Pure test collection 2

Permanent URI for this collectionhttps://dspace-test.anu.edu.au/handle/1885/733712421

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Now showing 1 - 20 of 121

Access status: Open Access ,
How to access the common bile duct
(2021) Aabakken, Lars; Bhat, Purnima
Biliary access is a prerequisite to all endoscopic interventions in the biliary tract. Successful cannulation of the papilla of Vater is the predominant challenge for the majority of endoscopists training in endoscopic retrograde cholangiopancreaticography (ERCP), and the skills required for success differ substantially from those of regular luminal endoscopy. This paper reviews some of the key elements to successful biliary cannulation, a range of options for problem-solving when cannulation is difficult, and some tips and tricks in select special situations as well. The techniques are described, and available evidence is reviewed.
Access status: Open Access ,
How to access the common bile duct
(2021) Aabakken, Lars; Bhat, Purnima
Biliary access is a prerequisite to all endoscopic interventions in the biliary tract. Successful cannulation of the papilla of Vater is the predominant challenge for the majority of endoscopists training in endoscopic retrograde cholangiopancreaticography (ERCP), and the skills required for success differ substantially from those of regular luminal endoscopy. This paper reviews some of the key elements to successful biliary cannulation, a range of options for problem-solving when cannulation is difficult, and some tips and tricks in select special situations as well. The techniques are described, and available evidence is reviewed.
Access status: Open Access ,
How to access the common bile duct
(2021) Aabakken, Lars; Bhat, Purnima
Biliary access is a prerequisite to all endoscopic interventions in the biliary tract. Successful cannulation of the papilla of Vater is the predominant challenge for the majority of endoscopists training in endoscopic retrograde cholangiopancreaticography (ERCP), and the skills required for success differ substantially from those of regular luminal endoscopy. This paper reviews some of the key elements to successful biliary cannulation, a range of options for problem-solving when cannulation is difficult, and some tips and tricks in select special situations as well. The techniques are described, and available evidence is reviewed.
Access status: Metadata only ,
3D box proposals from a single monocular image of an indoor scene
(2018) Zhuo, Wei; Salzmann, Mathieu; He, Xuming; Liu, Miaomiao
Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth-based 3D proposal generation.
Access status: Metadata only ,
3D box proposals from a single monocular image of an indoor scene
(2018) Zhuo, Wei; Salzmann, Mathieu; He, Xuming; Liu, Miaomiao
Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth-based 3D proposal generation.
Access status: Metadata only ,
3D box proposals from a single monocular image of an indoor scene
(2018) Zhuo, Wei; Salzmann, Mathieu; He, Xuming; Liu, Miaomiao
Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth-based 3D proposal generation.
Access status: Open Access ,
3D box proposals from a single monocular image of an indoor scene
(2018) Zhuo, Wei; Salzmann, Mathieu; He, Xuming; Liu, Miaomiao
Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth-based 3D proposal generation.
Access status: Metadata only ,
3D box proposals from a single monocular image of an indoor scene
(2018) Zhuo, Wei; Salzmann, Mathieu; He, Xuming; Liu, Miaomiao
Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth-based 3D proposal generation.
Access status: Open Access ,
3D box proposals from a single monocular image of an indoor scene
(2018) Zhuo, Wei; Salzmann, Mathieu; He, Xuming; Liu, Miaomiao
Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth-based 3D proposal generation.
Access status: Open Access ,
3D box proposals from a single monocular image of an indoor scene
(2018) Zhuo, Wei; Salzmann, Mathieu; He, Xuming; Liu, Miaomiao
Modern object detection methods typically rely on bounding box proposals as input. While initially popularized in the 2D case, this idea has received increasing attention for 3D bounding boxes. Nevertheless, existing 3D box proposal techniques all assume having access to depth as input, which is unfortunately not always available in practice. In this paper, we therefore introduce an approach to generating 3D box proposals from a single monocular RGB image. To this end, we develop an integrated, fully differentiable framework that inherently predicts a depth map, extracts a 3D volumetric scene representation and generates 3D object proposals. At the core of our approach lies a novel residual, differentiable truncated signed distance function module, which, accounting for the relatively low accuracy of the predicted depth map, extracts a 3D volumetric representation of the scene. Our experiments on the standard NYUv2 dataset demonstrate that our framework lets us generate high-quality 3D box proposals and that it outperforms the two-stage technique consisting of successively performing state-of-the-art depth prediction and depth-based 3D proposal generation.
Access status: Open Access ,
Halcyon - In Nature
(2019) Duck-Chong, Jenny; Sheldon, Jane; Walker, Sally; Gartner, Geoffrey; Gorbach, Vladimir; Brigden, Tim; Raval, Maharshi
In a concert of Contemporary Art Chamber Music, In Nature featured four world premieres by Larry Sitsky, Elena Kats Chernin, Melissa Hui and Madeleine Isaksson, the Australian premiere of Andrew Ford's new song cycle, "Nature" and Matthew Hindson's engaging "Insect Songs". The works, drawn from three continents and relationships old and new, featured texts and poetry from Australia, the UK, Finland, Sweden, France, Japan and China, performed by mezzo soprano Jenny Duck-Chong, soprano Jane Sheldon, flautist Sally Walker, cellist Geoffrey Gartner, guitarist Vladimir Gorbach, percussionist Tim Brigden and tabla player Maharshi Raval. Program: Andrew Ford "Nature", Elena Kats-Chernin's "Moondust" (Commissioned by flautist Sally Walker and dedicated to Dr Philip Spradbery, the renowned entomologist, CSIRO scientist and passionate environmental advocate) Madeleine Isaksson "Blad âˆšâˆ‚ver blad", Larry Sitsky "The Bamboo Flute" Catherine Milliken "Kazuko" Nigel Butterley "Nature changes", Melissa Hui "Two songs on Poems from Longfellow".
Access status: Open Access ,
Drifter
(2016) Whitelaw, Mitchell
Drifter is a multilayered portrait of the Murrumbidgee river system, made out of data. Historic images, newspaper articles, scientific observations and digital maps; tens of thousands of data points come together in three ever-changing views.
Access status: Open Access ,
Celestial Empire Life in China, 1644-1911
(2016) Woolley, Nathan
Experience 300 years of Chinese culture and tradition from two of the worlds great libraries. From life at court to life in the villages and fields, glimpse the world of Chinas last imperial dynasty and its wealth of cultural tradition. See exquisite and precious objects from the National Library of China. Marvel at drawings and plans for Beijings iconic palaces from the Yangshi Lei Archives, listed on UNESCOs Memory of the World Register in 2007 and never before seen in Australia. Beautiful maps, books and prints come alive in ornate detail. Discover our acclaimed Chinese Collection, including rare items from the London Missionary Society that offer a unique view of early western impressions of China. An exclusive exhibition in partnership with the National Library of China.
Access status: Open Access ,
After skiing my brother built a snowman with my father, I was jealous, so I hacked off its head with a shovel
(2015) Loughhead, Anja
two skii's a pole and an old photograph.
Access status: Open Access ,
A magic lantern show demonstrating scientific truths, Imagineers in Circus and Science: Scientific Knowledge and Creative Imagination
(2018) Jolly, Martyn; deCourcy, Elisa; Hunter, Alexander; McMahon, Paul
A magic lantern show demonstrating scientific truths, developed as part of the ARC DP16 Heritage in the Limelight: The Magic lantern in Australia and the World, with Dr Elisa deCourcy and Paul mCMahon, ANU, and Ben Keogh, for the Imagineers in Circus and Science: Scientific Knowledge and Creative Imagination conference, Australian National University, 3 5 April, and for the Mount Stromlo Open Night, Mount Stromlo Observatory, 20 April.
Access status: Open Access ,
Doing Diversity
(2015) Halse, Christine; Mansouri, Fethi; Moss, Julianne; Paradies, Yin; Oâ€™Mara, Joanne; Arber, Ruth; Denson, Nida; Arrowsmith, Colin; Priest, Naomi; Charles, Claire; Cloonan, Anne; Fox, Brandi; Hartung, Catherine; Mahoney, Caroline; Ohi, Sarah; Ovenden, Georgia; Shaw, Gary; Wright, Lesley
The study found that the most interculturally capable students attended schools that: i) have a strong, explicit and well-established culture of racial, religious and cultural equality in all areas of its operations; and ii) actively integrated the knowledge, attitudes and skills required for respectful engagement with diversity across all members of the school community, including students, teachers and parents (see Chapters 2, 7).
Access status: Open Access ,
Malware in spam email
(2020-09-22) Broadhurst, Roderic; Trivedi, Harshit
A 10 percent sample of a 2016 dataset of 25.76 million spam emails provided by the Australian Communications and Media Authority's Spam Intelligence Database was scanned for malware using the VirusTotal Malware database. Nearly one in 10 (9.9% or 255,222) emails were identified as malware compromised and, similarly, 9.9 percent were identified as inactive. Of the compromised URL sites, nearly one-third (31.8% or 81,176) could be further classified as phishing (58.4%) or trojan-compromised URLs (40.6%) or dedicated malicious websites (1%). All 115,025 unique file attachments found in the entire sample (0.5% of all spam) were also scanned and 31.4 percent (36,405) were compromised with various forms of malware. The majority of compromised attachments were found in images (55.6%), followed by PDFs (15.0%) and binary files (10.0%). Various trojans and ransomware were the most common malware, and these and others identified in the sample are described.
Access status: Metadata only ,
The Wisdom of the Gaming Crowd
(2020) Jeffrey, Robert; Ji, Fan; Bian, Pengze; Kyburz, Penny
In this paper, we report on three projects in which we are applying natural language processing techniques to analyse video game reviews. We present our process, techniques, and progress for extracting and analysing player reviews from the gaming platform Steam. Analysing video game reviews presents great opportunity to assist players to choose games to buy, to help developers to improve their games, and to aid researchers in further understanding player experience in video games. With limited previous research that specifically focuses on game reviews, we aim to provide a baseline for future research to tackle some of the key challenges. Our work shows promise for using natural language processing techniques to automatically identify features, sentiment, and spam in video game reviews on the Steam platform.
Access status: Open Access ,
Illicit firearms and other weapons on darknet markets
(2021) Broadhurst, Roderic; Jiang, Chuxuan; Ball, Matthew; Foye, Jack
This study provides a snapshot of the availability of weapons across eight omnibus or High Street and 12 specialist darknet or illicit cryptomarkets between July and December 2019. Overall, 2,124 weapons were identified, of which 11 percent were found on niche markets. On all markets, weapons for sale included 1,497 handguns, 218 rifles, 41 submachine guns and 34 shotguns. Also available were ammunition (n=79), explosives (n=37) and accessories such as silencers (n=24). Omnibus markets also sold other weapons (n=70) such as tasers, pepper spray and knives, and digital products (n=112), mostly DIY weapon manuals, as well as chemical, biological, nuclear and radiological weapons (n=12). The data allowed for estimates of the cost of weapons and some description of the 215 vendors identified, 18 (8.4%) of whom were active across more than one market.
Access status: Open Access ,
THE GOOD, THE BAD AND THE UNLIKELY: AUSTRALIAN'S PRIME MINISTERS: FROM BARTON TO ALBANESE
(2023) MacCALLUM, Mungo; Bongiorno, Frank

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