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278 results for “compatibility”
Relativistic description of dense matter equation of state and compatibility with neutron star observables: a Bayesian approach
<p>The general behavior of the nuclear equation of state (EOS), relevant for the description of neutron stars (NS), is studied within a Bayesian approach applied to a set of models based on a density-dependent relativistic mean-field description of nuclear matter <a href="https://arxiv.org/abs/2201.12552">Malik et al 2022</a>. The EOS is subjected to a minimal number of constraints based on nuclear saturation properties and the low-density pure neutron matter EOS obtained from a precise next-to-next-to-next-to-leading order (N$^{3}$LO) calculation in chiral effective field theory ($\chi$EFT). The number of final sample parameters corresponding to the posterior sets is around fourteen thousand. We present five EOSs among them, namely DDBl, DDBm, DDBu1, DDBu2, and DDBx. The DDBl, DDBm, DDBu2 were chosen so that the radius of the 1.4$M_\odot$ star has the lower limit, a medium value, and the upper limit of the 90% CI for the conditional probabilities $P(R|M)$. We have also included DDBu1 that has a slightly lower $R_{1.4}$ than the upper limit but lies completely inside the 90% CI for the conditional probabilities $P(R|M)$. The DDBx is the one that predicts a maximum mass of 2.5$M_\odot$ and has the following nuclear matter properties, $K_0=300$ MeV, $J_{sym,0}=30$ MeV and $L_{sym,0}=39$ MeV.</p> <p>We also release our entire sets of ~14K NS matter EOS. All the EOSs are for NS core and starting baryon density is 0.04 fm$^{-3}$. One needs to add their own choice of crust EOS for the star properties calculation. The uncertainty in star properties for the choice of the different crust has been discussed in Section 2.1 of the manuscript (arxiv: 2201.12552). </p> <pre> To extract the entire sets of ~14K NS matter EOS files, one needs to follow the steps, 1) unzip DDB_EOS_14K.zip ----------------------------Note------------------------------------- All the eos files have three columns baryon density (fm-3), energy density (MeV.fm-3), and pressure (MeV.fm-3). The starting density is 0.04 fm-3, as it is NS core eos. One needs to add their own choice of crust eos in order to calculate NS properties. ---------------------------------------------------------------</pre> <p> </p> <p> </p>
Example dataset for a SpaDES compatible moduels' project
<p>This mock data is just an example to be used with an integrated SpaDES-compatible project available at https://github.com/tati-micheletti/EFI_webinar/tree/main. This current version has improved the datasets to make the analysis more interesting.</p>
Biobased Structurally Compatible Polymer Blends Based on Lignin and Thermoplastic Elastomer Polyurethane as Carbon Fiber Precursors
<p>The production of carbon fibers based on lignin reduces the cost and the environmental impact associated with carbon fiber manufacturing. However, the melt processing of lignin as a carbon fiber precursor is challenging due to its brittleness and limited thermoplastic behavior. For this reason we produce biopolymer blends based on Alcell organosolv hardwood lignin, hydroxypropyl modified Kraft hardwood, and a thermoplastic elastomer polyurethane (TPU). Samples with TPU content greater than 30% showed excellent melt processability and carbonization yield (35% carbon yield for the samples containing 30% of TPU). The thermal properties were analyzed by differential scanning calorimetry, rheology and thermogravimetric analysis. Fourier infrared measurements were utilized to explain the lignin/TPU interactions which governed the thermal and rheological behavior of the blends. SEM analysis showed that the blends produce a homogeneous structure which was void free after carbonization. These structurally complementary biopolymeric blends should open up new avenues for lignin valorization and bring closer the realization of the production of carbon fibers from biosources.</p>
Replication Package for "Compatibility Issues in Deep Learning Systems: Problems and Opportunities"
<p>This dataset contains scripts and data used to generate relevant results for this paper. Detailed information and procedure to reproduce our results are described in README.md. </p> <p>code</p> <p>This folder contains two Python scripts: soextractor.py is used to extract 3,072 high-quality StackOverflow (SO) posts and soextractor_tags.py is used to extract the number of posts for the tags on SO. For detailed data collection criteria, please refer to Section 3.1 of our paper.</p> <p>DL compatibility issues.xlsx</p> <p>This file provides all the collected 3,072 issues, in which each line indicates whether the issue is a DL compatibility issue. Among them, 352 are DL compatibility issues. We also provide information on the library, stage, symptom, type, solution, root cause, and exception type for the DL compatibility issues. For the type CORE-TPL, we also provide backward-incompatible or forward-incompatible as well as API evolution patterns. For detailed manual classification of DL compatibility issues, please refer to Section 3.2 of our paper.</p> <p>Tool Survey.xlsx </p> <p>This file includes all the papers collected from the three top SE conferences (i.e., ICSE, FSE, and ASE) in recent five years (18-22). Each line of each sheet provides the following information: (a) Title, (b) Year, (c) Conference, and (d) Type. For the detailed paper collection procedure, please refer to Section 5 of our paper.</p>
Re-postprocessed POSYDON v1.0 dataset compatible with code release v2.0.0-pre1
<p>This dataset includes the downsampled data from the single- and binary-star models grids, as well as the trained classification and interpolation models, initially released with POSYDON v1 (see <a href="https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract">Fragos et al. 2023</a>), re-postprocessed to be compatible with POSYDON code release <strong>v2.0.0-pre1</strong> (see <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241102376A/abstract">Andrews et al. 2024</a>). This data release included the fix for reverse mass transfer introduced by <a href="https://ui.adsabs.harvard.edu/abs/2024A%26A...683A.144X/abstract">Xing et al. (2023)</a>. </p> <p>If you use this dataset, please cite the following papers:<br><a href="https://ui.adsabs.harvard.edu/abs/2023ApJS..264...45F/abstract">Fragos et al. (2023), The Astrophysical Journal Supplement Series, Volume 264, Issue 2, id.45, 46 pp.</a><br><a href="https://ui.adsabs.harvard.edu/abs/2024A%26A...683A.144X/abstract">Xing et al. (2023), Astronomy & Astrophysics, Volume 683, id.A144, 17 pp.</a><br><a href="https://ui.adsabs.harvard.edu/abs/2024arXiv241102376A/abstract">Andrews et al. (2024), eprint arXiv:2411.02376</a><br><br></p>
Data from: An Easily Compatible Eye Tracking System for Free-moving Small Animals
<p>These datasets are associated with human labled eye tracking datasets in DLC formate and pixel formate from the paper Huang et. al., <em>An Easily Compatible Eye Tracking System for Free-moving Small Animals, </em>2021.<em> </em></p>
Probing Ion Channel Functional Architecture and Domain Recombination Compatibility by Massively Parallel Domain Insertion Profiling
<p>Supplementary Data for a large insertional profiling study described in Coyote-Maestas et al. (2021) Nature Communications.</p>
Fig. 3 in Mating compatibility and competitiveness between wild and laboratory strains of Eldana saccharina (Lepidoptera: Pyralidae) afer radiation treatment
Fig. 3. The mean number of Eldana saccharina matings in a pair-wise comparison between non-irradiated laboratory adults and irradiated laboratory adults showing a significant 2-way interaction between time of night and location of trials. Data were pooled across the entire observation period to obtain total matings irrespective of cross type. Error bars denote 95% confidence limits.
Fig. 4 in Mating compatibility and competitiveness between wild and laboratory strains of Eldana saccharina (Lepidoptera: Pyralidae) afer radiation treatment
Fig. 4. The mean number of matings in a pair-wise comparison between irradiated laboratory reared and non-irradiated wild Eldana saccharina adults showing a significant 3-way interaction across time of night, type of cross and location of trials. The expected possible mating combinations/cross types were: (i) irradiated laboratory female and irradiated laboratory male (Sf x Sm); (ii) irradiated laboratory female and non-irradiated wild male (Sf x Wm); (iii) non-irradiated wild female and irradiated laboratory male (Wf x Sm); and/or (iv) non-irradiated wild female and non-irradiated wild male (Wf x Wm).
Fig. 2 in Mating compatibility and competitiveness between wild and laboratory strains of Eldana saccharina (Lepidoptera: Pyralidae) afer radiation treatment
Fig. 2. The mean number of matings in a pair-wise comparison between non-irradiated and irradiated laboratory reared Eldana saccharina adults. The expected possible mating combinations were: (i) non-irradiated laboratory female and non-irradiated laboratory male (Lf x Lm); (ii) non-irradiated laboratory female and irradiated laboratory male (Lf x Sm); (iii) irradiated laboratory female and non-irradiated laboratory male (Sf x Lm); and/or (iv) irradiated laboratory female and irradiated laboratory male (Sf x Sm).
Fig. 1 in Mating compatibility and competitiveness between wild and laboratory strains of Eldana saccharina (Lepidoptera: Pyralidae) afer radiation treatment
Fig. 1. The mean number of matings in a pair-wise comparison between non-irradiated laboratory and wild Eldana saccharina adults showing a significant 3-way interaction across time of night, type of cross and location of trials. The expected possible mating combinations were: (i) non-irradiated laboratory female and nonirradiated laboratory male (Lf x Lm); (ii) non-irradiated laboratory female and non-irradiated wild male (Lf x Wm); (iii) non-irradiated wild female and non-irradiated laboratory male (Wf x Lm); and/or (iv) non-irradiated wild female and non-irradiated wild male (Wf x Wm).
Figure 3 in Compatibility between the predators Cryptolaemus montrouzieri (Coleoptera: Coccinellidae) and Chrysoperla externa (Neuroptera: Chrysopidae) in the control of Planococcus citri (Hemiptera: Pseudococcidae) associated with rose crop
Figure 3 Behavior of Chrysoperla externa and Cryptolaemus montrouzieri acting in combination, against adult females of Planococcus citri. *Time averages (%) followed by the same letters do not differ by Tukey's Test, P <0.05. Lowercase letters compare predators within each category; uppercase letters compare each predator individually across categories.
Figure 2 in Compatibility between the predators Cryptolaemus montrouzieri (Coleoptera: Coccinellidae) and Chrysoperla externa (Neuroptera: Chrysopidae) in the control of Planococcus citri (Hemiptera: Pseudococcidae) associated with rose crop
Figure 2Behavior of Chrysoperla externa against nymphs and adult females of Planococcus citri. Time averages (%) followed by the same letter do not differ by Tukey's test, P <0.05.
Figure 4 in Compatibility between the predators Cryptolaemus montrouzieri (Coleoptera: Coccinellidae) and Chrysoperla externa (Neuroptera: Chrysopidae) in the control of Planococcus citri (Hemiptera: Pseudococcidae) associated with rose crop
Figure 4 Behavior of Chrysoperla externa and Cryptolaemus montrouzieri acting in combination, against first instar nymphs of Planococcus citri. *Time averages (%) followed by the same letters do not differ by Tukey's Test, P <0.05. Lowercase letters compare predators within each category; uppercase letters compare each predator individually across categories.
Figure 1 in Compatibility of entomopathogenic nematodes with plant extracts and post-exposure virulence test under laboratory condition
Figure 1. Percentage survival of the EPN Species in aqueous and ethanol extracts of A. amatymbica and E. elephantina after 72 h exposure. Different lower-case characters represent significant differences at p <0.05.
Replication Package for "PyTraceBERT: Python Traceback-based Language Model for Detecting Compatibility Issues in Deep Learning Systems"
<p>This package contains the traceback data, pre-trained models, and static word embeddings used in the paper, PyTraceBERT: Python Traceback-based Language Model for Detecting Compatibility Issues in Deep Learning Systems.</p>
Triadic influence as a proxy for compatibility in social relationships
<p>This dataset contains valuable information that allows for the recreation of the paper "Triadic influence as a proxy for compatibility in social relationships" which was published in PNAS. The dataset comprises 13 high school networks that represent the diverse social relationships that exist between students within each school.</p> <p>To construct each of these networks, you can utilize the "Edges_i.csv" and "Nodes_i.csv" files, which are available for download in the files section. The edges files provide data on weighted edges, where the weight varies between -2, -1, +1, and +2. The sign indicates the nature of the friendship, while the number corresponds to its intensity. On the other hand, the node files contain information about the age level, group, sex, and psychological attributes such as CRT and prosociality.</p> <p> </p> <p> </p> <p> </p>
compatibility for recycling of alloys
<p>Open access to simulated and experimental data generated by the ReINTEGRA project (GA 886609, H2020, European Union, through Clean Sky 2 JU) along the research, at single-stringer coupon level, of the End-of-Life of novel welded Al-Li aerostructures.Research pertaining to Task 3.1 (WP3), Deliverable D3.2 </p> <p>Al-Li alloys Compatibility-Recyclability predictions generated by the first version of the Excel tool (ReINTEGRA scrap recyclability tool v01.xlsx) developed by AZTERLAN for 18 scrap fractions of coupons; and predictions made by the second version of the software (ReINTEGRA scrap recyclability tool v02.xlsx) for 12 scrap fractions generated during full validation of the recommended EoL route for each coupon reference. Outputs of the excel tool printed as pdf files.</p> <p>Experimental data of the scrap chemical composition originated from chemical analysis by ICP-OES conducted by AZTERLAN on test samples of remelted scrap fractions and on test samples of pre-scrap material fractions. Scrap fractions of coupons supplied by SONACA by cutting (radial saw) coupons following one of four possible cutting strategies (0C, 1C, 2C, 3C).</p> <p>Each pdf file contains the description of the scrap being evaluated, the measured elemental composition of the scrap (%wt) and the results of the following evaluations: (1) scrap composition vs composition ranges of four registered Al-Li alloys (2060, 2196, 2099, 2198); (2) quantification of excess/deficit of elements in scrap vs composition of the four registered alloys; (3) chemical compatibility with each alloy; (4) critical chemical element for compatibility; (5) recyclability as max % scrap acceptable as raw material to manufacture each alloy; (6) alloying addition needs for recycling.</p> <p>The ReINTEGRA scrap recyclability tool has been developed as part of WP3 activities (Task 3.1).</p> <p>The first version of the tool only evaluates compatibility/recyclability of scrap with the four Al-Li alloys used in ecoTECH coupons. The user must enter the chemical composition data of the scrap to be evaluated. The second version of the tool implements corrections to the algorithm to identify the critical element in excess for setting max. % of scrap in the charge.</p> <p>The coupon scrap samples have been used for investigating cutting strategies (WP2), remelting set-up (WP3) and full validation of recommended EoL routes (WP3). Pre-scrap materials have been used in pre-scrap characterisation protocols (WP4).</p>
Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"
<p>This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md. </p> <p>code</p> <p>This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs.</p> <p>result</p> <p>This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects.</p> <p><br> Distribution of dfvc pairs.xlsx</p> <p>This file includes 6,926 DFVC pairs and their root causes.</p> <p>Tested framework versions.xlsx</p> <p>This file includes the framework versions tested and the Python versions that the framework versions are compatible with.</p> <p>Tested projects.xlsx</p> <p>This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.</p>
ECOBREED WP2 T2.2 Wheat AMF compatibility nursery
<p>Description of the winter common wheat (Triticum aestivum) AMF compatibility nursery. Germplasm to be tested within T2.2 in Austria (by BOKU) and the UK (by UNEW).</p>
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International Brain Laboratory public data
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OpenNeuro
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