The Khuri-Jones correction to the partial wave scattering amplitude at threshold is an automorphic function for a dihedron. An expression for the partial wave amplitude is obtained at the pole which the upper half-pla...The Khuri-Jones correction to the partial wave scattering amplitude at threshold is an automorphic function for a dihedron. An expression for the partial wave amplitude is obtained at the pole which the upper half-plane maps on to the interior of semi-infinite strip. The Lehmann ellipse exists below threshold for bound states. As the system goes from below to above threshold, the discrete dihedral (elliptic) group of Type 1 transforms into a Type 3 group, whose loxodromic elements leave the fixed points 0 and ∞ invariant. The transformation of the indifferent fixed points from -1 and +1 to the source-sink fixed points 0 and ∞ is the result of a finite resonance width in the imaginary component of the angular momentum. The change in symmetry of the groups, and consequently their tessellations, can be used to distinguish bound states from resonances.展开更多
数据流是一类具有高生成率、动态分布特性的数据,其异常检测旨在从这一类数据中发现偏离预期行为的数据流,从而为医疗、工业生产、金融等诸多领域的决策提供支持。现有数据流异常检测方法普遍面临参数敏感性高、时空开销大、阈值选取难...数据流是一类具有高生成率、动态分布特性的数据,其异常检测旨在从这一类数据中发现偏离预期行为的数据流,从而为医疗、工业生产、金融等诸多领域的决策提供支持。现有数据流异常检测方法普遍面临参数敏感性高、时空开销大、阈值选取难等问题。为了解决上述问题,提出一种基于变密度的自适应数据流的异常检测方法。首先定义了可变局部离群因子(Va-riable Local Outlier Factor,VLOF),VLOF通过对比数据点在并行的不同k值的邻域窗口下,其局部可达密度和局部异常因子的变化情况,度量数据点的密度分布,降低单一k近邻密度度量导致的结果不准确。其次,计算VLOF与k值的相对增长率和绝对均值率,以反映数据流的动态变化趋势,并将适应这种动态变化趋势的数据点定义为核心点,通过核心点加快对后续正常点的判断。最后,将相对增长率和绝对均值率作为数据点理论分布的度量指标,计算理论分布和新数据点实际分布的差异,从而自适应地将偏离理论分布的点识别为异常。为了验证提出算法的有效性,在多个UCI数据集和真实数据集下与8个算法进行对比实验,实验结果表明:与基线模型相比,所提方法在精确率、召回率、F1性能指标上表现良好,且时间和空间效率也有相应提升。展开更多
A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerabl...A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerable to landslides. The 2, 3, and 5-day antecedent rainfall data versus daily rainfall was clustered to identify a cluster of critical events that could potentially trigger landslides. Further, the cluster of critical events was utilized for regression analysis to develop the threshold equations. The 5-day antecedent(xvariable) vs. daily rainfall(y-variable) provided the best fit to the data with a threshold equation of y = 80.7-0.1981 x. The intercept of the equation indicates that if the 5-day antecedent rainfall is zero, the minimum daily rainfall needed to trigger the landslide in the Amboori region would be 80.7 mm. The negative coefficient of the antecedent rainfall indicates that when the cumulative antecedent rainfall increases, the amount of daily rainfall required to trigger monsoon landslide decreases. The coefficient value indicates that the contribution of the 5-day antecedent rainfall is～20% to the landslide trigger threshold. The slope stability analysis carried out for the area, using Probabilistic Infinite Slope Analysis Model(PISA-m), was utilized to identify the areas vulnerable to landslide in the region. The locations in the area where past landslides have occurred demonstrate lower Factors of Safety(FS) in the slope stability analysis. Thus, rainfall threshold analysis together with the FS values from slope stability can be suitable for developing a simple, cost-effective, and comprehensive early-warning system for shallow landslides in Amboori and similar regions.展开更多
文摘The Khuri-Jones correction to the partial wave scattering amplitude at threshold is an automorphic function for a dihedron. An expression for the partial wave amplitude is obtained at the pole which the upper half-plane maps on to the interior of semi-infinite strip. The Lehmann ellipse exists below threshold for bound states. As the system goes from below to above threshold, the discrete dihedral (elliptic) group of Type 1 transforms into a Type 3 group, whose loxodromic elements leave the fixed points 0 and ∞ invariant. The transformation of the indifferent fixed points from -1 and +1 to the source-sink fixed points 0 and ∞ is the result of a finite resonance width in the imaginary component of the angular momentum. The change in symmetry of the groups, and consequently their tessellations, can be used to distinguish bound states from resonances.
文摘数据流是一类具有高生成率、动态分布特性的数据,其异常检测旨在从这一类数据中发现偏离预期行为的数据流,从而为医疗、工业生产、金融等诸多领域的决策提供支持。现有数据流异常检测方法普遍面临参数敏感性高、时空开销大、阈值选取难等问题。为了解决上述问题,提出一种基于变密度的自适应数据流的异常检测方法。首先定义了可变局部离群因子(Va-riable Local Outlier Factor,VLOF),VLOF通过对比数据点在并行的不同k值的邻域窗口下,其局部可达密度和局部异常因子的变化情况,度量数据点的密度分布,降低单一k近邻密度度量导致的结果不准确。其次,计算VLOF与k值的相对增长率和绝对均值率,以反映数据流的动态变化趋势,并将适应这种动态变化趋势的数据点定义为核心点,通过核心点加快对后续正常点的判断。最后,将相对增长率和绝对均值率作为数据点理论分布的度量指标,计算理论分布和新数据点实际分布的差异,从而自适应地将偏离理论分布的点识别为异常。为了验证提出算法的有效性,在多个UCI数据集和真实数据集下与8个算法进行对比实验,实验结果表明:与基线模型相比,所提方法在精确率、召回率、F1性能指标上表现良好,且时间和空间效率也有相应提升。
文摘A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerable to landslides. The 2, 3, and 5-day antecedent rainfall data versus daily rainfall was clustered to identify a cluster of critical events that could potentially trigger landslides. Further, the cluster of critical events was utilized for regression analysis to develop the threshold equations. The 5-day antecedent(xvariable) vs. daily rainfall(y-variable) provided the best fit to the data with a threshold equation of y = 80.7-0.1981 x. The intercept of the equation indicates that if the 5-day antecedent rainfall is zero, the minimum daily rainfall needed to trigger the landslide in the Amboori region would be 80.7 mm. The negative coefficient of the antecedent rainfall indicates that when the cumulative antecedent rainfall increases, the amount of daily rainfall required to trigger monsoon landslide decreases. The coefficient value indicates that the contribution of the 5-day antecedent rainfall is～20% to the landslide trigger threshold. The slope stability analysis carried out for the area, using Probabilistic Infinite Slope Analysis Model(PISA-m), was utilized to identify the areas vulnerable to landslide in the region. The locations in the area where past landslides have occurred demonstrate lower Factors of Safety(FS) in the slope stability analysis. Thus, rainfall threshold analysis together with the FS values from slope stability can be suitable for developing a simple, cost-effective, and comprehensive early-warning system for shallow landslides in Amboori and similar regions.
