Understanding policy implementation processes as self-organizing systems

Implementation studies and related research in organizational theory can be enhanced by drawing on the field of complex systems to understand better and, as a consequence, more successfully manage change. This article reinterprets data previously published in the British Journal of Management to reveal a new contribution, that policy implementation processes should be understood as a self-organizing system in which adaptive abilities are extremely important for stakeholders. In other words, national policy is reinterpreted at the local level, with each local organization uniquely mixing elements of national policy with their own requirements making policy implementation unpredictable and more sketchy. The original article explained different paces and directions of change in terms of traditional management processes: leadership, politics, implementation and vision. By reinterpreting the data, it is possible to reveal that deeper level processes, which are more emergent, are also at work influencing change, which the authors label possibility space. Implications for theory, policy and practice are identified.

Recursive self-organizing map as a contractive iterative function system

Recently, there has been a considerable research activity in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, the representational capabilities and internal representations of the models are not well understood. We rigorously analyze a generalization of the Self-Organizing Map (SOM) for processing sequential data, Recursive SOM (RecSOM [1]), as a non-autonomous dynamical system consisting off a set of fixed input maps. We show that contractive fixed input maps are likely to produce Markovian organizations of receptive fields o the RecSOM map. We derive bounds on parameter $\beta$ (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed input maps is guaranteed.

Self-organizing Map and Cellular Automata Combined Technique for Advanced Mesh Generation in Urban and Architectural Design

This paper presents a technique for building complex and adaptive meshes for urban and architectural design. The combination of a self-organizing map and cellular automata algorithms stands as a method for generating meshes otherwise static. This intends to be an auxiliary tool for the architect or the urban planner, improving control over large amounts of spatial information. The traditional grid employed as design aid is improved to become more general and flexible.

String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors

* Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM)

LEÃO, Adriano de Castro; DÓRIA NETO, Adrião Duarte; SOUSA, Maria Bernardete Cordeiro de. New developmental stages for common marmosets (Callithrix jacchus) using mass and age variables obtained by K-means algorithm and self-organizing maps (SOM). Computers in Biology and Medicine, v. 39, p. 853-859, 2009

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

Artificial Immune Tissue using Self-Organizing Networks

As introduced by Bentley et al. (2005), artificial immune systems (AIS) are lacking tissue, which is present in one form or another in all living multi-cellular organisms. Some have argued that this concept in the context of AIS brings little novelty to the already saturated field of the immune inspired computational research. This article aims to show that such a component of an AIS has the potential to bring an advantage to a data processing algorithm in terms of data pre-processing, clustering and extraction of features desired by the immune inspired system. The proposed tissue algorithm is based on self-organizing networks, such as self-organizing maps (SOM) developed by Kohonen (1996) and an analogy of the so called Toll-Like Receptors (TLR) affecting the activation function of the clusters developed by the SOM.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.

A mathematical view of biological complexity

This essay is a trial on giving some mathematical ideas about the concept of biological complexity, trying to explore four different attributes considered to be essential to characterize a complex system in a biological context: decomposition, heterogeneous assembly, self-organization, and adequacy. It is a theoretical and speculative approach, opening some possibilities to further numerical and experimental work, illustrated by references to several researches that applied the concepts presented here. (C) 2008 Elsevier B.V. All rights reserved.

Untersuchung der Auswirkungen verschiedener Variationsbereiche des differenziellen Lernens und Lehrens im weiten Sinn auf ausgewählte technische Grundfertigkeiten im Hallenhockey

Die Frage wie großmotorische Bewegungen gelernt werden beschäftigt nicht nur Sportler, Trainer und Sportlehrer sondern auch Ärzte und Physiotherapeuten. Die sportwissenschaftlichen Teildisziplinen Bewegungs- und Trainingswissenschaft versuchen diese Frage sowohl im Sinne der Grundlagenforschung (Wie funktioniert Bewegungslernen?) als auch hinsichtlich der praktischen Konsequenzen (Wie lehrt man Bewegungen?) zu beantworten. Innerhalb dieser Themenfelder existieren Modelle, die Bewegungslernen als gezielte und extern unterstützte Ausbildung zentralnervöser Bewegungsprogramme verstehen und solche, die Lernen als Selbstorganisationsprozess interpretieren. Letzteren ist das Differenzielle Lernen und Lehren (Schöllhorn, 1999) zuzuordnen, das die Notwendigkeit betont, Bewegungen durch die Steigerung der Variationen während der Aneignungsphase zu lernen und zu lehren. Durch eine Vielzahl an Variationen, so die Modellannahme, findet der Lernende ohne externe Vorgaben selbstorganisiert ein individuelles situatives Optimum. Die vorliegende Arbeit untersucht, welchen Einfluss Variationen verschiedener Art und Größe auf die Lern- und Aneignungsleistung großmotorischer Bewegungen haben und in wie fern personenübergreifende Optima existieren. In zwei Experimenten wird der Einfluss von räumlichen (Bewegungsausführung, Bewegungsergebnis) und zeitlichen Variationen (zeitliche Verteilung der Trainingsreize) auf die Aneignungs- und Lernleistung großmotorischer sportlicher Bewegungen am Beispiel zweier technischer Grundfertigkeiten des Hallenhockeys untersucht. Die Ergebnisse der Experimente stützen die bisherige Befundlage zum Differenziellen Lernen und Lehren, wonach eine Zunahme an Variation in der Aneignungsphase zu größeren Aneignungs- und Lernleistungen führt. Zusätzlich wird die Annahme bestätigt, dass ein Zusammenhang von Variationsbereich und Lernrate in Form eines Optimaltrends vorliegt. Neu sind die Hinweise auf die Dynamik von motorischen Lernprozessen (Experiment 1). Hier scheinen individuelle Faktoren (z. B. die Lernbiografie) als auch die Phase im Lernprozess (Aneignung, Lernen) Einfluss zu haben auf den Umfang und die Struktur eines für die optimale Adaptation notwendigen Variationsbereichs. Darüber hinaus weisen die Befunde auf verschiedene Aneignungs- und Lerneffekte aufgrund alleiniger Variation der zeitlichen Verteilung bei ansonsten gleichen Trainingsreizen hin (Experiment 2). Für zukünftige Forschungsarbeiten zum Erlernen großmotorischer Bewegungen und für die sportliche Praxis dürfte es daher erkenntnisreich sein, die Historie der intrinsischen Dynamik der lernenden Systeme stärker zu berücksichtigen. Neben Fragestellungen für die Grundlagenforschung zum (Bewegungs-)Lernen ließen sich hieraus unmittelbar praxisrelevante Erkenntnisse darüber ableiten, wie Bewegungslernprozesse mittels verschiedener Variationsbereiche strukturiert und gesteuert werden könnten.

Synthesis, structural order and magnetic behavior of self-assembled epsilon-Co nanocrystal arrays

The synthesis of magnetic nanoparticles with monodispere size distributions, their self assembly into ordered arrays and their magnetic behavior as a function of structural order (ferrofluids and 2D assemblies) are presented. Magnetic colloids of monodispersed, passivated, cobalt nanocrystals were produced by the rapid pyrolysis of cobalt carbonyl in solution. The size, size distribution (std. dev.< 5%) and the shape of the nanocrystals were controlled by varying the surfactant, its concentration, the reaction rate and the reaction temperature. The Co particles are defect-free single crystals with a complex cubic structure related to the beta phase of manganese (epsilon-Co). In the 2D assembly, a collective behavior was observed in the low-field susceptibility measurements where the magnetization of the zero field cooled process increases steadily and the magnetization of the field cooling process is independent the temperature. This was different from the observed behavior in a sample comprised of disordered interacting particles. A strong paramagnetic contribution appears at very low temperatures where the magnetization increases drastically after field cooling the sample. This has been attributed to the Co surfactant-particle interface since no magnetic atomic impurities are present in these samples.

Bio-inspired Mechanisms for Artificial Self-organised Systems

Self-organization is a growing interdisciplinary field of research about a phenomenon that can be observed in the Universe, in Nature and in social contexts. Research on self-organization tries to describe and explain forms, complex patterns and behaviours that arise from a collection of entities without an external organizer. As researchers in artificial systems, our aim is not to mimic self-organizing phenomena arising in Nature, but to understand and to control underlying mechanisms allowing desired emergence of forms, complex patterns and behaviours. Rather than attempting to eliminate such self-organization in artificial systems, we think that this might be deliberately harnessed in order to reach desirable global properties. In this paper we analyze three forms of self-organization: stigmergy, reinforcement mechanisms and cooperation. The amplification phenomena founded in stigmergic process or in reinforcement process are different forms of positive feedbacks that play a major role in building group activity or social organization. Cooperation is a functional form for self-organization because of its ability to guide local behaviours in order to obtain a relevant collective one. For each forms of self-organisation, we present a case study to show how we transposed it to some artificial systems and then analyse the strengths and weaknesses of such an approach