Biogeochemical cycle in a coccolithophorid bloom

The biogeochemical properties of an extensive bloom of the coccolithophore, Emiliania huxleyi, at the shelf break in the northern Gulf of Biscay was investigated in June 2006. Total Alkalinity (TA) values in the water column showed strong non-conservative behaviour indicative of the impact of calcification, with the highest TA anomalies (up to 26μmol.kg-1) in the high reflectance coccolith patch. Partial pressure of CO2 (pCO2) values ranged from 250 to 338μatm and the area was found to act as a sink for atmospheric CO2.Overall, pCO2@13°C (pCO2 normalized at a constant temperature of 13°C) in the water column was negatively related to TA anomalies in agreement with an overall production of CO2 related to calcification. Hence, the calcifying phase of the E. huxleyi bloom decreased the sink of atmospheric pCO2, but did not reverse the direction of the flux. Rates of pelagic respiration up to 5.5mmol O2.m-3.d-1 suggested a close coupling between primary production and respiration and/or between organic carbon content and respiration. Benthic respiration rates were quite low and varied between 2 and 9mmol O2.m-3.d-1, in agreement with the fact that the study area consists of sandy sediments with low organic matter content. Benthic respiration was well correlated to the chlorophyll a content of the top 1cm of the sediment cores. Evidence was found for dissolution of CaCO3 due to the acidification of superficial sediments in relation to the production of CO2 and the oxidation of H2S in the oxic layers

Ants Constructing Rule-Based Classifiers

Book series: Studies in Computational Intelligencestatus: publishe

Explaining Support Vector Machines: A Color Based Nomogram.

PROBLEM SETTING: Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. OBJECTIVE: In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. RESULTS: Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. CONCLUSIONS: This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method

Kernel Spectral Clustering and applications

In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective. Just as in the classifier case, the binary clustering model is expressed by a hyperplane in a high dimensional space induced by a kernel. In addition, the multi-way clustering can be obtained by combining a set of binary decision functions via an Error Correcting Output Codes (ECOC) encoding scheme. Because of its model-based nature, the KSC method encompasses three main steps: training, validation, testing. In the validation stage model selection is performed to obtain tuning parameters, like the number of clusters present in the data. This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics. Once a KSC model is trained on a small subset of the entire data, it is able to generalize well to unseen test points. Beyond the basic formulation, sparse KSC algorithms based on the Incomplete Cholesky Decomposition (ICD) and $L_0$, $L_1, L_0 + L_1$, Group Lasso regularization are reviewed. In that respect, we show how it is possible to handle large scale data. Also, two possible ways to perform hierarchical clustering and a soft clustering method are presented. Finally, real-world applications such as image segmentation, power load time-series clustering, document clustering and big data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms

До питання підготовки музейної експозиції з історії культури первісного суспільства

In Europe, water management is moving from flood defense to a risk management approach, which takes both the probability and the potential consequences of flooding into account. In this report, we will look at Directives and (non-)EU- initiatives in place to deal with flood risk in Europe indirectly and directly. Emphasis will lie on the two Directives most specifically aimed at floods: the Water Framework Directive (WFD) and the Floods Directive (FD) – how are they related and how they have been or are implemented in the Member States (MSs)? In February 1995, the Netherlands and France took the initiative for a discussion on streamlining the water legislation of the European Union (EU) which resulted in the creation of the WFD in 2000. The WFD provided a new system for the protection and improvement of Europe’s water environment – its rivers, lakes, estuaries, coastal waters and groundwaters. Its main innovation is the requirement that water be managed in an integrated way, with river basin management as leading managing unit. Since flood protection is not explicitly addressed in the WFD, the need to clarify the role of the WFD in flood protection was put on the European agenda as early as 2003, and in 2007, the FD became a fact. The FD is to be implemented in coordination with the WFD, notably by coordinating Flood Risk Management Plans (FRMPs) and River Basin Management Plans (RBMPs). Both the WFD and the FD reflect a shift in EU-governance. Instead of the more traditional top-down legalistic approach they emphasise the importance of more bottom up initiatives from the actors who have to implement the Directives. Combined with the expanded freedom and flexibility for national and local governments, with this new approach, the FD is the first Water Directive in EU law that does not offer an equal minimum level of protection for EU citizens. While both Directives are meant to harmonise European legislation, much flexibility on objectives and measures in the FD is left to the MSs, justified by the nature of flooding and the subsidiarity principle. This creates multi-actor, multi-level and multi-sector challenges addressed in report D1.1.2 (Hegger et al. 2013). For instance, the FD sets out general obligations for transboundary cooperation, but at the national level, the scope and distributions of duties, rights and powers of the various organizations involved should be set out in law. Other challenges identified in the literature are concrete issues related to mandatory flood risks assessments, flood risk maps, and Flood Risk Management plans, but also the involvement of the public and stakeholders, the science-policy interface, uncertainties related to climate change predictions and effects, the coordination with the WFD, the lack of safety standards, the lack of possibilities for EU citizens to rely on substantive provisions before the administrative courts and finally, transboundary aspects such as issues of scale, mismatches between national policies, the assessment of transboundary effects and division of costs related to this. In sum, this report has clarified the development, content and implementation of the current European flood risk governance policies, possible synergies between the two most important Directives linked to floods, and identified topics and questions for more in-depth questions relevant for the next work packages, pertaining to, in no particular order, a) the level of implementation and level of ambition as well as the competent authorities in the case study countries; b) the transboundary nature of floods; c) synergies and conflicts between FD and WFD and other issues not mentioned in these Directives; d) the degree of harmonization, for instance when it comes to flood safety standards and e) the subsidiarity principle – is this conform the requirements set out in the FD? Because while current European flood regulation specified in the WFD and FD provides several potential opportunities for improving flood risk governance, it is not self-evident that all of these opportunities will materialise in all MSs

L2-norm multiple kernel learning and its application to biomedical data fusion

<p>Abstract</p> <p>Background</p> <p>This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as <it>L</it>_∞, <it>L</it>₁, and <it>L</it>₂MKL. In particular, <it>L</it>₂MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing <it>L</it>_∞MKL method. In real biomedical applications, <it>L</it>₂MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.</p> <p>Results</p> <p>We provide a theoretical analysis of the relationship between the <it>L</it>₂optimization of kernels in the dual problem with the <it>L</it>₂coefficient regularization in the primal problem. Understanding the dual <it>L</it>₂problem grants a unified view on MKL and enables us to extend the <it>L</it>₂method to a wide range of machine learning problems. We implement <it>L</it>₂MKL for ranking and classification problems and compare its performance with the sparse <it>L</it>_∞and the averaging <it>L</it>₁MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. <it>L</it>₂MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel <it>L</it>₂MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing.</p> <p>Conclusions</p> <p>This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in <it>L</it>_∞MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing <it>L</it>₂kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL.</p> <p>Availability</p> <p>The MATLAB code of algorithms implemented in this paper is downloadable from <url>http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html</url>.</p

A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology

Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very complex and non-interpretable mathematical models. In order to accomplish effective decision support, CDS systems should provide information on how the model arrives at a certain decision. To address the issue of incompatibility between performance, interpretability and applicability of CDS systems, this paper proposes an innovative model structure, automatically leading to interpretable and easily applicable models. The resulting models can be used to guide clinicians when deciding upon the appropriate treatment, estimating patient-specific risks and to improve communication with patients.We propose the interval coded scoring (ICS) system, which imposes that the effect of each variable on the estimated risk is constant within consecutive intervals. The number and position of the intervals are automatically obtained by solving an optimization problem, which additionally performs variable selection. The resulting model can be visualised by means of appealing scoring tables and color bars. ICS models can be used within software packages, in smartphone applications, or on paper, which is particularly useful for bedside medicine and home-monitoring. The ICS approach is illustrated on two gynecological problems: diagnosis of malignancy of ovarian tumors using a dataset containing 3,511 patients, and prediction of first trimester viability of pregnancies using a dataset of 1,435 women. Comparison of the performance of the ICS approach with a range of prediction models proposed in the literature illustrates the ability of ICS to combine optimal performance with the interpretability of simple scoring systems.The ICS approach can improve patient-clinician communication and will provide additional insights in the importance and influence of available variables. Future challenges include extensions of the proposed methodology towards automated detection of interaction effects, multi-class decision support systems, prognosis and high-dimensional data

Fuzzy Modeling for Uncertain Nonlinear Systems Using Fuzzy Equations and Z-Numbers

In this paper, the uncertainty property is represented by Z-number as the coefficients and variables of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. Here, we use fuzzy equations as the models for the uncertain nonlinear systems. The modeling of the uncertain nonlinear systems is to find the coefficients of the fuzzy equation. However, it is very difficult to obtain Z-number coefficients of the fuzzy equations. Taking into consideration the modeling case at par with uncertain nonlinear systems, the implementation of neural network technique is contributed in the complex way of dealing the appropriate coefficients of the fuzzy equations. We use the neural network method to approximate Z-number coefficients of the fuzzy equations