688 research outputs found

    Remote Camera and Trapping Survey of the Deep-water Shrimps Heterocarpus laevigatus and H. ensifer and the Geryonid Crab Chaceon granulatus in Palau

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    Time-lapse remote photo-sequences at 73-700 m depth off Palau, Western Caroline Islands, show that the caridean shrimp Heterocarpus laevigatus tends to be a solitary animal, occurring below ~350 m, that gradually accumulates around bait sites over a prolonged period. A smaller speies, H. ensifer, tends to move erratically in swarms, appearing in large numbers in the upper part of its range (<250 m) during the evening crepuscular period and disappearing at dawn. Trapping and photsequence data indicate the depth range of H. ensifer (during daylight) is ~250-550 M, while H. laevigatus ranges from 350 m to at least 800 m, along with the geryonid crab Chaceon granulatus. Combined trapping for Heterocarpus laevigatus and Chaceon granulatus, using a three-chamber box-trap and extended soak times (48-72 hr), may be an appropriate technique for small-scale deep-water fisheries along forereef slopes of Indo-Pacific archipelagoes

    Ommastrephes bartramii (Lesueur, 1821)

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    Continuous-wave Raman laser pumped within a semiconductor disk laser cavity

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    A KGd(WO4)(2) Raman laser was pumped within the cavity of a cw diode-pumped InGaAs semiconductor disk laser (SDL). The Raman laser threshold was reached for 5: 6W of absorbed diode pump power, and output power up to 0.8W at 1143nm, with optical conversion efficiency of 7.5% with respect to the absorbed diode pump power, was demonstrated. Tuning the SDL resulted in tuning of the Raman laser output between 1133 and 1157nm

    Todaropsis eblanae (Ball, 1841)

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    Comparative performance of some popular ANN algorithms on benchmark and function approximation problems

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    We report an inter-comparison of some popular algorithms within the artificial neural network domain (viz., Local search algorithms, global search algorithms, higher order algorithms and the hybrid algorithms) by applying them to the standard benchmarking problems like the IRIS data, XOR/N-Bit parity and Two Spiral. Apart from giving a brief description of these algorithms, the results obtained for the above benchmark problems are presented in the paper. The results suggest that while Levenberg-Marquardt algorithm yields the lowest RMS error for the N-bit Parity and the Two Spiral problems, Higher Order Neurons algorithm gives the best results for the IRIS data problem. The best results for the XOR problem are obtained with the Neuro Fuzzy algorithm. The above algorithms were also applied for solving several regression problems such as cos(x) and a few special functions like the Gamma function, the complimentary Error function and the upper tail cumulative χ2\chi^2-distribution function. The results of these regression problems indicate that, among all the ANN algorithms used in the present study, Levenberg-Marquardt algorithm yields the best results. Keeping in view the highly non-linear behaviour and the wide dynamic range of these functions, it is suggested that these functions can be also considered as standard benchmark problems for function approximation using artificial neural networks.Comment: 18 pages 5 figures. Accepted in Pramana- Journal of Physic

    Distribution and abundance of cephalopods in UK waters: long-term trends and environmental relationships

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    As part of a project which aimed to evaluate the feasibility of developing indicators of marine ecosystem status based on cephalopods, we analysed spatiotemporal variation in abundance,, and environmental relationships, using trawl survey catch data for cephalopods in UK waters (1980-2013) from Cefas and Marine Scotland Science databases. These data presented some challenges, notably the use of several different trawl gears, variable tow durations, and varying levels of taxonomic resolution. Accounting for gear type and tow duration, data were analysed separately for each cephalopod family and season to account for different phases of the life cycles being present at different times of year. The families investigated were Loliginidae, Octopodidae, Ommastrephidade, Sepiidae and Sepiolidae. A GAM framework was used to summarise spatiotemporal variation in abundance at family level and the relationships of spatial and long-term temporal variation with environmental variables, including depth, substrate (available for inshore waters) and several oceanographic variables (e.g., SST, chl signals), also considering fishing pressure. Long-term trends for each family varied between areas and seasons, although this may reflect the presence of several species within families. In Scotland, where Loligo vulgaris is rare and L. forbesii is normally distinguished from Alloteuthis spp., survey data suggested a peak in abundance of this species around 1990 and a generally increasing trend since the mid-1990s. Spatial patterns in distribution in all families were related to both physiographic and oceanographic features. As expected substrate type had most effect on those families in which eggs are attached to objects on the seabed

    Efficient Distributed Decision Trees for Robust Regression

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    The availability of massive volumes of data and recent advances in data collection and processing platforms have motivated the development of distributed machine learning algorithms. In numerous real-world applications large datasets are inevitably noisy and contain outliers. These outliers can dramatically degrade the performance of standard machine learning approaches such as regression trees. To this end, we present a novel distributed regression tree approach that utilizes robust regression statistics, statistics that are more robust to outliers, for handling large and noisy data. We propose to integrate robust statistics based error criteria into the regression tree. A data summarization method is developed and used to improve the efficiency of learning regression trees in the distributed setting. We implemented the proposed approach and baselines based on Apache Spark, a popular distributed data processing platform. Extensive experiments on both synthetic and real datasets verify the effectiveness and efficiency of our approach