Miami heat: Patent law, informed consent and benefit-sharing

This article considers whether the granting of patents in respect of biomedical genetic research should be conditional upon the informed consent of research participants. It focuses upon several case studies. In Moore v the Regents of the University Of California, a patient sued his physician for breach of fiduciary duty and lack of informed consent, because the doctor had obtained a patent on the patient's cell line, without the patient's authorisation. In Greenberg v Miami Children's Hospital, the research participants, the Greenbergs, the National Tay Sachs and Allied Diseases Association, and Dor Yeshorim brought a legal action against the geneticist Reubon Matalon and the Miami Children's Hospital over a patent obtained on a gene related to the Canavan disease and accompany genetic diagnostic test. PXE International entered into a joint venture with Charles Boyd and the University of Hawaii, and obtained a patent together for ‘methods for diagnosing Pseudoxanthoma elasticum’. In light of such case studies, it is contended that there is a need to reform patent law, so as to recognise the bioethical principles of informed consent and benefit-sharing. The 2005 UNESCO Declaration on Bioethics and Human Rights provides a model for future case law and policy-making.

Towards a unified service description language for the internet of services : requirements and first developments

Service-oriented Architectures (SOA) and Web services leverage the technical value of solutions in the areas of distributed systems and cross-enterprise integration. The emergence of Internet marketplaces for business services is driving the need to describe services, not only from a technical level, but also from a business and operational perspective. While, SOA and Web services reside in an IT layer, organizations owing Internet marketplaces are requiring advertising and trading business services which reside in a business layer. As a result, the gap between business and IT needs to be closed. This paper presents USDL (Unified Service Description Language), a specification language to describe services from a business, operational and technical perspective. USDL plays a major role in the Internet of Services to describe tradable services which are advertised in electronic marketplaces. The language has been tested using two service marketplaces as use cases.

VIVO as an Institutional Data Registry

The Queensland University of Technology (QUT) in Brisbane, Australia, is involved in a number of projects funded by the Australian National Data Service (ANDS). Currently, QUT is working on a project (Metadata Stores Project) that uses open source VIVO software to aid in the storage and management of metadata relating to data sets created/managed by the QUT research community. The registry (called QUT Research Data Finder) will support the sharing and reuse of research datasets, within and external to QUT. QUT uses VIVO for both the display and the editing of research metadata.

Preliminary bond-slip model for CFRP sheets bonded to steel plates

Carbon fiber reinforced polymer (CFRP) sheets have established a strong position as an effective method for innovative structural rehabilitation. However, the use of externally bonded CFRP in the repair and rehabilitation of steel structures is a relatively new technique that has the potential to improve the way structures are repaired. An important step toward understanding bond behaviour is to have an estimation of local bond stress versus slip relationship. The current study aims to establish the bond-slip model for CFRP sheets bonded to steel plate. To obtain the shear stress versus slippage relationship, a series of double strap tension type bond tests were conducted. This paper reports on the findings of the experimental studies. The strain and stress distributions measured in the specimens for two different bond lengths. The results show a preliminary bi-linear bond-slip model may be adopted for CFRP sheet bonded with steel plate.

Caribbean copyright showdown

The tiny Caribbean island of Antigua doesn’t make the news very often. Located 1800 kilometres east of Miami, just north of Montserrat, it is best known as a holiday destination for well-heeled Europeans and celebrities with private yachts. Now it is in the headlines for an unexpected reason...

FreeS: A fast algorithm to discover frequent free subtrees using a novel canonical form

Web data can often be represented in free tree form; however, free tree mining methods seldom exist. In this paper, a computationally fast algorithm FreeS is presented to discover all frequently occurring free subtrees in a database of labelled free trees. FreeS is designed using an optimal canonical form, BOCF that can uniquely represent free trees even during the presence of isomorphism. To avoid enumeration of false positive candidates, it utilises the enumeration approach based on a tree-structure guided scheme. This paper presents lemmas that introduce conditions to conform the generation of free tree candidates during enumeration. Empirical study using both real and synthetic datasets shows that FreeS is scalable and significantly outperforms (i.e. few orders of magnitude faster than) the state-of-the-art frequent free tree mining algorithms, HybridTreeMiner and FreeTreeMiner.

R1STM: One-class support tensor machine with randomised kernel

Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.