The seasonal sea-ice zone in the glacial Southern Ocean as a carbon sink, supplementary datasets

Reduced surface-deep ocean exchange and enhanced nutrient consumption by phytoplankton in the Southern Ocean have been linked to lower glacial atmospheric CO2. However, identification of the biological and physical conditions involved and the related processes remains incomplete. Here we specify Southern Ocean surface-subsurface contrasts using a new tool, the combined oxygen and silicon isotope measurement of diatom and radiolarian opal, in combination with numerical simulations. Our data do not indicate a permanent glacial halocline related to melt water from icebergs. Corroborated by numerical simulations, we find that glacial surface stratification was variable and linked to seasonal sea-ice changes. During glacial spring-summer, the mixed layer was relatively shallow, while deeper mixing occurred during fall-winter, allowing for surface-ocean refueling with nutrients from the deep reservoir, which was potentially richer in nutrients than today. This generated specific carbon and opal export regimes turning the glacial seasonal sea-ice zone into a carbon sink.

Monthly Arctic sea ice lead fraction in 0.5° x 0.5° resolution from SARAL/Altika altimeter, link to datasets in NetCDF 4 format

Sea ice leads play an essential role in ocean-ice-atmosphere exchange, in ocean circulation, geochemistry, and in ice dynamics. Their precise detection is crucial for altimetric estimations of sea ice thickness and volume. This study evaluates the performance of the SARAL/AltiKa (Satellite with ARgos and ALtiKa) altimeter to detect leads and to monitor their spatio-temporal dynamics. We show that a pulse peakiness parameter (PP) used to detect leads by Envisat RA-2 and ERS-1,-2 altimeters is not suitable because of saturation of AltiKa return echoes over the leads. The signal saturation results in loss of 6-10% of PP data over sea ice. We propose a different parameter-maximal power of waveform-and define the threshold to discriminate the leads. Our algorithm can be applied from December until May. It detects well the leads of small and medium size from 200 m to 3-4 km. So the combination of the high-resolution altimetric estimates with low-resolution thermal infra-red or radiometric lead fraction products could enhance the capability of remote sensing to monitor sea ice fracturing.

The Continuous Plankton Recorder survey: How can long-term phytoplankton datasets contribute to the assessment of Good Environmental Status?

Phytoplankton are crucial to marine ecosystem functioning and are important indicators of environmental change. Phytoplankton data are also essential for informing management and policy, particularly in supporting the new generation of marine legislative drivers, which take a holistic ecosystem approach to management. The Marine Strategy Framework Directive (MSFD) seeks to achieve Good Environmental Status (GES) of European seas through the implementation of such a management approach. This is a regional scale directive which recognises the importance of plankton communities in marine ecosystems; plankton data at the appropriate spatial, temporal and taxonomic scales are therefore required for implementation. The Continuous Plankton Recorder (CPR) survey is a multidecadal, North Atlantic basin scale programme which routinely records approximately 300 phytoplankton taxa. Because of these attributes, the survey plays a key role in the implementation of the MSFD and the assessment of GES in the Northeast Atlantic region. This paper addresses the role of the CPR's phytoplankton time-series in delivering GES through the development and informing of MSFD indicators, the setting of targets against a background of climate change and the provision of supporting information used to interpret change in non-plankton indicators. We also discuss CPR data in the context of other phytoplankton data types that may contribute to GES, as well as explore future possibilities for the use of new and innovative applications of CPR phytoplankton datasets in delivering GES. Efforts must be made to preserve long-term time series, such as the CPR, which supply vital ecological information used to informed evidence-based environmental policy.

The Continuous Plankton Recorder survey: How can long-term phytoplankton datasets contribute to the assessment of Good Environmental Status?

Phytoplankton are crucial to marine ecosystem functioning and are important indicators of environmental change. Phytoplankton data are also essential for informing management and policy, particularly in supporting the new generation of marine legislative drivers, which take a holistic ecosystem approach to management. The Marine Strategy Framework Directive (MSFD) seeks to achieve Good Environmental Status (GES) of European seas through the implementation of such a management approach. This is a regional scale directive which recognises the importance of plankton communities in marine ecosystems; plankton data at the appropriate spatial, temporal and taxonomic scales are therefore required for implementation. The Continuous Plankton Recorder (CPR) survey is a multidecadal, North Atlantic basin scale programme which routinely records approximately 300 phytoplankton taxa. Because of these attributes, the survey plays a key role in the implementation of the MSFD and the assessment of GES in the Northeast Atlantic region. This paper addresses the role of the CPR's phytoplankton time-series in delivering GES through the development and informing of MSFD indicators, the setting of targets against a background of climate change and the provision of supporting information used to interpret change in non-plankton indicators. We also discuss CPR data in the context of other phytoplankton data types that may contribute to GES, as well as explore future possibilities for the use of new and innovative applications of CPR phytoplankton datasets in delivering GES. Efforts must be made to preserve long-term time series, such as the CPR, which supply vital ecological information used to informed evidence-based environmental policy.

Identifying the Impact of Incomplete Datasets on Process Cycle Time Prediction in an Aerospace Assembly Line

Supply Chain Simulation (SCS) is applied to acquire information to support outsourcing decisions but obtaining enough detail in key parameters can often be a barrier to making well informed decisions.
One aspect of SCS that has been relatively unexplored is the impact of inaccurate data around delays within the SC. The impact of the magnitude and variability of process cycle time on typical performance indicators in a SC context is studied.
System cycle time, WIP levels and throughput are more sensitive to the magnitude of deterministic deviations in process cycle time than variable deviations. Manufacturing costs are not very sensitive to these deviations.
Future opportunities include investigating the impact of process failure or product defects, including logistics and transportation between SC members and using alternative costing methodologies.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

POWKist: visualising cultural heritage linked datasets.

A collaboration between dot.rural at the University of Aberdeen and the iSchool at Northumbria University, POWkist is a pilot-study exploring potential usages of currently available linked datasets within the cultural heritage domain. Many privately-held family history collections (shoebox archives) remain vulnerable unless a sustainable, affordable and accessible model of citizen-archivist digital preservation can be offered. Citizen-historians have used the web as a platform to preserve cultural heritage, however with no accessible or sustainable model these digital footprints have been ad hoc and rarely connected to broader historical research. Similarly, current approaches to connecting material on the web by exploiting linked datasets do not take into account the data characteristics of the cultural heritage domain. Funded by Semantic Media, the POWKist project is investigating how best to capture, curate, connect and present the contents of citizen-historians’ shoebox archives in an accessible and sustainable online collection. Using the Curios platform - an open-source digital archive - we have digitised a collection relating to a prisoner of war during WWII (1939-1945). Following a series of user group workshops, POWkist is now connecting these ‘made digital’ items with the broader web using a semantic technology model and identifying appropriate linked datasets of relevant content such as DBPedia (an archived linked dataset of Wikipedia) and Ordnance Survey Open Data. We are analysing the characteristics of cultural heritage linked datasets, so that these materials are better visualised, contextualised and presented in an attractive and comprehensive user interface. Our paper will consider the issues we have identified, the solutions we are developing and include a demonstration of our work-in-progress.

Comparing global land cover datasets through the Eagle matrix land cover components for continental Portugal

Global land cover maps play an important role in the understanding of the Earth's ecosystem dynamic. Several global land cover maps have been produced recently namely, Global Land Cover Share (GLC-Share) and GlobeLand30. These datasets are very useful sources of land cover information and potential users and producers are many times interested in comparing these datasets. However these global land cover maps are produced based on different techniques and using different classification schemes making their interoperability in a standardized way a challenge. The Environmental Information and Observation Network (EIONET) Action Group on Land Monitoring in Europe (EAGLE) concept was developed in order to translate the differences in the classification schemes into a standardized format which allows a comparison between class definitions. This is done by elaborating an EAGLE matrix for each classification scheme, where a bar code is assigned to each class definition that compose a certain land cover class. Ahlqvist (2005) developed an overlap metric to cope with semantic uncertainty of geographical concepts, providing this way a measure of how geographical concepts are more related to each other. In this paper, the comparison of global land cover datasets is done by translating each land cover legend into the EAGLE bar coding for the Land Cover Components of the EAGLE matrix. The bar coding values assigned to each class definition are transformed in a fuzzy function that is used to compute the overlap metric proposed by Ahlqvist (2005) and overlap matrices between land cover legends are elaborated. The overlap matrices allow the semantic comparison between the classification schemes of each global land cover map. The proposed methodology is tested on a case study where the overlap metric proposed by Ahlqvist (2005) is computed in the comparison of two global land cover maps for Continental Portugal. The study resulted with the overlap spatial distribution among the two global land cover maps, Globeland30 and GLC-Share. These results shows that Globeland30 product overlap with a degree of 77% with GLC-Share product in Continental Portugal.

Parcellation of fMRI datasets with ICA and PLS: a data driven approach

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.

Identifying wind power ramp causes from multivariate datasets: a methodological proposal and its application to reanalysis data.

Forecasting abrupt variations in wind power generation (the so-called ramps) helps achieve large scale wind power integration. One of the main issues to be confronted when addressing wind power ramp forecasting is the way in which relevant information is identified from large datasets to optimally feed forecasting models. To this end, an innovative methodology oriented to systematically relate multivariate datasets to ramp events is presented. The methodology comprises two stages: the identification of relevant features in the data and the assessment of the dependence between these features and ramp occurrence. As a test case, the proposed methodology was employed to explore the relationships between atmospheric dynamics at the global/synoptic scales and ramp events experienced in two wind farms located in Spain. The achieved results suggested different connection degrees between these atmospheric scales and ramp occurrence. For one of the wind farms, it was found that ramp events could be partly explained from regional circulations and zonal pressure gradients. To perform a comprehensive analysis of ramp underlying causes, the proposed methodology could be applied to datasets related to other stages of the wind-topower conversion chain.

Methodologies for the analysis and interpretation of environmental datasets from areas interested by offshore activities

The exploitation of hydrocarbon reservoirs by the oil and gas industries represents one of the most relevant and concerning anthropic stressor in various marine areas worldwide and the presence of extractive structures can have severe consequences on the marine environment. Environmental monitoring surveys are carried out to monitor the effects and impacts of offshore energy facilities. Macrobenthic communities, inhabiting the soft-bottom, represent a key component of these surveys given their great responsiveness to natural and anthropic changes. A comprehensive collection of monitoring data from four Italian seas was used to investigate distributional pattern of macrozoobenthos assemblages confirming a high spatial variability in relation to the environmental variables analyzed. Since these datasets could represent a powerful tool for the industrial and scientific research, the steps and standardized procedures needed to obtain robust and comparable high-quality data were investigated and outlined. Over recent years, decommissioning of old platforms is a growing topic in this sector, involving many actors in the various decision-making processes. A Multi-Criteria Decision Analysis, specific for the Adriatic Sea, was developed to investigate the impacts of decommissioning of a gas platform on environmental and socio-economic aspects, to select the best decommissioning scenario. From the scenarios studied, the most impacting one has resulted to be total removal, affecting all the faunal component considered in the study. Currently, the European nations are increasing the production of energy from offshore wind farms with an exponential expansion. A comparative study of methodologies used five countries of the North Sea countries was carried out to investigate the best approaches to monitor the effects of wind farms on the benthic communities. In the foreseeable future, collaboration between industry, scientific communities, national and international policies are needed to gain knowledge concerning the effects of these industrial activities on the ecological status of the ecosystems.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.