Dielectric recovery of short A-C arcs between low-boiling-point electrodes

The re-ignition characteristics (variation of re-ignition voltage with time after current zero) of short alternating current arcs between plane brass electrodes in air were studied by observing the average re-ignition voltages on the screen of a cathode-ray oscilloscope and controlling the rates of rise of voltage by varying the shunting capacitance and hence the natural period of oscillation of the reactors used to limit the current. The shape of these characteristics and the effects on them of varying the electrode separation, air pressure, and current strength were determined.

The results show that short arc spaces recover dielectric strength in two distinct stages. The first stage agrees in shape and magnitude with a previously developed theory that all voltage is concentrated across a partially deionized space charge layer which increases its breakdown voltage with diminishing density of ionization in the field-tree space. The second stage appears to follow complete deionization by the electric field due to displacement of the field-free region by the space charge layer, its magnitude and shape appearing to be due simply to increase in gas density due to cooling. Temperatures calculated from this second stage and ion densities determined from the first stage by means of the space charge equation and an extrapolation of the temperature curve are consistent with recent measurements of arc value by other methods. Analysis or the decrease with time of the apparent ion density shows that diffusion alone is adequate to explain the results and that volume recombination is not. The effects on the characteristics of variations in the parameters investigated are found to be in accord with previous results and with the theory if deionization mainly by diffusion be assumed.

Algorithmes pour la réconciliation d’un arbre de gènes avec un arbre d’espèces

Une réconciliation entre un arbre de gènes et un arbre d’espèces décrit une histoire d’évolution des gènes homologues en termes de duplications et pertes de gènes. Pour inférer une réconciliation pour un arbre de gènes et un arbre d’espèces, la parcimonie est généralement utilisée selon le nombre de duplications et/ou de pertes. Les modèles de réconciliation sont basés sur des critères probabilistes ou combinatoires. Le premier article définit un modèle combinatoire simple et général où les duplications et les pertes sont clairement identifiées et la réconciliation parcimonieuse n’est pas la seule considérée. Une architecture de toutes les réconciliations est définie et des algorithmes efficaces (soit de dénombrement, de génération aléatoire et d’exploration) sont développés pour étudier les propriétés combinatoires de l’espace de toutes les réconciliations ou seulement les plus parcimonieuses. Basée sur le processus classique nommé naissance-et-mort, un algorithme qui calcule la vraisemblance d’une réconciliation a récemment été proposé. Le deuxième article utilise cet algorithme avec les outils combinatoires décrits ci-haut pour calculer efficacement (soit approximativement ou exactement) les probabilités postérieures des réconciliations localisées dans le sous-espace considéré. Basé sur des taux réalistes (selon un modèle probabiliste) de duplication et de perte et sur des données réelles/simulées de familles de champignons, nos résultats suggèrent que la masse probabiliste de toute l’espace des réconciliations est principalement localisée autour des réconciliations parcimonieuses. Dans un contexte d’approximation de la probabilité d’une réconciliation, notre approche est une alternative intéressante face aux méthodes MCMC et peut être meilleure qu’une approche sophistiquée, efficace et exacte pour calculer la probabilité d’une réconciliation donnée. Le problème nommé Gene Tree Parsimony (GTP) est d’inférer un arbre d’espèces qui minimise le nombre de duplications et/ou de pertes pour un ensemble d’arbres de gènes. Basé sur une approche qui explore tout l’espace des arbres d’espèces pour les génomes considérés et un calcul efficace des coûts de réconciliation, le troisième article décrit un algorithme de Branch-and-Bound pour résoudre de façon exacte le problème GTP. Lorsque le nombre de taxa est trop grand, notre algorithme peut facilement considérer des relations prédéfinies entre ensembles de taxa. Nous avons testé notre algorithme sur des familles de gènes de 29 eucaryotes.

Referências históricas e o realismo mágico em Il barone rampante, de Italo Calvino

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Polytomies and bayesian phylogenetic inference

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.

Polytomies and bayesian phylogenetic inference

Bayesian phylogenetic analyses are now very popular in systematics and molecular evolution because they allow the use of much more realistic models than currently possible with maximum likelihood methods. There are, however, a growing number of examples in which large Bayesian posterior clade probabilities are associated with very short edge lengths and low values for non-Bayesian measures of support such as nonparametric bootstrapping. For the four-taxon case when the true tree is the star phylogeny, Bayesian analyses become increasingly unpredictable in their preference for one of the three possible resolved tree topologies as data set size increases. This leads to the prediction that hard (or near-hard) polytomies in nature will cause unpredictable behavior in Bayesian analyses, with arbitrary resolutions of the polytomy receiving very high posterior probabilities in some cases. We present a simple solution to this problem involving a reversible-jump Markov chain Monte Carlo (MCMC) algorithm that allows exploration of all of tree space, including unresolved tree topologies with one or more polytomies. The reversible-jump MCMC approach allows prior distributions to place some weight on less-resolved tree topologies, which eliminates misleadingly high posteriors associated with arbitrary resolutions of hard polytomies. Fortunately, assigning some prior probability to polytomous tree topologies does not appear to come with a significant cost in terms of the ability to assess the level of support for edges that do exist in the true tree. Methods are discussed for applying arbitrary prior distributions to tree topologies of varying resolution, and an empirical example showing evidence of polytomies is analyzed and discussed.

Radio propagation modeling and measurements for ZigBee based indoor wireless sensor networks

The deployment of nodes in Wireless Sensor Networks (WSNs) arises as one of the biggest challenges of this field, which involves in distributing a large number of embedded systems to fulfill a specific application. The connectivity of WSNs is difficult to estimate due to the irregularity of the physical environment and affects the WSN designers? decision on deploying sensor nodes. Therefore, in this paper, a new method is proposed to enhance the efficiency and accuracy on ZigBee propagation simulation in indoor environments. The method consists of two steps: automatic 3D indoor reconstruction and 3D ray-tracing based radio simulation. The automatic 3D indoor reconstruction employs unattended image classification algorithm and image vectorization algorithm to build the environment database accurately, which also significantly reduces time and efforts spent on non-radio propagation issue. The 3D ray tracing is developed by using kd-tree space division algorithm and a modified polar sweep algorithm, which accelerates the searching of rays over the entire space. Signal propagation model is proposed for the ray tracing engine by considering both the materials of obstacles and the impact of positions along the ray path of radio. Three different WSN deployments are realized in the indoor environment of an office and the results are verified to be accurate. Experimental results also indicate that the proposed method is efficient in pre-simulation strategy and 3D ray searching scheme and is suitable for different indoor environments.

A novel methodology for planning reliable wireless sensor networks

Wireless sensor networks (WSNs) have shown their potentials in various applications, which bring a lot of benefits to users from both research and industrial areas. For many setups, it is envisioned thatWSNs will consist of tens to hundreds of nodes that operate on small batteries. However due to the diversity of the deployed environments and resource constraints on radio communication, sensing ability and energy supply, it is a very challenging issue to plan optimized WSN topology and predict its performance before real deployment. During the network planning phase, the connectivity, coverage, cost, network longevity and service quality should all be considered. Therefore it requires designers coping with comprehensive and interdisciplinary knowledge, including networking, radio engineering, embedded system and so on, in order to efficiently construct a reliable WSN for any specific types of environment. Nowadays there is still a lack of the analysis and experiences to guide WSN designers to efficiently construct WSN topology successfully without many trials. Therefore, simulation is a feasible approach to the quantitative analysis of the performance of wireless sensor networks. However the existing planning algorithms and tools, to some extent, have serious limitations to practically design reliable WSN topology: Only a few of them tackle the 3D deployment issue, and an overwhelming number of works are proposed to place devices in 2D scheme. Without considering the full dimension, the impacts of environment to the performance of WSN are not completely studied, thus the values of evaluated metrics such as connectivity and sensing coverage are not sufficiently accurate to make proper decision. Even fewer planning methods model the sensing coverage and radio propagation by considering the realistic scenario where obstacles exist. Radio signals propagate with multi-path phenomenon in the real world, in which direct paths, reflected paths and diffracted paths contribute to the received signal strength. Besides, obstacles between the path of sensor and objects might block the sensing signals, thus create coverage hole in the application. None of the existing planning algorithms model the network longevity and packet delivery capability properly and practically. They often employ unilateral and unrealistic formulations. The optimization targets are often one-sided in the current works. Without comprehensive evaluation on the important metrics, the performance of planned WSNs can not be reliable and entirely optimized. Modeling of environment is usually time consuming and the cost is very high, while none of the current works figure out any method to model the 3D deployment environment efficiently and accurately. Therefore many researchers are trapped by this issue, and their algorithms can only be evaluated in the same scenario, without the possibility to test the robustness and feasibility for implementations in different environments. In this thesis, we propose a novel planning methodology and an intelligent WSN planning tool to assist WSN designers efficiently planning reliable WSNs. First of all, a new method is proposed to efficiently and automatically model the 3D indoor and outdoor environments. To the best of our knowledge, this is the first time that the advantages of image understanding algorithm are applied to automatically reconstruct 3D outdoor and indoor scenarios for signal propagation and network planning purpose. The experimental results indicate that the proposed methodology is able to accurately recognize different objects from the satellite images of the outdoor target regions and from the scanned floor plan of indoor area. Its mechanism offers users a flexibility to reconstruct different types of environment without any human interaction. Thereby it significantly reduces human efforts, cost and time spent on reconstructing a 3D geographic database and allows WSN designers concentrating on the planning issues. Secondly, an efficient ray-tracing engine is developed to accurately and practically model the radio propagation and sensing signal on the constructed 3D map. The engine contributes on efficiency and accuracy to the estimated results. By using image processing concepts, including the kd-tree space division algorithm and modified polar sweep algorithm, the rays are traced efficiently without detecting all the primitives in the scene. The radio propagation model iv is proposed, which emphasizes not only the materials of obstacles but also their locations along the signal path. The sensing signal of sensor nodes, which is sensitive to the obstacles, is benefit from the ray-tracing algorithm via obstacle detection. The performance of this modelling method is robust and accurate compared with conventional methods, and experimental results imply that this methodology is suitable for both outdoor urban scenes and indoor environments. Moreover, it can be applied to either GSM communication or ZigBee protocol by varying frequency parameter of the radio propagation model. Thirdly, WSN planning method is proposed to tackle the above mentioned challenges and efficiently deploy reliable WSNs. More metrics (connectivity, coverage, cost, lifetime, packet latency and packet drop rate) are modeled more practically compared with other works. Especially 3D ray tracing method is used to model the radio link and sensing signal which are sensitive to the obstruction of obstacles; network routing is constructed by using AODV protocol; the network longevity, packet delay and packet drop rate are obtained via simulating practical events in WSNet simulator, which to the best of our knowledge, is the first time that network simulator is involved in a planning algorithm. Moreover, a multi-objective optimization algorithm is developed to cater for the characteristics of WSNs. The capability of providing multiple optimized solutions simultaneously allows users making their own decisions accordingly, and the results are more comprehensively optimized compared with other state-of-the-art algorithms. iMOST is developed by integrating the introduced algorithms, to assist WSN designers efficiently planning reliable WSNs for different configurations. The abbreviated name iMOST stands for an Intelligent Multi-objective Optimization Sensor network planning Tool. iMOST contributes on: (1) Convenient operation with a user-friendly vision system; (2) Efficient and automatic 3D database reconstruction and fast 3D objects design for both indoor and outdoor environments; (3) It provides multiple multi-objective optimized 3D deployment solutions and allows users to configure the network properties, hence it can adapt to various WSN applications; (4) Deployment solutions in the 3D space and the corresponding evaluated performance are visually presented to users; and (5) The Node Placement Module of iMOST is available online as well as the source code of the other two rebuilt heuristics. Therefore WSN designers will be benefit from v this tool on efficiently constructing environment database, practically and efficiently planning reliable WSNs for both outdoor and indoor applications. With the open source codes, they are also able to compare their developed algorithms with ours to contribute to this academic field. Finally, solid real results are obtained for both indoor and outdoor WSN planning. Deployments have been realized for both indoor and outdoor environments based on the provided planning solutions. The measured results coincide well with the estimated results. The proposed planning algorithm is adaptable according to the WSN designer’s desirability and configuration, and it offers flexibility to plan small and large scale, indoor and outdoor 3D deployments. The thesis is organized in 7 chapters. In Chapter 1, WSN applications and motivations of this work are introduced, the state-of-the-art planning algorithms and tools are reviewed, challenges are stated out and the proposed methodology is briefly introduced. In Chapter 2, the proposed 3D environment reconstruction methodology is introduced and its performance is evaluated for both outdoor and indoor environment. The developed ray-tracing engine and proposed radio propagation modelling method are described in details in Chapter 3, their performances are evaluated in terms of computation efficiency and accuracy. Chapter 4 presents the modelling of important metrics of WSNs and the proposed multi-objective optimization planning algorithm, the performance is compared with the other state-of-the-art planning algorithms. The intelligent WSN planning tool iMOST is described in Chapter 5. RealWSN deployments are prosecuted based on the planned solutions for both indoor and outdoor scenarios, important data are measured and results are analysed in Chapter 6. Chapter 7 concludes the thesis and discusses about future works. vi Resumen en Castellano Las redes de sensores inalámbricas (en inglés Wireless Sensor Networks, WSNs) han demostrado su potencial en diversas aplicaciones que aportan una gran cantidad de beneficios para el campo de la investigación y de la industria. Para muchas configuraciones se prevé que las WSNs consistirán en decenas o cientos de nodos que funcionarán con baterías pequeñas. Sin embargo, debido a la diversidad de los ambientes para desplegar las redes y a las limitaciones de recursos en materia de comunicación de radio, capacidad de detección y suministro de energía, la planificación de la topología de la red y la predicción de su rendimiento es un tema muy difícil de tratar antes de la implementación real. Durante la fase de planificación del despliegue de la red se deben considerar aspectos como la conectividad, la cobertura, el coste, la longevidad de la red y la calidad del servicio. Por lo tanto, requiere de diseñadores con un amplio e interdisciplinario nivel de conocimiento que incluye la creación de redes, la ingeniería de radio y los sistemas embebidos entre otros, con el fin de construir de manera eficiente una WSN confiable para cualquier tipo de entorno. Hoy en día todavía hay una falta de análisis y experiencias que orienten a los diseñadores de WSN para construir las topologías WSN de manera eficiente sin realizar muchas pruebas. Por lo tanto, la simulación es un enfoque viable para el análisis cuantitativo del rendimiento de las redes de sensores inalámbricos. Sin embargo, los algoritmos y herramientas de planificación existentes tienen, en cierta medida, serias limitaciones para diseñar en la práctica una topología fiable de WSN: Sólo unos pocos abordan la cuestión del despliegue 3D mientras que existe una gran cantidad de trabajos que colocan los dispositivos en 2D. Si no se analiza la dimensión completa (3D), los efectos del entorno en el desempeño de WSN no se estudian por completo, por lo que los valores de los parámetros evaluados, como la conectividad y la cobertura de detección, no son lo suficientemente precisos para tomar la decisión correcta. Aún en menor medida los métodos de planificación modelan la cobertura de los sensores y la propagación de la señal de radio teniendo en cuenta un escenario realista donde existan obstáculos. Las señales de radio en el mundo real siguen una propagación multicamino, en la que los caminos directos, los caminos reflejados y los caminos difractados contribuyen a la intensidad de la señal recibida. Además, los obstáculos entre el recorrido del sensor y los objetos pueden bloquear las señales de detección y por lo tanto crear áreas sin cobertura en la aplicación. Ninguno de los algoritmos de planificación existentes modelan el tiempo de vida de la red y la capacidad de entrega de paquetes correctamente y prácticamente. A menudo se emplean formulaciones unilaterales y poco realistas. Los objetivos de optimización son a menudo tratados unilateralmente en los trabajos actuales. Sin una evaluación exhaustiva de los parámetros importantes, el rendimiento previsto de las redes inalámbricas de sensores no puede ser fiable y totalmente optimizado. Por lo general, el modelado del entorno conlleva mucho tiempo y tiene un coste muy alto, pero ninguno de los trabajos actuales propone algún método para modelar el entorno de despliegue 3D con eficiencia y precisión. Por lo tanto, muchos investigadores están limitados por este problema y sus algoritmos sólo se pueden evaluar en el mismo escenario, sin la posibilidad de probar la solidez y viabilidad para las implementaciones en diferentes entornos. En esta tesis, se propone una nueva metodología de planificación así como una herramienta inteligente de planificación de redes de sensores inalámbricas para ayudar a los diseñadores a planificar WSNs fiables de una manera eficiente. En primer lugar, se propone un nuevo método para modelar demanera eficiente y automática los ambientes interiores y exteriores en 3D. Según nuestros conocimientos hasta la fecha, esta es la primera vez que las ventajas del algoritmo de _image understanding_se aplican para reconstruir automáticamente los escenarios exteriores e interiores en 3D para analizar la propagación de la señal y viii la planificación de la red. Los resultados experimentales indican que la metodología propuesta es capaz de reconocer con precisión los diferentes objetos presentes en las imágenes satelitales de las regiones objetivo en el exterior y de la planta escaneada en el interior. Su mecanismo ofrece a los usuarios la flexibilidad para reconstruir los diferentes tipos de entornos sin ninguna interacción humana. De este modo se reduce considerablemente el esfuerzo humano, el coste y el tiempo invertido en la reconstrucción de una base de datos geográfica con información 3D, permitiendo así que los diseñadores se concentren en los temas de planificación. En segundo lugar, se ha desarrollado un motor de trazado de rayos (en inglés ray tracing) eficiente para modelar con precisión la propagación de la señal de radio y la señal de los sensores en el mapa 3D construido. El motor contribuye a la eficiencia y la precisión de los resultados estimados. Mediante el uso de los conceptos de procesamiento de imágenes, incluyendo el algoritmo del árbol kd para la división del espacio y el algoritmo _polar sweep_modificado, los rayos se trazan de manera eficiente sin la detección de todas las primitivas en la escena. El modelo de propagación de radio que se propone no sólo considera los materiales de los obstáculos, sino también su ubicación a lo largo de la ruta de señal. La señal de los sensores de los nodos, que es sensible a los obstáculos, se ve beneficiada por la detección de objetos llevada a cabo por el algoritmo de trazado de rayos. El rendimiento de este método de modelado es robusto y preciso en comparación con los métodos convencionales, y los resultados experimentales indican que esta metodología es adecuada tanto para escenas urbanas al aire libre como para ambientes interiores. Por otra parte, se puede aplicar a cualquier comunicación GSM o protocolo ZigBee mediante la variación de la frecuencia del modelo de propagación de radio. En tercer lugar, se propone un método de planificación de WSNs para hacer frente a los desafíos mencionados anteriormente y desplegar redes de sensores fiables de manera eficiente. Se modelan más parámetros (conectividad, cobertura, coste, tiempo de vida, la latencia de paquetes y tasa de caída de paquetes) en comparación con otros trabajos. Especialmente el método de trazado de rayos 3D se utiliza para modelar el enlace de radio y señal de los sensores que son sensibles a la obstrucción de obstáculos; el enrutamiento de la red se construye utilizando el protocolo AODV; la longevidad de la red, retardo de paquetes ix y tasa de abandono de paquetes se obtienen a través de la simulación de eventos prácticos en el simulador WSNet, y según nuestros conocimientos hasta la fecha, es la primera vez que simulador de red está implicado en un algoritmo de planificación. Por otra parte, se ha desarrollado un algoritmo de optimización multi-objetivo para satisfacer las características de las redes inalámbricas de sensores. La capacidad de proporcionar múltiples soluciones optimizadas de forma simultánea permite a los usuarios tomar sus propias decisiones en consecuencia, obteniendo mejores resultados en comparación con otros algoritmos del estado del arte. iMOST se desarrolla mediante la integración de los algoritmos presentados, para ayudar de forma eficiente a los diseñadores en la planificación de WSNs fiables para diferentes configuraciones. El nombre abreviado iMOST (Intelligent Multi-objective Optimization Sensor network planning Tool) representa una herramienta inteligente de planificación de redes de sensores con optimización multi-objetivo. iMOST contribuye en: (1) Operación conveniente con una interfaz de fácil uso, (2) Reconstrucción eficiente y automática de una base de datos con información 3D y diseño rápido de objetos 3D para ambientes interiores y exteriores, (3) Proporciona varias soluciones de despliegue optimizadas para los multi-objetivo en 3D y permite a los usuarios configurar las propiedades de red, por lo que puede adaptarse a diversas aplicaciones de WSN, (4) las soluciones de implementación en el espacio 3D y el correspondiente rendimiento evaluado se presentan visualmente a los usuarios, y (5) El _Node Placement Module_de iMOST está disponible en línea, así como el código fuente de las otras dos heurísticas de planificación. Por lo tanto los diseñadores WSN se beneficiarán de esta herramienta para la construcción eficiente de la base de datos con información del entorno, la planificación práctica y eficiente de WSNs fiables tanto para aplicaciones interiores y exteriores. Con los códigos fuente abiertos, son capaces de comparar sus algoritmos desarrollados con los nuestros para contribuir a este campo académico. Por último, se obtienen resultados reales sólidos tanto para la planificación de WSN en interiores y exteriores. Los despliegues se han realizado tanto para ambientes de interior y como para ambientes de exterior utilizando las soluciones de planificación propuestas. Los resultados medidos coinciden en gran medida con los resultados estimados. El algoritmo de planificación x propuesto se adapta convenientemente al deiseño de redes de sensores inalámbricas, y ofrece flexibilidad para planificar los despliegues 3D a pequeña y gran escala tanto en interiores como en exteriores. La tesis se estructura en 7 capítulos. En el Capítulo 1, se presentan las aplicaciones de WSN y motivaciones de este trabajo, se revisan los algoritmos y herramientas de planificación del estado del arte, se presentan los retos y se describe brevemente la metodología propuesta. En el Capítulo 2, se presenta la metodología de reconstrucción de entornos 3D propuesta y su rendimiento es evaluado tanto para espacios exteriores como para espacios interiores. El motor de trazado de rayos desarrollado y el método de modelado de propagación de radio propuesto se describen en detalle en el Capítulo 3, evaluándose en términos de eficiencia computacional y precisión. En el Capítulo 4 se presenta el modelado de los parámetros importantes de las WSNs y el algoritmo de planificación de optimización multi-objetivo propuesto, el rendimiento se compara con los otros algoritmos de planificación descritos en el estado del arte. La herramienta inteligente de planificación de redes de sensores inalámbricas, iMOST, se describe en el Capítulo 5. En el Capítulo 6 se llevan a cabo despliegues reales de acuerdo a las soluciones previstas para los escenarios interiores y exteriores, se miden los datos importantes y se analizan los resultados. En el Capítulo 7 se concluye la tesis y se discute acerca de los trabajos futuros.

Matchings and phylogenetic trees

This paper presents a natural coordinate system for phylogenetic trees using a correspondence with the set of perfect matchings in the complete graph. This correspondence produces a distance between phylogenetic trees, and a way of enumerating all trees in a minimal step order. It is useful in randomized algorithms because it enables moves on the space of trees that make random optimization strategies “mix” quickly. It also promises a generalization to intermediary trees when data are not decisive as to their choice of tree, and a new way of constructing Bayesian priors on tree space.

Searching for convergence in phylogenetic Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) is a methodology that is gaining widespread use in the phylogenetics community and is central to phylogenetic software packages such as MrBayes. An important issue for users of MCMC methods is how to select appropriate values for adjustable parameters such as the length of the Markov chain or chains, the sampling density, the proposal mechanism, and, if Metropolis-coupled MCMC is being used, the number of heated chains and their temperatures. Although some parameter settings have been examined in detail in the literature, others are frequently chosen with more regard to computational time or personal experience with other data sets. Such choices may lead to inadequate sampling of tree space or an inefficient use of computational resources. We performed a detailed study of convergence and mixing for 70 randomly selected, putatively orthologous protein sets with different sizes and taxonomic compositions. Replicated runs from multiple random starting points permit a more rigorous assessment of convergence, and we developed two novel statistics, delta and epsilon, for this purpose. Although likelihood values invariably stabilized quickly, adequate sampling of the posterior distribution of tree topologies took considerably longer. Our results suggest that multimodality is common for data sets with 30 or more taxa and that this results in slow convergence and mixing. However, we also found that the pragmatic approach of combining data from several short, replicated runs into a metachain to estimate bipartition posterior probabilities provided good approximations, and that such estimates were no worse in approximating a reference posterior distribution than those obtained using a single long run of the same length as the metachain. Precision appears to be best when heated Markov chains have low temperatures, whereas chains with high temperatures appear to sample trees with high posterior probabilities only rarely. [Bayesian phylogenetic inference; heating parameter; Markov chain Monte Carlo; replicated chains.]

Towards automatic tree crown detection and delineation in spectral feature space using PCNN and morphological reconstruction

The application of object-based approaches to the problem of extracting vegetation information from images requires accurate delineation of individual tree crowns. This paper presents an automated method for individual tree crown detection and delineation by applying a simplified PCNN model in spectral feature space followed by post-processing using morphological reconstruction. The algorithm was tested on high resolution multi-spectral aerial images and the results are compared with two existing image segmentation algorithms. The results demonstrate that our algorithm outperforms the other two solutions with the average accuracy of 81.8%.

K-tree : large scale document clustering

We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.

Tree of knowledge memorial

This article discusses the design of a memorial space of the Tree of Knowledge, located in Oak Street, Barcaldine, Queensland designed by Brian Hooper Architect and M3 Architecture. Features of the design include the dead tree trunk underneath pieces of timber hanging down to show the original size of the tree in the 1890s and the root ball visible under a glass floor.

Rethinking regional green space networks in China

Ecological networks are often represented as utopian webs of green meandering through cities, across states, through regions and even across a country (Erickson, 2006, p.28; Fabos, 2004, p.326; Walmsley, 2006). While this may be an inspiring goal for some in developed countries, the reality may be somewhat different in developing countries. China, in its shift to urbanisation and suburbanisation, is also being persuaded to adjust its planning schemes according to these aspirational representations of green spaces (Yu et al, 2006, p.237; Zhang and Wang, 2006, p.455). The failure of other countries to achieve regional goals of natural and cultural heritage protection on the ground in this way (Peterson et al, 2007; Ryan et al, 2006; von Haaren and Reich, 2006) suggests that there may be flaws in the underpinning concepts that are widely circulated in North American and Western European literature (Jongman et al, 2004; Walmsley, 2006). In China, regional open space networks, regional green infrastructure or regional ecological corridors as we know them in the West, are also likely to be problematic, at least in the foreseeable future. Reasons supporting this view can be drawn from lessons learned from project experience in landscape planning and related fields of study in China and overseas. Implementation of valuable regional green space networks is problematic because: • the concept of region as a spatial unit for planning green space networks is ambiguous and undefinable for practical purposes; • regional green space networks traditionally require top down inter-governmental cooperation and coordination which are generally hampered by inequalities of influence between and within government agencies; • no coordinating body with funding powers exists for regional green space development and infrastructure authorities are still in transition from engineering authorities; • like other infrastructure projects, green space is likely to become a competitive rather than a complementary resource for city governments; • stable long-term management, maintenance and uses of green space networks must fit into a ‘family’ social structure rather than a ‘public good’ social structure, particularly as rural and urban property rights are being re-negotiated with city governments; and • green space provision is a performance indicator of urban improvement in cities within the city hierarchy and remains quantitatively-based (land area, tree number and per capita share) rather than qualitatively-based with local people as the focus.

Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

Using incomplete online metric maps for topological exploration with the Gap Navigation Tree

This paper presents a general, global approach to the problem of robot exploration, utilizing a topological data structure to guide an underlying Simultaneous Localization and Mapping (SLAM) process. A Gap Navigation Tree (GNT) is used to motivate global target selection and occluded regions of the environment (called “gaps”) are tracked probabilistically. The process of map construction and the motion of the vehicle alters both the shape and location of these regions. The use of online mapping is shown to reduce the difficulties in implementing the GNT.