Composition and structural formulas of ODP Site 200-1224 Eocene ferrobasalt minerals

The ~46-m.y.-old igneous basement cored during Leg 200 in the North Pacific represents one of the few cross sections of Pacific oceanic crust with a total penetration into basalt of >100 m. The rocks, emplaced during the Eocene at a fast-spreading rate (~14 cm/yr; full rate) are strongly differentiated tholeiitic basalts (ferrobasalts) with 7-4.5 wt% MgO, relatively high TiO2 (2-3.5 wt%), and total iron as Fe2O3 (9.1-16.8 wt%). The differentiated character of these lavas is related to unusually large amounts of crystallization differentiation of plagioclase, clinopyroxene, and olivine. The lithostratigraphy of the basement (cored to ~170 meters below seafloor) is divided into three units. The deepest unit (lithologic Unit 3), is a succession of lava flows of no more that a few meters thickness each. The intermediate unit (lithologic Unit 2) is represented by intermixed thin flows and pillows, whereas the shallowest unit (lithologic Unit 1), comprises two massive flows. The rocks range from aphyric to sparsely clinopyroxene-plagioclase-phyric (phenocryst content = <3 vol%) and from holocrystalline to hypohyaline. Chilled margins of pillow fragments show holohyaline to sparsely vitrophyric textures. Site 1224 oxide minerals present a type of alteration not previously seen, where titanomagnetite is only partially destroyed and the pure magnetite component is partially removed from the mineral, leaving, in the most extreme case, a nearly pure ulvöspinel residuum. As a result of this dissolution, iron, mainly in the oxidized state, is added to the circulating solvent fluids. This means that a considerable metal source can result from low-temperature reactions throughout the upper ocean crust. The coarsest-grained lithologic Unit 1 rocks have interstitial myrmekitic intergrowths of quartz and sodic plagioclase (~An12), roughly similar in mineralogy and bulk composition to tonalite/trondhjemite veinlets in abyssal gabbros from the southwest Indian Ocean and Hess Deep, eastern equatorial Pacific. Based on idiomorphic relationships and projections into the simplified Q-Ab-Or-H2O granite ternary system, the myrmekitic intergrowths formed at the same time as, or just after, the oxide minerals coprecipitated and at low water vapor pressure (~0.5 kbar). Their compositions correspond to SiO2-oligoclase intergrowths that are considerably less potassic than dacitic glasses that erupt, although rarely, along the East Pacific Rise or that have been produced experimentally by partial melting of gabbro. Based on the crystallization history and comparison to experimental data, the original interstitial siliceous liquids resulted from late-stage immiscible separation of siliceous and iron-rich liquids. The rare andesitic lavas found along the East Pacific Rise may be hybrid rocks formed by mixing of these immiscible siliceous melts with basaltic magma.

Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plant

Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications

Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performance

Gaussian Multiscale Aggregation oriented to Hand Biometric Segmentation in Mobile Devices

New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile. However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation. For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task. Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments. The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics

This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage.