Nine new species of Bennelongia De Deckker & McKenzie, 1981 (Crustacea, Ostracoda) from Western Australia, with the description of a new subfamily

The genus Bennelongia De Deckker & McKenzie, 1981 is most likely endemic to Australia and New Zealand and, up to now, only two described species in this genus had been reported from Western Australia. Extensive sampling in Western Australia revealed a much higher specifi c diversity. Here, we describe nine new species in three lineages, within the genus Bennelongia: B. cygnus sp. nov. and B. frumenta sp. nov. in the B. cygnus lineage, B. gwelupensis sp. nov., B. coondinerensis sp. nov., B. cuensis sp. nov., B. lata sp. nov. and B. bidgelangensis sp. nov. in the B. australis lineage, and B. strellyensis sp. nov. and B. kimberleyensis sp. nov. (from the Pilbara and Kimberley regions respectively) in the B. pinpi-lineage. For six of the nine species, we were also able to construct molecular phylogenies and to test for cryptic diversity with two different methods based on the evolutionary genetic species concept, namely Birky’s 4 x rule and the GYMC model. These analyses support the specifi c nature of at least four of the fi ve new species in the B. australis lineage and of the two new species in the B. pinpi lineage. We also describe Bennelongiinae n.subfam. to accommodate the genus. With the nine new species described here, the genus Bennelongia now comprises 15 species, but several more await formal description

Feature Selection for Linear SVM with Provable Guarantees

We give two provably accurate feature-selection techniques for the linear SVM. The algorithms run in deterministic and randomized time respectively. Our algorithms can be used in an unsupervised or supervised setting. The supervised approach is based on sampling features from support vectors. We prove that the margin in the feature space is preserved to within $\epsilon$-relative error of the margin in the full feature space in the worst-case. In the unsupervised setting, we also provide worst-case guarantees of the radius of the minimum enclosing ball, thereby ensuring comparable generalization as in the full feature space and resolving an open problem posed in Dasgupta et al. We present extensive experiments on real-world datasets to support our theory and to demonstrate that our method is competitive and often better than prior state-of-the-art, for which there are no known provable guarantees.Comment: Appearing in Proceedings of 18th AISTATS, JMLR W&CP, vol 38, 201