Rôles des interactions entre loci dans l'organisation spatiale fonctionnelle et l'évolution des génomes de mammifères

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

A Poetics of Annotation: Alexander Pope's Footnotes

Studies of Alexander Pope's poetry tend to examine only the footnotes to his Dunciads, if they examine his footnotes at all. This dissertation will address this deficit in our understanding of Pope's poetics through an examination of Pope's use of footnotes in support of his verse throughout his career. With Gerard Genette's taxonomy of footnotes as variously paratext and text and Hugh Kenner's idea of the technological space of the printed page as frameworks within which Pope's footnotes operate, this dissertation will show that – over the course of his career – Pope developed a poetics of annotation that deployed footnotes rhetorically as appeals to ethos and pathos that both built up Pope's own authorial ethos for his audience in the literary market place of early eighteenth-century London and for posterity and used that authorial ethos in support of his literary and political friends.

D6.3 – Semantic Content Annotation Support

The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

Lehrende als ‘agents of change’ - Konzeptionelle Überlegungen zu einer kinderrechtlichen Annotation und Ausdeutung der Standards für die Lehrerbildung

Die Dissertation geht von der Überlegung aus, dass ein Wandel im deutschen Bildungssystem aufgrund kinderrechtlicher und demokratischer Überlegungen eine essenzielle Zukunftsaufgabe ist. Dies geschieht im Besonderen vor dem Hintergrund kinderrechtlicher Forderungen, wie sie beispielsweise aus dem Status Deutschlands als Mitgliedsland der UN-Kinderrechtskonvention und des UN-Übereinkommens über die Rechte von Menschen mit Behinderung entstehen: Die Korrelation zwischen Herkunft und Bildungserfolg sowie die noch konzeptionell ungenügend entwickelten Ansätze zur Inklusion werden als bestimmend für die Diskussion erkannt. Die Arbeit vertritt die These, dass Lehrende eine zentrale Rolle in einem notwendigen Prozess der Umwandlung des deutschen Bildungssystems spielen können und nimmt in emanzipatorischer Absicht die Lehrenden als Zielgruppe in den Blick. Aufgrund der Struktur der Lehrendenausbildung wird zudem dafür argumentiert, entsprechende Veränderungen im Sinne ihrer Nachhaltigkeit und flächendeckenden Relevanz in der Lehrendenausbildung des ersten universitären Ausbildungsabschnittes zu verankern. Als Instrument, welches die Kriterien von Nachhaltigkeit und umfassender Relevanz erfüllt, werden die Standards für die Lehrerbildung im Bereich Bildungswissenschaften benannt und theoretisch unterfüttert. Grundsätzlich spannt sich der Blickwinkel der Arbeit zwischen der Betrachtung der Struktur des Bildungssystems, des Wissens und Könnens von Lehrenden und Lernenden im Sinne der Kompetenzorientierung sowie der Haltung auf, welche Lehrende einnehmen. Dabei wird aufgezeigt, dass sich diese Faktoren wechselseitig auf vielfache Art und Weise beeinflussen. Auf der Basis dieser Überlegungen wird zunächst das deutsche Bildungssystem in seinen Strukturen beschrieben und die Lehrenden und Lernenden als Teilnehmende am Bildungsgeschehen skizziert. Ein exkursiver Vergleich dreier aufgrund kinderrechtlicher Parameter ausgewählter europäischer Bildungssysteme eruiert zudem, inwiefern die aufgezeigten Bestimmungsgrößen Struktur, Wissen/Können und Haltung Einfluss auf kinderrechtliche Verfasstheit des Bildungssystems nehmen, wobei die Grundprinzipien der UN-Kinderrechtskonvention hier als Messinstrument dienen. Davon ausgehend, erscheint pädagogische Haltung als eine wesentliche Einflussgröße, die im weiteren Verlauf der Arbeit im Sinne der kritischen Pädagogik zu einer Konzeption Pädagogischer Verantwortung verdichtet wird. Vor diesem Hintergrund erfolgt eine Betrachtung aktueller Problematiken im deutschen Bildungssystem, die sich an den in den Standards für die Lehrerbildung identifizierten Ausbildungsschwerpunkten orientiert und erneut die Prinzipien und rechtlichen Vorgaben der UN-Kinderrechtskonvention als Maßstab wählt. Auf der Grundlage dieser umfassenden Diskussion werden in einem sich anschließenden Analyseschritt Annotationen vorgenommen, die die Formulierungen der 11 Standards für die Lehrerbildung inhaltlich erweitern und ergänzen. In Verbindung mit einer Pädagogischen Verantwortung, die sich der Ausbildung kritischer Mündigkeit verpflichtet, werden die annotierten Standards als eine Möglichkeit der kinderrechtlichen (Selbst-)Evaluation von Lehrenden sowie als Instrument einer kinderrechtliche Gesichtspunkte fokussierenden Lehrendenbildung verstanden und dargestellt.

Annotation of Gyrodactylus salaris transcriptome

Relationship between organisms within an ecosystem is one of the main focuses in the study of ecology and evolution. For instance, host-parasite interactions have long been under close interest of ecology, evolutionary biology and conservation science, due to great variety of strategies and interaction outcomes. The monogenean ecto-parasites consist of a significant portion of flatworms. Gyrodactylus salaris is a monogenean freshwater ecto-parasite of Atlantic salmon (Salmo salar) whose damage can make fish to be prone to further bacterial and fungal infections. G. salaris is the only one parasite whose genome has been studied so far. The RNA-seq data analyzed in this thesis has already been annotated by using LAST. The RNA-seq data was obtained from Illumina sequencing i.e. yielded reads were assembled into 15777 transcripts. Last resulted in annotation of 46% transcripts and remaining were left unknown. This thesis work was started with whole data and annotation process was continued by the use of PANNZER, CDD and InterProScan. This annotation resulted in 56% successfully annotated sequences having parasite specific proteins identified. This thesis represents the first of Monogenean transcriptomic information which gives an important source for further research on this specie. Additionally, comparison of annotation methods interestingly revealed that description and domain based methods perform better than simple similarity search methods. Therefore it is more likely to suggest the use of these tools and databases for functional annotation. These results also emphasize the need for use of multiple methods and databases. It also highlights the need of more genomic information related to G. salaris.

Transcriptional dynamics of the developing sweet cherry (Prunus avium L.) fruit: sequencing, annotation and expression profiling of exocarp-associated genes

The exocarp, or skin, of fleshy fruit is a specialized tissue that protects the fruit, attracts seed dispersing fruit eaters, and has large economical relevance for fruit quality. Development of the exocarp involves regulated activities of many genes. This research analyzed global gene expression in the exocarp of developing sweet cherry (Prunus avium L., 'Regina'), a fruit crop species with little public genomic resources. A catalog of transcript models (contigs) representing expressed genes was constructed from de novo assembled short complementary DNA (cDNA) sequences generated from developing fruit between flowering and maturity at 14 time points. Expression levels in each sample were estimated for 34 695 contigs from numbers of reads mapping to each contig. Contigs were annotated functionally based on BLAST, gene ontology and InterProScan analyses. Coregulated genes were detected using partitional clustering of expression patterns. The results are discussed with emphasis on genes putatively involved in cuticle deposition, cell wall metabolism and sugar transport. The high temporal resolution of the expression patterns presented here reveals finely tuned developmental specialization of individual members of gene families. Moreover, the de novo assembled sweet cherry fruit transcriptome with 7760 full-length protein coding sequences and over 20 000 other, annotated cDNA sequences together with their developmental expression patterns is expected to accelerate molecular research on this important tree fruit crop.

The role of context in image annotation and recommendation

With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to “tag” their photos, however, these techniques are laborious and as a result have been poorly adopted; Sigurbjörnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an image’s appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the “bigger picture” surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an image’s context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the “what, who, where, when and how” of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios. In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections. In part III, we propose three new image retrieval scenarios, namely “visual event summarisation”, “image popularity prediction” and “lifelog summarisation”. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become “popular” (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a user’s “key moments” within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start).

AnnoTALE: Bioinformatics tools for identification, annotation, and nomenclature of TALEs from Xanthomonas genomic sequences

Transcription activator-like effectors (TALEs) are virulence factors, produced by the bacterial plant-pathogen Xanthomonas, that function as gene activators inside plant cells. Although the contribution of individual TALEs to infectivity has been shown, the specific roles of most TALEs, and the overall TALE diversity in Xanthomonas spp. is not known. TALEs possess a highly repetitive DNA-binding domain, which is notoriously difficult to sequence. Here, we describe an improved method for characterizing TALE genes by the use of PacBio sequencing. We present 'AnnoTALE', a suite of applications for the analysis and annotation of TALE genes from Xanthomonas genomes, and for grouping similar TALEs into classes. Based on these classes, we propose a unified nomenclature for Xanthomonas TALEs that reveals similarities pointing to related functionalities. This new classification enables us to compare related TALEs and to identify base substitutions responsible for the evolution of TALE specificities. © 2016, Nature Publishing Group. All rights reserved.

Annotation Pro. Enhancing analyses of linguistic and paralinguistic features in speech

Annotation Pro - a description of techniques, methods implemented in the tool, as well as the list of all built in functionalities and features of the user interface, and usage tips.

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.

Football Video Annotation Based on Player Motion Recognition Using Enhanced Entropy

This paper presents a semi-parametric Algorithm for parsing football video structures. The approach works on a two interleaved based process that closely collaborate towards a common goal. The core part of the proposed method focus perform a fast automatic football video annotation by looking at the enhance entropy variance within a series of shot frames. The entropy is extracted on the Hue parameter from the HSV color system, not as a global feature but in spatial domain to identify regions within a shot that will characterize a certain activity within the shot period. The second part of the algorithm works towards the identification of dominant color regions that could represent players and playfield for further activity recognition. Experimental Results shows that the proposed football video segmentation algorithm performs with high accuracy.

Thematic patterning in English and Spanish: contrastive annotation of a bilingual newspaper corpus for liguistic and computational applications

Thematization is recognized as a fundamental phenomenon in the construction of messages and texts by di erent linguistic schools. This location within a text privileges the elements that guide the reader in the orientation and interpretation of discourse at di erent levels. Thematizing a linguistic unit by locating it in the rst-initial position of a clause, paragraph, or text, confers upon it a special status: a signal of the organizational strategy which characterizes di erent text types playing a role as a variable in the distinction of registers, text types and genres. However, in spite of the importance of the study of thematization for message and textual structuring, to date there are no linguistic studies that have undertook the task of validating its aspects in a comparative manner, either for linguistic or computational purposes. This study, therefore, lls a research gap by implementing a methodology based on contrastive corpus annotation, which allows to empirically validate aspects of the phenomenon of Thematization in English and Spanish, it also seeks to develop a bilingual English-Spanish comparable corpus of newspaper texts automatically annotated with thematic features at clausal and discourse levels. The empirically validated categories (Thematic Field and its elements: Textual Theme, Interpersonal Theme, PreHead and Head) are used to annotate a larger corpus of three newspaper genres news reports, editorials and letters to the editor in terms of thematic choices. This characterization, reveals interesting results, such as the use of genre-speci c strategies in thematic position. In addition, the thesis investigates the possibility to automate the annotation of thematic features in the bilingual corpus through the development of a set of JAVA rules implemented in GATE. It also shows the e cacy of this method in comparison with the manual annotation results...

Program Annotations: Feedback for Students Learning to Program

This paper presents a novel program annotation mechanism which enables students to obtain feedback from tutors on their programs in a far simpler and more efficient way than is possible with, for example, email. A common scenario with beginning students is to email tutors with copies of their malfunctioning programs. Unfortunately the emailed program often bears little resemblance to the program the student has been trying to make work; often it is incomplete, a different version and corrupted. We propose an annotation mechanism enabling students to simply and easily annotate their programs with comments asking for help. Similarly our mechanism enables tutors to view students’ programs and to reply to their comments in a simple and structured fashion. This means students can get frequent and timely feedback on their programs; tutors can provide such feedback efficiently, and hence students’ learning is greatly improved.

Crowd Counting Using Multiple Local Features

In public venues, crowd size is a key indicator of crowd safety and stability. Crowding levels can be detected using holistic image features, however this requires a large amount of training data to capture the wide variations in crowd distribution. If a crowd counting algorithm is to be deployed across a large number of cameras, such a large and burdensome training requirement is far from ideal. In this paper we propose an approach that uses local features to count the number of people in each foreground blob segment, so that the total crowd estimate is the sum of the group sizes. This results in an approach that is scalable to crowd volumes not seen in the training data, and can be trained on a very small data set. As a local approach is used, the proposed algorithm can easily be used to estimate crowd density throughout different regions of the scene and be used in a multi-camera environment. A unique localised approach to ground truth annotation reduces the required training data is also presented, as a localised approach to crowd counting has different training requirements to a holistic one. Testing on a large pedestrian database compares the proposed technique to existing holistic techniques and demonstrates improved accuracy, and superior performance when test conditions are unseen in the training set, or a minimal training set is used.