Accumulation rates and grain sizes of eolian dust in sediments of the Intertropical Convergence Zone in the northeast Pacific

A 328 cm-long piston core (KODOS 02-01-02) collected from the northeast equatorial Pacific at 16°12'N, 125°59'W was investigated for eolian mass fluxes and grain sizes to test these proxies as a tool for the paleo-position of the Intertropical Convergence Zone (ITCZ). The eolian mass fluxes of the lower interval below 250 cm (15.5-7.6 Ma) are very uniform at 5 +/- 1 mg/cm**2/kyr, while those of the upper interval above 250 cm (from 7.6 Ma) are over 2 times higher than the lower interval at 12 +/- 1 mg/cm**2/kyr. The median grain size of the eolian dusts in the lower interval increases from 8.4 Phi to 8.0 Phi downward, while that of the upper interval varies in a narrow range from 8.8 Phi to 8.6 Phi. The determined values compare well in magnitude to those of central Pacific sediments for the upper interval and equatorial and southeast Pacific sediments for the lower interval. This result suggests a possibility that the study site had been under the influence of southeast trade winds at its earlier depositional period due to the northerly position of the ITCZ, and subsequently of the northeast trade winds for a later period when the upper sediments were deposited. This interpretation is consistent with a mineralogical and geochemical study published elsewhere that assigned the provenance of the study core dust to Central/South America for the lower interval and to Asia for the upper interval. This study suggests that the distinct differences in eolian mass flux and grain size observed across the ITCZ can be used to trace the paleo-latitude of the ITCZ.

Remanent and intrinsic properties of basalts at DSDP Leg 82 Holes

The magnetic properties of 56 samples of basalt from DSDP Leg 82 were studied in order to examine regional variations as well as the general question of the origin or remanence. Magnetization was carried, for the most part, by typical low temperature oxidized titanomagnetites, although two samples did show anomalous thermomagnetic curves. The natural remanence is distinctly different from an anhysteretic remanent magnetization and is hypothesized (by inference) to also be different from a thermoremanent magnetization (TRM) also. This suggests that alteration not only reduces the initial TRM but also changes it to chemical remanent magnetization with a significantly different magnetic character. An examination of thermomagnetic data tentatively suggests that the ulvospinel content of the titanomagnetites may be more variable than is commonly assumed. With the exception of a slight increase in saturation magnetization with decreasing latitude, no significant regional variations were evident.

Why Do Economies Enter into Preferential Agreements on Trade in Services? Assessing the Potential for Negotiated Regulatory Convergence in Asian Services Markets

More than one-third of the World Trade Organization-notified services trade agreements that were in effect between January 2008 and August 2015 involved at least one South or Southeast Asian trading partner. Drawing on Baier and Bergstrand’s (2004) determinants of preferential trade agreements and using the World Bank’s database on the restrictiveness of domestic services regimes (Borchert, Gootiiz, and Mattoo 2012), we examine the potential for negotiated regulatory convergence in Asian services markets. Our results suggest that Asian economies with high levels of preexisting bilateral merchandise trade and wide differences in services regulatory frameworks are more likely candidates for services trade agreement formation. Such results lend support to the hypothesis that the heightened “servicification” of production generates demand for the lowered services input costs resulting from negotiated market openings.

Why do Countries Enter into Preferential Agreements on Trade in Services? Assessing the Potential for Negotiated Regulatory Convergence in Asian Services Markets

More than a third of the World Trade Organization (WTO)-notified services trade agreements (STAs) in effect over January 2008 - August 2015 have involved at least one (South or Southeast) Asian trading partner. Drawing on Baier and Bergstrand's (2004) determinants of preferential trade agreements and using the World Bank's database on the restrictiveness of domestic services regimes (Borchert et.al. 2012), we examine the potential for negotiated regulatory convergence in Asian services markets. Our results suggest that countries within Asia with high levels of pre-existing bilateral merchandise trade and wide differences in services regulatory frameworks are more likely candidates for STA formation. Such results lend support to the hypothesis that the heightened "servicification" of production generates a demand for the lowered service input costs resulting from negotiated market opening.

Social cleavages and electoral support in Turkey -- toward convergence?

Studies on Western democracies have shown that deep-seated social cleavages stabilize the electoral behavior and thus reduce electoral volatility. But how do social cleavages affect a party system that is undergoing democratic consolidation, such as in Turkey? In this study, investigations were carried out on long- and short-term relationships between social cleavages (religiosity, ethnicity, and sectarism) and electoral volatility in Turkey during the 1961-2002 period. Cross-sectional multiple regressions were applied to electoral and demographic data at the provincial level. The results showed that in the long-term, social cleavages on the whole have increased volatility rather than reduced it. The cleavage-volatility relationship, however, has changed over time. Repeated elections have mitigated the volatile effect of social cleavages on the voting behavior, as political parties have become more representative of the existent social cleavages.

Network convergence and QoS for future multimedia services in the VISION project

The emerging use of real-time 3D-based multimedia applications imposes strict quality of service (QoS) requirements on both access and core networks. These requirements and their impact to provide end-to-end 3D videoconferencing services have been studied within the Spanish-funded VISION project, where different scenarios were implemented showing an agile stereoscopic video call that might be offered to the general public in the near future. In view of the requirements, we designed an integrated access and core converged network architecture which provides the requested QoS to end-to-end IP sessions. Novel functional blocks are proposed to control core optical networks, the functionality of the standard ones is redefined, and the signaling improved to better meet the requirements of future multimedia services. An experimental test-bed to assess the feasibility of the solution was also deployed. In such test-bed, set-up and release of end-to-end sessions meeting specific QoS requirements are shown and the impact of QoS degradation in terms of the user perceived quality degradation is quantified. In addition, scalability results show that the proposed signaling architecture is able to cope with large number of requests introducing almost negligible delay.

Work in progress - Exploiting videoconferencing possibilities to promote the European convergence process

This paper introduces the experience of using videoconferencing and recording as a mechanism to support courses which need to be promoted or discontinued within the framework of the European convergence process. Our objective is to make these courses accessible as live streaming during the lessons as well as recorded lectures and associated documents available to the students as soon as the lesson has finished. The technology used has been developed in our university and it is all open source. Although this is a technical project the key is the human factor involved. The people managing the virtual sessions are students of the courses being recorded. However, they lack technical knowledge, so we had to train them in audiovisuals and enhance the usability of the videoconferencing tool and platform. The validation process is being carried out in five real scenarios at our university. During the whole period we are evaluating technical and pedagogical issues of this experience for both students and teachers to guide the future development of the service. Depending on the final results, the service of lectures recording will be available as educational resource for all of the teaching staff of our university.

Theoretical Analysis of the No-Slip Boundary Condition Enforcement in SPH Methods

The aim of the present work is to provide an in-depth analysis of the most representative mirroring techniques used in SPH to enforce boundary conditions (BC) along solid profiles. We specifically refer to dummy particles, ghost particles, and Takeda et al. [Prog. Theor. Phys. 92 (1994), 939] boundary integrals. The analysis has been carried out by studying the convergence of the first- and second-order differential operators as the smoothing length (that is, the characteristic length on which relies the SPH interpolation) decreases. These differential operators are of fundamental importance for the computation of the viscous drag and the viscous/diffusive terms in the momentum and energy equations. It has been proved that close to the boundaries some of the mirroring techniques leads to intrinsic inaccuracies in the convergence of the differential operators. A consistent formulation has been derived starting from Takeda et al. boundary integrals (see the above reference). This original formulation allows implementing no-slip boundary conditions consistently in many practical applications as viscous flows and diffusion problems.

Suppression of the intrinsic stochastic pinning of domain walls in magnetic nanostripes

Nanofabrication has allowed the development of new concepts such as magnetic logic and race-track memory, both of which are based on the displacement of magnetic domain walls on magnetic nanostripes. One of the issues that has to be solved before devices can meet the market demands is the stochastic behaviour of the domain wall movement in magnetic nanostripes. Here we show that the stochastic nature of the domain wall motion in permalloy nanostripes can be suppressed at very low fields (0.6-2.7 Oe). We also find different field regimes for this stochastic motion that match well with the domain wall propagation modes. The highest pinning probability is found around the precessional mode and, interestingly, it does not depend on the external field in this regime. These results constitute an experimental evidence of the intrinsic nature of the stochastic pinning of domain walls in soft magnetic nanostripes

Evolvable 2D computing matrix model for intrinsic evolution in commercial FPGAs with native reconfiguration support

This paper addresses the modelling and validation of an evolvable hardware architecture which can be mapped on a 2D systolic structure implemented on commercial reconfigurable FPGAs. The adaptation capabilities of the architecture are exercised to validate its evolvability. The underlying proposal is the use of a library of reconfigurable components characterised by their partial bitstreams, which are used by the Evolutionary Algorithm to find a solution to a given task. Evolution of image noise filters is selected as the proof of concept application. Results show that computation speed of the resulting evolved circuit is higher than with the Virtual Reconfigurable Circuits approach, and this can be exploited on the evolution process by using dynamic reconfiguration

Semi-supervised subspace clustering and applications to neuroscience

Machine learning techniques are used for extracting valuable knowledge from data. Nowa¬days, these techniques are becoming even more important due to the evolution in data ac¬quisition and storage, which is leading to data with different characteristics that must be exploited. Therefore, advances in data collection must be accompanied with advances in machine learning techniques to solve new challenges that might arise, on both academic and real applications. There are several machine learning techniques depending on both data characteristics and purpose. Unsupervised classification or clustering is one of the most known techniques when data lack of supervision (unlabeled data) and the aim is to discover data groups (clusters) according to their similarity. On the other hand, supervised classification needs data with supervision (labeled data) and its aim is to make predictions about labels of new data. The presence of data labels is a very important characteristic that guides not only the learning task but also other related tasks such as validation. When only some of the available data are labeled whereas the others remain unlabeled (partially labeled data), neither clustering nor supervised classification can be used. This scenario, which is becoming common nowadays because of labeling process ignorance or cost, is tackled with semi-supervised learning techniques. This thesis focuses on the branch of semi-supervised learning closest to clustering, i.e., to discover clusters using available labels as support to guide and improve the clustering process. Another important data characteristic, different from the presence of data labels, is the relevance or not of data features. Data are characterized by features, but it is possible that not all of them are relevant, or equally relevant, for the learning process. A recent clustering tendency, related to data relevance and called subspace clustering, claims that different clusters might be described by different feature subsets. This differs from traditional solutions to data relevance problem, where a single feature subset (usually the complete set of original features) is found and used to perform the clustering process. The proximity of this work to clustering leads to the first goal of this thesis. As commented above, clustering validation is a difficult task due to the absence of data labels. Although there are many indices that can be used to assess the quality of clustering solutions, these validations depend on clustering algorithms and data characteristics. Hence, in the first goal three known clustering algorithms are used to cluster data with outliers and noise, to critically study how some of the most known validation indices behave. The main goal of this work is however to combine semi-supervised clustering with subspace clustering to obtain clustering solutions that can be correctly validated by using either known indices or expert opinions. Two different algorithms are proposed from different points of view to discover clusters characterized by different subspaces. For the first algorithm, available data labels are used for searching for subspaces firstly, before searching for clusters. This algorithm assigns each instance to only one cluster (hard clustering) and is based on mapping known labels to subspaces using supervised classification techniques. Subspaces are then used to find clusters using traditional clustering techniques. The second algorithm uses available data labels to search for subspaces and clusters at the same time in an iterative process. This algorithm assigns each instance to each cluster based on a membership probability (soft clustering) and is based on integrating known labels and the search for subspaces into a model-based clustering approach. The different proposals are tested using different real and synthetic databases, and comparisons to other methods are also included when appropriate. Finally, as an example of real and current application, different machine learning tech¬niques, including one of the proposals of this work (the most sophisticated one) are applied to a task of one of the most challenging biological problems nowadays, the human brain model¬ing. Specifically, expert neuroscientists do not agree with a neuron classification for the brain cortex, which makes impossible not only any modeling attempt but also the day-to-day work without a common way to name neurons. Therefore, machine learning techniques may help to get an accepted solution to this problem, which can be an important milestone for future research in neuroscience. Resumen Las técnicas de aprendizaje automático se usan para extraer información valiosa de datos. Hoy en día, la importancia de estas técnicas está siendo incluso mayor, debido a que la evolución en la adquisición y almacenamiento de datos está llevando a datos con diferentes características que deben ser explotadas. Por lo tanto, los avances en la recolección de datos deben ir ligados a avances en las técnicas de aprendizaje automático para resolver nuevos retos que pueden aparecer, tanto en aplicaciones académicas como reales. Existen varias técnicas de aprendizaje automático dependiendo de las características de los datos y del propósito. La clasificación no supervisada o clustering es una de las técnicas más conocidas cuando los datos carecen de supervisión (datos sin etiqueta), siendo el objetivo descubrir nuevos grupos (agrupaciones) dependiendo de la similitud de los datos. Por otra parte, la clasificación supervisada necesita datos con supervisión (datos etiquetados) y su objetivo es realizar predicciones sobre las etiquetas de nuevos datos. La presencia de las etiquetas es una característica muy importante que guía no solo el aprendizaje sino también otras tareas relacionadas como la validación. Cuando solo algunos de los datos disponibles están etiquetados, mientras que el resto permanece sin etiqueta (datos parcialmente etiquetados), ni el clustering ni la clasificación supervisada se pueden utilizar. Este escenario, que está llegando a ser común hoy en día debido a la ignorancia o el coste del proceso de etiquetado, es abordado utilizando técnicas de aprendizaje semi-supervisadas. Esta tesis trata la rama del aprendizaje semi-supervisado más cercana al clustering, es decir, descubrir agrupaciones utilizando las etiquetas disponibles como apoyo para guiar y mejorar el proceso de clustering. Otra característica importante de los datos, distinta de la presencia de etiquetas, es la relevancia o no de los atributos de los datos. Los datos se caracterizan por atributos, pero es posible que no todos ellos sean relevantes, o igualmente relevantes, para el proceso de aprendizaje. Una tendencia reciente en clustering, relacionada con la relevancia de los datos y llamada clustering en subespacios, afirma que agrupaciones diferentes pueden estar descritas por subconjuntos de atributos diferentes. Esto difiere de las soluciones tradicionales para el problema de la relevancia de los datos, en las que se busca un único subconjunto de atributos (normalmente el conjunto original de atributos) y se utiliza para realizar el proceso de clustering. La cercanía de este trabajo con el clustering lleva al primer objetivo de la tesis. Como se ha comentado previamente, la validación en clustering es una tarea difícil debido a la ausencia de etiquetas. Aunque existen muchos índices que pueden usarse para evaluar la calidad de las soluciones de clustering, estas validaciones dependen de los algoritmos de clustering utilizados y de las características de los datos. Por lo tanto, en el primer objetivo tres conocidos algoritmos se usan para agrupar datos con valores atípicos y ruido para estudiar de forma crítica cómo se comportan algunos de los índices de validación más conocidos. El objetivo principal de este trabajo sin embargo es combinar clustering semi-supervisado con clustering en subespacios para obtener soluciones de clustering que puedan ser validadas de forma correcta utilizando índices conocidos u opiniones expertas. Se proponen dos algoritmos desde dos puntos de vista diferentes para descubrir agrupaciones caracterizadas por diferentes subespacios. Para el primer algoritmo, las etiquetas disponibles se usan para bus¬car en primer lugar los subespacios antes de buscar las agrupaciones. Este algoritmo asigna cada instancia a un único cluster (hard clustering) y se basa en mapear las etiquetas cono-cidas a subespacios utilizando técnicas de clasificación supervisada. El segundo algoritmo utiliza las etiquetas disponibles para buscar de forma simultánea los subespacios y las agru¬paciones en un proceso iterativo. Este algoritmo asigna cada instancia a cada cluster con una probabilidad de pertenencia (soft clustering) y se basa en integrar las etiquetas conocidas y la búsqueda en subespacios dentro de clustering basado en modelos. Las propuestas son probadas utilizando diferentes bases de datos reales y sintéticas, incluyendo comparaciones con otros métodos cuando resulten apropiadas. Finalmente, a modo de ejemplo de una aplicación real y actual, se aplican diferentes técnicas de aprendizaje automático, incluyendo una de las propuestas de este trabajo (la más sofisticada) a una tarea de uno de los problemas biológicos más desafiantes hoy en día, el modelado del cerebro humano. Específicamente, expertos neurocientíficos no se ponen de acuerdo en una clasificación de neuronas para la corteza cerebral, lo que imposibilita no sólo cualquier intento de modelado sino también el trabajo del día a día al no tener una forma estándar de llamar a las neuronas. Por lo tanto, las técnicas de aprendizaje automático pueden ayudar a conseguir una solución aceptada para este problema, lo cual puede ser un importante hito para investigaciones futuras en neurociencia.

An approach to operational modal analysis using the expectation maximization algorithm

This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used.

Techniques to accelerate convergence of stress-controlled molecular dynamics simulations of dislocation motion

Dislocation mobility —the relation between applied stress and dislocation velocity—is an important property to model the mechanical behavior of structural materials. These mobilities reflect the interaction between the dislocation core and the host lattice and, thus, atomistic resolution is required to capture its details. Because the mobility function is multiparametric, its computation is often highly demanding in terms of computational requirements. Optimizing how tractions are applied can be greatly advantageous in accelerating convergence and reducing the overall computational cost of the simulations. In this paper we perform molecular dynamics simulations of ½ 〈1 1 1〉 screw dislocation motion in tungsten using step and linear time functions for applying external stress. We find that linear functions over time scales of the order of 10–20 ps reduce fluctuations and speed up convergence to the steady-state velocity value by up to a factor of two.

Maximum Likelihood Estimation of Modal Parameters in Structures Using the Expectation Maximization Algorithm

This paper presents a time-domain stochastic system identification method based on maximum likelihood estimation (MLE) with the expectation maximization (EM) algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. The benchmark structure is a four-story, two-bay by two-bay steel-frame scale model structure built in the Earthquake Engineering Research Laboratory at the University of British Columbia, Canada. This paper focuses on Phase I of the analytical benchmark studies. A MATLAB-based finite element analysis code obtained from the IASC-ASCE SHM Task Group web site is used to calculate the dynamic response of the prototype structure. A number of 100 simulations have been made using this MATLAB-based finite element analysis code in order to evaluate the proposed identification method. There are several techniques to realize system identification. In this work, stochastic subspace identification (SSI)method has been used for comparison. SSI identification method is a well known method and computes accurate estimates of the modal parameters. The principles of the SSI identification method has been introduced in the paper and next the proposed MLE with EM algorithm has been explained in detail. The advantages of the proposed structural identification method can be summarized as follows: (i) the method is based on maximum likelihood, that implies minimum variance estimates; (ii) EM is a computational simpler estimation procedure than other optimization algorithms; (iii) estimate more parameters than SSI, and these estimates are accurate. On the contrary, the main disadvantages of the method are: (i) EM algorithm is an iterative procedure and it consumes time until convergence is reached; and (ii) this method needs starting values for the parameters. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using both the SSI method and the proposed MLE + EM method. The numerical results show that the proposed method identifies eigenfrequencies, damping ratios and mode shapes reasonably well even in the presence of 10% measurement noises. These modal parameters are more accurate than the SSI estimated modal parameters.

Adaptive Refinements in BEM

Accuracy estimates and adaptive refinements is nowadays one of the main research topics in finite element computations [6,7,8, 9,11].Its extension to Boundary Elements has been tried as a means to better understand its capabilities as well as to impro ve its efficiency and its obvious advantages. The possibility of implementing adaptive techniques was shown [1,2] for h-conver gence and p-convergence respectively. Some posterior works [3,4 5,10] have shown the promising results that can be expected from those techniques. The main difficulty is associated to the reasonable establishment of “estimation” and “indication” factors related to the global and local errors in each refinement. Although some global measures have been used it is clear that the reduction in dimension intrinsic to boundary elements (3D→2D: 2D→1D) could allow a direct comparison among residuals using the graphic possibilities of modern computers and allowing a point-to-point comparison in place of the classical global approaches. Nevertheless an indicator generalizing the well known Peano’s one has been produced.