The changing nature of innovation networks in Taiwan: From imitation to innovation?

Taiwan is embarking on a new phase in its approach to building its national innovative capacity, through building the infrastructure for a biotechnology industry. Rather than acting as a “fast follower” of trends developed elsewhere, Taiwan is seeking to evolve the elements of a national innovation system, including upgrading the role of universities in providing fundamental R&D, in providing incubators for new, knowledge-based firms, in developing new funding models, and in establishing new biotech-focused science parks. This paper reviews the progress achieved to date, and the prospects for this new phase in Taiwan’s transition from imitation to innovation

Curvature-driven smoothing: a learning algorithm for feed-forward networks

The performance of feed-forward neural networks in real applications can be often be improved significantly if use is made of a-priori information. For interpolation problems this prior knowledge frequently includes smoothness requirements on the network mapping, and can be imposed by the addition to the error function of suitable regularization terms. The new error function, however, now depends on the derivatives of the network mapping, and so the standard back-propagation algorithm cannot be applied. In this paper, we derive a computationally efficient learning algorithm, for a feed-forward network of arbitrary topology, which can be used to minimize the new error function. Networks having a single hidden layer, for which the learning algorithm simplifies, are treated as a special case.

Topographic mappings and feed-forward neural networks

This thesis is a study of the generation of topographic mappings - dimension reducing transformations of data that preserve some element of geometric structure - with feed-forward neural networks. As an alternative to established methods, a transformational variant of Sammon's method is proposed, where the projection is effected by a radial basis function neural network. This approach is related to the statistical field of multidimensional scaling, and from that the concept of a 'subjective metric' is defined, which permits the exploitation of additional prior knowledge concerning the data in the mapping process. This then enables the generation of more appropriate feature spaces for the purposes of enhanced visualisation or subsequent classification. A comparison with established methods for feature extraction is given for data taken from the 1992 Research Assessment Exercise for higher educational institutions in the United Kingdom. This is a difficult high-dimensional dataset, and illustrates well the benefit of the new topographic technique. A generalisation of the proposed model is considered for implementation of the classical multidimensional scaling (¸mds}) routine. This is related to Oja's principal subspace neural network, whose learning rule is shown to descend the error surface of the proposed ¸mds model. Some of the technical issues concerning the design and training of topographic neural networks are investigated. It is shown that neural network models can be less sensitive to entrapment in the sub-optimal global minima that badly affect the standard Sammon algorithm, and tend to exhibit good generalisation as a result of implicit weight decay in the training process. It is further argued that for ideal structure retention, the network transformation should be perfectly smooth for all inter-data directions in input space. Finally, there is a critique of optimisation techniques for topographic mappings, and a new training algorithm is proposed. A convergence proof is given, and the method is shown to produce lower-error mappings more rapidly than previous algorithms.

Combining spatially distributed predictions from neural networks

In this report we discuss the problem of combining spatially-distributed predictions from neural networks. An example of this problem is the prediction of a wind vector-field from remote-sensing data by combining bottom-up predictions (wind vector predictions on a pixel-by-pixel basis) with prior knowledge about wind-field configurations. This task can be achieved using the scaled-likelihood method, which has been used by Morgan and Bourlard (1995) and Smyth (1994), in the context of Hidden Markov modelling

Training Bayesian networks for image segmentation

We are concerned with the problem of image segmentation in which each pixel is assigned to one of a predefined finite number of classes. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of segmentations. Markov Random Fields (MRFs) have been used to incorporate some of this prior knowledge, but this not entirely satisfactory as inference in MRFs is NP-hard. The multiscale quadtree model of Bouman and Shapiro (1994) is an attractive alternative, as this is a tree-structured belief network in which inference can be carried out in linear time (Pearl 1988). It is an hierarchical model where the bottom-level nodes are pixels, and higher levels correspond to downsampled versions of the image. The conditional-probability tables (CPTs) in the belief network encode the knowledge of how the levels interact. In this paper we discuss two methods of learning the CPTs given training data, using (a) maximum likelihood and the EM algorithm and (b) emphconditional maximum likelihood (CML). Segmentations obtained using networks trained by CML show a statistically-significant improvement in performance on synthetic images. We also demonstrate the methods on a real-world outdoor-scene segmentation task.

First year qualifying report: neural networks for extracting wind vectors from satellite scatterometer data

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about km800, carrying a C-band scatterometer. A scatterometer measures the amount of radar back scatter generated by small ripples on the ocean surface induced by instantaneous local winds. Operational methods that extract wind vectors from satellite scatterometer data are based on the local inversion of a forward model, mapping scatterometer observations to wind vectors, by the minimisation of a cost function in the scatterometer measurement space.par This report uses mixture density networks, a principled method for modelling conditional probability density functions, to model the joint probability distribution of the wind vectors given the satellite scatterometer measurements in a single cell (the `inverse' problem). The complexity of the mapping and the structure of the conditional probability density function are investigated by varying the number of units in the hidden layer of the multi-layer perceptron and the number of kernels in the Gaussian mixture model of the mixture density network respectively. The optimal model for networks trained per trace has twenty hidden units and four kernels. Further investigation shows that models trained with incidence angle as an input have results comparable to those models trained by trace. A hybrid mixture density network that incorporates geophysical knowledge of the problem confirms other results that the conditional probability distribution is dominantly bimodal.par The wind retrieval results improve on previous work at Aston, but do not match other neural network techniques that use spatial information in the inputs, which is to be expected given the ambiguity of the inverse problem. Current work uses the local inverse model for autonomous ambiguity removal in a principled Bayesian framework. Future directions in which these models may be improved are given.

Towards an inter-organisational knowledge transfer framework for SMEs

This paper aims to develop a framework for SMEs to help them understand, and thus to improve, the process of knowledge exchange with their customers or suppliers. Through a review of the literature on knowledge transfer, organisational learning, social network theory and electronic networks, the key actors, key factors and their relationships in the process are identified. Finally, a framework containing all above points is proposed.

Marking high-technology market evolution through the foci of market stories:the case of local area networks

Previous research suggests that changing consumer and producer knowledge structures play a role in market evolution and that the sociocognitive processes of product markets are revealed in the sensemaking stories of market actors that are rebroadcasted in commercial publications. In this article, the authors lend further support to the story-based nature of market sensemaking and the use of the sociocognitive approach in explaining the evolution of high-technology markets. They examine the content (i.e., subject matter or topic) and volume (i.e., the number) of market stories and the extent to which content and volume of market stories evolve as a technology emerges. Data were obtained from a content analysis of 10,412 article abstracts, published in key trade journals, pertaining to Local Area Network (LAN) technologies and spanning the period 1981 to 2000. Hypotheses concerning the evolving nature (content and volume) of market stories in technology evolution are tested. The analysis identified four categories of market stories - technical, product availability, product adoption, and product discontinuation. The findings show that the emerging technology passes initially through a 'technical-intensive' phase whereby technology related stories dominate, through a 'supply-push' phase, in which stories presenting products embracing the technology tend to exceed technical stories while there is a rise in the number of product adoption reference stories, to a 'product-focus' phase, with stories predominantly focusing on product availability. Overall story volume declines when a technology matures as the need for sensemaking reduces. When stories about product discontinuation surface, these signal the decline of current technology. New technologies that fail to maintain the 'product-focus' stage also reflect limited market acceptance. The article also discusses the theoretical and managerial implications of the study's findings. © 2002 Elsevier Science Inc. All rights reserved.

Cultural impact of wireless networks:emerging experiences in the healthcare sector

Culture defines collective behavior and interactions among people in groups. In organizations, it shapes group identity, work pattern, communication schemes, and interpersonal relations. Any change in organizational culture will lead to changes in these elements of organizational factors, and vice versa. From a managerial standpoint, how to cultivate an organizational culture that would enhance these aforementioned elements in organizational workplace should thus be taken into serious consideration. Based on cases studies in two hospitals, this paper investigates how organizational culture is shaped by a particular type of information and communication technology, wireless networks, a topic that is generally overlooked by the mainstream research community, and in turn implicates how such cultural changes in organizations renovate their competitiveness in the marketplace. Lessons learned from these cases provide valuable insights to emerging IT management and culture studies in general and in wireless network management in the healthcare sector in particular.

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.

Evolutionary neural networks : models and applications

The scaling problems which afflict attempts to optimise neural networks (NNs) with genetic algorithms (GAs) are disclosed. A novel GA-NN hybrid is introduced, based on the bumptree, a little-used connectionist model. As well as being computationally efficient, the bumptree is shown to be more amenable to genetic coding lthan other NN models. A hierarchical genetic coding scheme is developed for the bumptree and shown to have low redundancy, as well as being complete and closed with respect to the search space. When applied to optimising bumptree architectures for classification problems the GA discovers bumptrees which significantly out-perform those constructed using a standard algorithm. The fields of artificial life, control and robotics are identified as likely application areas for the evolutionary optimisation of NNs. An artificial life case-study is presented and discussed. Experiments are reported which show that the GA-bumptree is able to learn simulated pole balancing and car parking tasks using only limited environmental feedback. A simple modification of the fitness function allows the GA-bumptree to learn mappings which are multi-modal, such as robot arm inverse kinematics. The dynamics of the 'geographic speciation' selection model used by the GA-bumptree are investigated empirically and the convergence profile is introduced as an analytical tool. The relationships between the rate of genetic convergence and the phenomena of speciation, genetic drift and punctuated equilibrium arc discussed. The importance of genetic linkage to GA design is discussed and two new recombination operators arc introduced. The first, linkage mapped crossover (LMX) is shown to be a generalisation of existing crossover operators. LMX provides a new framework for incorporating prior knowledge into GAs.Its adaptive form, ALMX, is shown to be able to infer linkage relationships automatically during genetic search.

Informal boundary-spanning links and networks in successful technological innovation

The sources of ideas embodied within successful technological innovation has been a subject of interest in many studies since the 1950s. This research suggests that sources external to the innovating organisation account for between one and two-thirds of the inputs important to the innovation process. In addition, studies have long highlighted the important role played by the personal boundary-spanning relationships of engineers and scientists as a channel for the transference of such inputs. However, research concerning the role and nature of personal boundary-spanning links in the innovation process have either been primarily structurally orientated, seeking to map out the informal networks of scientists and engineers, or more typically, anecdotal. The objective of this research was to reveal and build upon our knowledge of the role, nature and importance of informal exchange activity in the innovation process. In order to achieve this, an empirical study was undertaken to determine the informal sources, channels and mechanisms employed in the development of thirty five award-winning innovations. Through the adoption of the network perspective, the multiple sources and pluralistic patterns of collaboration and communication in the innovation process were systematically explored. This approach provided a framework that allowed for the detailed study of both the individual dyadic links and morphology of the innovation action-sets in which these dyads were embedded. The research found, for example, that the mobilisation of boundary-spanning links and networks was an important or critical factor in nineteen (54%) of the development projects. Of these, informal boundary-spanning exchange activity was considered to be important or critical in eight (23%).

Learning to share knowledge in the Italian motorsport industry

The paper outlines a perspective on learning how to share knowledge in the context of inter-firm networks and highlights the essential role of participation in collaborative activities. This perspective suggests that knowledge sharing is not something achieved through the simple transfer of resources, but rather is an ongoing social accomplishment in which network firms constitute and re-constitute knowledge while engaging in collaborative activities. Empirical support for this view is offered by an in-depth and multiyear study of the development of collaborative relationships between a leading racing car manufacturer and its suppliers in the Italian motorsport industry. The study shows that knowledge is generated over time through the instigation of three knowledge sharing processes: the promotion of a culture of working together, co-location and the use of resident engineers, and shared education and training.

Managing wireless networks in the healthcare sector:emerging experiences of cultural impacts

The existing body of knowledge has generally supported that organizational culture plays a significant role in shaping group identity, work pattern, communication schemes, and interpersonal relations; all of these cultural elements are important organizational factors that shape workplaces and operational routines. In the context of emerging information technology, it has also been suggested that organizational culture could affect IT implementation and management. However, little is known about how emerging information technology shapes organizational culture, which in turn helps reshape the organization as a whole. The purpose of this paper is thus to build empirical understanding of how IT in general and emerging wireless networks in particular reshapes organizational culture. Case studies conducted in two hospitals situated in southwest U.S.A. illustrated that the implementation of wireless networks indeed helped shape and/or reshape organizational culture in the healthcare sector and in turn enhance healthcare organizations’ competitiveness in the marketplace. For IT managers and practitioners in healthcare institutions, effective strategy to plan and manage emerging ITs such as wireless networks will thus have long-term implications on cultivating organizational culture that could eventually reshape workplace and competitiveness.