Listening to late discovery adoption and donor offspring stories : adoption, ethics and implications for contemporary donor insemination practices

For most of the 20th Century a ‘closed’ system of adoption was practised throughout Australia and other modern Western societies. This ‘closed’ system was characterised by sealed records; amended birth certificates to conceal the adoption, and prohibited contact with all biological family. Despite claims that these measures protected these children from the taint of illegitimacy the central motivations were far more complex, involving a desire to protect couples from the stigma of infertility and to provide a socially acceptable family structure (Triseliotis, Feast, & Kyle, 2005; Marshall & McDonald, 2001). From the 1960s significant evidence began to emerge that many adopted children and adults were experiencing higher incidences of psychological difficulties, characterised by problems with psychological adjustment, building self-esteem and forming a secure personal identity. These difficulties became grouped under the term ‘genealogical bewilderment’. As a result, new policies and practices were introduced to try to place the best interests of the child at the forefront. These changes reflected new understandings of adoption; as not only an individual process but also as a social and relational process that continues throughout life. Secrecy and the withholding of birth information are now prohibited in the overwhelming majority of all domestic adoptions processed in Australia (Marshall & McDonald, 2001). One little known consequence of this ‘closed’ system of adoption was the significant number of children who were never told of their adoptive status. As a consequence, some have discovered or had this information disclosed to them, as adults. The first study that looked at the late discovery of genetic origins experiences was conducted by the Post Adoption Resource Centre in New South Wales in 1999. This report found that the participants in their study expressed feelings of disbelief, confusion, anger, sorrow and loss. Further, the majority of participants continued to struggle with issues arising from this intentional concealment of their genetic origins (Perl & Markham, 1999). A second and more recent study (Passmore, Feeney & Foulstone, 2007) looked at the issue of secrecy in adoptive families as part of a broader study of 144 adult adoptees. This study found that secrecy and/or lies or misinformation on the part of adoptive parents had negative effects on both personal identity and relationships with others. The authors noted that those adoptees who found out about their adoption as adults were ‘especially likely to feel a sense of betrayal’ (p.4). Over recent years, stories of secrecy and late discovery have also started to emerge from sperm donor conceived adults (Spencer, 2007; Turner & Coyle, 2000). Current research evidence shows that although a majority of couples during the donor assisted conception process indicate that they intend to tell the offspring about their origins, as many as two-thirds or more of couples continue to withhold this information from their children (Akker, 2006; Gottlieb, A. McWhinnie, 2001; Salter-Ling, Hunter, & Glover, 2001). Why do they keep this secret? Infertility involves a range of complex factors that are often left unresolved or poorly understood by those choosing insemination by donor as a form of family building (Schaffer, J. A., & Diamond, R., 1993). These factors may only impact after the child is born, when resemblance talk becomes most pronounced. Resemblance talk is an accepted form of public discourse and a social convention that legitimises the child as part of the family and is part of the process of constructing the child’s identity within the family. Couples tend to become focused on resemblance as this is where they feel most vulnerable, and the lack of resemblance to the parenting father may trigger his sense of loss (Becker, Butler, & Nachtigall, 2005).

Beyond discovery : a case study of teacher interaction, young children and computer tasks

The integration of computer technologies into everyday classroom life continues to provide pedagogical challenges for school systems, teachers and administrators. Data from an exploratory case study of one teacher and a multiage class of children in the first years of schooling in Australia show that when young children are using computers for set tasks in small groups, they require ongoing support from teachers, and to engage in peer interactions that are meaningful and productive. Classroom organization and the nature of teacher-child talk are key factors in engaging children in set tasks and producing desirable learning and teaching outcomes.

Vision only pose estimation and scene reconstruction on airborne platforms

We aim to demonstrate unaided visual 3D pose estimation and map reconstruction using both monocular and stereo vision techniques. To date, our work has focused on collecting data from Unmanned Aerial Vehicles, which generates a number of significant issues specific to the application. Such issues include scene reconstruction degeneracy from planar data, poor structure initialisation for monocular schemes and difficult 3D reconstruction due to high feature covariance. Most modern Visual Odometry (VO) and related SLAM systems make use of a number of sensors to inform pose and map generation, including laser range-finders, radar, inertial units and vision [1]. By fusing sensor inputs, the advantages and deficiencies of each sensor type can be handled in an efficient manner. However, many of these sensors are costly and each adds to the complexity of such robotic systems. With continual advances in the abilities, small size, passivity and low cost of visual sensors along with the dense, information rich data that they provide our research focuses on the use of unaided vision to generate pose estimates and maps from robotic platforms. We propose that highly accurate (�5cm) dense 3D reconstructions of large scale environments can be obtained in addition to the localisation of the platform described in other work [2]. Using images taken from cameras, our algorithm simultaneously generates an initial visual odometry estimate and scene reconstruction from visible features, then passes this estimate to a bundle-adjustment routine to optimise the solution. From this optimised scene structure and the original images, we aim to create a detailed, textured reconstruction of the scene. By applying such techniques to a unique airborne scenario, we hope to expose new robotic applications of SLAM techniques. The ability to obtain highly accurate 3D measurements of an environment at a low cost is critical in a number of agricultural and urban monitoring situations. We focus on cameras as such sensors are small, cheap and light-weight and can therefore be deployed in smaller aerial vehicles. This, coupled with the ability of small aerial vehicles to fly near to the ground in a controlled fashion, will assist in increasing the effective resolution of the reconstructed maps.

Privacy v IP Litigation : preliminary third party discovery on the Internet

The enforcement of Intellectual Property rights poses one of the greatest current threats to the privacy of individuals online. Recent trends have shown that the balance between privacy and intellectual property enforcement has been shifted in favour of intellectual property owners. This article discusses the ways in which the scope of preliminary discovery and Anton Piller orders have been overly expanded in actions where large amounts of electronic information is available, especially against online intermediaries (service providers and content hosts). The victim in these cases is usually the end user whose privacy has been infringed without a right of reply and sometimes without notice. This article proposes some ways in which the delicate balance can be restored, and considers some safeguards for user privacy. These safeguards include restructuring the threshold tests for discovery, limiting the scope of information disclosed, distinguishing identity discovery from information discovery, and distinguishing information preservation from preliminary discovery.

Who is the corporate entrepreneur? Insights from opportunity discovery and Creation Theory

Recent research has begun to address and even compare nascent entrepreneurship and nascent corporate entrepreneurship. An opportunity based view holds great potential to integrate both streams of research, but also presents challenges in how we define corporate entrepreneurship. We extend (corporate) entrepreneurship literature to the opportunity identification phase by providing a framework to classify different types of corporate entrepreneurship. Through analysis of a large dataset on nascent (corporate) entrepreneurship (PSEDII) we show that these corporate entrepreneurs differ largely from each other in terms of human capital. Prior studies have indicated that independent and corporate entrepreneurs pursue different types of opportunities and utilize different strategies. Our findings from the opportunity identification phase challenge those differences and seem to indicate a difference between the opportunities corporate entrepreneurs identify versus the opportunities they exploit.

Discovery & effective use of quality association rules in multi-level datasets

In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the knowledge within is not immediately accessible, but rather has to be mined and extracted. This requires automated tools and they need to be effective and efficient. Association rule mining is one approach to obtaining knowledge stored with datasets / databases which includes frequent patterns and association rules between the items / attributes of a dataset with varying levels of strength. However, this is also association rule mining’s downside; the number of rules that can be found is usually very big. In order to effectively use the association rules (and the knowledge within) the number of rules needs to be kept manageable, thus it is necessary to have a method to reduce the number of association rules. However, we do not want to lose knowledge through this process. Thus the idea of non-redundant association rule mining was born. A second issue with association rule mining is determining which ones are interesting. The standard approach has been to use support and confidence. But they have their limitations. Approaches which use information about the dataset’s structure to measure association rules are limited, but could yield useful association rules if tapped. Finally, while it is important to be able to get interesting association rules from a dataset in a manageable size, it is equally as important to be able to apply them in a practical way, where the knowledge they contain can be taken advantage of. Association rules show items / attributes that appear together frequently. Recommendation systems also look at patterns and items / attributes that occur together frequently in order to make a recommendation to a person. It should therefore be possible to bring the two together. In this thesis we look at these three issues and propose approaches to help. For discovering non-redundant rules we propose enhanced approaches to rule mining in multi-level datasets that will allow hierarchically redundant association rules to be identified and removed, without information loss. When it comes to discovering interesting association rules based on the dataset’s structure we propose three measures for use in multi-level datasets. Lastly, we propose and demonstrate an approach that allows for association rules to be practically and effectively used in a recommender system, while at the same time improving the recommender system’s performance. This especially becomes evident when looking at the user cold-start problem for a recommender system. In fact our proposal helps to solve this serious problem facing recommender systems.

Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

Entrepreneurial discovery and exploitation processes : sequence or symbiosis?

This study examined the effect that temporal order within the entrepreneurial discovery-exploitation process has on the outcomes of venture creation. Consistent with sequential theories of discovery-exploitation, the general flow of venture creation was found to be directed from discovery toward exploitation in a random sample of nascent ventures. However, venture creation attempts which specifically follow this sequence derive poor outcomes. Moreover, simultaneous discovery-exploitation was the most prevalent temporal order observed, and venture attempts that proceed in this manner more likely become operational. These findings suggest that venture creation is a multi-scale phenomenon that is at once directional in time, and simultaneously driven by symbiotically coupled discovery and exploitation.

Mining positive and negative patterns for relevance feature discovery

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.

Mesoporous bioactive glasses as drug delivery and bone tissue regeneration platforms

The use of mesoporous bioactive glasses (MBG) for drug delivery and bone tissue regeneration has grown significantly over the past 5 years. In this review, we highlight the recent advances made in the preparation of MBG particles, spheres, fibers and scaffolds. The advantages of MBG for drug delivery and bone scaffold applications are related to this material’s well-ordered mesopore channel structure, superior bioactivity, and the application for the delivery of both hydrophilic and hydrophobic drugs. A brief forward-looking perspective on the potential clinical applications of MBG in regenerative medicine is also discussed.

Online discovery of similarity mappings

We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated with the chosen assignment, and the goal is to minimize the cumulative loss of these choices relative to the best map on the entire sequence. Even though the offline problem of finding the best map is provably hard, we show that there is an equivalent online approximation algorithm, Randomized Map Prediction (RMP), that is efficient and performs nearly as well. While drawing upon results from the "Online Prediction with Expert Advice" setting, we show how RMP can be utilized as an online approach to several standard batch problems. We apply RMP to online clustering as well as online feature selection and, surprisingly, RMP often outperforms the standard batch algorithms on these problems.

Beam steering with high front-to-back ratio and high directivity on small platforms using decoupled antenna pairs

Beam steering with high front-to-back ratio and high directivity on a small platform is proposed. Two closely spaced antenna pairs with eigenmode port decoupling are used as the basic radiating elements. Two orthogonal radiation patterns are obtained for each antenna pair. High front-to-back ratio and high directivity are achieved by combining the two orthogonal radiation patterns. With an infinite groundplane, a front-to-back ratio of 21 dB with a directivity of 9.8 dB can be achieved. Beam steering, at the expense of a slight decrease in directivity, is achieved by placing the two antenna pairs 0.5λ apart. The simulated half power beamwidth is 58°. A prototype was designed and the 2-D radiation patterns were measured. The prototype supports three directions of beam steering. The half power beamwidth was measured as 46°, 48°, and 50° for the three respective beam directions. The measured front-to-back ratio in azimuth plane is 8.5 dB, 8.0 dB and 7.6 dB, respectively.