Azimuthal asymmetry in collective electron dynamics in relativistically transparent laser-foil interactions

Asymmetry in the collective dynamics of ponderomotively-driven electrons in the interaction of an ultraintense laser pulse with a relativistically transparent target is demonstrated experimentally. The 2D profile of the beam of accelerated electrons is shown to change from an ellipse aligned along the laser polarization direction in the case of limited transparency, to a double-lobe structure aligned perpendicular to it when a significant fraction of the laser pulse co-propagates with the electrons. The temporally-resolved dynamics of the interaction are investigated via particle-in-cell simulations. The results provide new insight into the collective response of charged particles to intense laser fields over an extended interaction volume, which is important for a wide range of applications, and in particular for the development of promising new ultraintense laser-driven ion acceleration mechanisms involving ultrathin target foils.

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.

Robust bimodal person identification using face and speech with limited training data and corruption of both modalities

This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.

The Muslim Brotherhood: the Arab Spring its Future Face

The Muslim Brotherhood is the most significant and enduring Sunni Islamist organization of the contemporary era. Its roots lie in the Middle East but it has grown into both a local and global movement, with its well-placed branches reacting effectively to take the opportunities for power and electoral competition offered by the Arab Spring.

Regarded by some as a force of moderation among Islamists, and by others as a façade hiding a terrorist fundamentalist threat, the potential influence of the Muslim Brotherhood on Middle Eastern politics remains ambiguous.The Muslim Brotherhood: The Arab Spring and its Future Face provides an essential insight into the organisation, with chapters devoted to specific cases where the Brotherhood has important impacts on society, the state and politics. Key themes associated with the Brotherhood, such as democracy, equality, pan-Islamism, radicalism, reform, the Palestine issue and gender, are assessed to reveal an evolutionary trend within the movement since its founding in Egypt in 1928 to its manifestation as the largest Sunni Islamist movement in the Middle East in the 21st century. The book addresses the possible future of the Muslim Brotherhood; whether it can surprise sceptics and effectively accommodate democracy and secular trends, and how its ascension to power through the ballot box might influence Western policy debates on their engagement with this manifestation of political Islam.

Drawing on a wide range of sources, this book presents a comprehensive study of a newly resurgent movement and is a valuable resource for students, scholars and policy makers focused on Middle Eastern Politics.

Performing in the face of the academy: challenges for a creative practitioner within a university

This piece of writing is an excerpt from a keynote talk given at the Symposium on Artistic Research in Borås, Sweden, on 28 November 2014.

Comparison of optimal designs of steel portal frames including topological asymmetry considering rolled, fabricated and tapered sections

A structural design optimisation has been carried out to allow for asymmetry and fully tapered portal frames. The additional weight of an asymmetric structural shape was found to be on average 5–13% with additional photovoltaic (PV) loading having a negligible effect on the optimum design. It was also shown that fabricated and tapered frames achieved an average percentage weight reduction of 9% and 11%, respectively, as compared to comparable hot-rolled steel frames. When the deflection limits recommended by the Steel Construction Institute were used, frames were shown to be deflection controlled with industrial limits yielding up to 40% saving.

Largest Matching Areas for Illumination and Occlusion Robust Face Recognition

In this paper, we introduce a novel approach to face recognition which simultaneously tackles three combined challenges: 1) uneven illumination; 2) partial occlusion; and 3) limited training data. The new approach performs lighting normalization, occlusion de-emphasis and finally face recognition, based on finding the largest matching area (LMA) at each point on the face, as opposed to traditional fixed-size local area-based approaches. Robustness is achieved with novel approaches for feature extraction, LMA-based face image comparison and unseen data modeling. On the extended YaleB and AR face databases for face identification, our method using only a single training image per person, outperforms other methods using a single training image, and matches or exceeds methods which require multiple training images. On the labeled faces in the wild face verification database, our method outperforms comparable unsupervised methods. We also show that the new method performs competitively even when the training images are corrupted.

Dual Classification Revisited: Rodney Needham and Vertical Asymmetry aboard Scottish Trawlers

Drawing on ethnographic data collected while working as a deckhand on two Scottish trawlers, this article analyses the spatialisation of social, religious and economic inequalities that marked relations between crew members while they hunted for prawns in the North Sea. Moreover, it explores these inequalities as a wider feature of life in Gamrie, Aberdeenshire, a Brethren and Presbyterian fishing village riven by disparities in wealth and religion. Inequalities identified by fishermen at sea mirrored those identified by residents onshore, resulting in fishing boats being experienced as small 'floating villages'. Drawing on the work of Rodney Needham, this article suggests that these asymmetries can be traced along a vertical axis, with greater to lesser wealth and religiosity moving from top/above to bottom/below. The article seeks to understand the presence and persistence of these hierarchies at sea and on land, by revisiting dual classification within anthropological theory.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.