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148 results for “entropy”
Data for the PPSN 2024 article: "Entropy, Trajectories, and Explainability for Frequency Fitness Assignment"
<p>Dataset for the PPSN 2024 article: "Entropy, Trajectories, and Explainability for Frequency Fitness Assignment"</p>
Accurate prediction of the solid-state region of the Ni-Al phase diagram including configurational and vibrational entropy and magnetic effects
<p>Documentation for the Dataset used in the publication entitled "Accurate prediction of the solid-state region of the Ni-Al phase diagram including configurational and vibrational entropy and magnetic effects" <br>** These datasets comprise all configurations uesd in Ni-Al system and their formation enthalpies at different temperatures, where fcc Al and fcc Ni were used as reference state. **<br>** More details about the methodology can be found in the paper "Wei Shao, José Manuel Guevara-Vela, Antonio Fernández-Caballero, Sha Liu, Javier LLorca, Accurate prediction of the solid-state region of the Ni-Al phase diagram including configurational and vibrational entropy and magnetic effects, Acta Materialia, 2023"**</p> <p>1. bcc-Ni-Al.zip<br>- Description: bcc-Ni-Al.zip is a compressed folder. It contains Al1-xNix configurations with bcc lattice used to fit the cluster expansion (CE). Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>2. bcc-with-vacancies-Ni-Al.zip<br>- Description: bcc-with-vacancies-Ni-Al.zip is a compressed folder. It contains (AlVa)x(AlNi)1-x configurations with bcc-with-vacancies lattice used to fit the CE. Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>3. fcc-Ni-Al.zip<br>- Description: fcc-Ni-Al.zip is a compressed folder. It contains Al1-xNix configurations with fcc lattice used to fit the CE. Each folder contains a POSCAR file that corresponds to a configuration. The POSCAR can be opened with Notepad and visualized with VESTA software.</p> <p><br>4. Formation enthalpies of bcc-Ni-Al.xlsx<br>- Description: Formation enthalpies of bcc lattice in Ni-Al system at different temperatures, which includes the effect of lattice vibration. The fcc Al and fcc Ni were used as reference states.</p> <p>- Variable description by columns:<br> 1-(Folder name) - type: numerical (integer)<br> Description: Each folder name in the bcc-Ni-Al.zip corresponds to a configuration<br> 2- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 3- (H_f^(conf)(DFT) (eV/atom) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by density functional theory (DFT).<br> 4- (H_f^(conf)(CE)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K fitted by CE. <br> 6- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 7- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K calculated by DFT and bond length vs. bond stiffness relationship (L-S).<br> 8- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K fitted by CE. <br> 10- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 11- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K calculated by DFT and L-S.<br> 12- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K fitted by CE. <br> 14- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 15- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K calculated by DFT and L-S.<br> 16- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K fitted by CE.<br> 18- (at. fraction of Ni (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 19- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K calculated by DFT and L-S.<br> 20- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K fitted by CE.</p> <p><br>5. Formation enthalpies of bcc-with-vacancies-Ni-Al.xlsx<br>- Description: Formation enthalpies of bcc lattice with vacancies in Ni-Al system at different temperatures, which includes the effect of lattice vibration and magnetism. The fcc Al and fcc Ni were used as reference states.</p> <p>- Variable description by columns:<br> 1-(Folder name) - type: numerical (integer)<br> Description: Each folder name in the bcc-with-vacancies-Ni-Al.zip corresponds to a configuration.<br> 2- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 3- (H_f^(conf)(DFT) (eV/atom) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K calculated by DFT.<br> 4- (H_f^(conf)(CE)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 0 K fitted by CE. <br> 6- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 7- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K calculated by DFT and L-S.<br> 8- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 300 K fitted by CE. <br> 10- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 11- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K calculated by DFT and L-S.<br> 12- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 600 K fitted by CE. <br> 14- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 15- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K calculated by DFT and L-S.<br> 16- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 900 K fitted by CE.<br> 18- (at. fraction of AlVa (%)) - type: numerical (float)<br> Description: The atomic fraction of Ni in each configuration<br> 19- (H_f^(conf+vib) (DFT+L-S) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K calculated by DFT and L-S.<br> 20- (H_f^(conf+vib) (CE) (eV/atom)) - type: numerical (float)<br> Description: Formation enthalpy of each configuration at 1200 K fitted by CE.</p> <p><br>6. Formation enthalpies of fcc-Ni-Al.xlsx<br>- Description: Formation enthalpies of fcc lattice in Ni-Al system at different temperatures, which includes the effect of lattice vibration. The fcc Al and fcc Ni were used as reference states.<br>- Variable descriptions by columns are the same as those of Formation enthalpies of bcc-Ni-Al.xlsx.</p> <p><br>7. ECIs of bcc-Ni-Al at different temperatures.txt<br>- Description: ECIs of bcc lattice in Ni-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>8. ECIs of bcc-with-vacancies-Ni-Al at different temperatures.txt<br>- Description: ECIs of bcc lattice with vacancies in Ni-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>9. ECIs of fcc-Ni-Al at different temperatures.txt<br>- Description: ECIs of hcp lattice in Ni-Al system from 0 to 2000 K with increment step of 10 K. The ECIs at different temperatures are separated by blank lines. ECIs at 0 K means that only configurational contribution was considered. ECIs at finite temperature means that both configurational and vibrational contributions were considered.</p> <p><br>10. Clusters of bcc-Ni-Al.txt<br>- Description: Cluster information of bcc lattice in Ni-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>11. Clusters of bcc-with-vacancies-Ni-Al.txt<br>- Description: Cluster information of bcc lattice with vacancies in Ni-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p> <p><br>12. Clusters of fcc-Ni-Al.txt<br>- Description: Cluster information of fcc lattice in Ni-Al system. Each cluster is separated by a blank line. Each cluster contains: multiplicity; Length of the longest pair within the cluster; number of points in cluster; coordinates of point. They are arranged in a row.</p>
Probing Rényi Entanglement Entropies via Randomized Measurements
<p>The data contained here is for the four figures in the main text of the manuscript Probing Rényi Entanglement Entropies via Randomized Measurements, which is available on the arXiv:<em>1806.05747. </em> The data is contained in tab delimited .csv files.</p>
Supporting data for "Quantifying process connectivity with transfer entropy in hydrologic models" [Paper #2018WR024555]
<p>This tarball contains the processed datasets used for the analysis of the process networks. It also contains the shapefiles used to define the domains of each sub-region.</p>
Linear cross-entropy certification of quantum computational advantage in Gaussian Boson Sampling
<p>This repository contains the data used to obtain the numerical results of the paper "Linear cross-entropy certification of quantum computational advantage in Gaussian Boson Sampling" (https://arxiv.org/abs/2403.15339).</p>
Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"
<p>Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"</p> <p>Includes scripts for generating raw results, postprocessing scripts, as well as scripts/notebooks for generating plots for the manuscript</p>
Fig. 6. Maximum entropy models for A in Weak Genetic Differentiation among Populations of the Andean Ground Beetle Pelmatellus columbianus (Reiche, 1843) (Coleoptera: Carabidae)
Fig. 6. Maximum entropy models for A) the past (21,000 years) and B) present distribution of Pelmatellus columbianus, using five bioclimatic variables. Maps show the limit of the montane forest (above 2,450 m) in green and páramo (above 3,000 m) in light brown.
Maximum entropy model estimates functional connectivity
<p>Tools to estimate brain connectivity have been useful to improve our understanding of brain functioning. Reduced models ofcultured neurons are often used to study the behavior of neuronal networks, including functional connectivity and how it mightbe affected by external stimuli. Cultured neurons tend to be active in ensembles, and when pairs of neurons show significantsynchronicity in their firing patterns they are said to be functionally connected. The most common methods to infer functionalconnections are based on pair-wise cross correlation between activity patterns of (small groups of) neurons. However, thesemethods are not designed to be used during external stimulation, and they are relatively insensitive to inhibitory connections.Maximum Entropy (MaxEnt) models may provide a conceptually different method to infer functional connectivity, with thepotential benefit to estimate functional connectivity in the presence of an external stimulus and to infer excitatory as well asinhibitory connections. These models do not use pairwise comparison, but are based on probability distributions of sets ofneurons that are synchronously active in discrete time bins. We investigate the ability of the MaxEnt models to infer functionalconnectivity, using electrophysiological recordings fromin vitroneuronal cultures on micro electrode arrays. We comparefunctional connectivity as inferred by MaxEnt models to that obtained by conditional firing probabilities (CFP), an establishedcross-correlation based method. We show that MaxEnt models provide connectivity estimates that correlate well with CFPoutcomes. In addition, stimulus-induced connectivity changes were detected by MaxEnt models, and were of the samemagnitude as those detected by CFP.</p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
Experiment-based data published in "Entropy, pitch, and noise: organization and disorganization in the perception of closure for different types of spectra", JNMR
<p>This folder contains both the sonic excerpts used for an experiment measuring the level of perceived closure for sound stimuli presenting pitch, bell, and noise spectral structures, and the web application created to run the experiment. The web application can be uploaded in any server for trying it our.</p>
The Use of Entropy to Assess Sleep Disordered Breathing in Chronic Respiratory Disease
ClinicalTrials.gov study NCT07060079. IPD Sharing: NO. Countries: 1. Publications: 2.
Intraoperative M-Entropy Measurements
ClinicalTrials.gov study NCT00599066. IPD Sharing: NO. Countries: 1. Publications: 2.
Target-controlled Infusion of Propofol and Remifentanil During General Anaesthesia Guided by Entropy
ClinicalTrials.gov study NCT00391885. IPD Sharing: NO. Countries: 1. Publications: 1.
ERPS, BIS and Entropy for Neuromonitoring in ICU Patients
ClinicalTrials.gov study NCT00639548. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Relationship Between Bispectral Index (BIS) and Spectral Entropy (SpEn) During Desflurane Anaesthesia
ClinicalTrials.gov study NCT01868178. IPD Sharing: Not stated. Countries: 1. Publications: 10.
Effect of Atracurium and Rocuronium on the State and Response Entropy During Isoflurane Anesthesia
ClinicalTrials.gov study NCT05097508. IPD Sharing: YES. Countries: 1. Publications: 1.
Monitoring of Anesthetic Depth and EEG Band Power Using PLE (Phase Lag Entropy) During Propofol Anesthesia
ClinicalTrials.gov study NCT03299621. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison of the Bispectral Index and the Entropy of the Electroencephalogram During Total Intravenous Anesthesia
ClinicalTrials.gov study NCT00391963. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Maximum entropy model estimates functional connectivity
Open the record for dataset details and reuse information.
Data from: Control of entropy in neural models of environmental state
Humans and animals construct internal models of their environment in order to select appropriate courses of action. The representation of uncertainty about the current state of the environment is a key feature of these models that controls the rate of learning as well as directly affecting choice behaviour. To maintain flexibility, given that uncertainty naturally decreases over time, most theoretical inference models include a dedicated mechanism to drive up model uncertainty. Here we probe the long-standing hypothesis that noradrenaline is involved in determining the entropy, and thus flexibility, of neural models. Pupil diameter, which indexes neuromodulatory state including noradrenaline release, predicted increases (but not decreases) in entropy in a neural state model encoded in human medial orbitofrontal cortex, as measured using multivariate functional MRI. Activity in anterior cingulate cortex predicted pupil diameter. These results provide evidence for top-down, neuromodulatory control of entropy in neural state models.
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