180 research outputs found

    Dynamic aperture factor analysis/target transformation (DAFA/TT) for Mg-serpentine and Mg-carbonate mapping on Mars with CRISM near-infrared data

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    © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Lin, H., Tarnas, J. D., Mustard, J. F., Zhang, X., Wei, Y., Wan, W., Klein, F., & Kellner, J. R. Dynamic aperture factor analysis/target transformation (DAFA/TT) for Mg-serpentine and Mg-carbonate mapping on Mars with CRISM near-infrared data. Icarus, 355, (2021): 114168, https://doi.org/10.1016/j.icarus.2020.114168.Serpentine and carbonate are products of serpentinization and carbonation processes on Earth, Mars, and other celestial bodies. Their presence implies that localized habitable environments may have existed on ancient Mars. Factor Analysis and Target Transformation (FATT) techniques have been applied to hyperspectral data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) to identify possible serpentine and Mg-carbonate-bearing outcrops. FATT techniques are capable of suggesting the presence of individual spectral signals in complex spectral mixtures. Applications of FATT techniques to CRISM data thus far only evaluate whether an entire analyzed image (≈ 3 × 105 pixels) may contain spectral information consistent with a specific mineral of interest. The spatial distribution of spectral signal from the possible mineral is not determined, making it difficult to validate a reported detection and also to understand the geologic context of any purported detections. We developed a method called Dynamic Aperture Factor Analysis/Target Transformation (DAFA/TT) to highlight the locations in a CRISM observation (or any similar laboratory or remotely acquired data set) most likely to contain spectra of specific minerals of interest. DAFA/TT determines the locations of possible target mineral spectral signals within hyperspectral images by performing FATT in small moving windows with different geometries, and only accepting pixels with positive detections in all cluster geometries as possible detections. DAFA/TT was applied to a hyperspectral image of a serpentinite from Oman for validation testing in a simplified laboratory setting. The mineral distribution determined by DAFA/TT application to the laboratory hyperspectral image was consistent with Raman analysis of the serpentinite sample. DAFA/TT also successfully mapped the spatial distribution of Mg-serpentine and Mg-carbonate previously detected in CRISM data using band parameter mapping and extraction of ratioed spectra. We applied DAFA/TT to CRISM images in some olivine-rich regions of Mars to characterize the spatial distribution of Mg-serpentine and Mg-carbonate-bearing outcrops.This work was supported by the National Natural Science Foundation of China (grant no. 41671360, 41525016, 41902318). JFM and JDT acknowledge NASA support through a subcontract from the Applied Physics Lab for CRISM investigations. H. Lin also acknowledges the support from the key research Program of the Institute of Geology and Geophysics, CAS (IGGCAS-201905). The Headwall imaging spectrometer was acquired using funds to JRK from The Institute at Brown for Environment and Society and Brown University. The DAFA/TT codes are available on GitHub (https://github.com/linhoml?tab=repositories)

    Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

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    Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R2 = 0.52–0.70), and photochemical reflectance index and blue N index from ground (PRIg and BNIg) performed consistently well across all growth stages (R2 = 0.48–0.65 and 0.39–0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNIg and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring

    Topological Magnetoresistance of Magnetic Skyrmionic Bubbles

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    Magnetic skyrmions offer promising prospects for constructing future energy-efficient and high-density information technology, leading to extensive explorations of new skyrmionic materials recently. The topological Hall effect has been widely adopted as a distinctive marker of skyrmion emergence. Alternately, here we propose a novel signature of skyrmion state by quantitatively investigating the magnetoresistance (MR) induced by skyrmionic bubbles in CeMn2Ge2. An intriguing finding was revealed: the anomalous MR measured at different temperatures can be normalized into a single curve, regardless of sample thickness. This behavior can be accurately reproduced by the recent chiral spin textures MR model. Further analysis of the MR anomaly allowed us to quantitatively examine the effective magnetic fields of various scattering channels. Remarkably, the analyses, combined with the Lorentz transmission electronic microscopy results, indicate that the in-plane scattering channel with triplet exchange interactions predominantly governs the magnetotransport in the Bloch-type skyrmionic bubble state. Our results not only provide insights into the quantum correction on MR induced by skyrmionic bubble phase, but also present an electrical probing method for studying chiral spin texture formation, evolution and their topological properties, which opens up exciting possibilities for identifying new skyrmionic materials and advancing the methodology for studying chiral spin textures.Comment: 17 pages,5 figures,submitte

    Interactive visual cluster analysis by contrastive dimensionality reduction

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    We propose a contrastive dimensionality reduction approach (CDR) for interactive visual cluster analysis. Although dimensionality reduction of high-dimensional data is widely used in visual cluster analysis in conjunction with scatterplots, there are several limitations on effective visual cluster analysis. First, it is non-trivial for an embedding to present clear visual cluster separation when keeping neighborhood structures. Second, as cluster analysis is a subjective task, user steering is required. However, it is also non-trivial to enable interactions in dimensionality reduction. To tackle these problems, we introduce contrastive learning into dimensionality reduction for high-quality embedding. We then redefine the gradient of the loss function to the negative pairs to enhance the visual cluster separation of embedding results. Based on the contrastive learning scheme, we employ link-based interactions to steer embeddings. After that, we implement a prototype visual interface that integrates the proposed algorithms and a set of visualizations. Quantitative experiments demonstrate that CDR outperforms existing techniques in terms of preserving correct neighborhood structures and improving visual cluster separation. The ablation experiment demonstrates the effectiveness of gradient redefinition. The user study verifies that CDR outperforms t-SNE and UMAP in the task of cluster identification. We also showcase two use cases on real-world datasets to present the effectiveness of link-based interactions

    Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods

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    The growth of the fusarium head blight (FHB) pathogen at the grain formation stage is a deadly threat to wheat production through disruption of the photosynthetic processes of wheat spikes. Real-time nondestructive and frequent proxy detection approaches are necessary to control pathogen propagation and targeted fungicide application. Therefore, this study examined the ch\lorophyll-related phenotypes or features from spectral and chlorophyll fluorescence for FHB monitoring. A methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments. The disease-sensitive features were selected using the Boruta feature-selection algorithm, and subjected to machine learning-sequential floating forward selection (ML-SFFS) for optimum feature combination. The results demonstrated that the biochemical parameters, HR, CFI, and HTP showed consistent alterations during the spike–pathogen interaction. Among the selected disease sensitive features, reciprocal reflectance (RR=1/700) demonstrated the highest coefficient of determination (R2) of 0.81, with root mean square error (RMSE) of 11.1. The multivariate k-nearest neighbor model outperformed the competing multivariate and univariate models with an overall accuracy of R2 = 0.92 and RMSE = 10.21. A combination of two to three kinds of features was found optimum for asymptomatic disease detection using ML-SFFS with an average classification accuracy of 87.04% that gradually improved to 95% for a disease severity level of 20%. The study demonstrated the fusion of chlorophyll-related phenotypes with the ML-SFFS might be a good choice for crop disease detection

    Interventions to treat premature ejaculation: a systematic review short report

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    Background: Premature ejaculation (PE) is commonly defined as ejaculation with minimal sexual stimulation before, on or shortly after penetration and before the person wishes it. PE can be either lifelong and present since first sexual experiences (primary), or acquired (secondary), beginning later (Godpodinoff ML. Premature ejaculation: clinical subgroups and etiology. J Sex Marital Ther 1989;15:130–4). Treatments include behavioural and pharmacological interventions. Objective: To systematically review evidence for clinical effectiveness of behavioural, topical and systemic treatments for PE. Data sources: The following databases were searched from inception to 6 August 2013 for published and unpublished research evidence: MEDLINE; EMBASE; Cumulative Index to Nursing and Allied Health Literature; The Cochrane Library including the Cochrane Systematic Reviews Database, Cochrane Controlled Trials Register, Database of Abstracts of Reviews of Effects and the Health Technology Assessment database; ISI Web of Science, including Science Citation Index, and the Conference Proceedings Citation Index-Science. The US Food and Drug Administration website and the European Medicines Agency (EMA) website were also searched. Methods: Randomised controlled trials (RCTs) in adult men with PE were eligible (or non-RCTs in the absence of RCTs). RCT data were extrapolated from review articles when available. The primary outcome was intravaginal ejaculatory latency time (IELT). Data were meta-analysed when possible. Other outcomes included sexual satisfaction, control over ejaculation, relationship satisfaction, self-esteem, quality of life, treatment acceptability and adverse events (AEs). Results: A total of 103 studies (102 RCTs, 65 from reviews) were included. RCTs were available for all interventions except yoga. The following interventions demonstrated significant improvements (p < 0.05) in arithmetic mean difference in IELT compared with placebo: topical anaesthetics – eutectic mixture of local anaesthetics (EMLA®, AstraZeneca), topical eutectic mixture for PE (Plethora Solutions Ltd) spray; selective serotonin reuptake inhibitors (SSRIs) – citalopram (Cipramil®, Lundbeck), escitalopram (Cipralex®, Lundbeck), fluoxetine, paroxetine, sertraline, dapoxetine (Priligy®, Menarini), 30 mg or 60 mg; serotonin–noradrenaline reuptake inhibitors – duloxetine (Cymbalta®, Eli Lilly & Co Ltd); tricyclic antidepressants – inhaled clomipramine 4 mg; phosphodiesterase-5 (PDE5) inhibitors – vardenafil (Levitra®, Bayer), tadalafil (Cialis®, Eli Lilly & Co Ltd); opioid analgesics – tramadol (Zydol SR®, Grünenthal). Improvements in sexual satisfaction and other outcomes compared with placebo were evident for SSRIs, PDE5 inhibitors and tramadol. Outcomes for interventions not compared with placebo were as follows: behavioural therapies – improvements over wait list control in IELT and other outcomes, behavioural therapy plus pharmacotherapy better than either therapy alone; alpha blockers – terazosin (Hytrin®, AMCO) not significantly different to antidepressants in ejaculation control; acupuncture – improvements over sham acupuncture in IELT, conflicting results for comparisons with SSRIs; Chinese medicine – improvements over treatment as usual; delay device – improvements in IELT when added to stop–start technique; yoga – improved IELT over baseline, fluoxetine better than yoga. Treatment-related AEs were evident with most pharmacological interventions. Limitations: Although data extraction from reviews was optimised when more than one review reported data for the same RCT, the reliability of the data extraction within these reviews cannot be guaranteed by this assessment report. Conclusions: Several interventions significantly improved IELT. Many interventions also improved sexual satisfaction and other outcomes. However, assessment of longer-term safety and effectiveness is required to evaluate whether or not initial treatment effects are maintained long term, whether or not dose escalation is required, how soon treatment effects end following treatment cessation and whether or not treatments can be stopped and resumed at a later time. In addition, assessment of the AEs associated with long-term treatment and whether or not different doses have differing AE profiles is required
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