Automated dental identification: A micro-macro decision-making approach

Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. In this work we consider three important problems in automated dental identification beyond the basic approach of tooth-to-tooth matching.;The first problem is on automatic classification of teeth into incisors, canines, premolars and molars as part of creating a data structure that guides tooth-to-tooth matching, thus avoiding illogical comparisons that inefficiently consume the limited computational resources and may also mislead the decision-making. We tackle this problem using principal component analysis and string matching techniques. We reconstruct the segmented teeth using the eigenvectors of the image subspaces of the four teeth classes, and then call the teeth classes that achieve least energy-discrepancy between the novel teeth and their approximations. We exploit teeth neighborhood rules in validating teeth-classes and hence assign each tooth a number corresponding to its location in a dental chart. Our approach achieves 82% teeth labeling accuracy based on a large test dataset of bitewing films.;Because dental radiographic films capture projections of distinct teeth; and often multiple views for each of the distinct teeth, in the second problem we look for a scheme that exploits teeth multiplicity to achieve more reliable match decisions when we compare the dental records of a subject and a candidate match. Hence, we propose a hierarchical fusion scheme that utilizes both aspects of teeth multiplicity for improving teeth-level (micro) and case-level (macro) decision-making. We achieve a genuine accept rate in excess of 85%.;In the third problem we study the performance limits of dental identification due to features capabilities. We consider two types of features used in dental identification, namely teeth contours and appearance features. We propose a methodology for determining the number of degrees of freedom possessed by a feature set, as a figure of merit, based on modeling joint distributions using copulas under less stringent assumptions on the dependence between feature dimensions. We also offer workable approximations of this approach

Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation

Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning (ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms have struggled to keep up, despite their superior capabilities. This is mainly attributed to the need for large amounts of data for training, which the scientific community is unable to satisfy. The number of promising DL algorithms is considerable, although solutions directly targeting the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on single, classical modalities and tend to complicate significantly with the amount of physiological effects they can simulate. This thesis aims at providing and validating a framework, specifically addressing the data deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was designed to generate large, annotated artificial signals. By expressing data through coefficients of pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and intra-modality associations were learned. Thereafter, new coefficients are sampled to generate artificial, multimodal signals with the original physiological dynamics. Moreover, normal and pathological beats along with artifacts were included by employing Markov models. Secondly, a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture and trained with synthesized data under real-world experimental conditions to evaluate how its performance is affected. Both the synthesizer and the CNN not only performed at state of the art level but also innovated with multiple types of generated data and detection error improvements, respectively. Cardiorespiratory data augmentation corrected performance drops when not enough data is available, enhanced the CNN’s ability to perform on noisy signals and to carry out new tasks when introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully validated showing potential to leverage future DL research on Cardiology into clinical standards

Knowledge acquisition for coreference resolution

Diese Arbeit befasst sich mit dem Problem der statistischen Koreferenzauflösung. Theoretische Studien bezeichnen Koreferenz als ein vielseitiges linguistisches Phänomen, das von verschiedenen Faktoren beeinflusst wird. Moderne statistiche Algorithmen dagegen basieren sich typischerweise auf einfache wissensarme Modelle. Ziel dieser Arbeit ist das Schließen der Lücke zwischen Theorie und Praxis. Ausgehend von den Erkentnissen der theoretischen Studien erfolgt die Bestimmung der linguistischen Faktoren die fuer die Koreferenz besonders relevant erscheinen. Unterschiedliche Informationsquellen werden betrachtet: von der Oberflächenübereinstimmung bis zu den tieferen syntaktischen, semantischen und pragmatischen Merkmalen. Die Präzision der untersuchten Faktoren wird mit korpus-basierten Methoden evaluiert. Die Ergebnisse beweisen, dass die Koreferenz mit den linguistischen, in den theoretischen Studien eingebrachten Merkmalen interagiert. Die Arbeit zeigt aber auch, dass die Abdeckung der untersuchten theoretischen Aussagen verbessert werden kann. Die Merkmale stellen die Grundlage für den Aufbau eines einerseits linguistisch gesehen reichen andererseits auf dem Machinellen Lerner basierten, d.h. eines flexiblen und robusten Systems zur Koreferenzauflösung. Die aufgestellten Untersuchungen weisen darauf hin dass das wissensreiche Model erfolgversprechende Leistung zeigt und im Vergleich mit den Algorithmen, die sich auf eine einzelne Informationsquelle verlassen, sowie mit anderen existierenden Anwendungen herausragt. Das System erreicht einen F-wert von 65.4% auf dem MUC-7 Korpus. In den bereits veröffentlichen Studien ist kein besseres Ergebnis verzeichnet. Die Lernkurven zeigen keine Konvergenzzeichen. Somit kann der Ansatz eine gute Basis fuer weitere Experimente bilden: eine noch bessere Leistung kann dadurch erreicht werden, dass man entweder mehr Texte annotiert oder die bereits existierende Daten effizienter einsetzt. Diese Arbeit beweist, dass statistiche Algorithmen fuer Koreferenzauflösung stark von den theoretischen linguistischen Studien profitiern können und sollen: auch unvollständige Informationen, die automatische fehleranfällige Sprachmodule liefern, können die Leistung der Anwendung signifikant verbessern.This thesis addresses the problem of statistical coreference resolution. Theoretical studies describe coreference as a complex linguistic phenomenon, affected by various different factors. State-of-the-art statistical approaches, on the contrary, rely on rather simple knowledge-poor modeling. This thesis aims at bridging the gap between the theory and the practice. We use insights from linguistic theory to identify relevant linguistic parameters of co-referring descriptions. We consider different types of information, from the most shallow name-matching measures to deeper syntactic, semantic, and discourse knowledge. We empirically assess the validity of the investigated theoretic predictions for the corpus data. Our data-driven evaluation experiments confirm that various linguistic parameters, suggested by theoretical studies, interact with coreference and may therefore provide valuable information for resolution systems. At the same time, our study raises several issues concerning the coverage of theoretic claims. It thus brings feedback to linguistic theory. We use the investigated knowledge sources to build a linguistically informed statistical coreference resolution engine. This framework allows us to combine the flexibility and robustness of a machine learning-based approach with wide variety of data from different levels of linguistic description. Our evaluation experiments with different machine learners show that our linguistically informed model, on the one side, outperforms algorithms, based on a single knowledge source and, on the other side, yields the best result on the MUC-7 data, reported in the literature (F-score of 65.4% with the SVM-light learning algorithm). The learning curves for our classifiers show no signs of convergence. This suggests that our approach makes a good basis for further experimentation: one can obtain even better results by annotating more material or by using the existing data more intelligently. Our study proves that statistical approaches to the coreference resolution task may and should benefit from linguistic theories: even imperfect knowledge, extracted from raw text data with off-the-shelf error-prone NLP modules, helps achieve significant improvements

Contributions to Vine-Copula Modeling

144 p.Regular vine-copula models (R-vines) are a powerful statistical tool for modeling thedependence structure of multivariate distribution functions. In particular, they allow modelingdierent types of dependencies among random variables independently of their marginaldistributions, which is deemed the most valued characteristic of these models. In this thesis, weinvestigate the theoretical properties of R-vines for representing dependencies and extend theiruse to solve supervised classication problems. We focus on three research directions.!In the rst line of research, the relationship between the graphical representations of R-vines!ÁREA LÍNEA1 2 0 3 0 4ÁREA LÍNEA1 2 0 3 1 7ÁREA LÍNEAÁREA LÍNEA!and Bayesian polytree networks is analyzed in terms of how conditional pairwise independence!relationships are represented by both models. In order to do that, we use an extended graphical!representation of R-vines in which the R-vine graph is endowed with further expressiveness,being possible to distinguish between edges representing independence and dependencerelationships. Using this representation, a separation criterion in the R-vine graph, called Rseparation,is dened. The proposed criterion is used in designing methods for building thegraphical structure of polytrees from that of R-vines, and vice versa. Moreover, possiblecorrespondences between the R-vine graph and the associated R-vine copula as well as dierentproperties of R-separation are analyzed. In the second research line, we design methods forlearning the graphical structure of R-vines from dependence lists. The main challenge of thistask lies in the extremely large size of the search space of all possible R-vine structures. Weprovide two strategies to solve the problem of learning R-vines that represent the largestnumber of dependencies in a list. The rst approach is a 0 -1 linear programming formulation forbuilding truncated R-vines with only two trees. The second approach is an evolutionaryalgorithm, which is able to learn complete and truncated R-vines. Experimental results show thesuccess of this strategy in solving the optimization problem posed. In the third research line, weintroduce a supervised classication approach where the dependence structure of the problemfeatures is modeled through R-vines. The ecacy of these classiers is validated in a mentaldecoding problem and in an image recognition task. While Rvines have been extensivelyapplied in elds such as economics, nance and statistics, only recently have they found theirplace in classication tasks. This contribution represents a step forward in understanding R-vinesand the prospect of extending their use to other machine learning tasks

Xerophilic Flightless Grasshoppers (Orthoptera: Acrididae: Melanoplinae: Melanoplus: the Puer Group) of the Southeastern U.S.A.: an Evolutionary History

The 27 flightless grasshopper species of the Puer Group (Orthoptera: Acrididae: Melanoplinae: Melanoplus) comprise a biological system of fascinating complexity in the southeastern U.S.A. that was heavily influenced by sea level fluctuations during the Pliocene and Pleistocene, especially during the latter. These shifts resulted in an oceanic island system that can now be classified as a landlocked archipelago, one that still reflects the patterns of its ancestral roots in terms of speciation and dispersal. To better understand speciation patterns in this group, I used several synergistic methods: molecular-based phylogenetic reconstruction, divergence time estimation, correlative microscopy and 3D model reconstruction of copulation, and shape analysis of male genitalia in an evolutionary time-based phylogenetic framework. As predicted, aside from general sea level changes, my evidence indicates that the biogeographical and speciation history of this system was shaped dominantly by allopatry in the form of oceanic islands in the past and, more recently, sympatry via sexual selection, especially for the species in peninsular Florida. My quantitative evidence also added strong support for the concept, especially in light of evolutionary time, that sexual selection can drive genital evolution divergently and rapidly. My investigation of the function of genital components (some new to science) during copulation was combined with shape analyses of five of those genital components to reveal that sexual selection’s evolutionary tempo on these components is accelerating and/or decelerating. The relative speed was found to be dependent upon the component and its associated function(s). I also discovered that one of the youngest Puer Group clades has speciated at a rapid rate that may possibly be the highest yet for insects. The obvious complexity of this biological system requires additional investigation at finer scales to further dissect the intriguing patterns and processes of evolution herein revealed to be at work. Continued analyses of the Puer Group, both quantitative and descriptive, are encouraged, especially because the threat of destruction and fragmentation of the group’s xeric habitats (especially scrub) looms large. Speciation is still largely a biological black box, but its inner workings will continue to be slowly revealed with further illuminating studies like this one

Argumentative zoning information extraction from scientific text

Let me tell you, writing a thesis is not always a barrel of laughs—and strange things can happen, too. For example, at the height of my thesis paranoia, I had a re-current dream in which my cat Amy gave me detailed advice on how to restructure the thesis chapters, which was awfully nice of her. But I also had a lot of human help throughout this time, whether things were going fine or beserk. Most of all, I want to thank Marc Moens: I could not have had a better or more knowledgable supervisor. He always took time for me, however busy he might have been, reading chapters thoroughly in two days. He both had the calmness of mind to give me lots of freedom in research, and the right judgement to guide me away, tactfully but determinedly, from the occasional catastrophe or other waiting along the way. He was great fun to work with and also became a good friend. My work has profitted from the interdisciplinary, interactive and enlightened atmosphere at the Human Communication Centre and the Centre for Cognitive Science (which is now called something else). The Language Technology Group was a great place to work in, as my research was grounded in practical applications develope

Connected image processing with multivariate attributes: an unsupervised Markovian classification approach

International audienceThis article presents a new approach for constructing connected operators for image processing and analysis. It relies on a hierarchical Markovian unsupervised algorithm in order to classify the nodes of the traditional Max-Tree. This approach enables to naturally handle multivariate attributes in a robust non-local way. The technique is demonstrated on several image analysis tasks: filtering, segmentation, and source detection, on astronomical and biomedical images. The obtained results show that the method is competitive despite its general formulation. This article provides also a new insight in the field of hierarchical Markovian image processing showing that morphological trees can advantageously replace traditional quadtrees

Natural Language Processing Resources for Finnish. Corpus Development in the General and Clinical Domains

Siirretty Doriast