1,721,394 research outputs found
Carson, K G, NX9186
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/376173Surname: CARSON
Given Name(s) or Initials: K G
Military Service Number or Last Known Location: NX9186
Missing, Wounded and Prisoner of War Enquiry Card Index Number: 3939188736
Item: [2016.0049.08481] "Carson, K G, NX9186
Dr. Carson K. Eoyang, Biography
Full text linkDr. Eoyang retired as the Chancellor of National Intelligence University and was a former Associate Provost at the Naval Postgraduate School. He has served in the Office of Science and Technology Policy in the White House, the Chief Training Officer for the FAA, and the Director of Training forNASA. He was awarded the Presidential Rank of Meritorious Executive in 1993 after serving on the Vice President Gore's National Performance Review. He obtained his B.S. from MIT, his M.B.A. from Harvard and Ph.D. from Stanford
Compliance with the British Thoracic Society guidelines in the management of pneumothoraces
No full textOral presentation TO 081Cheng J, Sarkar P, Carson K, Brinn M, Smith
Computing theoretically-sound upper bounds to expected support for frequent pattern mining problems over uncertain big data
No full textFrequent pattern mining aims to discover implicit, previously unknown, and potentially useful knowledge in the form of sets of frequently co-occurring items, events, or objects. To mine frequent patterns from probabilistic datasets of uncertain data, where each item in a transaction is usually associated with an existential probability expressing the likelihood of its presence in that transaction, the UF-growth algorithm captures important information about uncertain data in a UF-tree structure so that expected support can be computed for each pattern. A pattern is considered frequent if its expected support meets or exceeds the user-specified threshold. However, a challenge is that the UF-tree can be large. To handle this challenge, several algorithms use smaller trees such that upper bounds to expected support can be computed. In this paper, we examine these upper bounds, and determine which ones provide tighter upper bounds to expected support for frequent pattern mining of uncertain big data
Knowledge Discovery from Social Graph Data
No full textAbstractHigh volumes of a wide variety of valuable data can be easily collected and generated from a broad range of data sources of different veracities at a high velocity. In the current era of big data, many traditional data management and analytic approaches may not be suitable for handling the big data due to their well-known 5V's characteristics. Over the past few years, several systems and applications have developed to use cluster, cloud or grid computing to manage and analyze big data so as to support data science (e.g., knowledge discovery and data mining). In this paper, we present a knowledge-based system for social network analysis so as to support big data mining of interesting patterns from big social networks that are represented as graphs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Data analytics on the board game Go for the discovery of interesting sequences of moves in joseki
Full text linkData analytics on the board game Go for the discovery of interesting sequences of moves in josek
Physical training for asthma: a Cochrane systematic review
No full textOral presentations TO 078Brinn M, Carson K, Chandratilleke M, Picot J, Esterman A, Smith
Prolonged antibiotics for non-cystic fibrosis purulent bronchiectasis in children and adults: a Cochrane review
No full textOral presentation TO 054Hnin K, Nguyen C, Carson K, Evans D, Greenstone M, Smith
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