Finding the Most Descriptive Substructures in Graphs with Discrete and Numeric Labels

Many graph datasets are labelled with discrete and numeric attributes. Most frequent substructure discovery algorithms ignore numeric attributes; in this paper we show how they can be used to improve search performance and discrimination. Our thesis is that the most descriptive substructures are those which are normative both in terms of their structure and in terms of their numeric values. We explore the relationship between graph structure and the distribution of attribute values and propose an outlier-detection step, which is used as a constraint during substructure discovery. By pruning anomalous vertices and edges, more weight is given to the most descriptive substructures. Our method is applicable to multi-dimensional numeric attributes; we outline how it can be extended for high-dimensional data. We support our findings with experiments on transaction graphs and single large graphs from the domains of physical building security and digital forensics, measuring the effect on runtime, memory requirements and coverage of discovered patterns, relative to the unconstrained approach.

AGWAN: A Generative Model for Labelled, Weighted Graphs

Real-world graphs or networks tend to exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Much effort has been directed into creating realistic and tractable models for unlabelled graphs, which has yielded insights into graph structure and evolution. Recently, attention has moved to creating models for labelled graphs: many real-world graphs are labelled with both discrete and numeric attributes. In this paper, we presentAgwan (Attribute Graphs: Weighted and Numeric), a generative model for random graphs with discrete labels and weighted edges. The model is easily generalised to edges labelled with an arbitrary number of numeric attributes. We include algorithms for fitting the parameters of the Agwanmodel to real-world graphs and for generating random graphs from the model. Using real-world directed and undirected graphs as input, we compare our approach to state-of-the-art random labelled graph generators and draw conclusions about the contribution of discrete vertex labels and edge weights to graph structure.

Microneedle characterisation: the need for universal acceptance criteria and GMP specifications when moving towards commercialisation

With interest in microneedles as a novel drug transdermal delivery system increasing rapidly since the late 1990s (Margetts and Sawyer Contin Educ Anaesthesia Crit Care Pain. 7(5):171-76, 2007), a diverse range of microneedle systems have been fabricated with varying designs and dimensions. However, there are still very few commercially available microneedle products. One major issue regarding microneedle manufacture on an industrial scale is the lack of specific quality standards for this novel dosage form in the context of Good Manufacturing Practice (GMP). A range of mechanical characterisation tests and microneedle insertion analysis techniques are used by researchers working on microneedle systems to assess the safety and performance profiles of their various designs. The lack of standardised tests and equipment used to demonstrate microneedle mechanical properties and insertion capability makes it difficult to directly compare the in use performance of candidate systems. This review highlights the mechanical tests and insertion analytical techniques used by various groups to characterise microneedles. This in turn exposes the urgent need for consistency across the range of microneedle systems in order to promote innovation and the successful commercialisation of microneedle products.

The United Kingdom, the United Nations Human Rights Council and the first cycle of the Universal Periodic Review

The introduction of the Universal Periodic Review (UPR) mechanism as an innovative component of the new Human Rights Council in 2006 has suffered little academic scrutiny. This is partly because it holds as its objective an improvement in human rights situations on the ground, a goal that is difficult to test amongst so many possible causal factors attributable to law reform and policy change, and partly due to the fact that the mechanism has only completed one full cycle of review. This article seeks to remedy this absence of analysis by examining the experience of the United Kingdom during its first review. In doing so, the article first considers the conception of the UPR, before progressing to examine the procedure and recommendations made to the UK by its peers. Finally, the article considers the five year review of the UPR which occurred as a subset of the Human Rights Council Review in 2011 and the resulting changes to the process modalities.

Reclaiming the Radical in Universal Human Rights: Universality as Universalisation

The universality of human rights has been a fiercely contested issue throughout their history. This article contributes to scholarly engagements with the universality of human rights by proposing a re-engagement with this concept in a way that is compatible with the aims of radical politics. Instead of a static attribute or characteristic of rights this article proposes that universality can be thought of as, drawing from Judith Butler, an ongoing process of universalisation. Universality accordingly emerges as a site of powerful contest between competing ideas of what human rights should mean, do or say, and universal concepts are continually reworked through political activity. This leads to a differing conception of rights politics than traditional liberal approaches but, moreover, challenges such approaches. This understanding of universality allows human rights to come into view as potentially of use in interrupting liberal regimes and, crucially, opens possibilities to reclaim the radical in rights.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

The Casimir effect in the presence of compactified universal extra dimensions

The Casimir force in a system consisting of two parallel conducting plates in the presence of compactified universal extra dimensions (UXD) is analyzed. The Casimir force with UXDs differs from the force obtained without extra dimensions. A new power law for the Casimir force is derived. By comparison to experimental data the size R of the universal extra dimensions can be restricted to R⩽10 nm for one extra dimension.