235,705 research outputs found

    Control of the pinewood nematode Bursaphelenchus xylophilus by essential oils and extracts obtained from plants: a review.

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    The pinewood nematode (PWN), Bursaphelenchus xylophilus, is a serious threat to forest ecosystems at a global scale. The nematode has become a major quarantine problem due to its capability to completely destroy Pinus spp. trees, with great damage to the wood industry. Controlling the nematode inside a living tree is quite difficult, the techniques used being often ineffective and quite expensive. In the coming years, most chemicals used to control nematodes will be banned and replaced by safer and environmentally friendly products. As so, chemicals naturally produced by plants will play an important role in controlling diseases such as pine wilt. Plants, particularly aromatic ones, are commonly used due to the chemical properties of their secondary metabolites. Among these, essential oils and/or extracts are highly employed and are being tested as possible control of some organisms, like nematodes. Recent publications have evaluated essential oils derived from different plant species as natural nematicides [1; 2], antibacterial [3], anti-fungal [4] as well as insecticidal [5]. Concerning control of the PWN, a significant amount of information on plants tested, results obtained and employed techniques, is available. Our revision has extensively gathered this information, making it easier to search, read and use. It may become useful information for future studies on the subject, since it will be possible to check the plants already tested. Although numbers aren´t definitive, so far, tested plants are distributed amongst 148 families. The extracts or essential oils of plants belonging to the Asteraceae, Lamiaceae and Euphorbiaceae families show promising results on controlling the pinewood nematode

    DeltaTree: A Practical Locality-aware Concurrent Search Tree

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    As other fundamental programming abstractions in energy-efficient computing, search trees are expected to support both high parallelism and data locality. However, existing highly-concurrent search trees such as red-black trees and AVL trees do not consider data locality while existing locality-aware search trees such as those based on the van Emde Boas layout (vEB-based trees), poorly support concurrent (update) operations. This paper presents DeltaTree, a practical locality-aware concurrent search tree that combines both locality-optimisation techniques from vEB-based trees and concurrency-optimisation techniques from non-blocking highly-concurrent search trees. DeltaTree is a kk-ary leaf-oriented tree of DeltaNodes in which each DeltaNode is a size-fixed tree-container with the van Emde Boas layout. The expected memory transfer costs of DeltaTree's Search, Insert, and Delete operations are O(logBN)O(\log_B N), where N,BN, B are the tree size and the unknown memory block size in the ideal cache model, respectively. DeltaTree's Search operation is wait-free, providing prioritised lanes for Search operations, the dominant operation in search trees. Its Insert and {\em Delete} operations are non-blocking to other Search, Insert, and Delete operations, but they may be occasionally blocked by maintenance operations that are sometimes triggered to keep DeltaTree in good shape. Our experimental evaluation using the latest implementation of AVL, red-black, and speculation friendly trees from the Synchrobench benchmark has shown that DeltaTree is up to 5 times faster than all of the three concurrent search trees for searching operations and up to 1.6 times faster for update operations when the update contention is not too high

    Scaling Monte Carlo Tree Search on Intel Xeon Phi

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    Many algorithms have been parallelized successfully on the Intel Xeon Phi coprocessor, especially those with regular, balanced, and predictable data access patterns and instruction flows. Irregular and unbalanced algorithms are harder to parallelize efficiently. They are, for instance, present in artificial intelligence search algorithms such as Monte Carlo Tree Search (MCTS). In this paper we study the scaling behavior of MCTS, on a highly optimized real-world application, on real hardware. The Intel Xeon Phi allows shared memory scaling studies up to 61 cores and 244 hardware threads. We compare work-stealing (Cilk Plus and TBB) and work-sharing (FIFO scheduling) approaches. Interestingly, we find that a straightforward thread pool with a work-sharing FIFO queue shows the best performance. A crucial element for this high performance is the controlling of the grain size, an approach that we call Grain Size Controlled Parallel MCTS. Our subsequent comparing with the Xeon CPUs shows an even more comprehensible distinction in performance between different threading libraries. We achieve, to the best of our knowledge, the fastest implementation of a parallel MCTS on the 61 core Intel Xeon Phi using a real application (47 relative to a sequential run).Comment: 8 pages, 9 figure

    A grid-based infrastructure for distributed retrieval

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    In large-scale distributed retrieval, challenges of latency, heterogeneity, and dynamicity emphasise the importance of infrastructural support in reducing the development costs of state-of-the-art solutions. We present a service-based infrastructure for distributed retrieval which blends middleware facilities and a design framework to ‘lift’ the resource sharing approach and the computational services of a European Grid platform into the domain of e-Science applications. In this paper, we give an overview of the DILIGENT Search Framework and illustrate its exploitation in the field of Earth Science

    Prospects and limitations of full-text index structures in genome analysis

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    The combination of incessant advances in sequencing technology producing large amounts of data and innovative bioinformatics approaches, designed to cope with this data flood, has led to new interesting results in the life sciences. Given the magnitude of sequence data to be processed, many bioinformatics tools rely on efficient solutions to a variety of complex string problems. These solutions include fast heuristic algorithms and advanced data structures, generally referred to as index structures. Although the importance of index structures is generally known to the bioinformatics community, the design and potency of these data structures, as well as their properties and limitations, are less understood. Moreover, the last decade has seen a boom in the number of variant index structures featuring complex and diverse memory-time trade-offs. This article brings a comprehensive state-of-the-art overview of the most popular index structures and their recently developed variants. Their features, interrelationships, the trade-offs they impose, but also their practical limitations, are explained and compared

    Entity Personalized Talent Search Models with Tree Interaction Features

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    Talent Search systems aim to recommend potential candidates who are a good match to the hiring needs of a recruiter expressed in terms of the recruiter's search query or job posting. Past work in this domain has focused on linear and nonlinear models which lack preference personalization in the user-level due to being trained only with globally collected recruiter activity data. In this paper, we propose an entity-personalized Talent Search model which utilizes a combination of generalized linear mixed (GLMix) models and gradient boosted decision tree (GBDT) models, and provides personalized talent recommendations using nonlinear tree interaction features generated by the GBDT. We also present the offline and online system architecture for the productionization of this hybrid model approach in our Talent Search systems. Finally, we provide offline and online experiment results benchmarking our entity-personalized model with tree interaction features, which demonstrate significant improvements in our precision metrics compared to globally trained non-personalized models.Comment: This paper has been accepted for publication at ACM WWW 201
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