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.

Low Noise Amplifier With Integrated Balanced Antenna for 60 GHz Wireless Communications

The implementation of a dipole antenna co-designed and monolithically integrated with a low noise amplifier (LNA) on low resistivity Si substrate (20 Omega . cm) manufactured in 0.35 mu m commercial SiGe HBT process with f(T)/f(max) of 170 GHz and 250 GHz is investigated theoretically and experimentally. An air gap is introduced between the chip and a reflective ground plane, leading to substantial improvements in efficiency and gain. Moreover, conjugate matching conditions between the antenna and the LNA are exploited, enhancing power transfer between without any additional matching circuit. A prototype is fabricated and tested to validate the performance. The measured 10-dB gain of the standalone LNA is centered at 58 GHz with a die size of 0.7 mm x 0.6 mm including all pads. The simulated results showed antenna directivity of 5.1 dBi with efficiency higher than 70%. After optimization, the co-designed LNA-Antenna chip with a die size of 3 mm x 2.8 mm was characterized in anechoic chamber environment. A maximum gain of higher than 12 dB was obtained.

Size-balanced community reorganization in response to nutrients and warming

It is widely accepted that global warming will adversely affect ecological communities. As ecosystems are simultaneously exposed to other anthropogenic influences, it is important to address the effects of climate change in the context of many stressors. Nutrient enrichment might offset some of the energy demands that warming can exert on organisms by stimulating growth at the base of the food web. It is important to know whether indirect effects of warming will be as ecologically significant as direct physiological effects. Declining body size is increasingly viewed as a universal response to warming, with the potential to alter trophic interactions. To address these issues, we used an outdoor array of marine mesocosms to examine the impacts of warming, nutrient enrichment and altered top-predator body size on a community comprised of the predator (shore crab Carcinus maenas), various grazing detritivores (amphipods) and algal resources. Warming increased mortality rates of crabs, but had no effect on their moulting rates. Nutrient enrichment and warming had near diametrically opposed effects on the assemblage, confirming that the ecological effects of these two stressors can cancel each other out. This suggests that nutrient-enriched systems might act as an energy refuge to populations of species under metabolic constraints due to warming. While there was a strong difference in assemblages between mesocosms containing crabs compared to mesocosms without crabs, decreasing crab size had no detectable effect on the amphipod or algal assemblages. This suggests that in allometrically balanced communities, the expected long-term effect of warming (declining body size) is not of similar ecological consequence to the direct physiological effects of warming, at least not over the six week duration of the experiment described here. More research is needed to determine the long-term effects of declining body size on the bioenergetic balance of natural communities.

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.

On the Security of Balanced Encoding Countermeasures

Most cryptographic devices should inevitably have a resistance against the threat of side channel attacks. For this, masking and hiding schemes have been proposed since 1999. The security validation of these countermeasures is an ongoing research topic, as a wider range of new and existing attack techniques are tested against these countermeasures. This paper examines the side channel security of the balanced encoding countermeasure, whose aim is to process the secret key-related data under a constant Hamming weight and/or Hamming distance leakage. Unlike previous works, we assume that the leakage model coefficients conform to a normal distribution, producing a model with closer fidelity to real-world implementations. We perform analysis on the balanced encoded PRINCE block cipher with simulated leakage model and also an implementation on an AVR board. We consider both standard correlation power analysis (CPA) and bit-wise CPA. We confirm the resistance of the countermeasure against standard CPA, however, we find with a bit-wise CPA that we can reveal the key with only a few thousands traces.

Punishment, youth justice and cultural contingency: Towards a balanced approach

Reflecting developments in the broader penological realm, accounts have been advanced over the last number of decades about a ‘punitive turn’ in the youth justice systems of Western democracies. Against the background of this work, this project seeks to identify convergent and divergent trends in the youth justice systems of England, the Republic of Ireland and Northern Ireland as well as the rationalities and discourses animating these. The results lend support to research emphasising the continued salience of national, regional and local factors on penal outcomes but also suggest the need to steer an analytical path somewhere between nomothetic (convergent) and idiographic (divergent) accounts.