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opencc-by-4.0Jan 2012View details →

Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate count, yeast & mould count, and spore count were taken as input parameters, and sensory score as output parameter for developing TDNN single and multilayer models (Fig.1).

opencc-by-4.0Jan 2012View details →

Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

TDNN models with single and multi layers were developed taking soluble nitrogen, pH, standard plate count, yeast & mould count, spore count as input parameters, and sensory score as output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability of the developed TDNN models. Regression equations were developed for predicting the shelf life of processed cheese, which came out as 28.25 days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence from the study it can be concluded that TDNN artificial neural network models are quite efficient in predicting shelf life of processed cheese.

BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 2. Processes and Interfaces of the SAH Human Brain Model

The proposed SAH Human Brain Model starts assigning the main attributes to the “heavy pieces” (king, queen, rooks, bishops, knights) and assigning to pawns the interfaces as an advanced guard. The interface represents senses and processed human actions (equilibrium, movements, and speech) and it results from the brain activity (see Figure 2).

BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 4. Name and role of the Chess Pieces

In assigning the brain function to the computational processing units the strategy of the chess game will be pursued: 1 king – consciousness, mind, resolving undefined situations, undetermined risk analysis, feedback: 1 queen – implementation strategy, thinking, learning; 2 rooks – initial knowledge memory and learning memory; 2 bishops – good or updated, time or emergency decision; 2 knights – rules, open schemes, fixed processes, templates; 8 pawns – interfaces with own senses and actions. Double chess pieces will be assigned in the model with initial knowledge (‘ marked) that can be updated as a learning experience to a second set (“ marked).

BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 3. Assigning ` the "SAH" Human Brain Model the role of the Chess Pieces

The components are not topically subordinated to each other but in a strong interoperability and used for outputs reflected as result of thinking, actions to receiving information from the sensor of the interfaces, movement or speaking.

BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 5. Block Diagram of the SAH Human Brain Model

Three vertical areas are defined in the field of activities: Processes units, Computational Intelligence Block, and Smart Interfaces Block (Details are presented in Figure 5). The Processes units fully communicate with the Computational Intelligence Block, Central Processing Unit and Smart Interfaces. Some specific links and functions are not specified here. Smart interfaces defined for “sight, sound, taste, touch and hearing senses” are bidirectional and completed by input-output interfaces that ensure the communication for output actions like “speech, sound, movements” and other commands resulting in the thinking process. An important issue is the ‘equilibrium’ that must be treated in either “decision or movement” framework.

BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 1. Being Brain and Chess Game Strategy - similarities

Finally, the following similar reactions between a chess player and a human being must be mentioned and considered. The power of reason for every being, human brain, or chess game player lies in similarities and has three main directions (see Figure 1)

opencc-by-4.0Aug 2018View details →

Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro

This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>. In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are: <ul> <li>s, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li>theta, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li>p, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.

opencc-by-4.0Aug 2018View details →

Trained neural network data for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro

This archive contains data representing a trained-up neural network suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. The network generates coefficients that can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>. In this particular dataset, networks were trained on a training set of coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a> that is available as <a href="https://doi.org/10.5281/zenodo.1341154">DOI:10.5281/zenodo.1341154</a>. The data were generated using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, were: <ul> <li>s, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li>theta, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li>p, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> The training set was computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each, yielding about 22 million numbers. Training the networks took about 3 hours on an 8-core laptop. For the purposes of neurosynchro, the formats of the files in this package should be regarded as internal implementation details. The <a href="https://pypi.org/project/neurosynchro/">neurosynchro</a> Python package will load up the files in this archive and use them to predict synchrotron coefficients. For specifics, see <a href="https://neurosynchro.readthedocs.io/en/stable/">the neurosynchro documentation</a>.

opencc-by-4.0Mar 2019View details →

Dataset from Computational modeling of anthocyanin pathway evolution: Biases, hotspots, and trade-offs

This directory contains the scripts used to run simulations, Jupyter notebook with complete analysis, and serialized (pickled) raw simulated dataset from our paper "Computational modeling of anthocyanin pathway evolution: Biases, hotspots, and trade-offs". These materials are referenced in the main text and supplemental text of the publication. The purpose of this repository is to facilitate replication of our analysis by any interested parties.

FIGURE 3 in Heterochrony in helicoid spiral cones: a computer model for demonstrating heterochronic evolution

FIGURE 3. Modes of heterochronic change. The central simulation has parameters W0=2.5, ΔW=-0.12, D=0, S=0.8, T'0=0.4, ΔT'=0.06, and has six whorls. The other four simulations show the result of one increment of each mode of heterochronic change. The two forms on the left are paedomorphic; the forms on the right are peramorphic. Note that in this case neoteny results in larger size as the ontogenetic decline in whorl expansion is reduced, and acceleration results in a smaller size for the converse reason.

FIGURE 6. Heterochronic modelling experiment 3 in Heterochrony in helicoid spiral cones: a computer model for demonstrating heterochronic evolution

FIGURE 6. Heterochronic modelling experiment 3. The "ancestor" on the left has parameters W0=2, ΔW=-0.05, D=0, S=0.9, T'0=0.2, ΔT'=0.07, and has eight whorls. As in Figure 4, this is a common general type of gastropod shell shape. The central simulation is a "descendant" derived from this by two increments of neoteny (relative scale x 0.5); the simulation on the right has resulted from two further increments of neoteny (relative scale x 0.39). All three simulations have analogues in the Family Helicidae of the Pulmonata, and this result constitutes a hypothesis for the possible heterochronic evolution of forms with flattened spires (depressed and sub-planispiral).

FIGURE 2 in Heterochrony in helicoid spiral cones: a computer model for demonstrating heterochronic evolution

FIGURE 2. Isometric and allometric gastropods and simulations. 1, An example of a "Raupian" gastropod (Epitonium), showing isometric growth. 2, An example of a "Raupian" simulation with similar shape. 3, An example of an allometric gastropod (Megacochlea); note that there is no straight line that is tangential to the outside of all whorls. 4, A simulation using the program described here with similar shape. (Figures 2.1 and 2.3 © The Trustees of the Natural History Museum, London, reproduced from Anon, 1975, with kind permission.)

FIGURE 1 in Heterochrony in helicoid spiral cones: a computer model for demonstrating heterochronic evolution

FIGURE 1. Raup's definition of the shell dimensions on which the equations for parameters W, D and T are derived. Based on Raup (1966).

FIGURE 5. Heterochronic modelling experiment 2 in Heterochrony in helicoid spiral cones: a computer model for demonstrating heterochronic evolution

FIGURE 5. Heterochronic modelling experiment 2. The "ancestor" on the left has parameters W0=1.1, ΔW=0.05, D=0, S=2.3, T'0=2, ΔT'=-0.03, and has 10 whorls. It has a form, including the slightly concave profile of the spire, which is similar to some members of the Family Volutidae. The next simulations are "descendants" derived from this by successive increments of acceleration (relative scales x 0.5, x 0.225, x 0.0875, respectively). All of these have parallels in the Volutidae, with the final form resembling the giant volutids in the genus Melo. The observation that the simulation predicts the large size of the final form supports the heterochronic hypothesis in the evolution of these gastropods.

FIGURE 4. Heterochronic modelling experiment 1 in Heterochrony in helicoid spiral cones: a computer model for demonstrating heterochronic evolution

FIGURE 4. Heterochronic modelling experiment 1. The "ancestor" on the left has parameters W0=2.5, ΔW=-0.1, D=0, S=1, T'0=0, ΔT'=0.15, and has seven whorls. It has a general shape that is common in gastropods, and especially amongst the subclass Pulmonata. The central simulation is derived from this by one increment of acceleration (relative scale x 1.5) and the simulation on the right by two increments of acceleration (relative scale x 2). The incipient reduction in whorl diameter and incipient loss of whorl-to-whorl contact is typical of some Pulmonata, such as Family Chondrinidae. Note that if Raup's T were used, incipient reduction in whorl diameter (W<1) would lead to reversal of the translation direction, which is nonsensical; using T', this is not the case.

opencc-by-4.0Aug 2024View details →

Surrogate waveform model data for black hole binary systems computed in point-particle black hole perturbation theory

This repository contains all publicly available surrogate data for gravitational waveforms produced within the point-particle black hole perturbation theory framework and calibrated to numerical relativity simulations performed with the Spectral Einstein Code (SpEC).  Several surrogate models are currently available in this catalog: <ol> <li>BHPTNRSur2dq1e3, for aligned spin black hole binary systems with mass-ratios varying from 3 to 1000 and spins from −0.8≤χ1≤0.8 on the larger black hole. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with calibration to numerical relativity (NR) data. The waveforms include all spin-weighted spherical harmonic modes up to ℓ=4 except the (4,1) and m=0 modes. Model details can be found in <a href="https://arxiv.org/abs/2407.18319">Rink et al. 2024</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/blob/main/tutorials/BHPTNRSur2dq1e3.ipynb">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li>BHPTNRSur1dq1e4, an updated version of the EMRISur1dq1e4 model described below. The updated version includes better calibration to NR, a smoother transition to plunge model, and more harmonic modes. Model details can be found in <a href="https://arxiv.org/abs/2204.01972">Islam et al. 2022</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="https://bhptoolkit.org/BHPTNRSurrogate/">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/BHPTNRSurrogate/tree/main/tutorials/BHPTNRSur1dq1e4">tutorial</a>) or the GWSurrogate Python package, which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a> or <a href="https://anaconda.org/conda-forge/gwsurrogate">conda-forge</a>.</li> <li>EMRISur1dq1e4, for non-spinning black hole binary systems with mass-ratios varying from 3 to 10000. This surrogate model is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT), with the total mass rescaling parameter tuned to NR simulations. Available modes are [(2,2), (2,1), (3,3), (3,2), (3,1), (4,4), (4,3), (4,2), (5,5), (5,4), (5,3)]. The m<0 modes are deduced from the m>0 modes. Model details can be found in <a href="https://arxiv.org/abs/1910.10473">Rifat et al. 2019</a>. This data file is used to evaluate the surrogate model with either stand-alone Python code hosted by the <a href="http://github.com/BlackHolePerturbationToolkit/EMRISurrogate">Black Hole Perturbation Toolkit</a> (Jupyter notebook <a href="https://github.com/BlackHolePerturbationToolkit/EMRISurrogate/blob/master/EMRISur1dq1e4.ipynb">tutorial</a>) or the GWSurrogate Python package (Jupyter notebook <a href="https://github.com/sxs-collaboration/gwsurrogate/blob/master/tutorial/notebooks/nonspinning_nr_emri.ipynb">tutorial</a>), which can be found on <a href="https://pypi.python.org/pypi/gwsurrogate/">PyPI</a>.</li> </ol>

opencc-by-4.0Aug 2024View details →

Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform

This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform".  Source Data Raw.zip has the entire data set used to generate the images. Source Data.zip contains the processed data  from "Source Data Raw.zip".    Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab. For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.