49 research outputs found

    GPU Acceleration of Melody Accurate Matching in Query-by-Humming

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    With the increasing scale of the melody database, the query-by-humming system faces the trade-offs between response speed and retrieval accuracy. Melody accurate matching is the key factor to restrict the response speed. In this paper, we present a GPU acceleration method for melody accurate matching, in order to improve the response speed without reducing retrieval accuracy. The method develops two parallel strategies (intra-task parallelism and inter-task parallelism) to obtain accelerated effects. The efficiency of our method is validated through extensive experiments. Evaluation results show that our single GPU implementation achieves 20x to 40x speedup ratio, when compared to a typical general purpose CPU's execution time

    Detection of the Diffuse Supernova Neutrino Background with JUNO

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    As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

    Assessment of ensemble learning for object-based land cover mapping using multi-temporal Sentinel-1/2 images

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    Accurate land cover mapping is challenging in Southeast Asia where cloud coverage is prevalent and landscape is heterogenous. Object-based mapping, multi-temporal images and combined use of optical and microwave data, provide abundant features in spectral, spatial, temporal, geometric and polarimetric dimensions. And random forest is usually employed due to robustness and efficiency in handling high-dimensional and noisy data. This study assesses whether feature selection and ensemble analysis, which are rarely adopted, yield improved result. Recursive feature elimination decreases original 568 features into a subset of 53 features, achieving the optimal combination of features. Ensemble analysis of random forest, support vector machine, and K-nearest neighbors leads to an overall accuracy of 0.816. Comparison experiments demonstrated the merits of the multi-temporal, multi-source approach, feature elimination and ensemble analysis

    GPU Acceleration of Melody Accurate Matching in Query-by-Humming

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    Ligand Size and Conformation Affect the Behavior of Nanoparticles Coated with in Vitro and in Vivo Protein Corona

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    Protein corona is immediately established on the surface of nanoparticles upon their introduction into biological milieu. Several studies have shown that the targeting efficiency of ligand-modified nanoparticles is attenuated or abolished owing to the protein adsorption. Here, transferrin receptor-targeting ligands, including LT7 (CHAIYPRH), DT7 (hrpyiahc, all d-form amino acids), and transferrin, were used to identify the influence of the ligand size and conformation on protein corona formation. The results showed that the targeting capacity of ligand-modified nanoparticles was lost after incubation with plasma in vitro, whereas it was partially retained after in vivo corona formation. Results from sodium dodecyl sulfate polyacrylamide gel electrophoresis and liquid chromatography–mass spectrometry revealed the difference in the composition of in vitro and in vivo corona, wherein the ligand size and conformation played a critical role. Differences were observed in cellular internalization and exocytosis profiles on the basis of the ligand and corona source

    Normalizing Tumor Vessels To Increase the Enzyme-Induced Retention and Targeting of Gold Nanoparticle for Breast Cancer Imaging and Treatment

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    Abnormal tumor vessels impede the transport and distribution of chemotherapeutics, resulting in low drug concentration at tumor sites and compromised drug efficacy. Normalizing tumor vessels can modulate tumor vascular permeability, alleviate tumor hypoxia, increase blood perfusion, attenuate interstitial fluid pressure, and improve drug delivery. Herein, a novel strategy combining cediranib, a tumor vessel normalizing agent, with an enzyme responsive size-changeable gold nanoparticle (AuNPs-A&C) was developed. <i>In vivo</i> photoacoustic and fluorescence imaging showed that oral pretreatment with 6 mg/kg/day of cediranib for two consecutive days significantly enhanced the retention of AuNPs-A&C in 4T1 tumor. <i>In vivo</i> photoacoustic imaging for hemoglobin (Hb) and oxyhemoglobin (HbO<sub>2</sub>), Evans blue assay, and immunofluorescence assay showed that cediranib pretreatment markedly increased tumor vascular permeability and tumor oxygenation, while distinctly decreased the tumor microvessel density, demonstrating normalized tumor vessels and favorably altered microenvironment. Additionally, the combination strategy considerably elevated the tumor targeting capacity of different nanoparticle formulations (AuNPs-PEG, AuNPs-A&C), while coadministration of cediranib and AuNPs-A&C achieved prevailing tumor targeting and antitumor efficacy in 4T1 tumor bearing mouse model. In conclusion, we report a novel combined administration strategy to further improve tumor diagnosis and treatment
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