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We have developed a technique to calculate lateral density distribution of the sedimentary basin basement by combining linear gravity density inversion and 2D forward modeling. The procedure requires gravity anomaly data, depth-to...
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We have developed a technique to calculate lateral density distribution of the sedimentary basin basement by combining linear gravity density inversion and 2D forward modeling. The procedure requires gravity anomaly data, depth-to-basement data and density data for the sediments (density depth distribution). Gravity effect of density variations in the basement was extracted from the total gravity anomaly by removing the joint effect of the sediments with vertical density variations and homogeneous basement of average density contrast (calculated by 2D modeling). Gravity effect of the sediments was calculated using depth-to-basement data and density depth function, based on borehole logging data. Bouguer slab formula was used in order to simplify basement model and calculate the operator for the linear inversion of gravity effect of the basement into lateral density distribution. The results were tested by forward modeling, and differences between observed and modeled gravity data were used for the next iteration of the inversion and correction of density values in each point along the profile. The procedure was tested using synthetic models and field example, and both results were satisfying, especially taking into account the simplicity of the inversion technique. The main problem was the effect of abrupt changes in the basement topography on density distribution, but it was downsized by filtering. Basement density maps were compiled based upon the density distribution along the profiles.
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It is known that there exist classes of sets of positive integers which provide arbitrary given values as lower and upper logarithmic and asymptotic densities. The only restriction is the inequality between these densities. We gen...
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It is known that there exist classes of sets of positive integers which provide arbitrary given values as lower and upper logarithmic and asymptotic densities. The only restriction is the inequality between these densities. We generalize this result for a sequence of weighted densities, i.e., for countably infinitely many weighted densities.
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Let be a ψ-density topology for a fixed function ψ. This paper is concerned with the family of ψ-continuous functions, that means continuous functions from (ℝ, ) into (ℝ, ). The family of such functions forms a lattice and is...
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Let be a ψ-density topology for a fixed function ψ. This paper is concerned with the family of ψ-continuous functions, that means continuous functions from (ℝ, ) into (ℝ, ). The family of such functions forms a lattice and is not closed under addition and uniform convergence. There exist functions ψ for which even linear functions are not ψ-continuous. Keywords Density point - Density topology - ψ-density topology Mathematics Subject Classification (2000) 54 A 10 - 11 B 05 - 28 A 05
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Extending previous results, we give a new description of the density set, that is the set of all pairs of densities - upper and lower - of all subsets of a given set of positive integers. The extension consists in using the concep...
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Extending previous results, we give a new description of the density set, that is the set of all pairs of densities - upper and lower - of all subsets of a given set of positive integers. The extension consists in using the concept of weighted density with the weight function satisfying two standard conditions. In order to prove that the density set is convex, we establish and use the joint Darboux property of the weighted density. Finally we prove that the density set is closed through an explicit characterization of its upper boundary.
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Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is...
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Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly important. However, for multi-density data, when one parameter cannot satisfy all data, clustering often cannot achieve good results. Moreover, the subjective selection of cluster centers through decision diagrams is often not very convincing, and there are also certain errors. In view of the above problems, in order to achieve better clustering of multi-density data, this paper improves the density peak clustering algorithm. Aiming at the selection of parameter dc, the K-nearest neighbor idea is used to sort the neighbor distance of each data, draw a line graph of the K-nearest neighbor distance, and find the global bifurcation point to divide the data with different densities. Aiming at the selection of cluster centers, the local density and distance of each data point in each data division is found, a γ map is drawn, the average value of the γ height difference is calculated, and through two screenings the largest discontinuity point is found to automatically determine the cluster center and the number of cluster centers. The divided datasets are clustered by the DPC algorithm, and then the clustering results are perfected and integrated by using the cluster fusion rules. Finally, a variety of experiments are designed from various perspectives on various artificial simulated datasets and UCI real datasets, which demonstrate the superiority of the F-DPC algorithm in terms of clustering effect, clustering quality, and number of samples.
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Recently, density peaks clustering (DPC) has received much attention. The key step in DPC is to find the nearest denser point (N D P) for each point. If a point and its N D P are close, they are in the same cluster; otherwise, the...
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Recently, density peaks clustering (DPC) has received much attention. The key step in DPC is to find the nearest denser point (N D P) for each point. If a point and its N D P are close, they are in the same cluster; otherwise, they are in different clusters. However, the density gap between a point and its N D P is not considered in DPC. Once the point and its N D P are close, they are assigned to the same cluster, even if the density gap between them is large, which could result in poor clustering results. In this study, we propose a clustering algorithm based on finding prominent peaks (prominent peak clustering, PPC). Its main concept is to divide points into multiple potential clusters and then merge clusters whose density peaks are not prominent to obtain accurate clustering results. The prominence of cluster density peaks is measured by the density gap. Experimental results on low- and high-dimensional datasets demonstrate that PPC is competitive with other clustering techniques.
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In many business applications, large data workloads such as sales figures or process performance measures need to be monitored in real-time. The data analysts want to catch problems in flight to reveal the root cause of anomalies....
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In many business applications, large data workloads such as sales figures or process performance measures need to be monitored in real-time. The data analysts want to catch problems in flight to reveal the root cause of anomalies. Immediate actions need to be taken before the problems become too expensive or consume too many resources. In the meantime, analysts need to have the "big picture" of what the information is about. In this paper, we derive and analyze two real-time visualization techniques for managing density displays: (1) circular overlay displays which visualize large volumes of data without data shift movements after the display is full, thus freeing the analyst from adjusting the mental picture of the data after each data shift; and (2) variable resolution density displays which allow users to get the entire view without cluttering. We evaluate these techniques with respect to a number of evaluation measures, such as constancy of the display and usage of display space, and compare then? to conventional displays with periodic shifts. Our real time data monitoring system also provides advanced interactions such as a local root cause analysis for further exploration. The applications using a number of real-world data sets show the wide applicability and usefulness of our ideas.
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Let T_Ψbe a Ψ-density topology for fixed function Ψ. This paper isconcerned with the family of Ψ-continuous functions, that means continuousfunctions from (R, T_Ψ) into (R, T_Ψ).The family of such functions forms alattice an...
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Let T_Ψbe a Ψ-density topology for fixed function Ψ. This paper isconcerned with the family of Ψ-continuous functions, that means continuousfunctions from (R, T_Ψ) into (R, T_Ψ).The family of such functions forms alattice and is not closed under addition and uniform convergence. There existfunctions Ψ for which even linear functions are not Ψ-continuous.
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The Generator Coordinate Method (GCM) is used to construct the effective nuclear densityoperator suitable for calculations of EO transitional densities with collective eigenfunctions of the phe-nomenological Bohr Hamiltonian. For ...
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The Generator Coordinate Method (GCM) is used to construct the effective nuclear densityoperator suitable for calculations of EO transitional densities with collective eigenfunctions of the phe-nomenological Bohr Hamiltonian. For example, the O_(gs)~+9→O_2~+ transitional density is calculated for theshape-phase transitional nucleus ~(150)Ndusing the eigenfunctions of approximate X(5) solution theBohr Hamiltonian.
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Nuclear level densities of ~(47)V, ~(48)V, ~(49)V, ~(53)Mn, ~(54)Mn, ~(57)Co, ~(59)Ni and their energy dependences are determined from measurements of the neutron evaporation spectra in the (p, n) reaction. Neutron spectra from th...
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Nuclear level densities of ~(47)V, ~(48)V, ~(49)V, ~(53)Mn, ~(54)Mn, ~(57)Co, ~(59)Ni and their energy dependences are determined from measurements of the neutron evaporation spectra in the (p, n) reaction. Neutron spectra from the (p, n) reaction on nuclei of ~(47)Ti, ~(48)Ti, ~(49)Ti, ~(53)Cr, ~(54)Cr, ~(57)Fe, and ~(59)Co are measured at proton energies between 7 and 11 MeV. The data are analyzed in terms of statistical equilibrium and pre-equilibrium models of nuclear reactions.
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