235 resultados para Collecting of accounts.


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A large number of methods have been published that aim to evaluate various components of multi-view geometry systems. Most of these have focused on the feature extraction, description and matching stages (the visual front end), since geometry computation can be evaluated through simulation. Many data sets are constrained to small scale scenes or planar scenes that are not challenging to new algorithms, or require special equipment. This paper presents a method for automatically generating geometry ground truth and challenging test cases from high spatio-temporal resolution video. The objective of the system is to enable data collection at any physical scale, in any location and in various parts of the electromagnetic spectrum. The data generation process consists of collecting high resolution video, computing accurate sparse 3D reconstruction, video frame culling and down sampling, and test case selection. The evaluation process consists of applying a test 2-view geometry method to every test case and comparing the results to the ground truth. This system facilitates the evaluation of the whole geometry computation process or any part thereof against data compatible with a realistic application. A collection of example data sets and evaluations is included to demonstrate the range of applications of the proposed system.

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Once known as Crabb’s Creek, Katarapko Creek is a small anabranch of the Murray River, located between the towns of Berri and Loxton in the Riverland region of South Australia. Its 9 000 hectare grey clay floodplain is covered with blackbox, saltbush and lignum. The creek’s horseshoe lagoons, marshes and islands are the traditional lands of the Meru peoples. They fished the creek and surrounding waterways and hunted the wetlands. The ebb and flow of water guided their travels and featured in their stories. The Meru have seen their land and the river change...

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The Goulburn River’s cold, clear waters rush westward down from the steep hills and mountains of the Great Dividing Range toward Seymour. The river then turns northward and meanders through hills and plains until the river meets the Murray upstream of Echuca. These are the traditional lands of the Taungurung, Bangerang and Yorta Yorta peoples. However, the Goulburn River today is not the river the Taungurung, Bangerang and Yorta Yorta once knew and fished...

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The Upper Murrumbidgee cuts its way through the Snowy Mountains in south‐eastern New South Wales, snaking its way south, then turning north before dropping into the lowland and heading west to join the Murray downstream of Swan Hill. The Upper ‘Bidgee floodplain is only a couple of hundred metres wide, a stark contrast to the kilometres‐wide floodplains in other parts of the Murray‐ Darling Basin. When the floods come, they come up quickly and roar through the narrow valleys. These are the traditional lands of the Ngunnawal and Ngarigo peoples. They fished the river and surrounding waterways and hunted the wetlands. The seasonal rise and fall of the water guided their travels and featured in their stories. The Ngunnawal and Ngarigo people have seen their land and the river change...

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The Murray River is the boundary between NSW and Victoria. The river both defines boundaries and unites them with the waters that sustain townships, irrigation and the floodplain forests, including the 70 000ha of the iconic Barmah and Millewa Forest. The river and its floodplain are the traditional lands of the Yorta Yorta and Bangerang people. The Murray is a very different river to the one the Yorta Yorta and Bangerang peoples once knew and fished...

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The Lower Darling River and Great Darling Anabranch are located in south west New South Wales. Muddy waters meander over the grey soil floodplains past red dunes, spiky saltbush and gnarled red gums. These are the traditional lands of the Paakintji people. But the land and the river are no longer what the Paakintji once knew and fished...

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To say ‘Back o’ Bourke’ means ‘miles from anywhere’ to most Australians, however the Barwon and Darling Rivers that pass by the townships of Brewarrina and Bourke, respectively, are at the heart of the Murray‐Darling Basin. These are the traditional lands of the Ngiyampaa, Murawari and Yuwalaraay peoples (refer Aboriginal language groups in the Bringing back the fish section at the back of this booklet). They fished the river and surrounding waterways and hunted the wetlands. The Ngiyampaa, Murawari and Yuwalaraay people have seen their land and the rivers change...

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The Ovens River rises in the Victorian Alps where it is linked to significant freshwater meadows and marshes. It flows past Harrietville, Bright, Myrtleford and Wangaratta where it is joined by the King River on its way to meet the Murray near the top of Lake Mulwala. These the traditional lands of the Bangerang people and their neighbours the Taungurung and Yorta Yorta peoples. They have fished the river and surrounding waterways and hunted the wetlands. The ebb and flow of water guided their travels and featured in their stories. The Bangerang, Taungurung and Yorta Yorta have seen their land and the river change...

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After gathering water from 23 river valleys, the Murray empties into Lakes Alexandrina and Albert before making its way to the Coorong and out the Murray Mouth to Encounter Bay in South Australia. The entire Murray‐Darling Basin is upstream. Everything that happens there affects what goes on here...

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Much of what is written about digital technologies in preschool contexts focuses on young children’s acquisition of skills rather than their meaning-making during use of technologies. In this paper, we consider how the viewing of a YouTube video was used by a teacher and children to produce shared understandings about it. Conversation analysis of talk and interaction during the viewing of the video establishes some of the ways that individual accounts of events were produced for others and then endorsed as shared understandings. The analysis establishes how adults and children made use of verbal and embodied actions during interactions to produce shared understandings of the YouTube video, the events it recorded and written commentary about those events

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Since the inception of the first Joint Registry in Sweden in 1979, many countries including Finland, Norway, Denmark, Australia, New Zealand, Canada, Scotland, England and Wales now have more than 10 years experience and data, and are collecting data on more than 90% of the procedures performed nationally. There are also Joint Registries in Romania, Slovakia, Slovenia, Croatia, Hungary, France, Germany, Switzerland, Czech Republic, Italy, Austria and Portugal, and work is ongoing to develop a Joint Registry in the US...

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This paper describes the experimental evaluation of a novel Autonomous Surface Vehicle capable of navigating complex inland water reservoirs and measuring a range of water quality properties and greenhouse gas emissions. The 16 ft long solar powered catamaran is capable of collecting water column profiles whilst in motion. It is also directly integrated with a reservoir scale floating sensor network to allow remote mission uploads, data download and adaptive sampling strategies. This paper describes the onboard vehicle navigation and control algorithms as well as obstacle avoidance strategies. Experimental results are shown demonstrating its ability to maintain track and avoid obstacles on a variety of large-scale missions and under differing weather conditions, as well as its ability to continuously collect various water quality parameters complimenting traditional manual monitoring campaigns.

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Numerous research studies have evaluated whether distance learning is a viable alternative to traditional learning methods. These studies have generally made use of cross-sectional surveys for collecting data, comparing distance to traditional learners with intent to validate the former as a viable educational tool. Inherent fundamental differences between traditional and distance learning pedagogies, however, reduce the reliability of these comparative studies and constrain the validity of analyses resulting from this analytical approach. This article presents the results of a research project undertaken to analyze expectations and experiences of distance learners with their degree programs. Students were given surveys designed to examine factors expected to affect their overall value assessment of their distance learning program. Multivariate statistical analyses were used to analyze the correlations among variables of interest to support hypothesized relationships among them. Focusing on distance learners overcomes some of the limitations with assessments that compare off- and on-campus student experiences. Evaluation and modeling of distance learner responses on perceived value for money of the distance education they received indicate that the two most important influences are course communication requirements, which had a negative effect, and course logistical simplicity, which revealed a positive effect. Combined, these two factors accounted for approximately 47% of the variability in perceived value for money of the educational program of sampled students. A detailed focus on comparing expectations with outcomes of distance learners complements the existing literature dominated by comparative studies of distance and nondistance learners.

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"This invaluable companion to The Mammals of Australia is intended to be taken out into the field and used in conjunction with the more comprehensive volume. Genuinely practical in the outdoors, the book includes accounts of 389 species and newly developed, comprehensive identification keys. The Field Companion is introduced by a Mammal Distribution Matrix, which provides a classified checklist of all mammals in Australia (including those extinguished since European settlement) and the distribution of extant species in each state and territory. Species accounts provide initial differentiation, and include notes on identification, size, abundance, habitat and federal list/status, photograph and distribution map, as well as key references, which provide quick access to all relevant state identification keys in the Field Companion and to the longer entry in The Mammals of Australia. The authors have developed separate keys, illustrated with detailed drawings and maps, for the six states and the Northern Territory, to simplify the identification process and allow the reader to confidently separate all mammal species, no matter how subtle the differences. With the addition of these identification keys, this book becomes more than a field guide - although it is intended primarily to be used outdoors - and allows the user to finish identification based on more obscure characteristics, which is an advantage for some hard-to-identify species groups."--Libraries Australia

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Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.