Characterization of dynamic speckle sequences using principal component analysis and image descriptors

Speckle is being used as a characterization tool for the analysis of the dynamic of slow varying phenomena occurring in biological and industrial samples. The retrieved data takes the form of a sequence of speckle images. The analysis of these images should reveal the inner dynamic of the biological or physical process taking place in the sample. Very recently, it has been shown that principal component analysis is able to split the original data set in a collection of classes. These classes can be related with the dynamic of the observed phenomena. At the same time, statistical descriptors of biospeckle images have been used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, principal component analysis requires longer computation time but the results contain more information related with spatial-temporal pattern that can be identified with physical process. This contribution merges both descriptions and uses principal component analysis as a pre-processing tool to obtain a collection of filtered images where a simpler statistical descriptor can be calculated. The method has been applied to slow-varying biological and industrial processes

Prospective Estimation of Radiation Dose and Image Quality for Optimized CT Performance

X-ray computed tomography (CT) is a non-invasive medical imaging technique that generates cross-sectional images by acquiring attenuation-based projection measurements at multiple angles. Since its first introduction in the 1970s, substantial technical improvements have led to the expanding use of CT in clinical examinations. CT has become an indispensable imaging modality for the diagnosis of a wide array of diseases in both pediatric and adult populations [1, 2]. Currently, approximately 272 million CT examinations are performed annually worldwide, with nearly 85 million of these in the United States alone [3]. Although this trend has decelerated in recent years, CT usage is still expected to increase mainly due to advanced technologies such as multi-energy [4], photon counting [5], and cone-beam CT [6].

Despite the significant clinical benefits, concerns have been raised regarding the population-based radiation dose associated with CT examinations [7]. From 1980 to 2006, the effective dose from medical diagnostic procedures rose six-fold, with CT contributing to almost half of the total dose from medical exposure [8]. For each patient, the risk associated with a single CT examination is likely to be minimal. However, the relatively large population-based radiation level has led to enormous efforts among the community to manage and optimize the CT dose.

As promoted by the international campaigns Image Gently and Image Wisely, exposure to CT radiation should be appropriate and safe [9, 10]. It is thus a responsibility to optimize the amount of radiation dose for CT examinations. The key for dose optimization is to determine the minimum amount of radiation dose that achieves the targeted image quality [11]. Based on such principle, dose optimization would significantly benefit from effective metrics to characterize radiation dose and image quality for a CT exam. Moreover, if accurate predictions of the radiation dose and image quality were possible before the initiation of the exam, it would be feasible to personalize it by adjusting the scanning parameters to achieve a desired level of image quality. The purpose of this thesis is to design and validate models to quantify patient-specific radiation dose prospectively and task-based image quality. The dual aim of the study is to implement the theoretical models into clinical practice by developing an organ-based dose monitoring system and an image-based noise addition software for protocol optimization.

More specifically, Chapter 3 aims to develop an organ dose-prediction method for CT examinations of the body under constant tube current condition. The study effectively modeled the anatomical diversity and complexity using a large number of patient models with representative age, size, and gender distribution. The dependence of organ dose coefficients on patient size and scanner models was further evaluated. Distinct from prior work, these studies use the largest number of patient models to date with representative age, weight percentile, and body mass index (BMI) range.

With effective quantification of organ dose under constant tube current condition, Chapter 4 aims to extend the organ dose prediction system to tube current modulated (TCM) CT examinations. The prediction, applied to chest and abdominopelvic exams, was achieved by combining a convolution-based estimation technique that quantifies the radiation field, a TCM scheme that emulates modulation profiles from major CT vendors, and a library of computational phantoms with representative sizes, ages, and genders. The prospective quantification model is validated by comparing the predicted organ dose with the dose estimated based on Monte Carlo simulations with TCM function explicitly modeled.

Chapter 5 aims to implement the organ dose-estimation framework in clinical practice to develop an organ dose-monitoring program based on a commercial software (Dose Watch, GE Healthcare, Waukesha, WI). In the first phase of the study we focused on body CT examinations, and so the patient’s major body landmark information was extracted from the patient scout image in order to match clinical patients against a computational phantom in the library. The organ dose coefficients were estimated based on CT protocol and patient size as reported in Chapter 3. The exam CTDIvol, DLP, and TCM profiles were extracted and used to quantify the radiation field using the convolution technique proposed in Chapter 4.

With effective methods to predict and monitor organ dose, Chapters 6 aims to develop and validate improved measurement techniques for image quality assessment. Chapter 6 outlines the method that was developed to assess and predict quantum noise in clinical body CT images. Compared with previous phantom-based studies, this study accurately assessed the quantum noise in clinical images and further validated the correspondence between phantom-based measurements and the expected clinical image quality as a function of patient size and scanner attributes.

Chapter 7 aims to develop a practical strategy to generate hybrid CT images and assess the impact of dose reduction on diagnostic confidence for the diagnosis of acute pancreatitis. The general strategy is (1) to simulate synthetic CT images at multiple reduced-dose levels from clinical datasets using an image-based noise addition technique; (2) to develop quantitative and observer-based methods to validate the realism of simulated low-dose images; (3) to perform multi-reader observer studies on the low-dose image series to assess the impact of dose reduction on the diagnostic confidence for multiple diagnostic tasks; and (4) to determine the dose operating point for clinical CT examinations based on the minimum diagnostic performance to achieve protocol optimization.

Chapter 8 concludes the thesis with a summary of accomplished work and a discussion about future research.

CERAMICS IN BRITAIN (1840-90): MEANINGS AND METAPHORS

The intention of this thesis, “Ceramics in Britain (1840–90): Meanings and Metaphors” is to present new approaches for interpreting ceramics in nineteenth-century Britain by situating, problematizing, and contextualizing pottery and porcelain in the popular debates of the day within the methodologies of material culture, design, cultural and art histories. I ask how did ceramics—portable, functional, and often decorative objects—contribute to shaping modes of experiences? Crockery, tableware and blue-white-porcelain, admittedly largely mediated in texts and paintings, are at the centre of this research to examine how they imposed symbolism and influenced the engagement of their subjects beyond their intended meanings and functions. This thesis tracks a common rhetoric shared by writers and artists across genres and understood by readers and viewers: crockery in the cupboard, on the mantel, the table or the floor were popular motifs exemplifying class, gender, character, etiquette, and taste. This thesis also seeks to map ceramics’ relations with other objects and people depicted. Their meanings and metaphors changed, depending on their exchange with other objects in the room and who uses them. The conventions of representing ceramics dictated a particular grammar that writers and artists used, critiqued, discarded or personalized. The examination of ceramics mediated in text and image especially in comparison with extant objects invites a deeper probing of both material culture and artistic practice, which helps to situate the agency of the ceramic objects themselves. Also this thesis, in attempt to explore new methodological approaches for ceramic studies, examines the social life of the mid-Victorian relief-moulded “Minster” Jug in the Gardiner Museum in Toronto. The product originating in Staffordshire in 1843 and exported to the colonies holds significance due to its multiple life histories. Viewing the “Minster” through the lenses of curator, collector, consumer, and critic its layered lives unfold to reveal the protocols of museum praxis as well as important aspects of mid-nineteenth-century British society related to design reform, gender, imperialism and consumption patterns. This thesis contends that the British experienced ceramics in sometimes unexpected ways, unrelated to their original purpose, such as tools of violence or containers of solace, and transformative fantasy.

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.

Document analysis of PDF files: methods, results and implications

A strategy for document analysis is presented which uses Portable Document Format (PDF the underlying file structure for Adobe Acrobat software) as its starting point. This strategy examines the appearance and geometric position of text and image blocks distributed over an entire document. A blackboard system is used to tag the blocks as a first stage in deducing the fundamental relationships existing between them. PDF is shown to be a useful intermediate stage in the bottom-up analysis of document structure. Its information on line spacing and font usage gives important clues in bridging the semantic gap between the scanned bitmap page and its fully analysed, block-structured form. Analysis of PDF can yield not only accurate page decomposition but also sufficient document information for the later stages of structural analysis and document understanding.

A new technique to automatically quantify microstructures of fine grained carbonate mylonites: two-step etching combined with SEM imaging and image analysis

A two-step etching technique for fine-grained calcite mylonites using 0.37% hydrochloric and 0.1% acetic acid produces a topographic relief which reflects the grain boundary geometry. With this technique, calcite grain boundaries become more intensely dissolved than their grain interiors but second phase minerals like dolomite, quartz, feldspars, apatite, hematite and pyrite are not affected by the acid and therefore form topographic peaks. Based on digital backscatter electron images and element distribution maps acquired on a scanning electron microscope, the geometry of calcite and the second phase minerals can be automatically quantified using image analysis software. For research on fine-grained carbonate rocks (e.g. dolomite calcite mixtures), this low-cost approach is an attractive alternative to the generation of manual grain boundary maps based on photographs from ultra-thin sections or orientation contrast images.

Edge-aware Graph Attention Networks: Joint Reasoning On Text and Knowledge Graphs for Biomedical Question Answering

Much of the real-world dataset, including textual data, can be represented using graph structures. The use of graphs to represent textual data has many advantages, mainly related to maintaining a more significant amount of information, such as the relationships between words and their types. In recent years, many neural network architectures have been proposed to deal with tasks on graphs. Many of them consider only node features, ignoring or not giving the proper relevance to relationships between them. However, in many node classification tasks, they play a fundamental role. This thesis aims to analyze the main GNNs, evaluate their advantages and disadvantages, propose an innovative solution considered as an extension of GAT, and apply them to a case study in the biomedical field. We propose the reference GNNs, implemented with methodologies later analyzed, and then applied to a question answering system in the biomedical field as a replacement for the pre-existing GNN. We attempt to obtain better results by using models that can accept as input both node and edge features. As shown later, our proposed models can beat the original solution and define the state-of-the-art for the task under analysis.

Big Code Applications and Approaches

The availability of a huge amount of source code from code archives and open-source projects opens up the possibility to merge machine learning, programming languages, and software engineering research fields. This area is often referred to as Big Code where programming languages are treated instead of natural languages while different features and patterns of code can be exploited to perform many useful tasks and build supportive tools. Among all the possible applications which can be developed within the area of Big Code, the work presented in this research thesis mainly focuses on two particular tasks: the Programming Language Identification (PLI) and the Software Defect Prediction (SDP) for source codes. Programming language identification is commonly needed in program comprehension and it is usually performed directly by developers. However, when it comes at big scales, such as in widely used archives (GitHub, Software Heritage), automation of this task is desirable. To accomplish this aim, the problem is analyzed from different points of view (text and image-based learning approaches) and different models are created paying particular attention to their scalability. Software defect prediction is a fundamental step in software development for improving quality and assuring the reliability of software products. In the past, defects were searched by manual inspection or using automatic static and dynamic analyzers. Now, the automation of this task can be tackled using learning approaches that can speed up and improve related procedures. Here, two models have been built and analyzed to detect some of the commonest bugs and errors at different code granularity levels (file and method levels). Exploited data and models’ architectures are analyzed and described in detail. Quantitative and qualitative results are reported for both PLI and SDP tasks while differences and similarities concerning other related works are discussed.

A abordagem e o ensino da escrita na Educação Pré-Escolar e no ensino do 1º ciclo do Ensino Básico

Mestrado (PES II), Educação Pré-Escolar e Ensino do 1º Ciclo do Ensino Básico, 1 de Julho de 2014, Universidade dos Açores.

Kahden merkkijonon pisimmän yhteisen alijonon ongelma ja sen ratkaiseminen

Tämä tutkielma kuuluu merkkijonoalgoritmiikan piiriin. Merkkijono S on merkkijonojen X[1..m] ja Y[1..n] yhteinen alijono, mikäli se voidaan muodostaa poistamalla X:stä 0..m ja Y:stä 0..n kappaletta merkkejä mielivaltaisista paikoista. Jos yksikään X:n ja Y:n yhteinen alijono ei ole S:ää pidempi, sanotaan, että S on X:n ja Y:n pisin yhteinen alijono (lyh. PYA). Tässä työssä keskitytään kahden merkkijonon PYAn ratkaisemiseen, mutta ongelma on yleistettävissä myös useammalle jonolle. PYA-ongelmalle on sovelluskohteita – paitsi tietojenkäsittelytieteen niin myös bioinformatiikan osa-alueilla. Tunnetuimpia niistä ovat tekstin ja kuvien tiivistäminen, tiedostojen versionhallinta, hahmontunnistus sekä DNA- ja proteiiniketjujen rakennetta vertaileva tutkimus. Ongelman ratkaisemisen tekee hankalaksi ratkaisualgoritmien riippuvuus syötejonojen useista eri parametreista. Näitä ovat syötejonojen pituuden lisäksi mm. syöttöaakkoston koko, syötteiden merkkijakauma, PYAn suhteellinen osuus lyhyemmän syötejonon pituudesta ja täsmäävien merkkiparien lukumäärä. Täten on vaikeaa kehittää algoritmia, joka toimisi tehokkaasti kaikille ongelman esiintymille. Tutkielman on määrä toimia yhtäältä käsikirjana, jossa esitellään ongelman peruskäsitteiden kuvauksen jälkeen jo aikaisemmin kehitettyjä tarkkoja PYAalgoritmeja. Niiden tarkastelu on ryhmitelty algoritmin toimintamallin mukaan joko rivi, korkeuskäyrä tai diagonaali kerrallaan sekä monisuuntaisesti prosessoiviin. Tarkkojen menetelmien lisäksi esitellään PYAn pituuden ylä- tai alarajan laskevia heuristisia menetelmiä, joiden laskemia tuloksia voidaan hyödyntää joko sellaisinaan tai ohjaamaan tarkan algoritmin suoritusta. Tämä osuus perustuu tutkimusryhmämme julkaisemiin artikkeleihin. Niissä käsitellään ensimmäistä kertaa heuristiikoilla tehostettuja tarkkoja menetelmiä. Toisaalta työ sisältää laajahkon empiirisen tutkimusosuuden, jonka tavoitteena on ollut tehostaa olemassa olevien tarkkojen algoritmien ajoaikaa ja muistinkäyttöä. Kyseiseen tavoitteeseen on pyritty ohjelmointiteknisesti esittelemällä algoritmien toimintamallia hyvin tukevia tietorakenteita ja rajoittamalla algoritmien suorittamaa tuloksetonta laskentaa parantamalla niiden kykyä havainnoida suorituksen aikana saavutettuja välituloksia ja hyödyntää niitä. Tutkielman johtopäätöksinä voidaan yleisesti todeta tarkkojen PYA-algoritmien heuristisen esiprosessoinnin lähes systemaattisesti pienentävän niiden suoritusaikaa ja erityisesti muistintarvetta. Lisäksi algoritmin käyttämällä tietorakenteella on ratkaiseva vaikutus laskennan tehokkuuteen: mitä paikallisempia haku- ja päivitysoperaatiot ovat, sitä tehokkaampaa algoritmin suorittama laskenta on.

Convergences/divergences : le dialogue intermédial dans Vues et visions de Claude Cahun

Ce mémoire étudie les rapports texte/image dans Vues et visions de Claude Cahun, première œuvre composite créée en collaboration avec la peintre graphiste Marcel Moore. L’objet littéraire protéiforme, appartenant au genre de l’iconotexte (Alain Montandon), instaure un dialogue intermédial entre le textuel et le visuel au point de déconstruire l’horizon d’attente du lecteur : celui-ci est incité à lire et à voir alternativement les poèmes en prose ainsi que les dessins de sorte que les frontières qui définissent l’espace du littéral et du figural apparaissent poreuses. Subdivisé en deux chapitres, notre travail s’attachera dans un premier temps à mettre en lumière le rôle de l’écriture qui intègre certains dessins de Moore. En nous inspirant de l’iconolecture (Emmanuelle Pelard), nous tenterons d’effectuer des liens entre la plasticité et la signification littérale des signes linguistiques qu’illustrent ces images-textes tout en étudiant les correspondances thématiques et formelles qu’elles entretiennent avec les poèmes de Cahun. Le second chapitre étudiera la manière dont le figural investit le texte littéraire en adoptant une approche intermédiale. Après avoir abordé la figure du double, une partie de l’analyse sera consacrée à la figure de l’allusion, une stratégie d’écriture pour introduire le visuel au sein du textuel, ce qui nous permettra d’entrer en matière pour étudier « l’image-en-texte » (Liliane Louvel). Enfin, l’effet-tableau ainsi que l’anamorphose seront employés comme cadre d’analyse afin de penser le dialogue qui se noue entre le pictural et le texte littéraire dans Vues et visions.

Qu'importe : une faute au texte ; suivi de Mécanismes et dialogisme photographique dans Crimes passionnels et Darlinghurst Heroes d'André Martin

Toutes les photographies présentes à l'intérieur de ce mémoire ont été prises par l'auteur de ce dernier.

Cuerpos Intervenidos : Experiencias corporales diferenciadas de la feminidad y el cáncer de seno

Este proyecto de investigación se enfoca en estudiar la configuración de las feminidades en mujeres que han pasado por la experiencia del cáncer de seno. Teniendo en cuenta que cada una de ellas tiene una trayectoria social diferente que determina el desarrollo de la feminidad y de la experiencia de la enfermedad. La metodología que se utilizó dentro de la investigación fue de carácter etnográfico, ya que se pretendió dar cuentan de la experiencia de la enfermedad, la corporalidad y la subjetividad.

Parallel Monte Carlo algorithms for information retrieval

In any data mining applications, automated text and text and image retrieval of information is needed. This becomes essential with the growth of the Internet and digital libraries. Our approach is based on the latent semantic indexing (LSI) and the corresponding term-by-document matrix suggested by Berry and his co-authors. Instead of using deterministic methods to find the required number of first "k" singular triplets, we propose a stochastic approach. First, we use Monte Carlo method to sample and to build much smaller size term-by-document matrix (e.g. we build k x k matrix) from where we then find the first "k" triplets using standard deterministic methods. Second, we investigate how we can reduce the problem to finding the "k"-largest eigenvalues using parallel Monte Carlo methods. We apply these methods to the initial matrix and also to the reduced one. The algorithms are running on a cluster of workstations under MPI and results of the experiments arising in textual retrieval of Web documents as well as comparison of the stochastic methods proposed are presented. (C) 2003 IMACS. Published by Elsevier Science B.V. All rights reserved.