The Spatial Evolution of Innovation Networks: A Proximity Perspective

We propose an evolutionary perspective on the geography of network formation that is grounded in a dynamic proximity framework. In doing so, we root the proximity concept in an evolutionary approach to the geography of innovation networks. We discuss three topics. The first topic focuses on explaining the structure of networks. The second topic concentrates on explaining the effects of networks on the performance of actors. The third topic deals with the changing role of proximity dimensions in the formation and performance of innovation networks in the longer run.evolutionary economic geography, knowledge networks, innovation networks, dynamic proximity

Representation in Neural Networks

Artificial neural networks (ANNs) are computational systems that were inspired by biological neural networks in the brain. ANNs are trained to transform input into task appropriate output using learning algorithms rather than having all relevant aspects of the task explicitly encoded with symbolic rules. Despite the increasingly impressive performance and wide spread usage of ANNs in artificial intelligence (Krizhevsky et al.,2012., LeCun, et. al., 2015., Senjnowski, 2018., Floridi & Chiriatti, 2020), their operation remains somewhat mysterious. There is no widely accepted and comprehensive explanation of how these systems represent and process information (Bornstein, 2016., Habbis et. al., 2017, Schwartz-Ziv & Tishby, 2017). Approaches to explaining the operation of relatively simple neural network models have been discussed by philosophers since the inception of connectionist cognitive science. However, these discussions often relied on analysing the behaviour of a very small number of actual ANNs and there are important issues that still haven’t been resolved. I address this by using empirical data from my own unique analysis of a broad range of novel ANNs to evaluate some key philosophical approaches to understanding and comparing neural network models. I focus on structural-resemblance approaches and there has been a shift towards using structural approaches in cognitive neuroscience (Williams & Colling 2018., Boone & Piccinini, 2015., Kriegeskorte et al., 2008., Raizada and Conolly, 2012). Structural-resemblance explanations of representation rely on the idea that structural relations between representations might systematically correspond to the structural organisation of relevant aspects of the represented domain. My empirical work begins by extending Laakso and Cottrell’s (2000) method for assessing representation similarity in ANNs to explicitly compare the relevant structural relations between representations across distinct ANNs with diversely configured parameters. I apply this method to evaluate structural approaches to understanding representation in neural networks described by Churchland (1998,1989,1996,2007,2012) and O’Brien and Opie (2004,2006), along with other approaches including clustering (Shea, 2007) and mutual information (Azhar, 2017). My analysis of relatively simple facial recognition ANNs reveals that the structural relations between represented facial categories can vary between different ANNs and may reflect artificial relations rather than intuitive concepts of facial similarity. However, my analysis of a broad range of ANNs categorising various aspects of colour reveals that they develop robust and consistent task-dependent structural representations that do match the relational structure of corresponding human colour judgements. The task relevant structural arrangement of representations that are developed by these networks provides empirical support for the use of structural-resemblance approaches to explaining how ANNs represent and process information.Thesis (MPhil) -- University of Adelaide, School of Humanities, 202

Examining the roles of proximity in craft brewery knowledge-sharing and collaboration in Aotearoa New Zealand : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Albany, New Zealand

The research presented in this thesis examines the roles of proximity dimensions in inter-firm knowledge-sharing and collaboration between craft breweries in Aotearoa New Zealand. I sought to develop a deep understanding of proximity dimensions by responding to the following research questions: (1) What are the roles of proximity in knowledge-sharing between New Zealand’s craft breweries? (1a) How do other dimensions of proximity relate to geographic proximity in the New Zealand Craft Brewing Industry? (2) How are craft brewery collaboration modalities influenced by proximity dimensions in the New Zealand Craft Brewing Industry? Extant literature in this domain focuses on understanding the role of geography and contends that geographic proximity is neither necessary nor sufficient for inter-firm learning or collaboration. Such literature is constrained by static methodological approaches, grounded in positivism. Static positivistic approaches limit understanding as to how the roles of proximity dimensions inter-relate and change over time. Addressing this limitation with an exploratory qualitative approach deepens understanding of proximity in knowledge-sharing and collaboration. Towards this approach, I conduct this research following an interpretive research paradigm. Empirical material has been collected via semi-structured interviews with 24 participants, from 21 craft breweries, across six geographic regions of Aotearoa New Zealand. These interviews were conducted and subsequently analysed using a method devised from productive hermeneutic thinking. Findings show that the role of geographic proximity in craft brewery knowledge-sharing and collaboration is complex. It is a direct enabler of inter-firm knowledge-sharing, but it is foremost an enabler of other proximity dimensions that facilitate subsequent knowledge exchange and collaboration. By re-examining established proximity dimensions through a hermeneutic lens, this research presents alternate perspectives of institutional, cognitive, and organisational proximity. Contributions to knowledge are also made through the identification of three novel proximity dimensions: triadic proximity; adversarial proximity; and capacity proximity. The roles of each of these new proximities in craft brewery knowledge-sharing and collaboration are demonstrated in this research. The findings of this thesis may be used to inform New Zealand governmental policy, which has historically sought, and failed, to capitalise on proximity as a mechanism for enhancing national innovation performance. Findings may also be of value to industry practitioners, such as craft brewery managers seeking to learn from and collaborate with their industry counterparts

Development of a R package to facilitate the learning of clustering techniques

This project explores the development of a tool, in the form of a R package, to ease the process of learning clustering techniques, how they work and what their pros and cons are. This tool should provide implementations for several different clustering techniques with explanations in order to allow the student to get familiar with the characteristics of each algorithm by testing them against several different datasets while deepening their understanding of them through the explanations. Additionally, these explanations should adapt to the input data, making the tool not only adept for self-regulated learning but for teaching too.Grado en Ingeniería Informátic

Non-Metric Multi-Dimensional Scaling for Distance-Based Privacy-Preserving Data Mining

Recent advances in the field of data mining have led to major concerns about privacy. Sharing data with external parties for analysis puts private information at risk. The original data are often perturbed before external release to protect private information. However, data perturbation can decrease the utility of the output. A good perturbation technique requires balance between privacy and utility. This study proposes a new method for data perturbation in the context of distance-based data mining. We propose the use of non-metric multi-dimensional scaling (MDS) as a suitable technique to perturb data that are intended for distance-based data mining. The basic premise of this approach is to transform the original data into a lower dimensional space and generate new data that protect private details while maintaining good utility for distance-based data mining analysis. We investigate the extent the perturbed data are able to preserve useful statistics for distance-based analysis and to provide protection against malicious attacks. We demonstrate that our method provides an adequate alternative to data randomisation approaches and other dimensionality reduction approaches. Testing is conducted on a wide range of benchmarked datasets and against some existing perturbation methods. The results confirm that our method has very good overall performance, is competitive with other techniques, and produces clustering and classification results at least as good, and in some cases better, than the results obtained from the original data

Infinite feature selection: a graph-based feature filtering approach

We propose a filtering feature selection framework that considers a subset of features as a path in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles. By two different interpretations (exploiting properties of power series of matrices and relying on Markov chains fundamentals) we can evaluate the values of paths (i.e., feature subsets) of arbitrary lengths, eventually go to infinite, from which we dub our framework Infinite Feature Selection (Inf-FS). Going to infinite allows to constrain the computational complexity of the selection process, and to rank the features in an elegant way, that is, considering the value of any path (subset) containing a particular feature. We also propose a simple unsupervised strategy to cut the ranking, so providing the subset of features to keep. In the experiments, we analyze diverse setups with heterogeneous features, for a total of 11 benchmarks, comparing against 18 widely-known yet effective comparative approaches. The results show that Inf-FS behaves better in almost any situation, that is, when the number of features to keep are fixed a priori, or when the decision of the subset cardinality is part of the process

Strategies for image visualisation and browsing

PhDThe exploration of large information spaces has remained a challenging task even though the proliferation of database management systems and the state-of-the art retrieval algorithms is becoming pervasive. Signi cant research attention in the multimedia domain is focused on nding automatic algorithms for organising digital image collections into meaningful structures and providing high-semantic image indices. On the other hand, utilisation of graphical and interactive methods from information visualisation domain, provide promising direction for creating e cient user-oriented systems for image management. Methods such as exploratory browsing and query, as well as intuitive visual overviews of image collection, can assist the users in nding patterns and developing the understanding of structures and content in complex image data-sets. The focus of the thesis is combining the features of automatic data processing algorithms with information visualisation. The rst part of this thesis focuses on the layout method for displaying the collection of images indexed by low-level visual descriptors. The proposed solution generates graphical overview of the data-set as a combination of similarity based visualisation and random layout approach. Second part of the thesis deals with problem of visualisation and exploration for hierarchical organisation of images. Due to the absence of the semantic information, images are considered the only source of high-level information. The content preview and display of hierarchical structure are combined in order to support image retrieval. In addition to this, novel exploration and navigation methods are proposed to enable the user to nd the way through database structure and retrieve the content. On the other hand, semantic information is available in cases where automatic or semi-automatic image classi ers are employed. The automatic annotation of image items provides what is referred to as higher-level information. This type of information is a cornerstone of multi-concept visualisation framework which is developed as a third part of this thesis. This solution enables dynamic generation of user-queries by combining semantic concepts, supported by content overview and information ltering. Comparative analysis and user tests, performed for the evaluation of the proposed solutions, focus on the ways information visualisation a ects the image content exploration and retrieval; how e cient and comfortable are the users when using di erent interaction methods and the ways users seek for information through di erent types of database organisation

Multi-Input data ASsembly for joint Analysis (MIASA): A framework for the joint analysis of disjoint sets of variables

The joint analysis of two datasets and that describe the same phenomena (e.g. the cellular state), but measure disjoint sets of variables (e.g. mRNA vs. protein levels) is currently challenging. Traditional methods typically analyze single interaction patterns such as variance or covariance. However, problem-tailored external knowledge may contain multiple different information about the interaction between the measured variables. We introduce MIASA, a holistic framework for the joint analysis of multiple different variables. It consists of assembling multiple different information such as similarity vs. association, expressed in terms of interaction-scores or distances, for subsequent clustering/classification. In addition, our framework includes a novel qualitative Euclidean embedding method (qEE-Transition) which enables using Euclidean-distance/vector-based clustering/classification methods on datasets that have a non-Euclidean-based interaction structure. As an alternative to conventional optimization-based multidimensional scaling methods which are prone to uncertainties, our qEE-Transition generates a new vector representation for each element of the dataset union in a common Euclidean space while strictly preserving the original ordering of the assembled interaction-distances. To demonstrate our work, we applied the framework to three types of simulated datasets: samples from families of distributions, samples from correlated random variables, and time-courses of statistical moments for three different types of stochastic two-gene interaction models. We then compared different clustering methods with vs. without the qEE-Transition. For all examples, we found that the qEE-Transition followed by Ward clustering had superior performance compared to non-agglomerative clustering methods but had a varied performance against ultrametric-based agglomerative methods. We also tested the qEE-Transition followed by supervised and unsupervised machine learning methods and found promising results, however, more work is needed for optimal parametrization of these methods. As a future perspective, our framework points to the importance of more developments and validation of distance-distribution models aiming to capture multiple-complex interactions between different variables