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'.

JLMC: A clustering method based on Jordan-Form of Laplacian-Matrix

Among the current clustering algorithms of complex networks, Laplacian-based spectral clustering algorithms have the advantage of rigorous mathematical basis and high accuracy. However, their applications are limited due to their dependence on prior knowledge, such as the number of clusters. For most of application scenarios, it is hard to obtain the number of clusters beforehand. To address this problem, we propose a novel clustering algorithm - Jordan-Form of Laplacian-Matrix based Clustering algorithm (JLMC). In JLMC, we propose a model to calculate the number (n) of clusters in a complex network based on the Jordan-Form of its corresponding Laplacian matrix. JLMC clusters the network into n clusters by using our proposed modularity density function (P function). We conduct extensive experiments over real and synthetic data, and the experimental results reveal that JLMC can accurately obtain the number of clusters in a complex network, and outperforms Fast-Newman algorithm and Girvan-Newman algorithm in terms of clustering accuracy and time complexity.

Poor tolerance of motor cortex rTMS in chronic migraine

Background:
Two small studies had evaluated the efficacy of rTMS in migraine. One tested high frequency rTMS over the dorsolateral prefrontal cortex while the other evaluated 1 Hz rTMS over the vertex.
Aim:
To test the feasibility of 10 Hz rTMS of motor cortex as an adjunctive therapy in patients with chronic migraine.
Materials and Methods:
We randomized (2:1 ratio) chronic migraine patients on medical preventive treatment to receive either rTMS or sham therapy for 10 sessions. rTMS (80% resting motor threshold, 10Hz, 20 trains, 5 secs/train, inter-train interval 1 min, total 1000 stimuli/session) was applied over the right motor cortex.
Result:
Nine patients were randomized. Six received rTMS
and three had sham therapy. Three patients in the rTMS arm withdrew from the study due to increased headache frequency and discomfort from the treatment. The remaining six cases (3 rTMS, 3 sham) completed the study. The study was prematurely stopped due to the significant worsening of headache from rTMS. No significant differences in outcome measures were found between real and sham rTMS.
Conclusion:
Although the study was terminated prematurely, the high dropout rate (50%) due to worsening headaches suggested that rTMS over the motor cortex is poorly tolerated in chronic migraine.

Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the motion limitations.

Is exchange rate trading profitable?

We Test whether exchange rate trading is profitable in the emerging markets of Brazil, China, India, And South Africa. Using Momentum trading strategies applied to high frequency data, we discover that: (a) momentum-based trading strategies lead to statistically significant profits from the currencies of all four emerging markets; (b) The South African Rand Is generally the most profitable, followed by the Brazilian Real And the Indian Rupee; (c) Profits are persistent during the day and are trading frequency dependent; and (d) During the period of the global financial crisis currency profits were maximised.

The material poetics of Fabien Bürgy: reflections on spikes

Fabien Bürgy’s work creates interstitions in which the virtual becomes indistinguishable from the real, and the real becomes impregnated with the virtual. Spikes is a sculptureinstallation that exists in a threshold in which matter becomes digital and the digital becomes matter. This review addresses the many conceptual layers particular to this artwork, and wishes to preserve the aura of inscrutability that belongs to its poetics.

His daughter's father

His Daughter’s Father is an experimental multimedia-drawing about presence: of a subject that is depicted simultaneously with affect (alive/present) and stilled (dead/past), and of representation that continuously re-presents itself in real and imagined time. Imagery moves between performative drawing, the digital moving image, and the stilled representation.

Characterizing compactness of geometrical clusters using fuzzy measures

Certain tasks in image processing require the preservation of fine image details, while applying a broad operation to the image, such as image reduction, filtering, or smoothing. In such cases, the objects of interest are typically represented by small, spatially cohesive clusters of pixels which are to be preserved or removed, depending on the requirements. When images are corrupted by the noise or contain intensity variations generated by imaging sensors, identification of these clusters within the intensity space is problematic as they are corrupted by outliers. This paper presents a novel approach to accounting for spatial organization of the pixels and to measuring the compactness of pixel clusters based on the construction of fuzzy measures with specific properties: monotonicity with respect to the cluster size; invariance with respect to translation, reflection, and rotation; and discrimination between pixel sets of fixed cardinality with different spatial arrangements. We present construction methods based on Sugeno-type fuzzy measures, minimum spanning trees, and fuzzy measure decomposition. We demonstrate their application to generating fuzzy measures on real and artificial images.

Autoethnography: introducing 'I' into medical education research

Context: Autoethnography is a methodology that allows clinician-educators to research their own cultures, sharing insights about their own teaching and learning journeys in ways that will resonate with others. There are few examples of autoethnographic research in medical education, and many areas would benefit from this methodology to help improve understanding of, for example, teacher-learner interactions, transitions and interprofessional development. Objectives: We wish to share this methodology so that others may consider it in their own education environments as a viable qualitative research approach to gain new insights and understandings. Methods: This paper introduces autoethnography, discusses important considerations in terms of data collection and analysis, explores ethical aspects of writing about others and considers the benefits and limitations of conducting research that includes self. Results: Autoethnography allows medical educators to increasingly engage in self-reflective narration while analysing their own cultural biographies. It moves beyond simple autobiography through the inclusion of other voices and the analytical examination of the relationships between self and others. Autoethnography has achieved its goal if it results in new insights and improvements in personal teaching practices, and if it promotes broader reflection amongst readers about their own teaching and learning environments. Conclusions: Researchers should consider autoethnography as an important methodology to help advance our understanding of the culture and practices of medical education. Discuss ideas arising from the article at www.mededuc.com discuss. © 2015 John Wiley

Human perception-based washout filtering using genetic algorithm

The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator movement, aiming to regenerate high fidelity motion within the simulators physical limitations. Classical washout filters are widely used in commercial simulators because of their relative simplicity and reasonable performance. The main drawback of classical washout filters is the inappropriate empirical parameter tuning method that is based on trial-and-error, and is effected by programmers’ experience. This is the most important obstacle to exploiting the platform efficiently. Consequently, the conservative motion produces false cue motions. Lack of consideration for human perception error is another deficiency of classical washout filters and also there is difficulty in understanding the effect of classical washout filter parameters on generated motion cues. The aim of this study is to present an effortless optimization method for adjusting the classical MCA parameters, based on the Genetic Algorithm (GA) for a vehicle simulator in order to minimize human sensation error between the real and simulator driver while exploiting the platform within its physical limitations. The vestibular sensation error between the real and simulator driver as well as motion limitations have been taken into account during optimization. The proposed optimized MCA based on GA is implemented in MATLAB/Simulink. The results show the superiority of the proposed MCA as it improved the human sensation, maximized reference signal shape following and exploited the platform more efficiently within the motion constraints.

Displaced metaphors: collaborative poetic responses to language in a post-physical world

This paper discusses an ongoing creative and conceptual collaboration between three authors, in which poetry has been approached as a way of exploring how lived experience and language are being transformed by the rapid evolution of virtual reality and its lexicon. We recognise, via Bakhtin, that language is always shared, in-use and redolent with multiple meanings. We acknowledge that we have written within a metaphorical space where we, as avatars of ourselves, use word processing software loaded with its own metaphors of page and print. The poems we have collaborated on have interrupted the increasing invisibility of metaphors such as ‘cloud’ and ‘screen’ as applied to technology, by working in the disjunction between metaphor and what it describes. We now reflect on the collaborative process and on the influence of technology on our practice, whilst maintaining a collaborative strategy. The paper explores the poetics of longing (Stewart) and Baudrillard’s simulacra and argues that concerns over remembering the real and the effects of nostalgia are offset by the generative potential of collaborative writing and its surprising forms of heteroglossia, which have exciting possibilities for creative practice.

Three flying saucers

Three Flying Saucers is wall mounted set of three works which parody the iconic kitsch set of three flying ducks which inhabit the walls of many houses from the 1950’s to today. The project proposes a time in the future where contact with alien intelligent life has been established and we have finally obtained proof that Flying Saucers were real and had been visiting our world for many decades. Now these mysterious celestial forms have been adopted as the new kitsch and adorn the homes of the future.

Power quality impacts in a typical microgrid

Microgrid (MG) power system plays an important role to fulfill reliable and secure energy supply to critical loads of communities as well as for communities in remote area. Distributed Generation (DG) sources integrated in a MG provides numerous benefits, at the same time leads to power quality issues in the MG power distribution network. Power Quality (PQ) issue arises due to the integration of an intermittent nature of Renewable Energy (RE) sources with advanced Power Electronics (PE) converter technology. Also, presence of non-linear and unbalancing loads in MG seems to affect PQ of the energy supply in power distribution network. In this paper, PQ impacts like; power variation, voltage variation, Total Harmonic Distortion (THD), and Unbalance voltage level have been analysed in Low Voltage (LV) distribution network of typical MG power system model. In this study, development of MG model and PQ impact analysis through simulation has been done in PSS-Sincal software environment. Analysis results from the study can be used as a guideline for developing a real and independent MG power system with improved PQ conditions.

Hyperspherical cluster based distributed anomaly detection in wireless sensor networks

This article describes a distributed hyperspherical cluster based algorithm for identifying anomalies in measurements from a wireless sensor network, and an implementation on a real wireless sensor network testbed. The communication overhead incurred in the network is minimised by clustering sensor measurements and merging clusters before sending a compact description of the clusters to other nodes. An evaluation on several real and synthetic datasets demonstrates that the distributed hyperspherical cluster-based scheme achieves comparable detection accuracy with a significant reduction in communication overhead compared to a centralised scheme, where all the sensor node measurements are communicated to a central node for processing. .

An embedding scheme for detecting anomalous block structured graphs

Graph-based anomaly detection plays a vital role in various application domains such as network intrusion detection, social network analysis and road traffic monitoring. Although these evolving networks impose a curse of dimensionality on the learning models, they usually contain structural properties that anomaly detection schemes can exploit. The major challenge is finding a feature extraction technique that preserves graph structure while balancing the accuracy of the model against its scalability. We propose the use of a scalable technique known as random projection as a method for structure aware embedding, which extracts relational properties of the network, and present an analytical proof of this claim. We also analyze the effect of embedding on the accuracy of one-class support vector machines for anomaly detection on real and synthetic datasets. We demonstrate that the embedding can be effective in terms of scalability without detrimental influence on the accuracy of the learned model.