How should governments support the emergence of new industries in leading economies?

Over the past 30 years, developed economies' approaches to supporting growth have focused on competitiveness, entrepreneurship and innovation to varying degrees. However, following the credit crisis and global recession in 2008 there has been demand for an updated narrative of growth based on the emergence of new industries. This paper provides a brief review of the available literature on how governments in leading economies can support new industries to emerge to the benefit of their national economy, discusses a number of issues for governments trying to support emerging industries, provides a framework of activities which governments considering this type of intervention should consider, and discusses the case of the regenerative medicine industry in the UK using the framework. Copyright © 2013 Inderscience Enterprises Ltd.

The industrial emergence of commercial inkjet printing

Purpose: The purpose of this paper is to investigate how supply and demand interact during industrial emergence. Design/methodology/approach: The paper builds on previous theorising about co-evolutionary dynamics, exploring the interaction between supply and demand in a study of the industrial emergence of the commercial inkjet cluster in Cambridge, UK. Data are collected through 13 interviews with professionals working in the industry. Findings: The paper shows that as new industries emerge, asynchronies between technology supply and market demand create opportunities for entrepreneurial activity. In attempting to match innovative technologies to particular applications, entrepreneurs adapt to the system conditions and shape the environment to their own advantage. Firms that successfully operate in emerging industries demonstrate the functionality of new technologies, reducing uncertainty and increasing customer receptiveness. Research limitations/implications: The research is geographically bounded to the Cambridge commercial inkjet cluster. Further studies could consider commercial inkjet from a global perspective or test the applicability of the findings in other industries. Practical implications: Technology-based firms are often innovating during periods of industrial emergence. The insights developed in this paper help such firms recognise the emerging context in which they operate and the challenges that need to overcome. Originality/value: As an in depth study of a single industry, this research responds to calls for studies into industrial emergence, providing insights into how supply and demand interact during this phase of the industry lifecycle. © Emerald Group Publishing Limited.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.