Rise of oceanographic barriers in continuous populations of a cetacean: the genetic structure of harbour porpoises in Old World waters

<p>Abstract</p> <p>Background</p> <p>Understanding the role of seascape in shaping genetic and demographic population structure is highly challenging for marine pelagic species such as cetaceans for which there is generally little evidence of what could effectively restrict their dispersal. In the present work, we applied a combination of recent individual-based landscape genetic approaches to investigate the population genetic structure of a highly mobile extensive range cetacean, the harbour porpoise in the eastern North Atlantic, with regards to oceanographic characteristics that could constrain its dispersal.</p> <p>Results</p> <p>Analyses of 10 microsatellite loci for 752 individuals revealed that most of the sampled range in the eastern North Atlantic behaves as a 'continuous' population that widely extends over thousands of kilometres with significant isolation by distance (IBD). However, strong barriers to gene flow were detected in the south-eastern part of the range. These barriers coincided with profound changes in environmental characteristics and isolated, on a relatively small scale, porpoises from Iberian waters and on a larger scale porpoises from the Black Sea.</p> <p>Conclusion</p> <p>The presence of these barriers to gene flow that coincide with profound changes in oceanographic features, together with the spatial variation in IBD strength, provide for the first time strong evidence that physical processes have a major impact on the demographic and genetic structure of a cetacean. This genetic pattern further suggests habitat-related fragmentation of the porpoise range that is likely to intensify with predicted surface ocean warming.</p

Modeling Label Interactions in Multi-label Classification: A Multi-structure SVM Perspective

Multi-label classification has attracted much interest due to its wide applicability. Modeling label interactions and investigating their impact on classifier quality are crucial aspects of multi-label classification. In this paper, we propose a multi-structure SVM (called MSSVM) which allows the user to hypothesize multiple label interaction structures and helps to identify their importance in improving generalization performance. We design an efficient optimization algorithm to solve the proposed MSSVM. Extensive empirical evaluation provides fresh and interesting insights into the following questions: (a) How do label interactions affect multiple performance metrics typically used in multi-label classification? (b) Do higher order label interactions significantly impact a given performance metric for a particular dataset? (c) Can we make useful suggestions on the label interaction structure? and (d) Is it always beneficial to model label interactions in multi-label classification

An efficient analog Hamming distance comparator realized with a unipolar memristor array: a showcase of physical computing

We propose and demonstrate a novel physical computing paradigm based on an engineered unipolar memristor that exhibits symmetric SET switching with respect to voltage polarity. A one-dimensional array of these devices was sufficient to demonstrate an efficient Hamming distance comparator for two strings of analog states represented by voltages from the physical world. The comparator first simultaneously applies the two sets of voltages to the array of memristors, each of which is initially in its high resistance state and switches to its low resistance state only if the two voltages applied on that memristor differ by more than the switching threshold. An accurate analog representation of the Hamming distance is then obtained by applying a reading voltage to the memristors and summing all the resultant currents. The comparator with a small footprint can directly process analog signals and store computation results without power, representing a promising application for analog computing based on memristor crossbar arrays