Chemistry of igneous rocks from, the Celebes Sea basement

Major and trace elements, mineral chemistry, and Sr-Nd isotope ratios are reported for representative igneous rocks of Ocean Drilling Program Sites 767 and 770. The basaltic basement underlying middle Eocene radiolarianbearing red clays was reached at 786.7 mbsf and about 421 mbsf at Sites 767 and 770, respectively. At Site 770 the basement was drilled for about 106 m. Eight basaltic units were identified on the basis of mineralogical, petrographical, and geochemical data. They mainly consist of pillow lavas and pillow breccias (Units A, B, D, and H), intercalated with massive amygdaloidal lavas (Units Cl and C2) or relatively thin massive flows (Unit E). Two dolerite sills were also recognized (Units F and G). All the rocks studied show the effect of low-temperature seafloor alteration, causing almost total replacement of olivine and glass. Calcite, clays, and Fe-hydroxides are the most abundant secondary phases. Chemical mobilization due to the alteration processes has been evaluated by comparing elements that are widely considered mobile during halmyrolysis (such as low-field strength elements) with those insensitive to seafloor alteration (such as Nb). In general, MgO is removed and P2O5 occasionally enriched during the alteration of pillow lavas. Ti, Cs, Li, Rb, and K, which are the most sensitive indicators of rock/seawater interaction, are generally enriched. The most crystalline samples appear the least affected by chemical changes. Plagioclase and olivine are continuously present as phenocrysts, and clinopyroxene is confined in the groundmass. Textural and mineralogical features as well as crystallization sequences of Site 770 rocks are, in all, analogous to typical mid-ocean-ridge basalts (MORBs). Relatively high content of compatible trace elements, such as Ni and Cr, indicate that these rocks represent nearly primitive or weakly fractionated MORBs. All the studied rocks are geochemically within the spectrum of normal MORB compositional variation. Their Sr/Nd isotopic ratios plot on the mantle array (87Sr/87Sr 0.70324-0.70348 with 143Nd/144Nd 0.51298-0.51291) outside the field of Atlantic and Pacific MORBs. However, Sr and Nd isotopes are typical of both Indian Ocean MORBs and of some back-arc basalts, such as those of Lau Basin. The mantle source of Celebes basement basalts does not show a detectable influence of a subduction-related component. The geochemical and isotopic data so far obtained on the Celebes basement rocks do not allow a clear discrimination between mid-ocean ridge and back-arc settings.

Geochemistry of lavas from the Lau-Tonga arc and back-arc system

Detailed comparison of mineralogy, and major and trace geochemistry are presented for the modern Lau Basin spreading centers, the Sites 834-839 lavas, the modern Tonga-Kermadec arc volcanics, the northern Tongan boninites, and the Lau Ridge volcanics. The data clearly confirm the variations from near normal mid-ocean-ridge basalt (N-MORB) chemistries (e.g., Site 834, Central Lau Spreading Center) to strongly arc-like (e.g., Site 839, Valu Fa), the latter closely comparable to the modern arc volcanoes. Sites 835 and 836 and the East Lau Spreading Center represent transitional chemistries. Bulk compositions range from andesitic to basaltic, but lavas from Sites 834 and 836 and the Central Lau Spreading Center extend toward more silica-undersaturated compositions. The Valu Fa and modern Tonga-Kermadec arc lavas, in contrast, are dominated by basaltic andesites. The phenocryst and groundmass mineralogies show the strong arc-like affinities of the Site 839 lavas, which are also characterized by the existence of very magnesian olivines (up to Fo90-92) and Cr-rich spinels in Units 3 and 6, and highly anorthitic plagioclases in Units 2 and 9. The regional patterns of mineralogical and geochemical variations are interpreted in terms of two competing processes affecting the inferred magma sources: (1) mantle depletion processes, caused by previous melt extractions linked to backarc magmatism, and (2) enrichment in large-ion-lithophile elements, caused by a subduction contribution. A general trend of increasing depletion is inferred both eastward across the Lau Basin toward the modern arc, and northward along the Tongan (and Kermadec) Arc. Numerical modeling suggests that multistage magma extraction can explain the low abundances (relative to N-MORB) of elements such as Nb, Ta, and Ti, known to be characteristic of island arc magmas. It is further suggested that a subduction jump following prolonged slab rollback could account for the initiation of the Lau Basin opening, plausibly allowing a later influx of new mantle, as required by the recognition of a two-stage opening of the Lau Basin.

Lagrangian back-tracing results for Kohnen station; output of time dependent numerical experiment with a 3D nested Antarctic ice sheet model

A nested ice flow model was developed for eastern Dronning Maud Land to assist with the dating and interpretation of the EDML deep ice core. The model consists of a high-resolution higher-order ice dynamic flow model that was nested into a comprehensive 3-D thermomechanical model of the whole Antarctic ice sheet. As the drill site is on a flank position the calculations specifically take into account the effects of horizontal advection as deeper ice in the core originated from higher inland. First the regional velocity field and ice sheet geometry is obtained from a forward experiment over the last 8 glacial cycles. The result is subsequently employed in a Lagrangian backtracing algorithm to provide particle paths back to their time and place of deposition. The procedure directly yields the depth-age distribution, surface conditions at particle origin, and a suite of relevant parameters such as initial annual layer thickness. This paper discusses the method and the main results of the experiment, including the ice core chronology, the non-climatic corrections needed to extract the climatic part of the signal, and the thinning function. The focus is on the upper 89% of the ice core (appr. 170 kyears) as the dating below that is increasingly less robust owing to the unknown value of the geothermal heat flux. It is found that the temperature biases resulting from variations of surface elevation are up to half of the magnitude of the climatic changes themselves.

Isotopic composition of rocks from the Lau/Tonga back-arc basin, data of DSDP Leg 21 and ODP Legs 91 an 135

New trace element, Sr-, Nd-, Pb- and Hf isotope data provide insights into the evolution of the Tonga-Lau Basin subduction system. The involvement of two separate mantle domains, namely Pacific MORB mantle in the pre-rift and early stages of back-arc basin formation, and Indian MORB mantle in the later stages, is confirmed by these results. Contrary to models proposed in recent studies on the basis of Pb isotope and other compositional data, this change in mantle wedge character best explains the shift in the isotopic composition, particularly 143Nd/144Nd ratios, of modern Tofua Arc magmas relative to all other arc products from this region. Nevertheless, significant changes in the slab-derived flux during the evolution of the arc system are also required to explain second order variations in magma chemistry. In this region, the slab-derived flux is dominated by fluid; however, these fluids carry Pb with sediment-influenced isotopic signatures, indicating that their source is not restricted to the subducting altered mafic oceanic crust. This has been the case from the earliest magmatic activity in the arc (Eocene) until the present time, with the exception of two periods of magmatic activity recorded in samples from the Lau Islands. Both the Lau Volcanic Group, and Korobasaga Volcanic Group lavas preserve trace element and isotope evidence for a contribution from subducted sediment that was not transported as a fluid, but possibly in the form of a melt. This component shares similarities with that influencing the chemistry of the northern Tofua Arc magmas, suggesting some caution may be required in the adoption of constraints for the latter dependent upon the involvement of sediments from the Louisville Ridge. A key outcome of this study is to demonstrate that the models proposed to explain subduction zone magmatism cannot afford to ignore the small but important contributions made by the mantle wedge to the incompatible trace element inventory of arc magmas.

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

En esta tesis se aborda la detección y el seguimiento automático de vehículos mediante técnicas de visión artificial con una cámara monocular embarcada. Este problema ha suscitado un gran interés por parte de la industria automovilística y de la comunidad científica ya que supone el primer paso en aras de la ayuda a la conducción, la prevención de accidentes y, en última instancia, la conducción automática. A pesar de que se le ha dedicado mucho esfuerzo en los últimos años, de momento no se ha encontrado ninguna solución completamente satisfactoria y por lo tanto continúa siendo un tema de investigación abierto. Los principales problemas que plantean la detección y seguimiento mediante visión artificial son la gran variabilidad entre vehículos, un fondo que cambia dinámicamente debido al movimiento de la cámara, y la necesidad de operar en tiempo real. En este contexto, esta tesis propone un marco unificado para la detección y seguimiento de vehículos que afronta los problemas descritos mediante un enfoque estadístico. El marco se compone de tres grandes bloques, i.e., generación de hipótesis, verificación de hipótesis, y seguimiento de vehículos, que se llevan a cabo de manera secuencial. No obstante, se potencia el intercambio de información entre los diferentes bloques con objeto de obtener el máximo grado posible de adaptación a cambios en el entorno y de reducir el coste computacional. Para abordar la primera tarea de generación de hipótesis, se proponen dos métodos complementarios basados respectivamente en el análisis de la apariencia y la geometría de la escena. Para ello resulta especialmente interesante el uso de un dominio transformado en el que se elimina la perspectiva de la imagen original, puesto que este dominio permite una búsqueda rápida dentro de la imagen y por tanto una generación eficiente de hipótesis de localización de los vehículos. Los candidatos finales se obtienen por medio de un marco colaborativo entre el dominio original y el dominio transformado. Para la verificación de hipótesis se adopta un método de aprendizaje supervisado. Así, se evalúan algunos de los métodos de extracción de características más populares y se proponen nuevos descriptores con arreglo al conocimiento de la apariencia de los vehículos. Para evaluar la efectividad en la tarea de clasificación de estos descriptores, y dado que no existen bases de datos públicas que se adapten al problema descrito, se ha generado una nueva base de datos sobre la que se han realizado pruebas masivas. Finalmente, se presenta una metodología para la fusión de los diferentes clasificadores y se plantea una discusión sobre las combinaciones que ofrecen los mejores resultados. El núcleo del marco propuesto está constituido por un método Bayesiano de seguimiento basado en filtros de partículas. Se plantean contribuciones en los tres elementos fundamentales de estos filtros: el algoritmo de inferencia, el modelo dinámico y el modelo de observación. En concreto, se propone el uso de un método de muestreo basado en MCMC que evita el elevado coste computacional de los filtros de partículas tradicionales y por consiguiente permite que el modelado conjunto de múltiples vehículos sea computacionalmente viable. Por otra parte, el dominio transformado mencionado anteriormente permite la definición de un modelo dinámico de velocidad constante ya que se preserva el movimiento suave de los vehículos en autopistas. Por último, se propone un modelo de observación que integra diferentes características. En particular, además de la apariencia de los vehículos, el modelo tiene en cuenta también toda la información recibida de los bloques de procesamiento previos. El método propuesto se ejecuta en tiempo real en un ordenador de propósito general y da unos resultados sobresalientes en comparación con los métodos tradicionales. ABSTRACT This thesis addresses on-road vehicle detection and tracking with a monocular vision system. This problem has attracted the attention of the automotive industry and the research community as it is the first step for driver assistance and collision avoidance systems and for eventual autonomous driving. Although many effort has been devoted to address it in recent years, no satisfactory solution has yet been devised and thus it is an active research issue. The main challenges for vision-based vehicle detection and tracking are the high variability among vehicles, the dynamically changing background due to camera motion and the real-time processing requirement. In this thesis, a unified approach using statistical methods is presented for vehicle detection and tracking that tackles these issues. The approach is divided into three primary tasks, i.e., vehicle hypothesis generation, hypothesis verification, and vehicle tracking, which are performed sequentially. Nevertheless, the exchange of information between processing blocks is fostered so that the maximum degree of adaptation to changes in the environment can be achieved and the computational cost is alleviated. Two complementary strategies are proposed to address the first task, i.e., hypothesis generation, based respectively on appearance and geometry analysis. To this end, the use of a rectified domain in which the perspective is removed from the original image is especially interesting, as it allows for fast image scanning and coarse hypothesis generation. The final vehicle candidates are produced using a collaborative framework between the original and the rectified domains. A supervised classification strategy is adopted for the verification of the hypothesized vehicle locations. In particular, state-of-the-art methods for feature extraction are evaluated and new descriptors are proposed by exploiting the knowledge on vehicle appearance. Due to the lack of appropriate public databases, a new database is generated and the classification performance of the descriptors is extensively tested on it. Finally, a methodology for the fusion of the different classifiers is presented and the best combinations are discussed. The core of the proposed approach is a Bayesian tracking framework using particle filters. Contributions are made on its three key elements: the inference algorithm, the dynamic model and the observation model. In particular, the use of a Markov chain Monte Carlo method is proposed for sampling, which circumvents the exponential complexity increase of traditional particle filters thus making joint multiple vehicle tracking affordable. On the other hand, the aforementioned rectified domain allows for the definition of a constant-velocity dynamic model since it preserves the smooth motion of vehicles in highways. Finally, a multiple-cue observation model is proposed that not only accounts for vehicle appearance but also integrates the available information from the analysis in the previous blocks. The proposed approach is proven to run near real-time in a general purpose PC and to deliver outstanding results compared to traditional methods.

Reverse logistics practices for recovering mobile phones in Spain

The main objective of this article is to characterize the reverse logistics system for mobile phones in Spain. The study includes the characterization of the different actors involved in the reverse logistics system and the description of the most common logistics practices in the sector. We will also opose alternative practices for managing this complex reverse logistics system and finally, we analyse the challenges of the current reverse logistics model. Some alternatives for the current model are location of reception points for end-of-use mobiles, the need to legislate the secondhand mobile phone market, and the location of the necessary recycling centres according to current legislation.

Contribution to proactivity in mobile context-aware recommender systems

Los sistemas de recomendación son potentes herramientas de filtrado de información que permiten a usuarios solicitar sugerencias sobre ítems que cubran sus necesidades. Tradicionalmente estas recomendaciones han estado basadas en opiniones de los mismos, así como en datos obtenidos de su consumo histórico o comportamiento en el propio sistema. Sin embargo, debido a la gran penetración y uso de los dispositivos móviles en nuestra sociedad, han surgido nuevas oportunidades en el campo de los sistemas de recomendación móviles gracias a la información contextual que se puede obtener sobre la localización o actividad de los usuarios. Debido a este estilo de vida en el que todo tiende a la movilidad y donde los usuarios están plenamente interconectados, la información contextual no sólo es física, sino que también adquiere una dimensión social. Todo esto ha dado lugar a una nueva área de investigación relacionada con los Sistemas de Recomendación Basados en Contexto (CARS) móviles donde se busca incrementar el nivel de personalización de las recomendaciones al usar dicha información. Por otro lado, este nuevo escenario en el que los usuarios llevan en todo momento un terminal móvil consigo abre la puerta a nuevas formas de recomendar. Sustituir el tradicional patrón de uso basado en petición-respuesta para evolucionar hacia un sistema proactivo es ahora posible. Estos sistemas deben identificar el momento más adecuado para generar una recomendación sin una petición explícita del usuario, siendo para ello necesario analizar su contexto. Esta tesis doctoral propone un conjunto de modelos, algoritmos y métodos orientados a incorporar proactividad en CARS móviles, a la vez que se estudia el impacto que este tipo de recomendaciones tienen en la experiencia de usuario con el fin de extraer importantes conclusiones sobre "qué", "cuándo" y "cómo" se debe notificar proactivamente. Con este propósito, se comienza planteando una arquitectura general para construir CARS móviles en escenarios sociales. Adicionalmente, se propone una nueva forma de representar el proceso de recomendación a través de una interfaz REST, lo que permite crear una arquitectura independiente de dispositivo y plataforma. Los detalles de su implementación tras su puesta en marcha en el entorno bancario español permiten asimismo validar el sistema construido. Tras esto se presenta un novedoso modelo para incorporar proactividad en CARS móviles. Éste muestra las ideas principales que permiten analizar una situación para decidir cuándo es apropiada una recomendación proactiva. Para ello se presentan algoritmos que establecen relaciones entre lo propicia que es una situación y cómo esto influye en los elementos a recomendar. Asimismo, para demostrar la viabilidad de este modelo se describe su aplicación a un escenario de recomendación para herramientas de creación de contenidos educativos. Siguiendo el modelo anterior, se presenta el diseño e implementación de nuevos interfaces móviles de usuario para recomendaciones proactivas, así como los resultados de su evaluación entre usuarios, lo que aportó importantes conclusiones para identificar cuáles son los factores más relevantes a considerar en el diseño de sistemas proactivos. A raíz de los resultados anteriores, el último punto de esta tesis presenta una metodología para calcular cuán apropiada es una situación de cara a recomendar de manera proactiva siguiendo el modelo propuesto. Como conclusión, se describe la validación llevada a cabo tras la aplicación de la arquitectura, modelo de recomendación y métodos descritos en este trabajo en una red social de aprendizaje europea. Finalmente, esta tesis discute las conclusiones obtenidas a lo largo de la extensa investigación llevada a cabo, y que ha propiciado la consecución de una buena base teórica y práctica para la creación de sistemas de recomendación móviles proactivos basados en información contextual. ABSTRACT Recommender systems are powerful information filtering tools which offer users personalized suggestions about items whose aim is to satisfy their needs. Traditionally the information used to make recommendations has been based on users’ ratings or data on the item’s consumption history and transactions carried out in the system. However, due to the remarkable growth in mobile devices in our society, new opportunities have arisen to improve these systems by implementing them in ubiquitous environments which provide rich context-awareness information on their location or current activity. Because of this current all-mobile lifestyle, users are socially connected permanently, which allows their context to be enhanced not only with physical information, but also with a social dimension. As a result of these novel contextual data sources, the advent of mobile Context-Aware Recommender Systems (CARS) as a research area has appeared to improve the level of personalization in recommendation. On the other hand, this new scenario in which users have their mobile devices with them all the time offers the possibility of looking into new ways of making recommendations. Evolving the traditional user request-response pattern to a proactive approach is now possible as a result of this rich contextual scenario. Thus, the key idea is that recommendations are made to the user when the current situation is appropriate, attending to the available contextual information without an explicit user request being necessary. This dissertation proposes a set of models, algorithms and methods to incorporate proactivity into mobile CARS, while the impact of proactivity is studied in terms of user experience to extract significant outcomes as to "what", "when" and "how" proactive recommendations have to be notified to users. To this end, the development of this dissertation starts from the proposal of a general architecture for building mobile CARS in scenarios with rich social data along with a new way of managing a recommendation process through a REST interface to make this architecture multi-device and cross-platform compatible. Details as regards its implementation and evaluation in a Spanish banking scenario are provided to validate its usefulness and user acceptance. After that, a novel model is presented for proactivity in mobile CARS which shows the key ideas related to decide when a situation warrants a proactive recommendation by establishing algorithms that represent the relationship between the appropriateness of a situation and the suitability of the candidate items to be recommended. A validation of these ideas in the area of e-learning authoring tools is also presented. Following the previous model, this dissertation presents the design and implementation of new mobile user interfaces for proactive notifications. The results of an evaluation among users testing these novel interfaces is also shown to study the impact of proactivity in the user experience of mobile CARS, while significant factors associated to proactivity are also identified. The last stage of this dissertation merges the previous outcomes to design a new methodology to calculate the appropriateness of a situation so as to incorporate proactivity into mobile CARS. Additionally, this work provides details about its validation in a European e-learning social network in which the whole architecture and proactive recommendation model together with its methods have been implemented. Finally, this dissertation opens up a discussion about the conclusions obtained throughout this research, resulting in useful information from the different design and implementation stages of proactive mobile CARS.