Monotonically normal topological vector spaces are stratifiable

We prove that for any Hausdorff topological vector space E over the field R there exists A subset of E such that E is homeomorphic to a subset of A x R and A x R is homeomorphic to a subset of E. Using this fact we prove that E is monotonically normal if and only if E is stratifiable.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Experimental assessment of oyster transfers as a vector for macroalgal introductions

Introduction of non-indigenous species can alter marine communities and ecosystems. In shellfish farming, transfer of livestock, especially oysters, is a common practice and potentially constitutes a pathway for non-indigenous introductions. Many species of seaweeds are believed to have been accidentally introduced in association with these transfers, but there is little direct evidence.

Hull fouling on commercial ships as a vector of macroalgal introduction

Hull fouling is thought to have been the vector of introduction for many algal species. We studied ships arriving at a Mediterranean harbour to clarify the present role of commercial cargo shipping in algal introductions. A total of 31 macroalgal taxa were identified from 22 sampled hulls. The majority of records (58%) were of species with a known cosmopolitan geographical distribution. Due to a prevalence of cosmopolitan species and a high turnover of fouling communities, species composition of assemblages did not appear to be influenced by the area of origin, length of ship or age of coating. In the light of the present results, hull fouling on standard trading commercial vessels does not seem to pose a significant risk for new macroalgal species introductions. However, a high proportion of non-cosmopolitan species found on a ship with non-toxic coating may modify this assessment, especially in the light of the increasing use of such coatings and the potential future changes in shipping routes.

Insect faunas associated with Pinus sylvestris L. from the mid-Holocene of the Humberhead Levels, Yorkshire, U.K

This paper presents the results of recent palaeoentomological research carried out in the Humberhead Levels, South Yorkshire, UK, including the discovery of fossils of five species of beetle previously unknown in the British Isles. The significance of these and other Urwaldrelikt species is discussed in relation to the fragmentation of forest habitats, particularly those associated with Pinus sylvestris L. The Holocene history of this tree and its associated taxa is examined. The importance of fire habitats and the dependence of some pinicolous taxa on these habitats suggests that the decline of fire ecosystems may have had some impact on the changing biogeography of some species.

Can we characterise ‘openness’ in the Holocene palaeoenvironmental record? Modern analogue studies of insect faunas and pollen spectra from Dunham Massey deer park and Epping Forest, England

This paper examines the degree to which tree-associated Coleoptera (beetles) and pollen could be used to predict the degree of ‘openness’ in woodland. The results from two modern insect and pollen analogue studies from ponds at Dunham Massey, Cheshire and Epping Forest, Greater London are presented. We explore the reliability of modern pollen rain and sub-fossil beetle assemblages to represent varying degrees of canopy cover for up to 1000m from a sampling site. Modern woodland canopy structure around the study sites has been assessed using GIS-based mapping at increasing radial distances as an independent check on the modern insect and pollen data sets. These preliminary results suggest that it is possible to use tree-associated Coleoptera to assess the degree of local vegetation openness. Additionally, it appears that insect remains may indicate the relative intensity of land use by grazing animals. Our results also suggest most insects are collected from within a 100m to 200m radius of the sampling site. The pollen results suggest that local vegetation and density of woodland in the immediate area of the sampling site can have a strong role in determining the pollen signal.

A hierarchical multiclass support vector machine incorporated with holistic triple learning units

This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier. The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared to the popular 1-vs-1 alternative.