Shark tagging is used as a source of data for shark migration and behaviors. Different tags can collect temperature, salinity, or depth data points while the shark is in motion. There are a variety of shark tagging methods used to monitor sharks. Of these methods, shark bolting has caused some concern. Shark bolting is the method of drilling holes into the first dorsal fin of pelagic sharks and placing steel screws into the fin to secure a satellite (SAT) tag. Bolted SAT tags cause concern due to the limited ways of removal, high probability of excessive biofouling, infections, and increased drag while swimming in many species of sharks. The objective of this study is to assess if bolting is a non-impact shark tagging method in terms of resulting shark health and behavior and, further, whether it should be considered a humane or inhumane practice by examining the present knowledge of dorsal fin anatomy and functionality.

<p dir="ltr">Proper development of hypersonic vehicles requires knowledge of thermal loads, which are primarily dictated by the turbulent kinetic energy [1]. Accurate measurements of these values require measurements of velocity fluctuations, which are difficult to obtain using conventional seeded velocimetry methods [2]. However, molecular tagging velocimetry methods such as femtosecond laser electronic excitation tagging (FLEET) have been shown to be capable of measuring mean velocity within highly varying flows [3].</p><p dir="ltr">This work extends the available measurements provided by FLEET, through combination with optical thermometry and novel analysis methods of the signal. By performing FLEET velocimetry alongside thermometry, this shows capability to make instantaneous measurements of Mach number within supersonic flows [4]. Additionally, by tracking multiple characteristics of FLEET image signals, the ability to both capture instantaneous velocity fluctuations and improve measurement of mean velocity are demonstrated.</p><p dir="ltr">Furthermore, the uncertainty intrinsic to the analysis of FLEET signals is investigated. This is done using a combination of both classical statistical methods and uncertainty calculation methods commonly used in particle imaging velocimetry [5]. This is necessary to provide the best possible estimate of velocity fluctuations for the validation of computational fluid dynamic (CFD) models of boundary layer heat transfer.</p><p dir="ltr">Beyond simply improving the quality of velocity measurements, frequency analysis tools are developed and extended to analysis of fluid dynamic problems. These tools have been used prior for detecting extreme transitions within a signal [6], but are applied here to demonstrate their ability to detect physics captured within flow fields. These tools show promise in the ability to detect frequency couplings in time and can potentially be implemented to improve current control strategies in the field of fluid dynamics.</p>

In multimedia databases, data are images, audio, video, texts, etc. Research interests in these types of databases have increased in the last decade or so, especially with the advent of the Internet and Semantic Web. Fundamental research issues vary from unified data modelling, retrieval of data items and dynamic nature of updates. The thesis builds on findings in Semantic Web and retrieval techniques and explores novel tagging methods for identifying data items. Tagging systems have become popular which enable the users to add tags to Internet resources such as images, video and audio to make them more manageable. Collaborative tagging is concerned with the relationship between people and resources. Most of these resources have metadata in machine processable format and enable users to use free- text keywords (so-called tags) as search techniques. This research references some tagging systems, e.g. Flicker, delicious and myweb2.0. The limitation with such techniques includes polysemy (one word and different meaning), synonymy (different words and one meaning), different lexical forms (singular, plural, and conjugated words) and misspelling errors or alternate spellings. The work presented in this thesis introduces semantic characterization of web resources that describes the structure and organization of tagging, aiming to extend the existing Multimedia Query using similarity measures to cater for collaborative tagging. In addition, we discuss the semantic difficulties of tagging systems, suggesting improvements in their accuracies. The scope of our work is classified as follows: (i) Increase the accuracy and confidence of multimedia tagging systems. (ii) Increase the similarity measures of images by integrating varieties of measures. To address the first shortcoming, we use the WordNet based on a tagging system for social sharing and retrieval of images as a semantic lingual ontology resource. For the second shortcoming we use the similarity measures in different ways to recognise the multimedia tagging system. Fundamental to our work is the novel information model that we have constructed for our computation. This is based on the fact that an image is a rich object that can be characterised and formulated in n-dimensions, each dimension contains valuable information that will help in increasing the accuracy of the search. For example an image of a tree in a forest contains more information than an image of the same tree but in a different environment. In this thesis we characterise a data item (an image) by a primary description, followed by n-secondary descriptions. As n increases, the accuracy of the search improves. We give various techniques to analyse data and its associated query. To increase the accuracy of the tagging system we have performed different experiments on many images using similarity measures and various techniques from VoI (Value of Information). The findings have shown the linkage/integration between similarity measures and that VoI improves searches and helps/guides a tagger in choosing the most adequate of tags.

Digital images are being created and uploaded online in large numbers and this can be attributed to three main interconnected factors: a change in attitudes towards photography and its role in society; technological advancements in the camera industry; and changes in web technology. Many of these digital images are being uploaded to Flickr, one of the most popular of the new web 2.0 image management and sharing applications. Flickr supports secure storage, sharing, online communities, and tagging. Tagging is intended to aid with the organisation, description, and retrieval of images, and as tagging in Flickr generally relates to personal images (e.g., photographs), the tags assigned are highly subjective. Previous research has investigated motivations to upload and tag images in web 2.0 image management and sharing applications, and types of tags used in web 2.0 image management and sharing applications, and a limited number of studies have attempted to correlate the two, however no research has attempted to correlate the two whilst also taking into account the subjective nature of image tagging. Identifying the discrepancies between why people want to use Flickr, and how they use it can help system designers and users to get the best out of these applications. This thesis compares users’ motivations to upload and tag their images in Flickr with how they tag their images in practice. The study used a quantitative survey methodology consisting of a semi-structured questionnaire to explore user motivations. Tagging practices were investigated via a manual tag classification scheme applied to automatically extracted Flickr tags. The questionnaire results show that Flickr users are primarily motivated to upload their images to Flickr for the purposes of social-communication (i.e., to draw attention to their images for comments and feedback and to express and present aspects of their personality and identity) and for socialorganisation (i.e., so other people can access and view the images uploaded). However, tagging images in Flickr is not associated with the motivation of social-organisation and is instead more closely aligned to social-communication, and self-organisation (i.e., as a way of organising images for personal search and retrieval). Self-communication (i.e., documenting and recording for memory and personal reflection) was not found to be a popular motivation for either uploading or tagging images. Flickr users that are motivated to upload their images for the purposes of self-organisation have the clearest tagging practice and they predominantly use tags that are only meaningful to themselves. Gender, pro account status, number of images, and number of contacts are also strong predictors of tagging practice. However, overall, tagging practice is more closely associated with image content than with the motivation the user has. Although the results show that socialcommunication is the most prominent factor in motivating users to upload their images and in motivating users to tag their images, the findings reveal that users who are motivated to upload their images for the purposes of social-organisation are not using the system to its full potential.

Activation-tagging is a functional genomics technique where strong enhancers are inserted randomly into target genomes to over-activate endogenous genes. Phenotypes of interest can be selected for investigation of genetic factors contributing to the mutant phenotype. From initial screens of a population of activation-tagged potato, a mutant with chocolate-coloured tuber skin has been identified. In this thesis, a novel sequence capture method for identifying T-DNA loci in activation tagged potato was used to characterize chocolate’s single T-DNA insertion locus. Transcriptome analysis of tuber periderm tissue was used to identify major processes occurring in the chocolate mutant. Our data suggest activation of a chitin-binding receptor-like kinase located 65 kb from T-DNA insert may cause activation of immune signaling pathways in chocolate. The present work explores a putative model of transcriptional and cellular responses involved in gain-of-function immune receptor activation. Selectively, these findings illustrate the periderm tissue as an important area of defense charged against biotic and abiotic stresses. Periderm development and anatomy are highly important for tuber storage. Further characterization of potato tuber periderm may contribute knowledge to model periderm systems and have implications for molecular breeding strategies to improve tuber storage quality. / Thesis (Master, Biology) -- Queen's University, 2012-09-27 11:45:16.478

A measurement of the top-pair production cross-section at a centre-of-mass energy of 7 TeV using proton-proton collisions with 36 pb-1 of data collected by CMS at the Large Hadron Collider is presented. The analysis is performed using the nal state that consists of one isolated electron with jets, one of which is required to be identified as being consistent with including the decay of a B hadron. The measured cross-section with three or more selected jets is 169 +/- 13(stat:)+37 +32(sys:)+8 -7(lumi:) pb and 197 +/- 17(stat:)+38 -35(sys:)+9 -8(lumi:) pb for four or more jets. The results are consistent with NLO and approximate NNLO theoretical predictions.

Globally distributed software teams are widespread throughout industry. But finding reliable methods that can properly assess a team's activities is a real challenge. Methods such as surveys and manual coding of activities are too time consuming and are often unreliable. Recent advances in information retrieval and linguistics, however, suggest that automated and/or semi-automated text classification algorithms could be an effective way of finding differences in the communication patterns among individuals and groups. Communication among group members is frequent and generates a significant amount of data. Thus having a web-based tool that can automatically analyze the communication patterns among global software teams could lead to a better understanding of group performance. The goal of this thesis, therefore, is to compare automatic and semi-automatic measures of communication and evaluate their effectiveness in classifying different types of group activities that occur within a global software development project. In order to achieve this goal, we developed a web-based component that can be used to help clean and classify communication activities. The component was then used to compare different automated text classification techniques on various group activities to determine their effectiveness in correctly classifying data from a global software development team project.

Language sample analysis is accepted as the gold standard in child language assessment. Unfortunately it is often viewed as too time consuming for the practicing clinician. Over the last 15 years a great deal of research has been invested in the automated analysis of child language samples to make the process more time efficient. One step in the analysis process may be precoding the sample, as is used in the Systematic Analysis of Language Transcripts (SALT) software. However, a claim has been made (MacWhinney, 2008) that such precoding in fact leads to lower accuracy because of manual coding errors. No data on this issue have been published. The current research measured the accuracy of language samples analyzed with and without SALT precoding. This study also compared the accuracy of current software to an older version called GramCats (Channell & Johnson 1999). The results presented support the use of precoding schemes such as SALT and suggest that the accuracy of automated analysis has improved over time.

The task of tagging, annotating or labelling image content automatically with semantic keywords is a challenging problem. To automatically tag images semantically based on the objects that they contain is essential for image retrieval. In addressing these problems, we explore the techniques developed to combine textual description of images with visual features, automatic region tagging and region-based ontology image retrieval. To evaluate the techniques, we use three corpora comprising: Lonely Planet travel guide articles with images, Wikipedia articles with images and Goats comic strips. In searching for similar images or textual information specified in a query, we explore the unification of textual descriptions and visual features (such as colour and texture) of the images. We compare the effectiveness of using different retrieval similarity measures for the textual component. We also analyse the effectiveness of different visual features extracted from the images. We then investigate the best weight combination of using textual and visual features. Using the queries from the Multimedia Track of INEX 2005 and 2006, we found that the best weight combination significantly improves the effectiveness of the retrieval system. Our findings suggest that image regions are better in capturing the semantics, since we can identify specific regions of interest in an image. In this context, we develop a technique to tag image regions with high-level semantics. This is done by combining several shape feature descriptors and colour, using an equal-weight linear combination. We experimentally compare this technique with more complex machine-learning algorithms, and show that the equal-weight linear combination of shape features is simpler and at least as effective as using a machine learning algorithm. We focus on the synergy between ontology and image annotations with the aim of reducing the gap between image features and high-level semantics. Ontologies ease information retrieval. They are used to mine, interpret, and organise knowledge. An ontology may be seen as a knowledge base that can be used to improve the image retrieval process, and conversely keywords obtained from automatic tagging of image regions may be useful for creating an ontology. We engineer an ontology that surrogates concepts derived from image feature descriptors. We test the usability of the constructed ontology by querying the ontology via the Visual Ontology Query Interface, which has a formally specified grammar known as the Visual Ontology Query Language. We show that synergy between ontology and image annotations is possible and this method can reduce the gap between image features and high-level semantics by providing the relationships between objects in the image. In this thesis, we conclude that suitable techniques for image retrieval include fusing text accompanying the images with visual features, automatic region tagging and using an ontology to enrich the semantic meaning of the tagged image regions.

This thesis explores the development of parallel treebanks, collections of language data consisting of texts and their translations, with syntactic annotation and alignment, linking words, phrases, and sentences to show translation equivalence. We describe the semi-manual annotation of the SMULTRON parallel treebank, consisting of 1,000 sentences in English, German and Swedish. This description is the starting point for answering the first of two questions in this thesis. What issues need to be considered to achieve a high-quality, consistent,parallel treebank? The units of annotation and the choice of annotation schemes are crucial for quality, and some automated processing is necessary to increase the size. Automatic quality checks and evaluation are essential, but manual quality control is still needed to achieve high quality. Additionally, we explore improving the automatically created annotation for one language, using information available from the annotation of the other languages. This leads us to the second of the two questions in this thesis. Can we improve automatic annotation by projecting information available in the other languages? Experiments with automatic alignment, which is projected from two language pairs, L1–L2 and L1–L3, onto the third pair, L2–L3, show an improvement in precision, in particular if the projected alignment is intersected with the system alignment. We also construct a test collection for experiments on annotation projection to resolve prepositional phrase attachment ambiguities. While majority vote projection improves the annotation, compared to the basic automatic annotation, using linguistic clues to correct the annotation before majority vote projection is even better, although more laborious. However, some structural errors cannot be corrected by projection at all, as different languages have different wording, and thus different structures. / I denna doktorsavhandling utforskas skapandet av parallella trädbanker. Dessa är språkliga data som består av texter och deras översättningar, som har märkts upp med syntaktisk information samt länkar mellan ord, fraser och meningar som motsvarar varandra i översättningarna. Vi beskriver den delvis manuella uppmärkningen av den parallella trädbanken SMULTRON, med 1.000 engelska, tyska och svenska meningar. Denna beskrivning är utgångspunkt för att besvara den första av två frågor i avhandlingen. Vilka frågor måste beaktas för att skapa en högkvalitativ parallell trädbank? De enheter som märks upp samt valet av uppmärkningssystemet är viktiga för kvaliteten, och en viss andel automatisk bearbetning är nödvändig för att utöka storleken. Automatiska kvalitetskontroller och automatisk utvärdering är av vikt, men viss manuell granskning är nödvändig för att uppnå hög kvalitet. Vidare utforskar vi att använda information som finns i uppmärkningen, för att förbättra den automatiskt skapade uppmärkningen för ett annat språk. Detta leder oss till den andra av de två frågorna i avhandlingen. Kan vi förbättra automatisk uppmärkning genom att överföra information som finns i de andra språken? Experimenten visar att automatisk länkning som överförs från två språkpar, L1–L2 och L1–L3, till det tredje språkparet, L2–L3, får förbättrad precision, framför allt för skärningspunkten mellan den överförda länkningen och den automatiska länkningen. Vi skapar även en testsamling för experiment med överföring av uppmärkning för att lösa upp strukturella flertydigheter hos prepositionsfraser. Överföring enligt majoritetsprincipen förbättrar uppmärkningen, jämfört med den grundläggande automatiska uppmärkningen, men att använda språkliga ledtrådar för att korrigera uppmärkningen innan majoritetsöverföring är ännu bättre, om än mer arbetskrävande. Vissa felaktiga strukturer kan dock inte korrigeras med hjälp av överföring, eftersom de olika språken använder olika formuleringar, och därmed har olika strukturer.