Self-organizing Routing Algorithm fo Wireless Sensors Networks (WSN) using Ant Colony Optimization (ACO) with Tinyos.

This paper describes the basic tools to work with wireless sensors. TinyOShas a componentbased architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools ? all of which can be used asia or be further refined for a custom application. TinyOS was originally developed as a research project at the University of California Berkeley, but has since grown to have an international community of developers and users. Some algorithms concerning packet routing are shown. Incar entertainment systems can be based on wireless sensors in order to obtain information from Internet, but routing protocols must be implemented in order to avoid bottleneck problems. Ant Colony algorithms are really useful in such cases, therefore they can be embedded into the sensors to perform such routing task.

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

A game theory based strategy for reducing energy consumption in cognitive WSN

Wireless sensor networks (WSNs) are one of the most important users of wireless communication technologies in the coming years and some challenges in this area must be addressed for their complete development. Energy consumption and spectrum availability are two of the most severe constraints of WSNs due to their intrinsic nature. The introduction of cognitive capabilities into these networks has arisen to face the issue of spectrum scarcity but could be used to face energy challenges too due to their new range of communication possibilities. In this paper a new strategy based on game theory for cognitive WSNs is discussed. The presented strategy improves energy consumption by taking advantage of the new change-communication-channel capability. Based on game theory, the strategy decides when to change the transmission channel depending on the behavior of the rest of the network nodes. The strategy presented is lightweight but still has higher energy saving rates as compared to noncognitive networks and even to other strategies based on scheduled spectrum sensing. Simulations are presented for several scenarios that demonstrate energy saving rates of around 65% as compared to WSNs without cognitive techniques.

Ecología política y economía de la visibilidad de los dispositivos tecnológicos de escala urbana durante el siglo XX Abriendo la caja negra

En paralelo a la superficie cotidiana de la ciudad moderna, discurre otra "ciudad invisible" o "caja negra" tecnológica, que opera, de manera silenciosa e imperceptible, al servicio de los ciudadanos. Paradójicamente, en este espacio invisible, se toman decisiones de la máxima relevancia para la ciudad: en la "caja negra", las ciudades pactan sus relaciones con la naturaleza; a través de ella, se produce la administración y distribución de los recursos que componen su metabolismo urbano. En definitiva, la "caja negra" es el conjunto de las arquitecturas donde se urbaniza la naturaleza, donde ésta pasa a ser ciudad. Y, sin embargo, ha tendido a permanecer invisible. No obstante, en el último tercio del siglo XX, se ha "abierto la caja negra" urbana y lo que era invisible ha dejado de serlo o, al menos, ha alterado su estatuto de visibilidad. El objetivo de esta tesis doctoral es evaluar las repercusiones arquitectónicas, urbanísticas y ecológicas, que conlleva este reciente fenómeno urbano que, desde hace unas décadas, está teniendo lugar en muchas de las ciudades herederas de las grandes reformas urbanas acometidas en las metrópolis europeas durante el siglo XIX, bajo el paradigma simbólico de un proyecto moderno "prometeico" y emancipador. Para abordar dicho análisis, se pondrán en relación dos parámetros urbanos elementales que han tendido a ser estudiados por separado. Por un lado, la "ecología política urbana", es decir, la red de relaciones socio-ecológicas que acontecen en la ciudad. Por otro lado, la "economía de la visibilidad", es decir, las formas de articular lo visible y lo invisible, en relación a los marcos de gobernanza. La intersección entre la "ecología política urbana" y la "economía de la visibilidad" proporciona un marco de análisis efectivo para comprender el fenómeno de la "apertura de la caja negra" y conlleva un cuestionamiento de algunas nociones dominantes en la teoría urbana y arquitectónica clásicas, como la segregación de la naturaleza, las infraestructuras y la sociedad o como las formas tradicionales de explotación ecológica del medio. Además, ofrece un criterio de análisis privilegiado para la comprensión del proceso de deslegitimación (filosófica, arquitectónica, económica, así como desde perspectivas ecológicas, sociales, de género o queer) de los modelos urbanísticos integrales modernos, herederos de los marcos antropocéntricos del siglo XIX. Por último, proporciona algunas herramientas arquitectónicas para afrontar los desafíos ecosistémicos del siglo XXI. A través del estudio de autores relevantes que han analizado esta problemática para la arquitectura, así como del estudio de casos arquitectónicos que han marcado hitos fundamentales en la consolidación urbana de los procesos asociados a la "caja negra", se concluirá que, en términos ecológicos, la ciudad moderna ha movilizado una "ecología política urbana" basada en fórmulas de sometimiento del entorno, a partir de operaciones arquitectónicas y tecnológicas invisibles. En esta tesis doctoral se estudiará la organización arquitectónica de las arquitecturas de la "caja negra" y se evaluará si el fenómeno de la "apertura de la caja negra" puede ser considerado como un síntoma de la alteración en la "ecología política urbana". 'Abriremos la caja negra" para estudiar cómo se integran en el espacio urbano los dispositivos tecnológicos de escala urbana, toda vez éstos han dejado de ser invisibles. Cómo participan, como un actor más, en la configuración de otros marcos de cohabitación, dentro de la ciudad contemporánea. ABSTRACT An 'invisible city' or technological 'black box' runs parallel to the day-to-day surface of modern cities, remaining silent, unnoticed, at the service of the citizenry. Paradoxically, this invisible space is where some of the most relevant decisions concerning the city are made: the 'black box' is where cities agree on their relationships with nature; it is used to manage and distribute the resources that form its urban metabolism. In short, the 'black box' is the collection of architectures where nature is urbanised, where it becomes a city. And in spite of all this, it has mostly remained invisible. Nevertheless, this urban 'black box' was opened during the last third of the 20th century, so what used to be invisible is invisible no more, or at least the laws governing its visibility have been altered. The purpose of this doctoral thesis is to evaluate the architectural, urban planning and ecological repercussions of this recent urban phenomenon that has been taking place for several decades in many of the cities that followed in the footsteps of the large European metropolises of the 19th century, under the symbolic paradigm of a modern 'prometheic' and emancipating project. This analysis shall be done by juxtaposing two basic urban parameters that in general have been studied separately: frstly the ‘urban political ecology', that is, the network of socio-ecological relationships within the city. Secondly, the 'economy of visibility', that is, the way the visible and invisible spheres are structured in relation with the governance frameworks. The intersection between the 'urban political ecology' and the 'economy of visibility' provides an effective analysis framework to understand the phenomenon of the 'opening of the black box'. It entails calling into question some of the predominant notions in classical urban and architectural theory, such as the segregation of nature, infrastructures and society, or the traditional forms of ecological usage of the environment. It also offers an exceptional analysis criterion to understand the discrediting process (from a philosophical, architectural, economic perspective, but also from the point of view of ecology, society, gender or queerness) of modern all-encompassing urban models that draw from the anthropocentric frameworks of the 19th century. Finally, it provides some architectural tools to tackle 21st-century ecosystemic challenges. The study of relevant authors that have analysed these problems for architecture, as well as the study of milestone architectural cases for the urban consolidation of processes associated to the 'black box', shall serve to reach the conclusion that, in ecological terms, modern cities have mobilised an 'urban political ecology' based on formulas of subjugation of the environment, through invisible architectural and technological operations. This thesis shall present an evaluation of whether the phenomenon of the 'opening of the black box' can be considered a symptom of the alteration of the 'urban political ecology'. We shall 'open the black box' to study the integration of the various urbanscale technological devices within the urban landscape, now that they have ceased to be invisible. We shall see how they participate, like any other actor, in the confguration of other cohabitation frameworks within today's cities.

O AGENDAMENTO DE TELEJORNAIS BRASILEIROS EM SITES NOTICIOSOS DE CONTEÚDO COLABORATIVO

The thesis investigates if with the free news production, people who post information on collaborative content sites, known as interacting, tend to reproduce information that was scheduled for Tv news. This study is a comparison of the collaborative content vehicles Vc reporter, Vc no G1 and Eu reporter with TV news SBT Brasil, Jornal Nacional, Jornal da Record and Jornal da Band. We sought to determine whether those newscasts guide the collaborative platforms. The hypothesis assumes that Brazilian TV news have been building over time a credible relationship with the viewer, so it is possible to think that the interacting use the same criteria for selecting the broadcasts and reproduce similar information in collaborative content sites. The method used was content analysis, based on the study of Laurence Bardin and the type of research used was quantitative. This research concluded that, within a small portion of the universe surveyed, there are schedules of television news across the collaborative content.

BIND—The Biomolecular Interaction Network Database

The Biomolecular Interaction Network Database (BIND; http://binddb.org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD.

Forming classes by stimulus frequency: Behavior and theory

Visual classification is the way we relate to different images in our environment as if they were the same, while relating differently to other collections of stimuli (e.g., human vs. animal faces). It is still not clear, however, how the brain forms such classes, especially when introduced with new or changing environments. To isolate a perception-based mechanism underlying class representation, we studied unsupervised classification of an incoming stream of simple images. Classification patterns were clearly affected by stimulus frequency distribution, although subjects were unaware of this distribution. There was a common bias to locate class centers near the most frequent stimuli and their boundaries near the least frequent stimuli. Responses were also faster for more frequent stimuli. Using a minimal, biologically based neural-network model, we demonstrate that a simple, self-organizing representation mechanism based on overlapping tuning curves and slow Hebbian learning suffices to ensure classification. Combined behavioral and theoretical results predict large tuning overlap, implicating posterior infero-temporal cortex as a possible site of classification.

Theory of orientation tuning in visual cortex.

The role of intrinsic cortical connections in processing sensory input and in generating behavioral output is poorly understood. We have examined this issue in the context of the tuning of neuronal responses in cortex to the orientation of a visual stimulus. We analytically study a simple network model that incorporates both orientation-selective input from the lateral geniculate nucleus and orientation-specific cortical interactions. Depending on the model parameters, the network exhibits orientation selectivity that originates from within the cortex, by a symmetry-breaking mechanism. In this case, the width of the orientation tuning can be sharp even if the lateral geniculate nucleus inputs are only weakly anisotropic. By using our model, several experimental consequences of this cortical mechanism of orientation tuning are derived. The tuning width is relatively independent of the contrast and angular anisotropy of the visual stimulus. The transient population response to changing of the stimulus orientation exhibits a slow "virtual rotation." Neuronal cross-correlations exhibit long time tails, the sign of which depends on the preferred orientations of the cells and the stimulus orientation.

Monetary Effervescence: A Sociological Theory of Religion Applied to Money

This project attempts to answer the question "What holds the construction of money together?" by asserting that it is money's religious nature which provides the moral compulsion for people to use, and continue to uphold, money as a socially constructed concept. This project is primarily descriptive and focuses on the religious nature of money by employing a sociological theory of religion in viewing money as a technical concept. This is an interdisciplinary work between religious studies, economics, and sociology and draws heavily from Emile Durkheim's 'The Elementary Forms of Religious Life' as well as work related to heterodox theories of money developed by Geoffrey Ingham, A. Mitchell Innes, and David Graeber. Two new concepts are developed: the idea of monetary sacrality and monetary effervescence, both of which serve to recharge the religious saliency of money. By developing the concept of monetary sacrality, this project shows how money acts to interpret our economic relations while also obfuscating complex power dynamics in society, making them seem naturally occurring and unchangeable. The project also shows how our contemporary fractional reserve banking system contributes to money's collective effervescence and serves to animate economic acting within a monetary network. The project concludes by outlining multiple implications for religious studies, economics, sociology, and central banking.

Shifting Between Hegemony and Dominance? A Neo-Gramscian Analysis of the EU as a Structural Foreign Policy Actor: The Singular Case of the Cariforum-EU Economic Partnership Agreement. Bruges Regional Integration & Global Governance Paper 01/2014

The groundbreaking scope of the Economic Partnership Agreement (EPA) between the European Union (EU) and Cariforum (CF) irrefutably marks a substantive shift in trade relations between the regions and also has far-reaching implications across several sectors and levels. Supplementing the framework of analysis of Structural Foreign Policy (SFP) with neo-Gramscian theory allows for a thorough investigation into the details of structural embeddedness based on the EU's historic directionality towards the Caribbean region; notably, encouraging integration into the global capitalist economy by adapting to and adopting the ideals of neoliberal economics. Whilst the Caribbean – as the first and only signatory of a ‘full’ EPA – may be considered the case par excellence of the success of the EPAs, this paper demonstrates that there is no cause-effect relationship between the singular case of the ‘full’ CF-EU EPA and the success of the EPA policy towards the ACP in general. The research detailed throughout this paper responds to two SFP-based questions: (1) To what extent is the EPA a SFP tool aimed at influencing and shaping the structures in the Caribbean? (2) To what extent is the internalisation of this process reflective of the EU as a hegemonic SFP actor vis-à-vis the Caribbean? This paper affirms both the role of the EU as a hegemonic SFP actor and the EPA as a hegemonic SFP tool. Research into the negotiation, agreement and controversy that surrounds every stage of the EPA confirmed that through modern diplomacy and an evolution in relations, consensus is at the fore of contemporary EU-Caribbean relations. Whilst at once dealing with the singular case of the Caribbean, the author offers a nuanced approach beyond 'EU navel-gazing' by incorporating an ‘outside-in’ perspective, which thereafter could be applied to EU-ACP relations and the North-South dialogue in general.

Sustainable Development and Collective Learning: Theory and a European Case Study. Bruges European Economic Policy (BEEP) Briefing 7/2004

Network governance of collective learning processes is an essential approach to sustainable development. The first section of the article briefly refers to recent theories about both market and government failures that express scepticism about the way framework conditions for market actors are set. For this reason, the development of networks for collective learning processes seems advantageous if new solutions are to be developed in policy areas concerned with long-term changes and a stepwise internalisation of externalities. With regard to corporate actors’ interests, the article shows recent insights from theories about the knowledge-based firm, where the creation of new knowledge is based on the absorption of societal views. This concept shifts the focus towards knowledge generation as an essential element in the evolution of sustainable markets. This involves at the same time the development of new policies. In this context innovation-inducing regulation is suggested and discussed. The evolution of the Swedish, German and Dutch wind turbine industries are analysed based on the approach of governance put forward in this article. We conclude that these coevolutionary mechanisms may take for granted some of the stabilising and orientating functions previously exercised by basic regulatory activities of the state. In this context, the main function of the governments is to facilitate learning processes that depart from the government functions suggested by welfare economics.

An approach to the general synthesis of a threshold element network.

Vita.

Parallel algorithms for network routing problems and recurrences /

"Grant no. US NSF MCS75-21758."

Network computer analysis,

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