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The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 6. Drawing of auto correlation function and partial correlation for females primary stage students

The instability of the time series is noticed and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to affirm the stability according to the figure 6.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 4. Drawing of autocorrelation function and partial correlation for males primary stage students

We get to notice the stability of the time series, and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure (4).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 8. Drawing of autocorrelation function and partial correlation for Females primary stage students

The stability of the time series is observed and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (8).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 2. Drawing of autocorrelation function and partial correlation for males primary stage

We get to notice the instability of the time series, and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure 2.

opencc-by-4.0Nov 2024View details →

Dataset of the cross-correlation functions and 3-D Vs model in the central and western NCC

The file "CCFs_NCC.dat" contains all ZZ components cross-correlation fuctions for all available station pairs. The file "Vs_NCC.dat" contains the 3D crustal and uppermost mantle model of central and western NCC via multimodal dispersion inversion.

opencc-zeroDec 2017View details →

Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models

1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided.

opencc-by-4.0Jan 2022View details →

Stacked cross correlation functions for the MeSO-net network and derived 3D Vs model

This dataset contains two major types of data. 1) The yearly stacked cross-correlation functions between 296 MeSO-net stations covering the Kanto basin, Japan. 2) A 3D radially anisotropic Vs model for the Kanto basin. The grid increment for the longitude and latitude is 0.01 degrees and that for the depth is 0.1 km. The complete station list for the 296 MeSO-net stations can be found on the Github page of https://github.com/chengxinjiang/Jiang_Kanto_anisotropy.

opencc-zeroMay 2022View details →

Phenotypic correlates of pelvic spine coloration in the Threespine Stickleback (Gasterosteus aculeatus): Implications for function and evolution

Animal color patches may be static or plastic in expression and concealable or continuously visible, yet these aspects of coloration, and their consequences, have been little studied. We address them here using the threespine stickleback (Gasterosteus aculeatus). Despite a rich history of study of stickleback nuptial color pattern evolution, disagreement persists regarding selection pressures and function. However, little research has addressed the role of pelvic spine coloration, a potentially important, and substantially concealable, color pattern element. We investigated (i) whether male pelvic spine (along with throat and body) coloration is relatively static or plastic across the reproductive cycle, (ii) when pelvic spines are raised versus concealed across behavioral contexts, and (iii) associations between color patches and behavior in males. We found no significant variation in spine color across reproductive stages whereas body color was more plastic and intensely red during courtship and egg/fry care. Conspicuousness of pelvic spine coloration instead varied behaviorally, through increased erection frequency during social interactions and in response to a model predator. Spine erection frequency was positively associated with behaviors that enhance spine color visibility, i.e. flees and leads to nest. These findings suggest that stickleback use pelvic spines to display an intensely red color patch facultatively, either as a complement to similar body coloration or possibly as a substitute. In addition, elevated spine raising in the presence of a model predator, together with the presence of red spine coloration in females, raises the possibility that red spine coloration may also have an anti-predator function.

opencc-by-4.0Jul 2022View details →

Auto Correlation Functions Mt Ontake

Auto Correlation Functions at station ONTA computed using MSNoise software (http://www.msnoise.org/) between 2013-01-01 and 2015-01-01. We used 4 different frequency bands and stacks of 5 days (last day and 4 preceding ones). The pre-processing parameters are described in the corresponding supplementary material of the paper. Filter 01 is 0.1-1 Hz, Filter 02 is 0.5-1.0 Hz and Filter 03 1.0-2.0 Hz.

opencc-zeroApr 2024View details →

Data for: A linear response framework for simulating bosonic and fermionic correlation functions on quantum computers

Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiments. Within the context of quantum computing, and by using a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and is ancilla-free. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material on {\emph{ibm\_auckland}}. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.

opencc-by-4.0Jun 2024View details →

Assessing exchange-correlation functionals for heterogeneous catalysis of nitrogen species: VASP input and output

This contains all VASP data used in the paper titled "Assessing exchange-correlation functionals for heterogeneous catalysis of nitrogen species". Please read README.md file for the description of each file. Author list: Honghui Kim, Neung-Kyung Yu, Nianhan Tian, and Andrew J. Medford* arxiv:<a href="https://arxiv.org/abs/2403.14482"> https://arxiv.org/abs/2403.14482</a>

opencc-by-4.0Jun 2024View details →

Data Set for Self-adapting short-range correlation functional for complete active space-based approximations

Data Set to accompany:  Self-adapting short-range correlation functional for complete active space-based approximations

opencc-zeroAug 2021View details →

Biodiversity scale-dependence and opposing multi-level correlations underlie differences among taxonomic, phylogenetic, and functional diversity

Aim: Biodiversity is a multi-dimensional property of biological communities that represents different information depending on how it is measured, but how dimensions relate to one another and under what conditions is not well understood. We explore how taxonomic, phylogenetic, and functional diversity can differ in scale-of-effect dependence and habitat-biodiversity relationships, and subsequently how spatial differences among biodiversity dimensions may arise. Location: Nebraska, United States Time period: May-July 2016, 2017 Major taxa studied: Birds Methods: Across 2016 and 2017, we conducted 2,641 point counts at 781 sites. We modeled the occupancy of 141 species using Bayesian Bernoulli-Bernoulli hierarchical logistic regressions. We calculated species richness (SR), phylogenetic diversity (PD), and functional diversity (FD) for each site and year based on predicted occupancy, accounting for imperfect detection. Using Bayesian latent indicator scale selection and multivariate modeling, we quantified the spatial scales-of-effect that best explained the relationships between environmental characteristics and SR, PD, and FD. Additionally, we decomposed the residual between- and within-site biodiversity correlations using our repeated measures design. Results: We demonstrate spatial differences among biodiversity predictions, arising from scale-dependence in habitat-biodiversity relationships and variation in correlation structure among biodiversity dimensions. Although relationships between specific land cover types and SR, PD and FD were qualitatively similar, the spatial scales at which these variables were important in explaining biodiversity differed among dimensions. Between-site residual biodiversity correlations were negative, yet within-site biodiversity residual correlations were positive. Main conclusions: Our results demonstrate how spatial differences among biodiversity dimensions may arise from biodiversity-specific scale-dependent habitat relationships, low shared environmental correlations and opposing residual correlations between dimensions, which suggest that single-scale and single-dimension analyses are not entirely appropriate for quantifying habitat-biodiversity relationships. After accounting for shared habitat relationships, we found positive within-site residual correlations between taxonomic, phylogenetic, and functional diversity, suggesting that habitat change over time influenced all biodiversity dimensions relatively similarly. However, negative between-site residual correlation among biodiversity dimensions may indicate trade-offs in achieving maximum biodiversity across multiple biodiversity dimensions at any given location. Although habitat management can to a limited degree improve biodiversity relatively across all metrics, other environmental effects may ensure that not all facets of biodiversity can be maximized at once. If maximizing a specific biodiversity dimension is the goal, then care should be taken to consider these within-site residual correlations.

opencc-by-4.0Mar 2023View details →

Height covariance correlation function in Contact Process

Data of the correlation function, namely, the height covariance c1(r,t) for 1, 2 and 3 dimensions.  It has been measured for the interface representation of a contact process system at its critical point. See <a href="https://arxiv.org/abs/2304.10883">arXiv:2304.10883</a> for more details. Each file contain two headers starting with #. The columns show, in order, the time, the coordinate r, the value of c1(r,t), the error estimate by the jackknife procedure and the numbers of files used. All the columns are separated by space. Data for one-dimensional system in: c1JK-1D-L8192-t4000000-f2000.dat Data for two-dimensional system in: c1JK-2D-L512-t200000-f498.dat Data for three-dimensional system in: c1JK-3D-L128-t40000-f20.dat The datasets have been produced and employed in the context of a scientific work currently published in <a href="https://arxiv.org/abs/2304.10883">arXiv:2304.10883</a>.

opencc-by-4.0Apr 2023View details →

Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data

ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper. Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper. GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp]. Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)]. Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude]. Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].

opencc-by-4.0Jul 2023View details →

Real-space correlation functions of nearest-neighbour spin ice

Real-space correlation functions \(\langle \sigma_i\sigma_j\rangle\) of the nearest-neighbour spin ice Hamiltonian \(H = J \sum_{\langle ij\rangle} \sigma_i\sigma_j\) on the pyrochlore lattice, from Monte Carlo simulations on 128×128×128 cubic unit cells. File formats The file name indicates the temperature (in units of J) where the measurement was taken. Files with extension .dat contain the correlation functions as double-precision real floats. Files with extension .err contain the standard errors as single-precision real floats. Both file types contain an array of shape (4, 4, 4, 128, 128, 128). The meaning of the indices (from major to minor): <ul> <li>sublattice index μ</li> <li>sublattice index ν</li> <li>index λ of FCC lattice point in the cubic unit cell</li> <li>offset of cubic lattice cells</li> </ul> Entry [μ, ν, λ, x, y, z] corresponds to the correlator \(\langle \sigma(\vec R + \vec r^\mathrm{FCC}_\lambda + \vec r_\mu)\sigma(\vec r_\nu)\rangle\), where \(\vec R = [xyz] \), the FCC index λ corresponds to the lattice points \(\vec r^\mathrm{FCC}_0 = [000], \vec r^\mathrm{FCC}_1=[011]/2, \vec r^\mathrm{FCC}_2=[101]/2,\vec r^\mathrm{FCC}_3 = [110]/2\), and the sublattice  indices correspond to the site offsets \(r_0 = [111]/8, r_1=[1\bar1\bar1]/8, r_2=[\bar11\bar1]/8,r_3=[\bar1\bar1 1]/8\) from the nearest FCC lattice point. Coordinates x,y,z run between 0 and 127 in periodic boundary conditions. The utility loader.py loads the files, shapes them in the correct array format, and extracts individual correlators. Details of data generation We performed Monte Carlo simulations of nearest-neighbour spin ice using the efficient loop-string algorithm introduced in <a href="https://doi.org/10.1103/PhysRevB.90.220406">Phys. Rev. B 90, 220406(R)</a>. For nonzero temperatures, correlations between different strings were excluded, which effectively averages all spin configurations compatible with a given loop graph. We ran 32 independent Markov chains at each temperature point and obtained 4096 Monte Carlo samples in each. The reported standard errors are the error on the mean of the 32 Markov chains.

opencc-by-4.0Jul 2023View details →

Reciprocal-space correlation functions of nearest-neighbour spin ice

Reciprocal-space correlation functions \(\langle \sigma_\mu(k)\sigma_\nu(-k)\rangle\) of the nearest-neighbour spin ice Hamiltonian \(H = J \sum_{\langle ij\rangle} \sigma_i\sigma_j\) on the pyrochlore lattice, from Monte Carlo simulations on 128×128×128 cubic unit cells. File formats The file name indicates the temperature (in units of J) where the measurement was taken. Files with extension .dat contain the correlation functions as double-precision complex floats. Files with extension .err contain the standard errors as single-precision real floats. Both file types contain an array of shape (4, 4, 128, 256, 256). The meaning of the indices (from major to minor): <ul> <li>sublattice index μ</li> <li>sublattice index ν</li> <li>wave vector components in units of \(2\pi/(128a_0)\)</li> </ul> The sublattice indices correspond to the site offsets \(r_0 = [111]/8, r_1=[1\bar1\bar1]/8, r_2=[\bar11\bar1]/8,r_3=[\bar1\bar1 1]/8\) from the nearest FCC lattice point. For the Fourier transforms, all sublattices are shifted to these lattice points, so the correlators stored in the files are periodic with respect to the FCC reciprocal lattice. The wave vector range covered is \(0\le k_x< 2\pi/a_0, 0\le k_y,k_z< 4\pi/a_0\), an (unconventional) reciprocal-space unit cell of the FCC pyrochlore lattice. The utility loader.py loads the files, shapes them in the correct array format, and extracts single k-points. Details of data generation We performed Monte Carlo simulations of nearest-neighbour spin ice using the efficient loop-string algorithm introduced in <a href="https://doi.org/10.1103/PhysRevB.90.220406">Phys. Rev. B 90, 220406(R)</a>. For nonzero temperatures, correlations between different strings were excluded, which effectively averages all spin configurations compatible with a given loop graph. We ran 32 independent Markov chains at each temperature point and obtained 4096 Monte Carlo samples in each. The reported standard errors are the error on the mean of the 32 Markov chains.