620,769 research outputs found

    Dendritic Spine Shape Analysis: A Clustering Perspective

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    Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis. We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types.Comment: Accepted for BioImageComputing workshop at ECCV 201

    Representation of transport: A Rural Destination Analysis

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    Moscovici’s social representations perspective is applied to a study of transport in a rural destination. The principles are demonstrated using empirical data from a questionnaire survey, developed following in-depth qualitative research. The data analysis strategy was founded on inductive reasoning, by employing cluster analysis and correspondence analysis. A social representations analysis demonstrates how individuals draw on socially accepted explanations of transport where they have little or no direct knowledge or experience of the actual transport modes (notably the alternatives to the car). By so doing, ideas are further perpetuated. Importantly there is ambiguity surrounding responsibility to take positive action yet a key to addressing transport issues is acknowledgement of responsibility. Keywords: social representations, transport, rural destinations

    How Similar Are the East Asian Economies? A Cluster Analysis Perspective on Economic Cooperation in the Region

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    Recent economic calamities such as the 1997 Asian financial crisis have amply demonstrated the need for increased economic integration in the East Asian economic region. While various forms of economic cooperation are possible, it is important to identify groups, or clusters, of countries that are similar to each other economically. Such similarity not only has been shown to be associated with the increased bilateral trade flows, but also with the increased net welfare gains to the participating countries. I employ a variety of clustering techniques and come up with a clustering solution containing four groups of economically similar countries. The clusters are robust across the estimation procedures. Hierarchical clustering also conducted in this study suggests a sequential agglomerating path for the countries to follow. The results of this study are intended as one of the (many) decision tools used by the parties considering multilateral economic cooperation and trade agreements in the region

    Territorial innovation dynamics: a knowledge based perspective

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    A great deal of studies has focused on the role played by geographical location on the emergence and the building of localised learning capacities (Maskell, Malmberg, 1999). In this perspective, empirical studies have demonstrated that innovation dynamics of clusters results from the quality of interactions and coordination inside the cluster as well as interactions with external, often global, networks. In this context, knowledge exchange between firms and institutions are claimed to be the main drivers of spatial agglomeration (Canals et al, 2008). Hence, cluster policies have followed the main idea that geographic proximity facilitates collective innovation in so far as firms can capture knowledge externalities more easily. This idea is in fact very attractive but contains some limits (Suire et Vicente, 2007): if some clusters are successful others seem to decline. Therefore, in order to understand the territorial dynamics of clusters, the analysis of the specific nature of knowledge and information flows within a cluster is crucial. The objective of the paper is to enhance the analysis of the role of cognitive and relational dimensions of interactions on territorial dynamics of innovation. We focus on the key sub process of innovation: knowledge creation, which is above all a social process based on two key complex social mechanisms: the exchange and the combination of knowledge (Nahapiet and Goshal, 1996). We suggest building a theoretical framework that hinges on these two key mechanisms. In this perspective, we mobilise Boisot's I-Space model (Boisot, 1998) for the diffusion and exchange of knowledge and suggest completing the model by introducing the concept of architectural knowledge (Henderson and Clark, 1990) so as to take the complexity of the combination process into consideration. This analysis is conducted through the illustrative analysis of three different case studies. We will draw upon the case of Aerospace Valley Pole of Competitiveness (PoC), The Secured Communicating Solutions PoC, and Fabelor Competence Cluster. The cases show that the existence of architectural knowledge is pivotal to territorial innovation.Architectural Knowledge, I-Space Model, Territorial Innovation, Geographical Clusters, Knowledge Management

    Long-Range Dependence in Financial Markets: a Moving Average Cluster Entropy Approach

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    A perspective is taken on the intangible complexity of economic and social systems by investigating the underlying dynamical processes that produce, store and transmit information in financial time series in terms of the \textit{moving average cluster entropy}. An extensive analysis has evidenced market and horizon dependence of the \textit{moving average cluster entropy} in real world financial assets. The origin of the behavior is scrutinized by applying the \textit{moving average cluster entropy} approach to long-range correlated stochastic processes as the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Fractional Brownian motion (FBM). To that end, an extensive set of series is generated with a broad range of values of the Hurst exponent HH and of the autoregressive, differencing and moving average parameters p,d,qp,d,q. A systematic relation between \textit{moving average cluster entropy}, \textit{Market Dynamic Index} and long-range correlation parameters HH, dd is observed. This study shows that the characteristic behaviour exhibited by the horizon dependence of the cluster entropy is related to long-range positive correlation in financial markets. Specifically, long range positively correlated ARFIMA processes with differencing parameter d≃0.05 d\simeq 0.05, d≃0.15d\simeq 0.15 and d≃0.25 d\simeq 0.25 are consistent with \textit{moving average cluster entropy} results obtained in time series of DJIA, S\&P500 and NASDAQ

    Perspectives on industrial clustering and the product, resource and knowledge based views of management

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    This project examines the theoretical basis for linking industrial clustering to the strategic management of firms. Specifically, a recently deployed theory building framework defined three perspectives on clustering, the competitiveness perspective, the externalities perspective and the territorial perspective, but stopped short of explaining when, where and to whom these perspectives are relevant. This thesis proposes that firms are the central recipient of cluster effects and that the product-based, resource-based and knowledge-based approaches to management provide the theoretical base from which the operational contexts of each cluster perspective can be defined. Three cluster-management relationships are modelled and beta-tested on a sample of cluster-based firms. The empirical analysis is designed to provide feedback to the theory building process and not to prove or disprove the theory itself. The analysis yielded little if any evidence that the proposed cluster-management relationships are present in the sample that was studied. This result was a surprise as the exuberance with which clusters and their benefits are often promoted suggests that in a cluster there should be a pronounced correlation between firm performance and cluster attributes. The statistical limitations of this analysis mean the results can not be inferred to the general population and that the theoretical propositions are not actually disproved. Nonetheless, the muted observations do cast attention on the need for better modelling and measurement instruments in the field of cluster research. In addition, this project initiates a deductive process by which subsequent research can focus on the causal pathways that comprise the phenomenon of industrial clustering; including the pathway that links clusters to firms and then to economic performance

    The European metropolitan region of Zurich - a cluster of economic clusters?

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    Switzerland is a small country and possesses only two or three major economic regions of a metropolitan character. From a Swiss perspective the most important region is the European Metropolitan Region of Zurich (EMRZ). The EMRZ covers the whole of the Zurich economic area as well as adjacent areas like Zug, Lucerne and Basel. In empirical terms the EMRZ shows an above average percentage share of manufacturing industries and advanced service companies. Although the EMRZ is recognised as the prime Swiss economic engine there is only vague perception about the locational situation of its more important industries such as pharmaceuticals, machinery, financial services and others. A true-type cluster analysis for the EMRZ is still lacking. This deficit of fundamental knowledge about the region seems all the more important since several economic promotion agencies market the greater Zurich economic area as a region with clusters in financial service industries and medical equipment. The paper thus presents in a first step the EMRZ delimitation using some selected statistical data which are put into relation with the whole of Switzerland. The emphasis of this analysis lies on the identification of the major manufacturing and service industries that are located within the EMRZ. Followed by a short overview of the different cluster theories and a working definition for an empirical cluster analysis. The next section produces a cluster analysis based on data from the Swiss Federal Office of Statistics. This results in the identification of major clusters, locational coefficients as well as a first glimpse at the interrelations between selected clusters. The paper finishes off with a hypothesis whether the spatial proximity of economic clusters in the case of the EMRZ bears any causality with its economic development. Key words: cluster analysis, European metropolitan region of Zurich, regional innovation system, spillovers
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