4,372 research outputs found

    Theoretical and experimental evidence of non-symmetric doubly localized rogue waves

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    We present determinant expressions for vector rogue wave solutions of the Manakov system, a two-component coupled nonlinear Schr\"odinger equation. As special case, we generate a family of exact and non-symmetric rogue wave solutions of the nonlinear Schr\"odinger equation up to third-order, localized in both space and time. The derived non-symmetric doubly-localized second-order solution is generated experimentally in a water wave flume for deep-water conditions. Experimental results, confirming the characteristic non-symmetric pattern of the solution, are in very good agreement with theory as well as with numerical simulations, based on the modified nonlinear Schr\"odinger equation, known to model accurately the dynamics of weakly nonlinear wave packets in deep-water.Comment: 15 pages, 7 figures, accepted by Proceedings of the Royal Society

    EASY AND EFFICIENT SPLIT-ROOT METHOD TO STUDY MORPHOLOGY AND ANATOMY OF RICE (Oryza sativa L.)

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    ABSTRACT: Objective: This study was intended to investigate the efficacy of PVC pipe method for split-root experiment in rice (Oryza sativa L.) using soil medium at all the growth stages of rice.Methods: Rice seeds were sown in small PVC pipes and allowed to grow for one month, which were then transferred to split-root setup by equally splitting the roots of these seedlings into two halves and were allowed to grow for different time periods of their growth stages to analyze their health and stability.Results: We report an easy split-root study for rice grown in soil. Unlike the field grown plants, the efficient PVC tube method enables simple and systematic growth and harvesting for proper analysis of the plant samples without damaging the tissue. In our experiments, although the rice plants were transferred to the split-root setup by splitting their roots, they were healthy and stable after 7days, 15 days, 70 days and even at 120 days (maturity) of growth in split-root condition.Conclusion: Morphology and anatomy of plants can be easily and efficiently studied at any growth stage using PVC tube method as opposed to field method where sample harvesting requires inconvenient process of uprooting the plant while losing and damaging the tissue

    TYK2 in cancer metastases: Genomic and proteomic discovery

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    Advances in genomic analysis and proteomic tools have rapidly expanded identification of biomarkers and molecular targets important to cancer development and metastasis. On an individual basis, personalized medicine approaches allow better characterization of tumors and patient prognosis, leading to more targeted treatments by detection of specific gene mutations, overexpression, or activity. Genomic and proteomic screens by our lab and others have revealed tyrosine kinase 2

    RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

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    Inferring road attributes such as lane count and road type from satellite imagery is challenging. Often, due to the occlusion in satellite imagery and the spatial correlation of road attributes, a road attribute at one position on a road may only be apparent when considering far-away segments of the road. Thus, to robustly infer road attributes, the model must integrate scattered information and capture the spatial correlation of features along roads. Existing solutions that rely on image classifiers fail to capture this correlation, resulting in poor accuracy. We find this failure is caused by a fundamental limitation -- the limited effective receptive field of image classifiers. To overcome this limitation, we propose RoadTagger, an end-to-end architecture which combines both Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to infer road attributes. The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers. We evaluate RoadTagger on both a large real-world dataset covering 688 km^2 area in 20 U.S. cities and a synthesized micro-dataset. In the evaluation, RoadTagger improves inference accuracy over the CNN image classifier based approaches. RoadTagger also demonstrates strong robustness against different disruptions in the satellite imagery and the ability to learn complicated inductive rules for aggregating scattered information along the road network
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