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325 results for “network structure”
BRAIN Journal-High Performance Data mining by Genetic Neural Network-Figure 3. The Structure of Neural Network
<p>A neural network (NN), in the case of artificial neurons called artificial neural<br> network (ANN) or simulated neural network (SNN), is an interconnected group of natural<br> or artificial neurons that uses a mathematical or computational model for information<br> processing based on a connectionist approach to computation. In most cases an ANN is an adaptive<br> system that changes its structure based on external or internal information that flows through the<br> network[9].<br> In more practical terms neural networks are nonlinear statistical data modelling or decision<br> making tools. They can be used to model complex relationships between inputs and outputs or<br> to find patterns in data.<br> Two neurons neural network active in memory (ON or 1) or disable (Off or 0), and each<br> edge (synapses or connections between nodes) is a weight. Edges with positive weight, stimulate or<br> activate next active node, and edges with negative weight, disable or inhibit the next connected<br> node (if it is active) ones.</p>
Figure 1. Brain Structure-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>EEG data have collected from<br> desirable subjects. Each and every EEG signal has different kind of bands like Alpha, Beta,<br> Gamma, Theta, and Delta. Each band stores the particular information about the emotions. Alpha<br> band (8-13 Hz) which located in Frontal Occipital, Beta band (13-30 Hz) which located in Frontal<br> Central, Gamma band (30-100 Hz), Theta band (4- 7 Hz) which located in Midline Temp, Delta<br> band (0-4Hz) which located in Frontal Lobe. Before processing the EEG signal and extracting these<br> bands, preprocess the signal and reduce the noise. The basic brain figure is shown in below.</p>
Data and code for: Community structure in co-inventor networks affects time to first citation for patents
<p>This package provides the datasets and programming code needed to reproduce the results reported in the article "Community structure in co-inventor networks affects time to first citation for patents".</p> <p>v2: Added data and code pertaining to randomized-community-association test and updated README file.</p>
Figure 3. Median-joining networks for J in Mitochondrial evidence indicates a shallow phylogeographic structure for Jaculus blanfordi (Murray, 1884) populations (Rodentia: Dipodidae)
Figure 3. Median-joining networks for J. blanfordi mitochondrial DNA haplotypes in the different datasets for cyt b (1110 bp), COI (618 bp) and COI + cyt b (313 bp + 284 bp). The numbers of mutations (greater than 1) between the haplotypes are indicated near the branches and circle sizes are proportional to the number of similar haplotypes. See Table 1 for the haplotype designations.
Data from: Personality and social network structure influence cooperative dynamics across canid species
<p>In canids, cooperative behaviour occurs in many scenarios. However, most studies focus on single-species observations, not accounting for variation beyond the species-level. We modelled cooperative behaviour using Eigenvalue centrality as well as boldness combined with biological traits such as kinship, sex, age, mating system and foraging strategy in multiple canid species with Bayesian inference, Tukey HSD and distance correlation.</p>
Network of reference tree-ring chronologies for forensic botanical (dendrochronological) examinations and dating of architectural structures in the Tyva Republic.
<p>The database consists of tables. First sheet - general description of tree-ring chronologies (general information: name of chronology, authors, data type, tree-ring parameter, notes, key words; description of sample collection site: site name, location, region, latitude, longitude, height; description of sample collection: collection code designation, number of series, year of first ring, year of last ring, maximum length of sample, average width of year ring; species affiliation - species; support - grant number). Second sheet, first column - years, second column - standardized growth value. The third sheet is a PDF document containing the results of independent testing in the program COFECA (the file is opened by the command: right-click/Acrobat Document object/open). The database is implemented in the OpenOffice.org Calc spreadsheet processor. The table file format is an internal OpenOffice.org Calc format, with the extension .ods. The data is accessed and structured using the standard tools "Sort", "Autofilter", etc. In the database, the integrity restriction control is not implemented, the user is invited to monitor the integrity of the database himself. Computer type: IBM PC. PC; OS: Windows 10.</p> <p>Type and version of the database management system: OpenOffice.org Calc.</p> <p>Database size: 5.6 MB</p>
Neural Networks for Structure-Informed Prediction of Formation Energy (employed in SIPFENN)
<p>pySIPFENN Documentation: <a href="https://pysipfenn.org">pysipfenn.org</a></p> <p>pySIPFENN GitHub: <a href="https://github.com/PhasesResearchLab/pySIPFENN">git.pysipfenn.org</a></p> <p>Original SIPFENN Paper: <a href="https://doi.org/10.1016/j.commatsci.2022.111254">10.1016/j.commatsci.2022.111254</a></p> <p> </p> <p>Network Changelog:</p> <p>V 0.10 - All models moved to the open ONNX format for improved interchangeability; NN30 neural network similar to NN20 but accepting the new KS2022 feature vector; Python code migrated to public GitHub repository.</p> <p>V 0.9 - Python code updated to the release version; paper published</p> <p>V 0.8 - Python code (beta) to run models included</p> <p>V 0.7 - Original upload of development models </p> <p> </p> <p>Selected works with SIPFENN alongside DFT and experiments:</p> <p>- <a href="https://doi.org/10.1016/j.actamat.2021.117448">10.1016/j.actamat.2021.117448</a></p> <p>- <a href="https://doi.org/10.1038/s41598-021-03578-0">10.1038/s41598-021-03578-0</a></p> <p> </p> <p>SIPFENN Abstract (original publication, 2021):</p> <p>In recent years, numerous studies have employed machine learning (ML) techniques to enable orders of magnitude faster high-throughput materials discovery by augmentation of existing methods or as standalone tools. In this paper, we introduce a new neural network-based tool for the prediction of formation energies based on elemental and structural features of Voronoi-tessellated materials. We provide a self-contained overview of the ML techniques used. Of particular importance is the connection between the ML and the true material-property relationship, how to improve the generalization accuracy by reducing overfitting, and how new data can be incorporated into the model to tune it to a specific material system.<br> <br> In the course of this work, over 30 novel neural network architectures were designed and tested. This lead to three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve mean average error (MAE) of 28, 40, and 42 meV/atom respectively. The second model provides better predictions on materials far from ones reported in OQMD, while the third reduces the computational cost by a factor of 8.<br> <br> We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models but also ships in a ready-to-use form with pre-trained neural networks and a user interface. </p> <p> </p> <p>Contacts:</p> <p>- Adam Krajewski: ak@psu.edu</p> <p>- Prof. Zi-Kui Liu: zxl15@psu.edu</p>
Colloidal network oleogels structured by sonothermal conjugates of sodium caseinate and anionic gums
<p>The raw data of Maillard conjugates, Maillard conjugate-stabilized emulsions and oleogels.</p> <p>The optical microscopic images of Maillard conjugate-stabilized emulsions.</p> <p>Centrifugation and storage stability of emulsions.</p> <p> </p>
Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
<p>Described in 2009, the Patch-nosed Salamander (<em>Urspelerpes brucei</em>) is a miniature species of lungless salamander with a geographic range of only ~45 km<sup>2</sup>. This species is endemic to the foothills of the Appalachian Mountains in extreme northeastern Georgia and northwestern South Carolina. The Tugaloo River—a waterway of some 50 m in width that forms the political boundary between the two states—bisects the tiny range of <em>U. brucei</em> and likely acts as a barrier to gene flow. Using RADcap data and a suite of complementary population genomic analyses, we evaluated the role that this river and its tributaries may play in enabling and/or interrupting gene flow among populations of <em>U. brucei</em>, and we investigated patterns of within-population and between-population genetic variation. Our results revealed a general pattern of isolation-by-stream distance and indicated that a population separated by the Tugaloo River is moderately more differentiated than what is explainable by stream distance alone. Unique in both its physiography and geologic history, this region in which <em>U. brucei</em> lives also harbors more than a dozen other species of lungless salamanders. Therefore, the genetic patterns that we have elucidated may have larger implications for differentiation among populations of other species with similar dispersal abilities.</p>
Structural Characterization of amphiphilic co-networks in selective and non-selective solvents using 1H-NMR and SAXS
<p>We investigate structural properties of model amphiphilic co-networks made by heterocomplementary end-linking of tetra-PEG tetra-PCL star polymers in selective and non-selective solvent using small-angle X-ray scattering, NMR diffusometry and double-quantum magic-angle spinning (DQ MAS) NMR techniques.<br><br>Here, the data used is made accessible</p>
In Vivo Effects of Fibrinogen Concentrate (FC) Versus Cryoprecipitate on the Neonatal Fibrin Network Structure After Cardiopulmonary Bypass (CPB)
ClinicalTrials.gov study NCT03932240. IPD Sharing: YES. Countries: 1. Publications: 1.
Group composition of individual personalities alters social network structure in experimental populations of forked fungus beetles
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Early insight into social network structure predicts climbing the social ladder
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Structure and dynamics of enterovirus genotype networks
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Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)
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Data from: Effects of the control of an invasive tree on the structure of a plant-frugivore network
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Data from: Emergence of structure in plant-pollinator networks: Low floral resource constrains network specialisation
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Fast-slow traits predict competition network structure and its response to resources and enemies
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Ecological network structure in response to community assembly processes over evolutionary time
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Estimates of Forest Structure, Successional Trajectory, and Carbon and Nitrogen Pools Across the 2004 Wildfire Network Sites
This dataset contains stand structure and ecosystem carbon and nitrogen pool data measured in 2005 and 2006. It includes in situ measurements of post-fire residual above and belowground carbon and nitrogen pools, reconstructions of pre-fire pools and estimates of losses. Successional trajectory was calculated from measurements of tree seedling density and biomass made in 2017.
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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.