJECTIVES: To assess the microbiological outcome of local administration of minocycline hydrochloride microspheres 1 mg (Arestin) in cases with peri-implantitis and with a follow-up period of 12 months. MATERIAL AND METHODS: After debridement, and local administration of chlorhexidine gel, peri-implantitis cases were treated with local administration of minocycline microspheres (Arestin). The DNA-DNA checkerboard hybridization method was used to detect bacterial presence during the first 360 days of therapy. RESULTS: At Day 10, lower bacterial loads for 6/40 individual bacteria including Actinomyces gerensceriae (P<0.1), Actinomyces israelii (P<0.01), Actinomyces naeslundi type 1 (P<0.01) and type 2 (P<0.03), Actinomyces odontolyticus (P<0.01), Porphyromonas gingivalis (P<0.01) and Treponema socranskii (P<0.01) were found. At Day 360 only the levels of Actinobacillus actinomycetemcomitans were lower than at baseline (mean difference: 1x10(5); SE difference: 0.34x10(5), 95% CI: 0.2x10(5) to 1.2x10(5); P<0.03). Six implants were lost between Days 90 and 270. The microbiota was successfully controlled in 48%, and with definitive failures (implant loss and major increase in bacterial levels) in 32% of subjects. CONCLUSIONS: At study endpoint, the impact of Arestin on A. actinomycetemcomitans was greater than the impact on other pathogens. Up to Day 180 reductions in levels of Tannerella forsythia, P. gingivalis, and Treponema denticola were also found. Failures in treatment could not be associated with the presence of specific pathogens or by the total bacterial load at baseline. Statistical power analysis suggested that a case control study would require approximately 200 subjects.

Classification and selection of biomarkers in genomic data using LASSO

High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been on assessing univariate associations between gene expression with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.