Mobile device-to-device distributed computing using data sets

The rapidly increasing computing power, available storage and communication capabilities of mobile devices makes it possible to start processing and storing data locally, rather than offloading it to remote servers; allowing scenarios of mobile clouds without infrastructure dependency. We can now aim at connecting neighboring mobile devices, creating a local mobile cloud that provides storage and computing services on local generated data. In this paper, we describe an early overview of a distributed mobile system that allows accessing and processing of data distributed across mobile devices without an external communication infrastructure. Copyright © 2015 ICST.

Modelo de data mining para deteção de embolias pulmonares

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Determining the number of clusters in categorical data

Cluster analysis for categorical data has been an active area of research. A well-known problem in this area is the determination of the number of clusters, which is unknown and must be inferred from the data. In order to estimate the number of clusters, one often resorts to information criteria, such as BIC (Bayesian information criterion), MML (minimum message length, proposed by Wallace and Boulton, 1968), and ICL (integrated classification likelihood). In this work, we adopt the approach developed by Figueiredo and Jain (2002) for clustering continuous data. They use an MML criterion to select the number of clusters and a variant of the EM algorithm to estimate the model parameters. This EM variant seamlessly integrates model estimation and selection in a single algorithm. For clustering categorical data, we assume a finite mixture of multinomial distributions and implement a new EM algorithm, following a previous version (Silvestre et al., 2008). Results obtained with synthetic datasets are encouraging. The main advantage of the proposed approach, when compared to the above referred criteria, is the speed of execution, which is especially relevant when dealing with large data sets.

Data analytics in the cloud with flexible mapreduced workflows

Data analytic applications are characterized by large data sets that are subject to a series of processing phases. Some of these phases are executed sequentially but others can be executed concurrently or in parallel on clusters, grids or clouds. The MapReduce programming model has been applied to process large data sets in cluster and cloud environments. For developing an application using MapReduce there is a need to install/configure/access specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. It would be desirable to provide more flexibility in adjusting such configurations according to the application characteristics. Furthermore the composition of the multiple phases of a data analytic application requires the specification of all the phases and their orchestration. The original MapReduce model and environment lacks flexible support for such configuration and composition. Recognizing that scientific workflows have been successfully applied to modeling complex applications, this paper describes our experiments on implementing MapReduce as subworkflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). A text mining data analytic application is modeled as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. As in typical MapReduce environments, the end user only needs to define the application algorithms for input data processing and for the map and reduce functions. In the paper we present experimental results when using the AWARD framework to execute MapReduce workflows deployed over multiple Amazon EC2 (Elastic Compute Cloud) instances.

Efficient feature selection filters for high-dimensional data

Feature selection is a central problem in machine learning and pattern recognition. On large datasets (in terms of dimension and/or number of instances), using search-based or wrapper techniques can be cornputationally prohibitive. Moreover, many filter methods based on relevance/redundancy assessment also take a prohibitively long time on high-dimensional. datasets. In this paper, we propose efficient unsupervised and supervised feature selection/ranking filters for high-dimensional datasets. These methods use low-complexity relevance and redundancy criteria, applicable to supervised, semi-supervised, and unsupervised learning, being able to act as pre-processors for computationally intensive methods to focus their attention on smaller subsets of promising features. The experimental results, with up to 10(5) features, show the time efficiency of our methods, with lower generalization error than state-of-the-art techniques, while being dramatically simpler and faster.

Deep crustal structure across a young passive margin from wide-angle and reflection seismic data (The Sardinia Experiment) - I. Gulf of Lion's margin

The conjugate margins system of the Gulf of Lion and West Sardinia (GLWS) represents a unique natural laboratory for addressing fundamental questions about rifting due to its landlocked situation, its youth, its thick sedimentary layers, including prominent palaeo-marker such as the MSC event, and the amount of available data and multidisciplinary studies. The main goals of the SARDINIA experiment, were to (i) investigate the deep structure of the entire system within the two conjugate margins: the Gulf of Lion and West Sardinia, (ii) characterize the nature of the crust, and (iii) define the geometry of the basin and provide important constrains on its genesis. This paper presents the results of P-wave velocity modelling on three coincident near-vertical reflection multi-channel seismic (MCS) and wide-angle seismic profiles acquired in the Gulf of Lion, to a depth of 35 km. A companion paper [part II Afilhado et al., 2015] addresses the results of two other SARDINIA profiles located on the oriental conjugate West Sardinian margin. Forward wide-angle modelling of both data sets confirms that the margin is characterised by three distinct domains following the onshore unthinned, 33 km-thick continental crust domain: Domain I is bounded by two necking zones, where the crust thins respectively from 30 to 20 and from 20 to 7 km over a width of about 170 km; the outermost necking is imprinted by the well-known T-reflector at its crustal base; Domain II is characterised by a 7 km-thick crust with anomalous velocities ranging from 6 to 7.5 km/s; it represents the transition between the thinned continental crust (Domain I) and a very thin (only 4-5 km) "atypical" oceanic crust (Domain III). In Domain II, the hypothesis of the presence of exhumed mantle is falsified by our results: this domain may likely consist of a thin exhumed lower continental crust overlying a heterogeneous, intruded lower layer. Moreover, despite the difference in their magnetic signatures, Domains II and III present the very similar seismic velocities profiles, and we discuss the possibility of a connection between these two different domains.

Deep crustal structure across a young passive margin from wide-angle and reflection seismic data (The Sardinia Experiment) - II. Sardinia's margin

Geophysical data acquired on the conjugate margins system of the Gulf of Lion and West Sardinia (GLWS) is unique in its ability to address fundamental questions about rifting (i.e. crustal thinning, the nature of the continent-ocean transition zone, the style of rifting and subsequent evolution, and the connection between deep and surface processes). While the Gulf of Lion (GoL) was the site of several deep seismic experiments, which occurred before the SARDINIA Experiment (ESP and ECORS Experiments in 1981 and 1988 respectively), the crustal structure of the West Sardinia margin remains unknown. This paper describes the first modeling of wide-angle and near-vertical reflection multi-channel seismic (MCS) profiles crossing the West Sardinia margin, in the Mediterranean Sea. The profiles were acquired, together with the exact conjugate of the profiles crossing the GoL, during the SARDINIA experiment in December 2006 with the French R/V L'Atalante. Forward wide-angle modeling of both data sets (wide-angle and multi-channel seismic) confirms that the margin is characterized by three distinct domains following the onshore unthinned, 26 km-thick continental crust : Domain V, where the crust thins from 26 to 6 km in a width of about 75 km; Domain IV where the basement is characterized by high velocity gradients and lower crustal seismic velocities from 6.8 to 7.25 km/s, which are atypical for either crustal or upper mantle material, and Domain III composed of "atypical" oceanic crust.The structure observed on the West Sardinian margin presents a distribution of seismic velocities that is symmetrical with those observed on the Gulf of Lion's side, except for the dimension of each domain and with respect to the initiation of seafloor spreading. This result does not support the hypothesis of simple shear mechanism operating along a lithospheric detachment during the formation of the Liguro-Provencal basin.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Learning techniques for automatic email message tagging

A organização automática de mensagens de correio electrónico é um desafio actual na área da aprendizagem automática. O número excessivo de mensagens afecta cada vez mais utilizadores, especialmente os que usam o correio electrónico como ferramenta de comunicação e trabalho. Esta tese aborda o problema da organização automática de mensagens de correio electrónico propondo uma solução que tem como objectivo a etiquetagem automática de mensagens. A etiquetagem automática é feita com recurso às pastas de correio electrónico anteriormente criadas pelos utilizadores, tratando-as como etiquetas, e à sugestão de múltiplas etiquetas para cada mensagem (top-N). São estudadas várias técnicas de aprendizagem e os vários campos que compõe uma mensagem de correio electrónico são analisados de forma a determinar a sua adequação como elementos de classificação. O foco deste trabalho recai sobre os campos textuais (o assunto e o corpo das mensagens), estudando-se diferentes formas de representação, selecção de características e algoritmos de classificação. É ainda efectuada a avaliação dos campos de participantes através de algoritmos de classificação que os representam usando o modelo vectorial ou como um grafo. Os vários campos são combinados para classificação utilizando a técnica de combinação de classificadores Votação por Maioria. Os testes são efectuados com um subconjunto de mensagens de correio electrónico da Enron e um conjunto de dados privados disponibilizados pelo Institute for Systems and Technologies of Information, Control and Communication (INSTICC). Estes conjuntos são analisados de forma a perceber as características dos dados. A avaliação do sistema é realizada através da percentagem de acerto dos classificadores. Os resultados obtidos apresentam melhorias significativas em comparação com os trabalhos relacionados.

Stratification of the earth beneath the Azores from P and S receiver functions

Seismic recordings of IRIS/IDA/GSN station CMLA and of several temporary stations in the Azores archipelago are processed with P and S receiver function (PRF and SRF) techniques. Contrary to regional seismic tomography these methods provide estimates of the absolute velocities and of the Vp/Vs ratio up to a depth of similar to 300 km. Joint inversion of PRFs and SRFs for a few data sets consistently reveals a division of the subsurface medium into four zones with a distinctly different Vp/Vs ratio: the crust similar to 20 km thick with a ratio of similar to 1.9 in the lower crust, the high-Vs mantle lid with a strongly reduced VpNs velocity ratio relative to the standard 1.8, the low-velocity zone (LVZ) with a velocity ratio of similar to 2.0, and the underlying upper-mantle layer with a standard velocity ratio. Our estimates of crustal thickness greatly exceed previous estimates (similar to 10 km). The base of the high-Vs lid (the Gutenberg discontinuity) is at a depth of-SO km. The LVZ with a reduction of S velocity of similar to 15% relative to the standard (IASP91) model is terminated at a depth of similar to 200 km. The average thickness of the mantle transition zone (TZ) is evaluated from the time difference between the S410p and SKS660p, seismic phases that are robustly detected in the S and SKS receiver functions. This thickness is practically similar to the standard IASP91 value of 250 km. and is characteristic of a large region of the North Atlantic outside the Azores plateau. Our data are indicative of a reduction of the S-wave velocity of several percent relative to the standard velocity in a depth interval from 460 to 500 km. This reduction is found in the nearest vicinities of the Azores, in the region sampled by the PRFs, but, as evidenced by SRFs, it is missing at a distance of a few hundred kilometers from the islands. We speculate that this anomaly may correspond to the source of a plume which generated the Azores hotspot. Previously, a low S velocity in this depth range was found with SRF techniques beneath a few other hotspots.

From unthinned continent to ocean: The deep structure of the West Iberia passive continental margin at 38 degrees N

The crustal and lithospheric mantle structure at the south segment of the west Iberian margin was investigated along a 370 km long seismic transect. The transect goes from unthinned continental crust onshore to oceanic crust, crossing the ocean-continent transition (OCT) zone. The wide-angle data set includes recordings from 6 OBSs and 2 inland seismic stations. Kinematic and dynamic modeling provided a 2D velocity model that proved to be consistent with the modeled free-air anomaly data. The interpretation of coincident multi-channel near-vertical and wide-angle reflection data sets allowed the identification of four main crustal domains: (i) continental (east of 9.4 degrees W); (ii) continental thinning (9.4 degrees W-9.7 degrees W): (iii) transitional (9.7 degrees W-similar to 10.5 degrees W); and (iv) oceanic (west of similar to 10.5 degrees W). In the continental domain the complete crustal section of slightly thinned continental crust is present. The upper (UCC, 5.1-6.0 km/s) and the lower continental crust (LCC, 6.9-7.2 km/s) are seismically reflective and have intermediate to low P-wave velocity gradients. The middle continental crust (MCC, 6.35-6.45 km/s) is generally unreflective with low velocity gradient. The main thinning of the continental crust occurs in the thinning domain by attenuation of the UCC and the LCC. Major thinning of the MCC starts to the west of the LCC pinchout point, where it rests directly upon the mantle. In the thinning domain the Moho slope is at least 13 degrees and the continental crust thickness decreases seaward from 22 to 11 km over a similar to 35 km distance, stretched by a factor of 1.5 to 3. In the oceanic domain a two-layer high-gradient igneous crust (5.3-6.0 km/s; 6.5-7.4 km/s) was modeled. The intra-crustal interface correlates with prominent mid-basement, 10-15 km long reflections in the multi-channel seismic profile. Strong secondary reflected PmP phases require a first order discontinuity at the Moho. The sedimentary cover can be as thick as 5 km and the igneous crustal thickness varies from 4 to 11 km in the west, where the profile reaches the Madeira-Tore Rise. In the transitional domain the crust has a complex structure that varies both horizontally and vertically. Beneath the continental slope it includes exhumed continental crust (6.15-6.45 km/s). Strong diffractions were modeled to originate at the lower interface of this layer. The western segment of this transitional domain is highly reflective at all levels, probably due to dykes and sills, according to the high apparent susceptibility and density modeled at this location. Sub-Moho mantle velocity is found to be 8.0 km/s, but velocities smaller than 8.0 km/s confined to short segments are not excluded by the data. Strong P-wave wide-angle reflections are modeled to originate at depth of 20 km within the lithospheric mantle, under the eastern segment of the oceanic domain, or even deeper at the transitional domain, suggesting a layered structure for the lithospheric mantle. Both interface depths and velocities of the continental section are in good agreement to the conjugate Newfoundland margin. A similar to 40 km wide OCT having a geophysical signature distinct from the OCT to the north favors a two pulse continental breakup.

Paleomagnetic study of the Messejana Plasencia dyke (Portugal and Spain): A lower Jurassic paleopole for the Iberian plate

The only Iberian lower Jurassic palcomagnetic pole come from the "Central Atlantic Magmatic Province"-related Messejana Plasencia dyke, but the age and origin of its remanence have been a matter of discussion. With the aim of solving this uncertainty, and to go further into a better understanding of its emplacement and other possible tectonic features, a systematic paleomagnetic investigation of 40 sites (625 specimens) distributed all along the 530 kin of the Messejana Plasencia dyke has been carried out. Rock magnetic experiments indicate PSD low Ti-titanomagnetite and magnetite as the minerals carrying the NRM. The samples were mostly thermally demagnetized. Most sites exhibit a characteristic remanent component of normal polarity with the exception of two sites, where samples with reversed polarities have been observed. The paleomagnetic pole derived from a total of 35 valid sites is representative of the whole structure of the dyke, and statistically well defined, with values of PLa = 70.4 degrees N, PLo = 237.6 degrees E, K= 47.9 and A(95) = 3.5 degrees. Paleomagnetic data indicates that: (i) there is no evidence of a Cretaceous remagnetization in the dyke, as it was suggested; (ii) most of the dyke had a brief emplacement time; furthermore, two dyke intrusion events separated in time from it by at least 10,000 y have been detected; (iii) the high grouping of the VGPs directions suggests no important tectonic perturbations of the whole structure of the dyke since its intrusion time; (iv) the pole derived from this study is a good quality lower Jurassic paleopole for the Iberian plate; and (v) the Messejana Plasencia dyke paleopole for the Iberian plate is also in agreement with quality-selected European and North American lower Jurassic paleopoles and the magnetic anomalies data sets that are available for rotate them to Iberia.

The problem of estimating the volatility of zero coupon bond interest rate

Financial literature and financial industry use often zero coupon yield curves as input for testing hypotheses, pricing assets or managing risk. They assume this provided data as accurate. We analyse implications of the methodology and of the sample selection criteria used to estimate the zero coupon bond yield term structure on the resulting volatility of spot rates with different maturities. We obtain the volatility term structure using historical volatilities and Egarch volatilities. As input for these volatilities we consider our own spot rates estimation from GovPX bond data and three popular interest rates data sets: from the Federal Reserve Board, from the US Department of the Treasury (H15), and from Bloomberg. We find strong evidence that the resulting zero coupon bond yield volatility estimates as well as the correlation coefficients among spot and forward rates depend significantly on the data set. We observe relevant differences in economic terms when volatilities are used to price derivatives.