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30 results for “Learning theory”
Data: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
<p>Network parameters of continuous normalizing flows trained for the <span class="math-tex">\(\varphi^4\)</span> theory.</p> <p>Corresponding article: Learning Lattice Quantum Field Theories with Equivariant Continuous Flows [<a href="https://arxiv.org/abs/2207.00283">2207.00283</a>]</p> <p>Abstract: We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem. We test our model on the <span class="math-tex">\(\varphi^4\)</span> theory, showing that it systematically outperforms previously proposed flow-based methods in sampling efficiency, and the improvement is especially pronounced for larger lattices. Furthermore, we demonstrate that our model can learn a continuous family of theories at once, and the results of learning can be transferred to larger lattices. Such generalizations further accentuate the advantages of machine learning methods.</p>
An information theory-based machine learning approach to detecting functionally conserved and coordinated protein dynamics
<p>The application of machine learning classification to the molecular dynamics of the functional states of protein allows for application of an information theoretic framework familiar to traditional bioinformatics. The functional states of proteins involving binding interactions with partners comprised of protein, DNA or small molecules can first be defined in a binary fashion (i.e. bound vs unbound), subsequently simulated in molecular dynamics software, and then employed as a comparative training set for a binary machine learning classifier capable of discerning the complex dynamical consequences of binding interaction. This learner can subsequently be deployed on new simulations of the functionally bound state to validate its ability to recognize the molecular motions that are supporting binding function. Regions of proteins with functionally conserved dynamics will induce significant local correlations in learning performance across independent validation runs. Through case studies of Rbp subunit 4/7 interaction in RNA Pol II and DNA-protein interactions of TATA binding protein, we demonstrate this method of detecting functionally conserved protein dynamics. We also demonstrate how Shannon information, relative entropy and mutual information can be applied to these binary classification states of dynamic simulations in order to compare dynamics and identify concerted motions involved in dynamic interactions across sites.</p>
Attentional Bias for Uncertain Cues of Shock in Human Fear Conditioning: Evidence for Attentional Learning Theory
<p>Eye tracking data and statistical analysis of:</p> <p>Koenig, S., Uengoer, M., & Lachnit, H. (2017). Attentional bias for uncertain cues of shock in human fear conditioning: Evidence for attentional learning theory. Frontiers in Human Neuroscience. doi: 10.3389/fnhum.2017.00266.</p> <p>Abstract: We conducted a human fear conditioning experiment in which three different color cues were followed by an aversive electric shock on 0, 50, and 100% of the trials, and thus induced low (L), partial (P), and high (H) shock expectancy respectively. The cues differed with respect to the strength of their shock association (L < P < H) and the uncertainty of their prediction (L < P > H). During conditioning we measured pupil dilation and ocular fixations to index differences in the attentional processing of the cues.<br> After conditioning, the shock-associated colors were introduced as irrelevant distracters during visual search for a shape target while shocks were no longer administered and we analyzed the cues’ potential to capture and hold overt attention automatically.<br> Our findings suggest that fear conditioning creates an automatic attention bias for the conditioned cues that depends on their correlation with the aversive outcome. This bias was exclusively linked to the strength of the cues’ shock association for the early<br> attentional processing of cues in the visual periphery, but additionally was influenced by the uncertainty of the shock prediction after participants fixated on the cues. These findings are in accord with attentional learning theories that formalize how associative learning shapes automatic attention.</p>
Dataset for "Solving deep-learning density-functional theory via variational autoencoder"
<p>The dataset contains the ground state energies, the ground state density profiles, and the external potentials of a 3D single particle system with a Gaussian-like external potential.<br>The number of grid points for each dimension is \(N_g=18\), the linear length of the box is \(L=a_0\) with \(a_0\) the unit of length. The unit of energy is \(E_0=\frac{ \hbar^2}{(m a_0^2)}\).</p> <p> </p> <p> </p> <p>The dataset is zip file of a Python npz file with the following keys:</p> <p>- "density" that corresponds to the ground state density profile.<br>- "potential" is the external potential.<br>-"energy" is the ground state energy.</p> <p><br>The number of instances is 36000. </p> <p>-3D_gaussian.zip -> 3D_gaussian.npz</p> <p> a dictionary with three keys -density, potential, energy-.<br> The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\).<br> The shape of energy is \([N_d]\).<br> \(N_d=36000\)</p> <p>-3D_gaussian_transfer_test_1.npz</p> <p> a dictionary with three keys -density, potential, energy-.<br> The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\).<br> The shape of energy is \([N_d]\).<br> \(N_d=500\)</p> <p>-3D_gaussian_transfer_test_2.npz</p> <p> a dictionary with three keys -density, potential, energy-.<br> The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\).<br> The shape of energy is \([N_d]\).<br> \(N_d=500\)</p>
Assessing Student Sustainable Learning Engagement in Mobile Learning through Social Cognitive Theory and Social Learning Theory
<p><span>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381)</em>.</span><span> the data was collected as part of ODDEA WP2.</span></p>
Gaussian16 data for "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning"
<p>This is the Gaussian 16 input and corresponding output, which was used as input into the machine learning presented in the paper titled "Dynamic electronic structure fluctuations in the de novo peptide ACC-dimer revealed by first-principles theory and machine learning". This upload is required before submission of the paper.<br><br>The 1001 and 100 snapshots from different extractions are preserved in separated directories. Each snapshot directory <code>*_snapshot</code> has the initial GROMACS snapshot <code>test_*.pdb</code> , the geometry after truncating the solvation shell in various formats, the Gaussian16 input, qsub input and the output directory <code>*.1</code> with a JobID number assigned by qsub. The output directory has the standard output from Gaussian in a <code>.log</code> file and <code>grep</code>ed output from the <code>.fchk</code> file in <code>*.out</code> .</p>
Do children learn from their mistakes? A registered report evaluating error-based theories of language acquisition
<p>Error-based theories of language acquisition suggest that children, like adults, continuously make and evaluate predictions in order to reach an adult-like state of language use. However, while these theories have become extremely influential, their central claim - that unpredictable input leads to higher rates of lasting change in linguistic representations – has scarcely been tested. We designed a prime surprisal-based intervention study to assess this claim.</p> <p>As predicted, both 5- to 6-year-old children (n=72) and adults (n=72) showed a pre- to post-test shift towards producing the dative syntactic structure they were exposed to in surprising sentences. The effect was significant in both age groups together, and in the child group separately when participants with ceiling performance in the pre-test were excluded. Secondary predictions were not upheld: there were no verb-based learning effects and there was only reliable evidence for immediate prime surprisal effects in the adult, but not in the child group. To our knowledge this is the first published study demonstrating enhanced learning rates for the same syntactic structure when it appeared in surprising as opposed to predictable contexts, thus providing crucial support for error-based theories of language acquisition.</p>
Data from Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory and machine learning. Parts I and II.
<p>Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning; Parts I and II.</p> <p>This data-set is published in Refs. [1-2] and it is now made openly accessible. The data-set consists of computational X-ray spectroscopy fingerprints of plain and functionalized amorphous carbon. This data can be used in interpretation of experimental spectroscopy data (XAS and XPS). The spectra are averages of certain atomic environments, that are described in the publications. Standard deviation is included in the third column. Please, feel free to use the data-set, and if you do so, remember to cite Refs. [1-2] and this source. If there are any questions, please contact the corresponding author. </p> <p> </p> <p>[1] A. Aarva, V. L. Deringer, S. Sainio, T. Laurila, andM. A. Caro, “Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part I: Fingerprint spectra,” Chem. Mater. 31, 9243–9255 (2019).</p> <p>[2] A. Aarva, V. L. Deringer, S. Sainio, T. Laurila, andM. A. Caro, “Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part II: Quantitative fitting of spectra,” Chem. Mater. 31, 9256–9267(2019).</p> <p> </p> <p>Funding and resources for the work are acknowledged as follows:</p> <p>Funding from the Academy of Finland (project no.285526) and the computational resources provided for this project by CSC – IT Center for Science are gratefully acknowledged. M. A. C. acknowledges personal funding from the Academy of Finland under project no. 310574.V. L. D. acknowledges a Leverhulme Early Career Fellowship and support from the Isaac Newton Trust. M. A. C.and V. L. D. are grateful for travelling support from the HPC-Europa3 program under the auspices of the European Union’s Horizon 2020 framework (grant agreement no. 730897). Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under contract no. DE-AC02-76SF00515. S. S. acknowledges personal funding from Instrumentarium Science Foundation and the Walter Ahlström Foundation.</p> <p> </p>
Reinforcement learning theory reveals the cognitive requirements for solving the cleaner fish market task
<p>Learning is an adaptation that allows individuals to respond to environmental stimuli in ways that improve their reproductive outcomes. The degree of sophistication in learning mechanisms potentially explains variation in behavioural responses. Here, we present a model of learning that is inspired by documented intra- and interspecific variation in the performance in a simultaneous two-choice task, the 'biological market task'. The task presents a problem that cleaner fish often face in nature: the decision of choosing between two client types; one that is willing to wait for inspection and one that may leave if ignored. The cleaners' choice hence influences the future availability of clients, i.e. it influences food availability. We show that learning the preference that maximizes food intake requires subjects to represent in their memory different combinations of pairs of client types rather than just individual client types. In addition, subjects need to account for future consequences of actions, either by estimating expected long-term reward or by experiencing a client leaving as a penalty (negative reward). Finally, learning is influenced by the absolute and relative abundance of client types. Thus, cognitive mechanisms and ecological conditions jointly explain intra and interspecific variation in the ability to learn the adaptive response.</p>
Kinetics of N2 Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study
<p>Total potential (E) and Thermal correction to Gibbs Free Energies obtained using SMD/M06-2X/def2-TZVP//SMD/M06-2X/6-31G(d) level of theory in dichloroethane and Cartesian coordinates for all of the calculated structures.</p> <p>dataset</p> <p>Python Machine Learning Script</p> <p> </p>
Stable and accurate orbital-free density functional theory powered by machine learning
Open the record for dataset details and reuse information.
Do children learn from their mistakes? A registered report evaluating error-based theories of language acquisition
Open the record for dataset details and reuse information.
Reinforcement learning theory reveals the cognitive requirements for solving the cleaner fish market task
Open the record for dataset details and reuse information.
Exploring the Global Reaction Coordinate for Retinal Photoisomerization: A Graph Theory-Based Machine Learning Approach
<p>This repository contains i. optimized geometry of the cis and trans retinal, ii. figure labelling the internal coordinates of retinal. </p>
Dataset for deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation
<p>Dataset files of atomic structures and Hamiltonian matrices of graphene, MoS<sub>2</sub>, bilayer graphene and bilayer bismuthene.</p> <p>Please note that the DFT results in this dataset were calculated using OpenMX. This means that if you want to use a DeepH model trained on this dataset to calculate properties, you need to use the <a href="https://github.com/mzjb/overlap-only-OpenMX">overlap calculated using OpenMX</a>. The orbital information required for overlap calculations can be found in the <a href="https://www.nature.com/articles/s43588-022-00265-6">paper</a>.</p>
Distillation of crop models to learn plant physiology theories using machine learning
<p>Full Paper available for download at LINK</p>
Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries
<p>The uploaded data contains (i) "<strong>01_Data</strong>" optimized molecular structure in XYZ format and the primary attributes and SMILES, (ii) "<strong>02_Datasets</strong>" datasets used in the publication, and (iv) "<strong>03_pynb_script</strong>" a Jupyter-Notebook. The <strong>01_Data </strong>directory contains more than 8000 subdirectories. Each is for a molecule that undergoes a two-proton two-electron transfer reaction. In each subdirectory, one finds the following files:</p> <p>(1) directories named corresponding to the ones in Figure 1 of the paper. Inside each, there are geometries and properties in XYZ and CSV format, respectively.</p> <p>(2)<strong> "freeEnergy.dat" </strong>contains the free energy of different states.</p> <p>(3) <strong>"schemesquare.dat" </strong>has the parameters of the electrochemical scheme of square representation.</p> <p>├── A<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── A1-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── A2-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH1+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH1-<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH2<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH21+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── AH22+<br> │ ├── info.csv<br> │ └── pos.xyz<br> ├── <strong>freeEnergy.dat</strong><br> └── <strong>schemesquare.dat</strong><br> ******************************************************<br> The new version (v1.1) contains some updates around:<br> (i) The DFT calculations workflow in a folder called "<strong>04_workflow_of_DFT</strong>"</p> <p> The Gaussian input files have been explained in the "README" file.</p> <p>(ii) The Python scripts for data extraction have been added and can be found in "<strong>05_how_to_extracted_data</strong>"</p> <p>(iii) We explained how to compute the Purbaix diagram in great detail "<strong>06_how_to_compute_Pourbaix_diagram</strong>/"</p> <p>All these changes/improvements were applied/made following the Referee of Digital Discovery Journal. Here, we would like to thank him/her.</p>
Examining the Effectiveness of Education Based on Social Learning Theory in Fostering Self-care and Social Skills
ClinicalTrials.gov study NCT06485882. IPD Sharing: NO. Countries: 1. Publications: 3.
The Effect of a Mobile Application Based on the Social Cognitive Learning Theory on Medication Adherence and Hypertension Self-Efficacy in Patients With Hypertension: A Randomized Controlled Trial
ClinicalTrials.gov study NCT07050303. IPD Sharing: NO. Countries: 1. Publications: 13.
Learning Theory to Improve Obesity Treatment
ClinicalTrials.gov study NCT01708785. IPD Sharing: NO. Countries: 1. Publications: 4.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.