Spatial variability of the plankton trophic interaction in the North Sea: a new feature after the early 1970s

Traditionally, marine ecosystem structure was thought to be bottom-up controlled. In recent years, a number of studies have highlighted the importance of top-down regulation. Evidence is accumulating that the type of trophic forcing varies temporally and spatially, and an integrated view – considering the interplay of both types of control – is emerging. Correlations between time series spanning several decades of the abundances of adjacent trophic levels are conventionally used to assess the type of control: bottom-up if positive or top-down if this is negative. This approach implies averaging periods which might show time-varying dynamics and therefore can hide part of this temporal variability. Using spatially referenced plankton information extracted from the Continuous Plankton Recorder, this study addresses the potential dynamic character of the trophic structure at the planktonic level in the North Sea by assessing its variation over both temporal and spatial scales. Our results show that until the early-1970s a bottom-up control characterized the base of the food web across the whole North Sea, with diatoms having a positive and homogeneous effect on zooplankton filter-feeders. Afterwards, different regional trophic dynamics were observed, in particular a negative relationship between total phytoplankton and zooplankton was detected off the west coast of Norway and the Skagerrak as opposed to a positive one in the southern reaches. Our results suggest that after the early 1970s diatoms remained the main food source for zooplankton filter-feeders east of Orkney–Shetland and off Scotland, while in the east, from the Norwegian Trench to the German Bight, filter-feeders were mainly sustained by dinoflagellates.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.

Unraveling EU Policies: the ““Common Basic Principles”” and the ““European Integration Fund”” as a technology of government

this paper is about EU “soft policies” on immigrant integration. It analyzes the “Common Basic Principles” (CBPs) and the “European Integration Fund” (EIF), two devices that have been recently established within this framework. It adopts the theoretical perspective of the “anthropology of policy” and “governmentality studies”. It shows the context of birth of the aforementioned devices, as well as their functioning and the assessment done by the actors implied in the elaboration/implementation/evaluation of the related policies. It is based both on documentary research as well as direct observation and interviews done to the actors implied. It concludes that the PBC and the EIF should be considered as a “technology of government”, that strives to align the conduct of the actors with the governmental aims, as well as it produces specific practices and knowledge. It also underlines an intrinsic feature of many policies: their “congenital failure”, since they are (often) disputed and resignified by situated actors, who are embedded in asymmetrical power relations.

Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.

Fast model based feature matching technique applied to airport lighting

The use of image processing techniques to assess the performance of airport landing lighting using images of it collected from an aircraft-mounted camera is documented. In order to assess the performance of the lighting, it is necessary to uniquely identify each luminaire within an image and then track the luminaires through the entire sequence and store the relevant information for each luminaire, that is, the total number of pixels that each luminaire covers and the total grey level of these pixels. This pixel grey level can then be used for performance assessment. The authors propose a robust model-based (MB) featurematching technique by which the performance is assessed. The development of this matching technique is the key to the automated performance assessment of airport lighting. The MB matching technique utilises projective geometry in addition to accurate template of the 3D model of a landing-lighting system. The template is projected onto the image data and an optimum match found, using nonlinear least-squares optimisation. The MB matching software is compared with standard feature extraction and tracking techniques known within the community, these being the Kanade–Lucus–Tomasi (KLT) and scaleinvariant feature transform (SIFT) techniques. The new MB matching technique compares favourably with the SIFT and KLT feature-tracking alternatives. As such, it provides a solid foundation to achieve the central aim of this research which is to automatically assess the performance of airport lighting.