Governance, representation and the ‘monstrous regiment’: is the collective feminine?

Alternatives to the individualistic emphasis of liberal theory focus attention on collective dimensions of social life with implications for legal and political analysis of the state, of representation, and of international law. In this context, relationships between the individual–collective dichotomy and the dichotomy of gender demand attention because of the claimed affiliations of individualism with social understandings of masculinity.

Stylised configurations of trauma: faking identity in Holocaust memoirs

Exploring a series of fraudulent Holocaust memoirs-Herman Rosenblat's Angel at the Fence, Misha Defonseca's Misha: A Mémoire of the Holocaust, Binjamin Wilkomirski's Fragments and Helen Demidenko's The Hand That Signed the Paper-, this paper argues that fakes are not some 'bogus Other' (Ruthven 3) of 'genuine' literature but in fact parodic works that reflect on the tenuous nature of both the past and the notion of self. Indeed, the revelation of a fraudulent memoir exposes the investments of a public culture in notions of the real-firstly, in terms of an authentic identity and secondly, in relation to a genuine literary experience. The Holocaust frauds perpetuated by Rosenblat, Defonseca, Demidenko and Wilkomirski, in exploiting an historical phenomena regarded as sacrosanct, highlight and utilise the commodification of trauma in both public and literary arenas, manipulating discourses of victimhood and authenticity in order to interrogate the boundaries of the real and the unreal and, indeed, to reveal the faultlines in literary culture per se. Less interested in literary classifications, however, than in notions of history and identity, this paper contends that the scandals surrounding fakes are fundamental to understanding anxieties about the connection between word and world, and the strange expectation that literature is able to provide access to something 'true'.

Numerical modeling of dual phase microstructure behavior under deformation conditions on the basis of digital material representation

Development of a digital material representation (DMR) model of dual phase steel is presented within the paper. Subsequent stages involving generation of a reliable representation of microstructure morphology, assignment of material properties to component phases and incorporation of the model into the commercial finite element software are described within the paper. Different approaches used to recreate dual phase morphology in a digital manner are critically assessed. However, particular attention is placed on innovative identification of phase properties at the micro scale by using micro-pillar compression tests. The developed DMR model is finally applied to model influence of micro scale features on failure initiation and propagation under loading conditions.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

Enhancing medical named entity recognition with an extended segment representation technique

Objective : The objective of this paper is to formulate an extended segment representation (SR) technique to enhance named entity recognition (NER) in medical applications.

Methods : An extension to the IOBES (Inside/Outside/Begin/End/Single) SR technique is formulated. In the proposed extension, a new class is assigned to words that do not belong to a named entity (NE) in one context but appear as an NE in other contexts. Ambiguity in such cases can negatively affect the results of classification-based NER techniques. Assigning a separate class to words that can potentially cause ambiguity in NER allows a classifier to detect NEs more accurately; therefore increasing classification accuracy.

Results : The proposed SR technique is evaluated using the i2b2 2010 medical challenge data set with eight different classifiers. Each classifier is trained separately to extract three different medical NEs, namely treatment, problem, and test. From the three experimental results, the extended SR technique is able to improve the average F1-measure results pertaining to seven out of eight classifiers. The kNN classifier shows an average reduction of 0.18% across three experiments, while the C4.5 classifier records an average improvement of 9.33%.

Neural spike representation using Cepstrum

Neural spikes define the human brain function. An accurate extraction of spike features leads to better understanding of brain functionality. The main challenge of feature extraction is to mitigate the effect of strong background noises. To address this problem, we introduce a new feature representation for neural spikes based on Cepstrum of multichannel recordings. Simulation results indicated that the proposed method is more robust than the existing Haar wavelet method.

Numerical investigation of influence of the Martensite Volume Fraction on dp steels fracture behavior on the basis of digital material representation model

Development of the methodology for creating reliable digital material representation (DMR) models of dual-phase steels and investigation of influence of the martensite volume fraction on fracture behavior under tensile load are the main goals of the paper. First, an approach based on image processing algorithms for creating a DMR is described. Then, obtained digital microstructures are used as input for the numerical model of deformation, which takes into account mechanisms of ductile fracture. Ferrite and martensite material model parameters are evaluated on the basis of micropillar compression tests. Finally, the model is used to investigate the impact of the martensite volume fraction on the DP steel behavior under plastic deformation. Results of calculations are presented and discussed in the paper.

Migration exhibitions and the question of identity: reflections on the history of the representation of migration in Australian museums, 1986-2011

Museums and Migration explores the ways in which museum spaces - local, regional, national - have engaged with the history of migration, including internal migration, emigration and immigration.

Digital material representation and testing of metal foams

In recent years, metal foams are becoming more and more popular due to their high energy absorption ability and low density, which are being widely used in automotive engineering and aerospace engineering. As a design guide, foams can be characterised by several main geometric parameters, such as pore size, pore shape, spatial distribution and arrangement and so on. Considering most foam materials have random distributions of cell size and cell shape, the digital material representation and modelling of such materials become more complex. Cell size and shape effects on mechanical behaviours of metal foams have been found and investigated numerically and experimentally in authors' previous studies in which the authors have developed a digital framework for the representation, modelling and evaluation of multi-phase materials including metal foams. In this study, 2-/3-D finite element models are both developed to represent metal foams with random cell distributions and then a series of digital testing are simulated to investigate the mechanical behaviours of such foams. For validation and verification purpose, the results obtained from 2-/3-D models have been compared and good agreement has been found which demonstrated the effectiveness of the digital framework developed for metal forms. © (2014) Trans Tech Publications, Switzerland.