Towards technological rules for designing innovation networks: a dynamic capabilities view.

Inter-organizational innovation networks provide opportunities to exploit complementary resources that reside beyond the boundary of the firm. The shifting locus of innovation and value creation away from the “sole firm as innovator” poses important questions about the nature of these resources and the capabilities needed to leverage them for competitive advantage. The purpose of this paper is to describe research into producing design-oriented knowledge, for configuring inter-organizational networks as a means of accessing such resources for innovation

A PCA approach to the object constancy for faces using view-based models of the face

The analysis of object and face recognition by humans attracts a great deal of interest, mainly because of its many applications in various fields, including psychology, security, computer technology, medicine and computer graphics. The aim of this work is to investigate whether a PCA-based mapping approach can offer a new perspective on models of object constancy for faces in human vision. An existing system for facial motion capture and animation developed for performance-driven animation of avatars is adapted, improved and repurposed to study face representation in the context of viewpoint and lighting invariance. The main goal of the thesis is to develop and evaluate a new approach to viewpoint invariance that is view-based and allows mapping of facial variation between different views to construct a multi-view representation of the face. The thesis describes a computer implementation of a model that uses PCA to generate example- based models of the face. The work explores the joint encoding of expression and viewpoint using PCA and the mapping between viewspecific PCA spaces. The simultaneous, synchronised video recording of 6 views of the face was used to construct multi-view representations, which helped to investigate how well multiple views could be recovered from a single view via the content addressable memory property of PCA. A similar approach was taken to lighting invariance. Finally, the possibility of constructing a multi-view representation from asynchronous view-based data was explored. The results of this thesis have implications for a continuing research problem in computer vision – the problem of recognising faces and objects from different perspectives and in different lighting. It also provides a new approach to understanding viewpoint invariance and lighting invariance in human observers

New mobilities across the lifecourse: a framework for analysing demographically-linked drivers of migration

Migration, along with fertility and mortality, is one of the fundamental drivers of population change. Taking the lifecourse as the central concern, the authors set out a theoretical framework and define some key research questions for a programme of research that explores how the linked lives of mobile people are situated in time-space within the economic, social and cultural structures of contemporary society. Drawing on methodologically innovative techniques, these perspectives can offer conceptually significant and policy relevant insights into the changing nature and meanings of migration across the lifecourse

Anticipating Visual Representations from Unlabeled Video

Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. Visual representations are a promising prediction target because they encode images at a higher semantic level than pixels yet are automatic to compute. We then apply recognition algorithms on our predicted representation to anticipate objects and actions. We experimentally validate this idea on two datasets, anticipating actions one second in the future and objects five seconds in the future.Comment: CVPR 201

Living with contradictions: the dynamics of senior managers in relation to sustainability

In this article, we investigate how senior managers located in Northern Europe in the energy and power industry coordinate their recognition of sustainability challenges with other things they say and do. Identity theory is used to examine the fine-grained work through which the managers navigate identities and potentially competing narratives. In contrast with other studies we find that pursuing cohering identities and resolving potential tensions and contradictions does not appear to matter for most of the managers. We explore the dynamics of how managers live with apparent contradictions and tensions without threat to their narrative coherence. We extend existing research into managerial identities and sustainability by: showing how managers combine different potentially contrasting identity types; identifying nine discursive processes through which the majority of managers distance and deflect sustainability issues away from themselves and their companies; and, showing the contrasting identity dynamics in the case of one manager to whom narrative coherence becomes important and prompts alternative action

Geometric Expression Invariant 3D Face Recognition using Statistical Discriminant Models

Currently there is no complete face recognition system that is invariant to all facial expressions. Although humans find it easy to identify and recognise faces regardless of changes in illumination, pose and expression, producing a computer system with a similar capability has proved to be particularly di cult. Three dimensional face models are geometric in nature and therefore have the advantage of being invariant to head pose and lighting. However they are still susceptible to facial expressions. This can be seen in the decrease in the recognition results using principal component analysis when expressions are added to a data set. In order to achieve expression-invariant face recognition systems, we have employed a tensor algebra framework to represent 3D face data with facial expressions in a parsimonious space. Face variation factors are organised in particular subject and facial expression modes. We manipulate this using single value decomposition on sub-tensors representing one variation mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained environments and still preserves the integrity of the 3D data. The results show improved recognition rates for faces and facial expressions, even recognising high intensity expressions that are not in the training datasets. We have determined, experimentally, a set of anatomical landmarks that best describe facial expression e ectively. We found that the best placement of landmarks to distinguish di erent facial expressions are in areas around the prominent features, such as the cheeks and eyebrows. Recognition results using landmark-based face recognition could be improved with better placement. We looked into the possibility of achieving expression-invariant face recognition by reconstructing and manipulating realistic facial expressions. We proposed a tensor-based statistical discriminant analysis method to reconstruct facial expressions and in particular to neutralise facial expressions. The results of the synthesised facial expressions are visually more realistic than facial expressions generated using conventional active shape modelling (ASM). We then used reconstructed neutral faces in the sub-tensor framework for recognition purposes. The recognition results showed slight improvement. Besides biometric recognition, this novel tensor-based synthesis approach could be used in computer games and real-time animation applications

Performance measurement : challenges for tomorrow

This paper demonstrates that the context within which performance measurement is used is changing. The key questions posed are: Is performance measurement ready for the emerging context? What are the gaps in our knowledge? and Which lines of enquiry do we need to pursue? A literature synthesis conducted by a team of multidisciplinary researchers charts the evolution of the performance-measurement literature and identifies that the literature largely follows the emerging business and global trends. The ensuing discussion introduces the currently emerging and predicted future trends and explores how current knowledge on performance measurement may deal with the emerging context. This results in identification of specific challenges for performance measurement within a holistic systems-based framework. The principle limitation of the paper is that it covers a broad literature base without in-depth analysis of a particular aspect of performance measurement. However, this weakness is also the strength of the paper. What is perhaps most significant is that there is a need for rethinking how we research the field of performance measurement by taking a holistic systems-based approach, recognizing the integrated and concurrent nature of challenges that the practitioners, and consequently the field, face

A gender perspective on entrepreneurial leadership:female leaders in Kazakhstan

The paper proposes a conceptual model to understand female entrepreneurial leadership through an exploration of the perceptions and experiences of women entrepreneurs within their leadership roles. The paper addresses an existing knowledge gap on entrepreneurial leadership by bringing together three key constructs of gender, leadership and entrepreneurship. We apply Stewart's model of role demands-constraints-choices (DCC) to women entrepreneurs in Kazakhstan in order to understand their perceptions of the demands, constraints and choices they experience within their leadership roles. The results of in-depth interviews with women entrepreneurs present deeper conceptualization of their leadership enactment as a co-developing, co-constructed relational activity between leaders and others in their wider business environments and context