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162 results for “invariability”
Data, multiscale dataset and supplementary information for 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process'
<p>This repository contains the analytical Supplementary Information, the data, the multiscale dataset and the codes used to construct the dataset and plot figures used in the manuscript 'Pulsed fluid release from subducting slabs caused by a scale-invariant dehydration process' (accepted in Earth and Planetary Science Letters). </p> <p> </p> <p> </p>
Arbitrage equilibrium, invariance and the emergence of spontaneous order in the dynamics of birds flocking
<p>This is the dataset used to report the findings in the paper "Arbitrage equilibrium, invariance and the emergence of spontaneous order in the dynamics of birds flocking". </p>
Data and graphics from: "Three invariants of strange attractors derived through hypergeometric entropy"
<p>This package contains data and graphics related to the publication:</p><p>Keisuke Okamura, "Three invariants of chaotic attractors derived through hypergeometric entropy", Chaos, Solitons & Fractals 170 (2023) 113392; <a href="https://doi.org/10.1016/j.chaos.2023.113392">https://doi.org/10.1016/j.chaos.2023.113392</a></p><p> </p><p><strong>Abstract of the original article</strong></p><p>A new description of strange attractor systems through three geometrical and dynamical invariants is provided. They are the correlation dimension (\(\mathcal{D}\)) and the correlation entropy (\(\mathcal{K}\)), both having attracted attention over the past decades, and a new invariant called the correlation concentration (\(\mathcal{A}\)) introduced in the present study. The correlation concentration is defined as the normalised mean distance between the reconstruction vectors, evaluated by the underlying probability measure on the infinite-dimensional embedding space. These three invariants determine the scaling behaviour of the system's Rényi-type extended entropy, modelled by Kummer's confluent hypergeometric function, with respect to the gauge parameter (\(\rho\)) coupled to the distance between the reconstruction vectors. The entropy function reproduces the known scaling behaviours of \(\mathcal{D}\) and \(\mathcal{K}\) in the 'microscopic' limit \(\rho\to\infty\) while exhibiting a new scaling behaviour of \(\mathcal{A}\) in the other, 'macroscopic' limit \(\rho\to 0\). The three invariants are estimated simultaneously via nonlinear regression analysis without needing separate estimations for each invariant. The proposed method is verified through simulations in both discrete and continuous systems.</p><p> </p><p><strong>Data files</strong></p><p>The following files are provided (see the original article for the notation):</p><ul><li>The comma-separated values (CSV) file named 'est_sum_all.csv' summarises the results of the correlation dimension (\(\mathcal{D}\)), correlation entropy (\(\mathcal{K}\)) and correlation concentration (\(\mathcal{A}\)) estimates for two discrete (logistic and Hénon maps) and four continuous (Lorenz, Rössler, Duffing-Ueda and Langford attractors) chaotic systems in tabular form.</li><li>The six CSV files whose file names begin with 'H_' record the extended Rényi entropy values calculated for each \(\sigma\) value (in increments of 0.1) for the chaotic system whose name follows the prefix, for each embedding dimension (\(m\)). The column 'mN' shows the results for the embedding dimension \(N\).</li><li>The six PDF files whose file names begin with 'gr_' illustrate, for the named chaotic systems following their prefixes, their i) appearance (left), ii) graph of the extended Rényi entropy as a function of \(\sigma\) (centre) and iii) the chaotic invariants (\(\mathcal{D}, \mathcal{K}, \mathcal{A}\)) estimation results (right).</li></ul>
The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"
<p>The Experimental Data for the Study "Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value"</p> <p><strong>1. Introduction</strong></p> <p>Frequency Fitness Assignment (FFA) replaces the objective value in the selection step of an optimization method with its encounter frequency in any selection step so far. It turns static problems into dynamic ones. Here we experimentally investigated this approach in two important contexts: First, we integrated it into a basic (1+1)-EA, obtaining the (1+1)-FEA. We applied both algorithms to several well-known benchmark problems with bit-string based search spaces, including the OneMax, LeadingOnes, TwoMax, Jump, Plateau, and W-Model functions. We also applied them to the Max-3-Sat instances from SATLib. We then also integrated FFA into a Memetic Algorithm for the Job Shop Problem.</p> <p><strong>2. Paper</strong></p> <p>This data is used as the basis for the following article: Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value, originally submitted to <a href="https://arxiv.org/abs/2001.01416">arxiv</a> on 2020-01-06 (under the title Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function), updated with the new data in June 2020, and submitted to the IEEE Transactions on Evolutionary Computation.</p> <p><strong>3. Data</strong></p> <p>This data set contains all the results of these experiments, the source codes used in the experiments (i.e., the algorithm implementations), as well as the scripts used for evaluating the results.</p> <p><strong>4. Version History</strong></p> <p>This is the second version of the data set, including extended experiments and more evaluation results. Most importantly, data for larger scales of OneMax and LeadingOnes has been added. The original version is at <a href="http://dx.doi.org/10.5281/zenodo.3598172">10.5281/zenodo.3598172</a>.</p> <p><strong>5. Contact</strong></p> <p>If you have any questions or suggestions, please contact <a href="http://iao.hfuu.edu.cn/team/director">Prof. Dr. Thomas Weise</a> of the <a href="http://iao.hfuu.edu.cn/">Institute of Applied Optimization</a> at <a href="http://www.hfuu.edu.cn">Hefei University</a> in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.</p>
Scripts, Data, and Figures for "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems"
<div> <p>This archive contains the scripts required to run all calculations presented in "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems," the figure generation scripts and attendant processed data, and a copy of MesoHOPS version 1.4.0, as used in the paper.</p> </div>
Figure 27 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 27. Stratigraphical ranges of the lepidocyclinids species at the Cuban sections and their correlation with the American Larger Foraminifera Zones (ABZ) proposed by Mitchel et al. (2022). ABZ zonation scheme calibrated against planktic foraminifera and calcareous nannofossils zonations along with Shallow Benthic Zonation (Mitchell et al., 2022).
Figure 25 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 25. Lepidocyclina ocalana Cushman. Nepionts of megalospheric individuals. A, CA-216-E3(7a); B, CA-216-E3(7b); C, CA-216-E3(12); D, CA-216-F3(3a); E, CA-216-F3(3b); F, CA-216-F3(3c); G, P-559 (513); H, P-559 (509); I, P-562 (491). Blanco Formation (CA-216); Jicotea Formation (P-559; P-562).
Figure 24. A–D, Lepidocyclina ocalana Cushman. A, B in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 24. A–D, Lepidocyclina ocalana Cushman. A, B, equatorial sections of megalospheric individuals; C, D, axial sections of megalospheric individuals. A, P-562(491); B, P-562(481); C, P-562(499); D, P-562(498). Jicotea Formation (P-562).
Figure 23. A–G in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 23. A–G, Lepidocyclina ocalana Cushman. Nepionts of megalospheric individuals. A, CA-216-E3(7a); B, CA-216-E3(7b); C, CA-216-F3(3b); D, P-559 (513); E, CA-216-F3(3a); F,CA-216-E3(12); G, CA-216-F3(3c). Blanco Formation (CA-216); Jicotea Formation (P-559).
Figure 22. Nepionts. A–G in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 22. Nepionts. A–G, Lepidocyclina macdonaldi Cushman. Equatorial sections of megalospheric individuals. A, 98LC-1(640); B, 98LC-1(642); C, 98LC-1(616); D, 98LC-1(617); E, CA-215 (66); F, CA-215 (74); G, LM-23 (429). H, I, Lepidocyclina ariana Cole & Ponton. Equatorial sections of megalospheric individuals; H, LM-23(431); I, LM-23 (433). Loma Candela Formation (98LC-1), Arroyo Blanco Formation (CA-215) and Hatillo Formation (LM-23).
Figure 20 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 20. Geometric reconstruction of chamberlets based on the means of character states for the species (Tables 5–8).
Figure 19 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 19. Box plots of nepiont character states for the Lepidocyclina groups (species). Significant differences checked by the Kruskal-Wallis test. L. group 1 = L. macdonaldi, L. group 2 = L.?macdonaldi (small specimens), L. group 3 = L. ariana, L. group 4 = L. pustulosa, L. group 5 = L. ocalana.
Figure 26 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 26. Lepidocyclina pustulosa (H. Douvilĺe). A–C, equatorial sections of megalospheric individuals; D–F, axial sections of megalospheric individuals; G–I, nepiont enlargement. A, G, LM-52(734); B, I, LM-52(738); C, H, LM-52(735); D, LM-52(742); E, LM-52(744); F, LM-52(751). Jabaco Formation (LM-52).
Figure 21. A–G in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 21. A–G, Lepidocyclina macdonaldi Cushman. Equatorial sections of megalospheric individuals. A, 98LC-1(640); B, 98LC-1(642); C, 98LC-1(616); D, 98LC-1(617); E, CA-215 (66); F, CA-215 (74); G, LM-23 (429); H, I, Lepidocyclina ariana Cole & Ponton. Equatorial sections of megalospheric individuals; H, LM-23(431); I, LM-23 (433). Loma Candela Formation (98LC-1), Arroyo Blanco Formation (CA-215) and Hatillo Formation (LM-23).
Figure 15 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 15. Box plots of neanic character states for the Lepidocyclina groups (species). Significant differences checked by the Kruskal-Wallis test. L. group 1 = L. macdonaldi, L. group 2 = L.?macdonaldi (small specimens), L. group 3 = L. ariana, L. group 4 = L. pustulosa, L. group 5 = L. ocalana.
Figure 17 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 17. Box plots of neanic character states for the Lepidocyclina groups (species). Significant differences checked by the Kruskal-Wallis test. L. group 1 = L. macdonaldi, L. group 2 = L.?macdonaldi (small specimens), L. group 3 = L. ariana, L. group 4 = L. pustulosa, L. group 5 = L. ocalana.
Figure 13 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 13. Principal component analysis based on growth-independent characters of nepionts. Grouping by samples (point colours) and groups (background colour of convex hulls). Points with yellow margins represent the position of 'L. group 2'.
Figure 12 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 12. Principal component analysis based on growth-invariant characters of neanic chamberlets. Grouping by samples (point colours) and groups (background colour of convex hulls). Points with yellow margins represent the position of 'L. group 2'.
Figure 11 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 11. Loadings of the first and second axes in principal component analysis based on growth-independent and growth-invariant characters of neanic chamberlets and nepionts.
Figure 14 in Objective identification of Lepidocyclina (Foraminifera) species from the Eocene of Cuba based on growth-invariant morphometric characters
Figure 14. Discriminant analysis separating species. Ellipses point to the 95% confidence limits. Axis values of characters shown in Table 3.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.