6,706 research outputs found
How do Clusters/Pipelines and Core/Periphery Structures Work Together in Knowledge Processes?
This paper contributes to the empirical identification of geographical and structural properties of innovative networks, focusing on the particular case of Global Navigation Satellite Systems (GNSS) at the European level. We show that knowledge bases of organizations and knowledge phases of the innovation process are the critical factors in determining the nature of the interplay between structural and geographical features of knowledge networks. Developing a database of R&D collaborative projects of the 5th and 6th European Framework Programs, we propose a methodology based on social network analysis. Its originality consists in starting from a bimodal network, in order to deduce two affiliation matrixes that allow us to study both the properties of the organization network and the properties of the project network. The results are discussed in the light of the mutual influence of the cognitive, structural and geographical dimensions on knowledge production and diffusion, and in the light of the knowledge drivers that give rise to the coexistence of a relational core-periphery structure with a geographical cluster and pipeline structure.Economic Geography, Knowledge networks, Social network analysis, EU Framework Programs, GNSS
Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases
In many applications, such as physiology and finance, large time series data
bases are to be analyzed requiring the computation of linear, nonlinear and
other measures. Such measures have been developed and implemented in commercial
and freeware softwares rather selectively and independently. The Measures of
Analysis of Time Series ({\tt MATS}) {\tt MATLAB} toolkit is designed to handle
an arbitrary large set of scalar time series and compute a large variety of
measures on them, allowing for the specification of varying measure parameters
as well. The variety of options with added facilities for visualization of the
results support different settings of time series analysis, such as the
detection of dynamics changes in long data records, resampling (surrogate or
bootstrap) tests for independence and linearity with various test statistics,
and discrimination power of different measures and for different combinations
of their parameters. The basic features of {\tt MATS} are presented and the
implemented measures are briefly described. The usefulness of {\tt MATS} is
illustrated on some empirical examples along with screenshots.Comment: 25 pages, 9 figures, two tables, the software can be downloaded at
http://eeganalysis.web.auth.gr/indexen.ht
Fuzzy C-ordered medoids clustering of interval-valued data
Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The
Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a
partitioning around medoids approach. However, one of the greatest disadvantages of this method is its
sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering
method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Huber's
M-estimators and the Yager's Ordered Weighted Averaging (OWA) operators are used in the method
proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids
method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided
Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases
In many applications, such as physiology and finance, large time series data bases are to be analyzed requiring the computation of linear, nonlinear and other measures. Such measures have been developed and implemented in commercial and freeware softwares rather selectively and independently. The Measures of Analysis of Time Series (MATS) MATLAB toolkit is designed to handle an arbitrary large set of scalar time series and compute a large variety of measures on them, allowing for the specification of varying measure parameters as well. The variety of options with added facilities for visualization of the results support different settings of time series analysis, such as the detection of dynamics changes in long data records, resampling (surrogate or bootstrap) tests for independence and linearity with various test statistics, and discrimination power of different measures and for different combinations of their parameters. The basic features of MATS are presented and the implemented measures are briefly described. The usefulness of MATS is illustrated on some empirical examples along with screenshots.
The Impact of Export and Foreign Direct Investments on Macedonian GDP Growth - Empirical Analysis
In this paper in chronological order is analyzed the Macedonia's economic development in general, considering that the country has a liberal trade regime which is characterized by simplicity and neutrality. R. of Macedonia should utilize this trade regime in direction of creating policies and conditions for promoting the private sector development and its possibilities for export that will contribute for greater macroeconomic development. The paper will have a detailed look to the overall economic development and the GDP growth, the components and the main factors influencing this growth, techniques and approaches of assessment of the economic system and its development. It will also analyze the role of exports and the foreign direct investments in Macedonian GDP growth. Numerous theoretical researches related to the role of exports and FDI in GDP growth, have shown a positive relationship between them. The data used in this paper were provided by the Statistical Office of Macedonia and the Macedonian Customs in different periods, while for the empirical analysis I have included the period from 2014-2015. Within the empirical analysis is applied a model of multiple linear regression, where is defined the dependent variable "GDP growth" as well as the independent variables: the growth of FDI and the growth of export
Low-income children's pretend play: The contributory influences of individual and contextual factors
The purpose of the present study was to examine the contributory influences of individual and contextual factors that are associated with the complexity of pretend play in low-income preschool children. Forty-seven children and their teachers from six Head Start classrooms in Guilford County, North Carolina, participated in the study. Children's play behavior and their verbalization were video recorded using a camcorder and a wireless microphone for 10 minutes on two separate days during free play period. In addition, information on children's current language competence was collected using the Expressive Vocabulary Test (EVT). Lead teachers completed the Penn Interactive Peer Play Scale (PIPPS), a teacher rating scale of children's social skills. A teacher survey on children's pretend play provided descriptive information regarding teachers' beliefs about the importance of pretend play and relevant teaching practice. Individual factors investigated in the study included age, gender, language competence, and social skills. Contextual factors examined in the study included use of low-structure materials, level of peer involvement, peer language competence, and social configuration of the play group. The results showed that a combination of contextual factors (use of low-structure materials, level of peer involvement, and peer language competence) strongly predicted the complexity of pretend play. The social configuration of the play group was also found to be associated with the complexity of pretend play. Level of peer involvement and peer language competence were the most significant predictors in the complexity of pretend play in the study. Limitations as well as implications for future research are discussed
Extending the Modern Synthesis: The evolution of ecosystems
The Modern Evolutionary Synthesis formalizes the role of variation, heredity, differential reproduction and mutation in population genetics. Here we explore a mathematical structure, based on the asymptotic limit theorems of information theory, that instantiates the punctuated dynamic relations of organisms and their embedding environments. The mathematical overhead is considerable, and we conclude that the model must itself be extended even further to allow the possibility of the transfer of heritage information between different classes of organisms. In essence, we provide something of a formal roadmap for the modernization of the Modern Synthesis
MASISCo—Methodological Approach for the Selection of Information Security Controls
As cyber-attacks grow worldwide, companies have begun to realize the importance of being protected against malicious actions that seek to violate their systems and access their information assets. Faced with this scenario, organizations must carry out correct and efficient management of their information security, which implies that they must adopt a proactive attitude, implementing standards that allow them to reduce the risk of computer attacks. Unfortunately, the problem is not only implementing a standard but also determining the best way to do it, defining an implementation path that considers the particular objectives and conditions of the organization and its availability of resources. This paper proposes a methodological approach for selecting and planning security controls, standardizing and systematizing the process by modeling the situation (objectives and constraints), and applying optimization techniques. The work presents an evaluation of the proposal through a methodology adoption study. This study showed a tendency of the study subjects to adopt the proposal, perceiving it as a helpful element that adapts to their way of working. The main weakness of the proposal was centered on ease of use since the modeling and resolution of the problem require advanced knowledge of optimization techniques.This research was funded by Universidad de La Frontera, research direction, research project DIUFRO DI22-0043
Practical recommendations for gradient-based training of deep architectures
Learning algorithms related to artificial neural networks and in particular
for Deep Learning may seem to involve many bells and whistles, called
hyper-parameters. This chapter is meant as a practical guide with
recommendations for some of the most commonly used hyper-parameters, in
particular in the context of learning algorithms based on back-propagated
gradient and gradient-based optimization. It also discusses how to deal with
the fact that more interesting results can be obtained when allowing one to
adjust many hyper-parameters. Overall, it describes elements of the practice
used to successfully and efficiently train and debug large-scale and often deep
multi-layer neural networks. It closes with open questions about the training
difficulties observed with deeper architectures
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