902 resultados para Hierarchical document
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Objective: To assess the risk factors for delayed diagnosis of uterine cervical lesions. Materials and Methods: This is a case-control study that recruited 178 women at 2 Brazilian hospitals. The cases (n = 74) were composed of women with a late diagnosis of a lesion in the uterine cervix (invasive carcinoma in any stage). The controls (n = 104) were composed of women with cervical lesions diagnosed early on (low-or high-grade intraepithelial lesions). The analysis was performed by means of logistic regression model using a hierarchical model. The socioeconomic and demographic variables were included at level I (distal). Level II (intermediate) included the personal and family antecedents and knowledge about the Papanicolaou test and human papillomavirus. Level III (proximal) encompassed the variables relating to individuals' care for their own health, gynecologic symptoms, and variables relating to access to the health care system. Results: The risk factors for late diagnosis of uterine cervical lesions were age older than 40 years (odds ratio [OR] = 10.4; 95% confidence interval [CI], 2.3-48.4), not knowing the difference between the Papanicolaou test and gynecological pelvic examinations (OR, = 2.5; 95% CI, 1.3-4.9), not thinking that the Papanicolaou test was important (odds ratio [OR], 4.2; 95% CI, 1.3-13.4), and abnormal vaginal bleeding (OR, 15.0; 95% CI, 6.5-35.0). Previous treatment for sexually transmissible disease was a protective factor (OR, 0.3; 95% CI, 0.1-0.8) for delayed diagnosis. Conclusions: Deficiencies in cervical cancer prevention programs in developing countries are not simply a matter of better provision and coverage of Papanicolaou tests. The misconception about the Papanicolaou test is a serious educational problem, as demonstrated by the present study.
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Spin systems in the presence of disorder are described by two sets of degrees of freedom, associated with orientational (spin) and disorder variables, which may be characterized by two distinct relaxation times. Disordered spin models have been mostly investigated in the quenched regime, which is the usual situation in solid state physics, and in which the relaxation time of the disorder variables is much larger than the typical measurement times. In this quenched regime, disorder variables are fixed, and only the orientational variables are duly thermalized. Recent studies in the context of lattice statistical models for the phase diagrams of nematic liquid-crystalline systems have stimulated the interest of going beyond the quenched regime. The phase diagrams predicted by these calculations for a simple Maier-Saupe model turn out to be qualitative different from the quenched case if the two sets of degrees of freedom are allowed to reach thermal equilibrium during the experimental time, which is known as the fully annealed regime. In this work, we develop a transfer matrix formalism to investigate annealed disordered Ising models on two hierarchical structures, the diamond hierarchical lattice (DHL) and the Apollonian network (AN). The calculations follow the same steps used for the analysis of simple uniform systems, which amounts to deriving proper recurrence maps for the thermodynamic and magnetic variables in terms of the generations of the construction of the hierarchical structures. In this context, we may consider different kinds of disorder, and different types of ferromagnetic and anti-ferromagnetic interactions. In the present work, we analyze the effects of dilution, which are produced by the removal of some magnetic ions. The system is treated in a “grand canonical" ensemble. The introduction of two extra fields, related to the concentration of two different types of particles, leads to higher-rank transfer matrices as compared with the formalism for the usual uniform models. Preliminary calculations on a DHL indicate that there is a phase transition for a wide range of dilution concentrations. Ising spin systems on the AN are known to be ferromagnetically ordered at all temperatures; in the presence of dilution, however, there are indications of a disordered (paramagnetic) phase at low concentrations of magnetic ions.
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Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.
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[EN]We present a new strategy for constructing spline spaces over hierarchical T-meshes with quad- and octree subdivision scheme. The proposed technique includes some simple rules for inferring local knot vectors to define C 2 -continuous cubic tensor product spline blending functions. Our conjecture is that these rules allow to obtain, for a given T-mesh, a set of linearly independent spline functions with the property that spaces spanned by nested T-meshes are also nested, and therefore, the functions can reproduce cubic polynomials. In order to span spaces with these properties applying the proposed rules, the T-mesh should fulfill the only requirement of being a 0- balanced mesh...
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This PhD Thesis is part of a long-term wide research project, carried out by the "Osservatorio Astronomico di Bologna (INAF-OABO)", that has as primary goal the comprehension and reconstruction of formation mechanism of galaxies and their evolution history. There is now substantial evidence, both from theoretical and observational point of view, in favor of the hypothesis that the halo of our Galaxy has been at least partially, built up by the progressive accretion of small fragments, similar in nature to the present day dwarf galaxies of the Local Group. In this context, the photometric and spectroscopic study of systems which populate the halo of our Galaxy (i.e. dwarf spheroidal galaxy, tidal streams, massive globular cluster, etc) permits to discover, not only the origin and behaviour of these systems, but also the structure of our Galactic halo, combined with its formation history. In fact, the study of the population of these objects and also of their chemical compositions, age, metallicities and velocity dispersion, permit us not only an improvement in the understanding of the mechanisms that govern the Galactic formation, but also a valid indirect test for cosmological model itself. Specifically, in this Thesis we provided a complete characterization of the tidal Stream of the Sagittarius dwarf spheroidal galaxy, that is the most striking example of the process of tidal disruption and accretion of a dwarf satellite in to our Galaxy. Using Red Clump stars, extracted from the catalogue of the Sloan Digital Sky Survey (SDSS) we obtained an estimate of the distance, the depth along the line of sight and of the number density for each detected portion of the Stream (and more in general for each detected structure along our line of sight). Moreover comparing the relative number (i.e. the ratio) of Blue Horizontal Branch stars and Red Clump stars (the two features are tracers of different age/different metallicity populations) in the main body of the galaxy and in the Stream, in order to verify the presence of an age-metallicity gradient along the Stream. We also report the detection of a population of Red Clump stars probably associated with the recently discovered Bootes III stellar system. Finally, we also present the results of a survey of radial velocities over a wide region, extending from r ~ 10' out to r ~ 80' within the massive star cluster Omega Centauri. The survey was performed with FLAMES@VLT, to study the velocity dispersion profile in the outer regions of this stellar system. All the results presented in this Thesis, have already been published in refeered journals.
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This thesis aims at investigating a new approach to document analysis based on the idea of structural patterns in XML vocabularies. My work is founded on the belief that authors do naturally converge to a reasonable use of markup languages and that extreme, yet valid instances are rare and limited. Actual documents, therefore, may be used to derive classes of elements (patterns) persisting across documents and distilling the conceptualization of the documents and their components, and may give ground for automatic tools and services that rely on no background information (such as schemas) at all. The central part of my work consists in introducing from the ground up a formal theory of eight structural patterns (with three sub-patterns) that are able to express the logical organization of any XML document, and verifying their identifiability in a number of different vocabularies. This model is characterized by and validated against three main dimensions: terseness (i.e. the ability to represent the structure of a document with a small number of objects and composition rules), coverage (i.e. the ability to capture any possible situation in any document) and expressiveness (i.e. the ability to make explicit the semantics of structures, relations and dependencies). An algorithm for the automatic recognition of structural patterns is then presented, together with an evaluation of the results of a test performed on a set of more than 1100 documents from eight very different vocabularies. This language-independent analysis confirms the ability of patterns to capture and summarize the guidelines used by the authors in their everyday practice. Finally, I present some systems that work directly on the pattern-based representation of documents. The ability of these tools to cover very different situations and contexts confirms the effectiveness of the model.
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Zusammenfassung: Die vorliegende Untersuchung zum deutschen Kriegsgefangenenwesen (KGW) im Zweiten Weltkrieg schließt eine wichtige Lücke innerhalb der geschichtswissenschaftlichen Forschungen zum Themenkreis der Kriegsgefangenschaft in deutschem Gewahrsam. Bisherige Studien (bis einschließlich 1997) behandeln vor allem sozial- und kulturgeschichtliche Aspekte der Kriegsgefangenen (Kgf.), der Lagergesellschaft und dem Alltag von Soldaten in Kriegsgefangenschaft. Der Verfasser indes legt mit seiner Magisterarbeit erstmals eine Organisations- und Strukturgeschichte des deutschen Kriegsgefangenenwesens von 1939 bis 1945 vor, welche fundamentale Grundlagen der deutschen militärischen Lagerorganisation und Verwaltung dokumentiert. So wird die Entwicklung von den Vorkriegsplanungen bis zum Kriegsende anhand der zentralen Dienststellen herausgearbeitet und im Kontext des Genfer Kriegsgefangenenabkommens von 1929 und völkerrechtlicher Implikationen gewichtet. Hiermit untrennbar verbundene Einflußnahmen nichtmilitärischer Stellen in die Entscheidungsgewalt der Streitkräfte im Heimatkriegsgebiet und in den Wehrmachtbefehlshaberbereichen werden nicht zuletzt auch anhand mehrerer Organigramme veranschaulicht. Zudem dokumentiert und analysiert die Untersuchung die im Kriegsverlauf stetig verschärften Maßnahmen zur Fluchtprävention und der konzertierten Fahndung nach geflohenen Kriegsgefangenen: Die Machterosion des Oberkommandos der Wehrmacht (OKW) zugunsten des Reichsführers-SS, des Reichssicherheitshauptamts und nicht zuletzt der Parteikanzlei der NSDAP wird so augenfällig. Trotz eminenter Schriftgutverluste kann der Verfasser vor allem anhand einer nahezu vollständig erhaltenen Schlüsselquelle die Stellenbesetzung und Organisationsstruktur der mit Kriegsgefangenenfragen befassten Stellen im OKW rekonstruieren. Die Auswertung dieser Sammelmitteilungen / Befehlssammlung für das Kriegsgefangenenwesen sowie an anderer Stelle überlieferter Organisationsbefehle ermöglicht wichtige Änderungen am derzeitigen Forschungsstand. Darüber hinaus beschäftigt sich die vorliegende Untersuchung mit dem Arbeitseinsatz Kriegsgefangener in der deutschen Wirtschaft als in der zweiten Kriegshälfte zentralem Element der Kriegsgefangenschaft. Außerdem wird beleuchtet, welche politischen, (rassen)ideologischen oder reziprok konnotierten Faktoren den Stellenwert gefangener Soldaten unterschiedlicher Nationalität innerhalb der Gefangenenhierarchie im deutschen Kriegsgefangenenwesen bestimmten. Inhalt: 1. Einführung; 2. Die Entwicklung des Kriegsvölkerrechts und das Genfer Kriegsgefangenenabkommen von 1929; 3. Einleitende Bemerkungen zum deutschen Kriegsgefangenenwesen: Quellenlage, Grundlagen; 4. Organisationsstruktur und Aufgaben des KGW: Zuständigkeiten für Kgf. in OKW und OKH, Abt. Wehrmachtverluste und Kriegsgefangene, der General z.b.V. für das KGW 1939 bis Ende 1941, Allgemeine und Organisationsabteilung seit Januar 1942, Generalinspekteur und Inspekteur des KGW von Juli 1943 bis Oktober 1944, das Kriegsgefangenenwesen unter Himmler seit Oktober 1944; 5. Die Kriegsgefangenenlager: Lagertypen, Anzahl und Verwendung, die Gesamtzahl Kgf. und Belegstärken ausgewählter Lager; 6. Richtlinien für KGL: Die Sammelmitteilungen / Befehlssammlung für das KGW, Lagerorganisation und Behandlung Kriegsgefangener; 7. Die Post der Kriegsgefangenen: Tätigkeit von Auslandsbriefprüfstelle, Abwehr III Referat Kgf. und Abwehrstellen der Wehrkreise, Vorgaben für Postüberwachung und Stimmungsberichte der Asten, Befehle zur Kgf-Post und Kooperation mit Hilfsorganisationen und Schutzmächten; 8. Fluchtprävention: Bestimmungen und Maßnahmen zur Fluchtvereitelung, der Fluchterlass vom 22.09.1942, der Sonderfahndungsplan der Sicherheitspolizei und des SD vom 28.09.1942, Erlass zur Kriegsfahndung vom 5.12.1942, der Fluchterlaß vom 02.07.1943, der Erlaß zur Mitarbeit NSDAP bei Groß- und Kriegsfahndungen vom 10.07.1943, Schulung zur Fluchtprävention auf Wehrkreisebene 1944, Preisausschreiben "Wie verhindere ich Fluchten?" vom 09.04.1945, Anwerbung von V-Leuten durch die Abwehr; 9. Arbeitseinsatz Kriegsgefangener in der deutschen Wirtschaft und beteiligte Stellen; 10. Der Status Kriegsgefangener unterschiedlicher Nationalitäten im Vergleich; 11. Schluss
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In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
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We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z-score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b-high/CD10-low/CD221-high) and a second group clustering close to fibroblasts (CD49b-low/CD10-high/CD221-low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes.
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In the field of computer assisted orthopedic surgery (CAOS) the anterior pelvic plane (APP) is a common concept to determine the pelvic orientation by digitizing distinct pelvic landmarks. As percutaneous palpation is - especially for obese patients - known to be error-prone, B-mode ultrasound (US) imaging could provide an alternative means. Several concepts of using ultrasound imaging to determine the APP landmarks have been introduced. In this paper we present a novel technique, which uses local patch statistical shape models (SSMs) and a hierarchical speed of sound compensation strategy for an accurate determination of the APP. These patches are independently matched and instantiated with respect to associated point clouds derived from the acquired ultrasound images. Potential inaccuracies due to the assumption of a constant speed of sound are compensated by an extended reconstruction scheme. We validated our method with in-vitro studies using a plastic bone covered with a soft-tissue simulation phantom and with a preliminary cadaver trial.
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Three-dimensional (3D) models of teeth and soft and hard tissues are tessellated surfaces used for diagnosis, treatment planning, appliance fabrication, outcome evaluation, and research. In scientific publications or communications with colleagues, these 3D data are often reduced to 2-dimensional pictures or need special software for visualization. The portable document format (PDF) offers a simple way to interactively display 3D surface data without additional software other than a recent version of Adobe Reader (Adobe, San Jose, Calif). The purposes of this article were to give an example of how 3D data and their analyses can be interactively displayed in 3 dimensions in electronic publications, and to show how they can be exported from any software for diagnostic reports and communications among colleagues.
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Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.