Trial by market: the Brightstar incubation experiment

Established firms accumulate a significant body of knowledge, expertise and capabilities that are often secondary to their central revenue generating activities. How do they leverage this expertise in non-core technology into future value creation opportunities? In this paper we examine an attempt by the telecommunications firm BT to create value from the accumulated knowledge within its laboratories by setting up an incubator. While conceived by the board as a mechanism for leveraging the value of non-core technology into the workplace, corporate support for the incubator was withdrawn after only three years and prompted the incubator to partner with a venture capital firm, NVP, in the spin-out of ventures. Through analysis of this single case we observe how entering into such a relationship reduces the transaction costs of accessing complementary resources, capabilities and competences, while simultaneously reducing a number of the risks associated with venturing for both parties. Partnering with the venture capitalist allows the established firm to get its intellectual property into the market, for it to be tested by the market and further developed. © 2010 Inderscience Enterprises Ltd.

Engineering as a tool for improving human habitat

The conventional approaches to poverty alleviation in the slums entail a cocktail of interventions in health, education, governance and physical improvements, often stretching the scarce resources far and thin. Driven by the 'poverty' mindset, physical measures such as minimal paving, public water posts and community latrines actually brand the slums apart instead of assimilating them into the urban infrastructure fabric. The concept of Slum Networking proposes comprehensive water and environmental sanitation infrastructure as the central and catalytic leverage for holistic development. At costs less than the conventional 'slum' solutions, it tries to penetrate a high quality urban infrastructure net deeply into the slums to assimilate them into the city rather than lock them in as disadvantaged islands. Further, it transcends resource barriers and 'aid' through innovative partnerships and the latent resource mobilisation potential of the so-called 'poor'. This paper examines Slum Networking as implemented in Sanjaynagar in Ahmedabad, India and compares it with a similar settlement with no interventions in Ahmedabad. It assesses the knock-on impact of physical infrastructure on health, education and poverty. Finally, it evaluates the multiplier effect of physical infrastructure and the partnerships on the subsequent investments by the community in its own shelter and habitat. Copyright © 2009 Inderscience Enterprises Ltd.

One style does not fit all

The different types of outsourcing and the management requirements for each of them are discussed. An outsourcing has a high strategic importance to the buying company when it is aligned with the organization's long term strategies and when its results are either positive or negative. Functional outsourcings are characterized by both low strategic importance and financial impact, and the products/services that fall in this category belong to support activities which are highly standardized. Leverage outsourcings are those that have a low strategic importance, but high financial impact to the outsourcer, and they concentrate on operational aspects, leaving strategic issues.

An analysis of factors improving technology roadmap credibility: A communications theory assessment of roadmapping processes

In recent years, many industrial firms have been able to use roadmapping as an effective process methodology for projecting future technology and for coordinating technology planning and strategy. Firms potentially realize a number of benefits in deploying technology roadmapping (TRM) processes. Roadmaps provide information identifying which new technologies will meet firms' future product demands, allowing companies to leverage R&D investments through choosing appropriately out of a range of alternative technologies. Moreover, the roadmapping process serves an important communication tool helping to bring about consensus among roadmap developers, as well as between participants brought in during the development process, who may communicate their understanding of shared corporate goals through the roadmap. However, there are few conceptual accounts or case studies have made the argument that roadmapping processes may be used effectively as communication tools. This paper, therefore, seeks to elaborate a theoretical foundation for identifying the factors that must be considered in setting up a roadmap and for analyzing the effect of these factors on technology roadmap credibility as perceived by its users. Based on the survey results of 120 different R&D units, this empirical study found that firms need to explore further how they can enable frequent interactions between the TRM development team and TRM participants. A high level of interaction will improve the credibility of a TRM, with communication channels selected by the organization also positively affecting TRM credibility. © 2011 Elsevier Inc.

Evidence-informed development of engineering practical work: Effecting change through staff and student engagement

This paper discusses innovations in curriculum development in the Department of Engineering at the University of Cambridge as a participant in the Teaching for Learning Network (TFLN), a teaching and learning development initiative funded by the Cambridge-MIT Institute a pedagogic collaboration and brokerage network. A year-long research and development project investigated the practical experiences through which students traditionally explore engineering disciplines, apply and extend the knowledge gained in lectures and other settings, and begin to develop their professional expertise. The research project evaluated current practice in these sessions and developed an evidence-base to identify requirements for new activities, student support and staff development. The evidence collected included a novel student 'practice-value' survey highlighting effective practice and areas of concern, classroom observation of practicals, semi-structured interviews with staff, a student focus group and informal discussions with staff. Analysis of the data identified three potentially 'high-leverage' strategies for improvement: development of a more integrated teaching framework, within which practical work could be contextualised in relation to other learning; a more transparent and integrated conceptual framework where theory and practice were more closely linked; development of practical work more reflective of the complex problems facing professional engineers. This paper sets out key elements of the evidence collected and the changes that have been informed by this evidence and analysis, leading to the creation of a suite of integrated practical sessions carefully linked to other course elements and reinforcing central concepts in engineering, accompanied by a training and support programme for teaching staff.

Dynamical structure function identifiability conditions enabling signal structure reconstruction

Networks of controlled dynamical systems exhibit a variety of interconnection patterns that could be interpreted as the structure of the system. One such interpretation of system structure is a system's signal structure, characterized as the open-loop causal dependencies among manifest variables and represented by its dynamical structure function. Although this notion of structure is among the weakest available, previous work has shown that if no a priori structural information is known about the system, not even the Boolean structure of the dynamical structure function is identifiable. Consequently, one method previously suggested for obtaining the necessary a priori structural information is to leverage knowledge about target specificity of the controlled inputs. This work extends these results to demonstrate precisely the a priori structural information that is both necessary and sufficient to reconstruct the network from input-output data. This extension is important because it significantly broadens the applicability of the identifiability conditions, enabling the design of network reconstruction experiments that were previously impossible due to practical constraints on the types of actuation mechanisms available to the engineer or scientist. The work is motivated by the proteomics problem of reconstructing the Per-Arnt-Sim Kinase pathway used in the metabolism of sugars. © 2012 IEEE.

Unconstrained monocular 3D human pose estimation by action detection and cross-modality regression forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts. © 2013 IEEE.

Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.