Seafloor spreading magnetic anomaly isochron map compilation for the Weddell Sea and Scotia Sea

The deep sea sedimentary record is an archive of the pre-glacial to glacial development of Antarctica and changes in climate, tectonics and ocean circulation. Identification of the pre-glacial, transitional and full glacial components in the sedimentary record is necessary for ice sheet reconstruction and to build circum-Antarctic sediment thickness grids for past topography and bathymetry reconstructions, which constrain paleoclimate models. A ~3300 km long Weddell Sea to Scotia Sea transect consisting of multichannel seismic reflection data from various organisations, were used to interpret new horizons to define the initial basin-wide seismostratigraphy and to identify the pre-glacial to glacial components. We mapped seven main units of which three are in the inferred Cretaceous-Paleocene pre-glacial regime, one in the Eocene-Oligocene transitional regime and three units in the Miocene-Pleistocene full glacial climate regime. Sparse borehole data from ODP leg 113 and SHALDRIL constrain the ages of the upper three units. Compiled seafloor spreading magnetic anomalies constrain the basement ages and the hypothetical age model. In many cases, the new horizons and stratigraphy contradict the interpretations in local studies. Each seismic sedimentary unit and its associated base horizon are continuous and traceable for the entire transect length, but reflect a lateral change in age whilst representing the same deposition process. The up to 1240 m thick pre-glacial seismic units form a mound in the central Weddell Sea basin and, in conjunction with the eroded flank geometry, support the interpretation of a Cretaceous proto-Weddell Gyre. The base reflector of the transitional seismic unit, which marks the initial ice sheet advances to the outer shelf, has a lateral model age of 26.6-15.5 Ma from southeast to northwest. The Pliocene-Pleistocene glacial deposits reveals lower sedimentations rates, indicating a reduced sediment supply. Sedimentation rates for the pre-glacial, transitional and full glacial components are highest around the Antarctic Peninsula, indicating higher erosion and sediment supply of a younger basement. We interpret an Eocene East Antarctic Ice Sheet expansion, Oligocene grounding of the West Antarctic Ice Sheet and Early Miocene grounding of the Antarctic Peninsula Ice Sheet.

Mineral and chemical compositions of a ferromanganese nodule from Station DM41-3911-76

Processes of authigenic manganese ore formation in sediments of the North Equatorial Pacific are considered on the basis of a study of the surface layer (<2 mm) of a ferromanganese nodule and four micronodule size fractions from associated surface sediment (0-7 cm). Inhomogeneity of nodule composition is shown. Mn/Fe ratio is maximal in samples from lateral sectors of the nodule at the water-sediment interface. Compositional differences of nodules are related to preferential accumulation of trace elements in iron oxyhydroxides (P, Sr, Pb, U, Bi, Th, Y, and REE), manganese hydroxides (Co, Ni, Cu, Zn, Cd, Mo, Tl, W), and lithogenic component trapped during nodule growth (Ga, Rb, Ba, and Cs). Ce accumulation in the REE composition is maximal in the upper and lower parts of the nodule characterized by minimal Mn/Fe values. A compositional comparison of manganese micronodules and surface layers of the nodule demonstrates that micronodule material was subjected to more intense reworking during diagenesis of sediments. The micronodules are characterized by higher Mn/Fe and P/Fe, but lower Ni/Cu and Co/Ni ratios. The micronodules and nodules do not differ in terms of contents of Ce and Th that are the least mobile elements during diagenesis. Differences in chemical composition of the micronodules and nodules are related not only to additional input of Mn in the process of diagenesis, but also to transformation of iron oxyhydroxides after removal of Mn from the close association with Fe formed in suspended matter during sedimentation.

Rare earth element concentrations and geochemistry of carbonate minerals from the Logatchev Hydrothermal Field and the Gakkel Ridge

The ultramafic-hosted Logatchev Hydrothermal Field (LHF) at 15°N on the Mid-Atlantic Ridge and the Arctic Gakkel Ridge (GR) feature carbonate precipitates (aragonite, calcite, and dolomite) in voids and fractures within different types of host rocks. We present chemical and Sr isotopic compositions of these different carbonates to examine the conditions that led to their formation. Our data reveal that different processes have led to the precipitation of carbonates in the various settings. Seawater-like 87Sr/86Sr ratios for aragonite in serpentinites (0.70909 to 0.70917) from the LHF are similar to those of aragonite from the GR (0.70912 to 0.70917) and indicate aragonite precipitation from seawater at ambient conditions at both sites. Aragonite veins in sulfide breccias from LHF also have seawater-like Sr isotope compositions (0.70909 to 0.70915), however, their rare earth element (REE) patterns show a clear positive europium (Eu) anomaly indicative of a small (< 1%) hydrothermal contribution. In contrast to aragonite, dolomite from the LHF has precipitated at much higher temperatures (~100 °C), and yet its 87Sr/86Sr ratios (0.70896 to 0.70907) are only slightly lower than those of aragonite. Even higher temperatures are calculated for the precipitation of deformed calcite veins in serpentine-talc fault schists form north of the LHF. These calcites show unradiogenic 87Sr/86Sr ratios (0.70460 to 0.70499) indicative of precipitation from evolved hydrothermal fluids. A simple mixing model based on Sr mass balance and enthalpy conservation indicates strongly variable conditions of fluid mixing and heat transfers involved in carbonate formation. Dolomite precipitated from a mixture of 97% seawater and 3% hydrothermal fluid that should have had a temperature of approximately 14 °C assuming that no heat was transferred. The much higher apparent precipitation temperatures based on oxygen isotopes (~ 100 °C) may be indicative of conductive heating, probably of seawater prior to mixing. The hydrothermal calcite in the fault schist has precipitated from a mixture of 67% hydrothermal fluid and 33% seawater, which should have had an isenthalpic mixing temperature of ~ 250 °C. The significantly lower temperatures calculated from oxygen isotopes are likely due to conductive cooling of hydrothermal fluid discharging along faults. Rare earth element patterns corroborate the results of the mixing model, since the hydrothermal calcite, which formed from waters with the greatest hydrothermal contribution, has REE patterns that closely resemble those of vent fluids from the LHF. Our results demonstrate, for the first time, that (1) precipitation from pure seawater, (2) conductive heating of seawater, and (3) conductive cooling of hydrothermal fluids in the sub-seafloor all can lead to carbonate precipitation within a single ultramafic-hosted hydrothermal system.

(Table S1) Trace element and isotopic compositions of anhydrites sampled in the Manus Basin, Papua New Guinea

Microchemical analyses of rare earth element (REE) concentrations and Sr and S isotope ratios of anhydrite are used to identify sub-seafloor processes governing the formation of hydrothermal fluids in the convergent margin Manus Basin, Papua New Guinea. Samples comprise drill-core vein anhydrite and seafloor massive anhydrite from the PACMANUS (Roman Ruins, Snowcap and Fenway) and SuSu Knolls (North Su) active hydrothermal fields. Chondrite-normalized REE patterns in anhydrite show remarkable heterogeneity on the scale of individual grains, different from the near uniform REEN patterns measured in anhydrite from mid-ocean ridge deposits. The REEN patterns in anhydrite are correlated with REE distributions measured in hydrothermal fluids venting at the seafloor at these vent fields and are interpreted to record episodes of hydrothermal fluid formation affected by magmatic volatile degassing. 87Sr/86Sr ratios vary dramatically within individual grains between that of contemporary seawater and that of endmember hydrothermal fluid. Anhydrite was precipitated from a highly variable mixture of the two. The intra-grain heterogeneity implies that anhydrite preserves periods of contrasting hydrothermal versus seawater dominant near-seafloor fluid circulation. Most sulfate d34S values of anhydrite cluster around that of contemporary seawater, consistent with anhydrite precipitating from hydrothermal fluid mixed with locally entrained seawater. Sulfate d34S isotope ratios in some anhydrites are, however, lighter than that of seawater, which are interpreted as recording a source of sulfate derived from magmatic SO2 degassed from underlying felsic magmas in the Manus Basin. The range of elemental and isotopic signatures observed in anhydrite records a range of sub-seafloor processes including high-temperature hydrothermal fluid circulation, varying extents of magmatic volatile degassing, seawater entrainment and fluid mixing. The chemical and isotopic heterogeneity recorded in anhydrite at the inter- and intra-grain scale captures the dynamics of hydrothermal fluid formation and sub-seafloor circulation that is highly variable both spatially and temporally on timescales over which hydrothermal deposits are formed. Microchemical analysis of hydrothermal minerals can provide information about the temporal history of submarine hydrothermal systems that are variable over time and cannot necessarily be inferred only from the study of vent fluids.

Geochemistry of ODP Hole 108-658C sediments

During the African Humid Period (AHP), much of the modern hyperarid Saharan desert was vegetated and covered with numerous lakes. In marine sediments off northwestern Africa, the AHP is represented by markedly reduced siliciclastic sediment flux between ~ 12.3 and 5.5 ka. Changes in the origin of this terrigenous sediment fraction can be constrained by sediment chemistry and radiogenic isotope tracers. At Ocean Drilling Program (ODP) Site 658, Hole C (20°44.95'N, 18°34.85'W, 2263 mbsl), the neodymium (Nd) isotope composition of terrigenous detritus shows little variability throughout the last 25 kyr, indicating that the contributing geological terranes have not changed appreciably since the last glacial period. In contrast, there were large and abrupt changes in strontium (Sr) isotope ratios and chemical compositions associated with the AHP, during which 87Sr/86Sr ratios were markedly less radiogenic, and sediments show higher chemical indices of alteration. We show that sediment geochemical changes during the AHP cannot be attributed to changes in the source terranes, physical sorting, or intensity of chemical weathering. The low 87Sr/86Sr and high Sr concentrations of AHP-age samples also conflict with the interpretation of increased fine-grained, fluvially derived sediments. We propose that the most significant compositional changes at ODP 658C are due to the addition of an aluminosilicate component that has a highly altered major element signature but is enriched in soluble elements like Sr and magnesium (Mg) compared to aluminum (Al) and has low 87Sr/86Sr relative to local terrigenous source areas. We interpret these characteristics to reflect authigenic sediment supply from extensive North African paleolake basins that were prevalent during the AHP.

Video anomaly detection in real-time on a Power-Aware Heterogeneous Platform

FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

The changing epidemiology of Ebstein's anomaly and its relationship with maternal mental health conditions: a European registry-based study

OBJECTIVES: The aim of this study was to describe the epidemiology of Ebstein's anomaly in Europe and its association with maternal health and medication exposure during pregnancy.

DESIGN: We carried out a descriptive epidemiological analysis of population-based data.

SETTING: We included data from 15 European Surveillance of Congenital Anomalies Congenital Anomaly Registries in 12 European countries, with a population of 5.6 million births during 1982-2011. Participants Cases included live births, fetal deaths from 20 weeks gestation, and terminations of pregnancy for fetal anomaly. Main outcome measures We estimated total prevalence per 10,000 births. Odds ratios for exposure to maternal illnesses/medications in the first trimester of pregnancy were calculated by comparing Ebstein's anomaly cases with cardiac and non-cardiac malformed controls, excluding cases with genetic syndromes and adjusting for time period and country.

RESULTS: In total, 264 Ebstein's anomaly cases were recorded; 81% were live births, 2% of which were diagnosed after the 1st year of life; 54% of cases with Ebstein's anomaly or a co-existing congenital anomaly were prenatally diagnosed. Total prevalence rose over time from 0.29 (95% confidence interval (CI) 0.20-0.41) to 0.48 (95% CI 0.40-0.57) (p<0.01). In all, nine cases were exposed to maternal mental health conditions/medications (adjusted odds ratio (adjOR) 2.64, 95% CI 1.33-5.21) compared with cardiac controls. Cases were more likely to be exposed to maternal β-thalassemia (adjOR 10.5, 95% CI 3.13-35.3, n=3) and haemorrhage in early pregnancy (adjOR 1.77, 95% CI 0.93-3.38, n=11) compared with cardiac controls.

CONCLUSIONS: The increasing prevalence of Ebstein's anomaly may be related to better and earlier diagnosis. Our data suggest that Ebstein's anomaly is associated with maternal mental health problems generally rather than lithium or benzodiazepines specifically; therefore, changing or stopping medications may not be preventative. We found new associations requiring confirmation.

Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Monoamniotic twins discordant for body stalk anomaly

Body stalk anomaly is a rare malformation. This anomaly in monozygotic twins is extremely unusual. We describe a case of monoamniotic pregnancy discordant for body stalk anomaly diagnosed at 11 weeks. Ultrasound showed a fetus with a large anterior abdominal wall defect, anomaly of the spine and no evidence of lower extremities and other with a normal morphology. As far as our concern, only three monoamniotic pregnancies discordant for this malformation were reported. Our case represents the fourth reported monoamniotic pregnancy discordant for body stalk anomaly with diagnosis made by ultrasound and the second diagnosed in the first trimester.