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486 results for “hierarchic”
Research data supporting "Fractal-like hierarchical organisation of bone begins at the nanoscale"
<p>Raw research data supporting: N. Reznikov et al., Science 360, eaao2189 (2018). DOI: 10.1126/science.aao2189</p>
New Ideas for Brain Modelling 4-Figure 3. Neuron Pairing: an ensemble neuron links with a hierarchal neuron. Also figure 4 in Greer (2016)
<p>The model is also based on the idea of an auto-associative neural network. The Hopfield neural network (Hopfield, 1982), and its stochastic equivalents are auto-associative or memory networks. With the memory networks, information is sent between the input and the output until a stable state is reached, when the information does not then change. These are resonance networks, such as bidirectional associative memory (BAM), or others (Rojas, 1996), but they can only provide a memory recall – they map the input pattern directly to the output pattern. If some of the input pattern is missing however, they can still provide an accurate recall of the whole pattern. They also prefer the data vectors to be orthogonal without overlap. This is however ideal for the binding that only wants to reproduce the base ensemble in the hierarchy.</p>
Fig. 2 in Hierarchical establishment of information sources during foraging decision-making process involving Acromyrmex subterraneus (Forel, 1893) (Hymenoptera, Formicidae)
Fig. 2. Decision time (s) spent by the A. subterraneus target worker according to number of trips (n) made by the respective target worker and the concentration of pheromone manipulated on the branch that does not lead to the food, estimated by total flow of foragers.
Fig. 1. Y in Hierarchical establishment of information sources during foraging decision-making process involving Acromyrmex subterraneus (Forel, 1893) (Hymenoptera, Formicidae)
Fig. 1. Y-trail system with branches of equal length (225 mm). Branches arranged at an angle of 60◦ connected to a bifurcation. Decision lines (LD) established at fixed points 140 mm far from the bifurcation center on the right and left branches, and 25 mm from the base branch to calculate the A. subterraneus workers' frequency of passage when half of their bodies had crossed each LD.
HiDy: A Large-scale Hierarchical Dynamic Financial Knowledge Base
<p>HiDy is a hierarchical, dynamic, robust, diverse, and large-scale financial KB that aims to provide various valuable financial knowledge as critical benchmarking data for fair model testing in different financial tasks. Specifically, HiDy currently contains 34 relation types, more than 506,444 relations, 17 entity types, and more than 51,095 entities. The scale of HiDy is steadily growing due to its continuous updates. To make HiDy easily accessible and retrieved, HiDy is organized in a well-formed financial hierarchy with four branches, <em>Macro</em>, <em>Meso</em>,<em> Micro</em>, and<em> Others</em>.</p> <p>We then give explanations on the various csv files as follows.</p> <ul> <li>"hidy.nodes.entity_type.csv" includes a mapping dictionary of entities with a specific entity type. The meta data is ID, name, (code), Label. For example, "hidy.nodes.company.csv" includes "0,东诚药业,002675.SZ,company", "1,大庆华科,000985.SZ,company".</li> <li>"hidy.relationships.relation_type.csv" includes quadruple knowledge with a specific relation type. The meta data is START_ID, END_ID, TYPE, time. For example, "hidy.relationships.cooperate.csv" includes "2197,245,cooperate,2019/9/17 12:00, "1165,756,cooperate,2020/8/2 17:10".</li> </ul> <p>For details of HiDy, please refer to our <a href="https://github.com/K-Quant/HiDy">GitHub</a>.</p>
Pre-existing natural variations of adult neurogenesis and anxiety predict hierarchical social status of inbred male mice.
<p>CVS files and image files of all data presented in the correcponding figures. </p>
Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders
<p>#########</p> <p>Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders</p> <p>#########</p> <p>Authors: Lucas Stoffl, Andy Bonnetto, Stéphane D'Ascoli & Alexander Mathis</p> <p>Affiliation: Ecole Polytechnique de Lausanne (EPFL)</p> <p>Date: 25/09/2024</p> <p>Link to the BiorXiv article : https://doi.org/10.1101/2024.08.06.606796</p> <p>-----------------</p> <h2>Provided data (hBehaveMAE checkpoints)</h2> <p>We provide a collection of pre-trained models that were reported in our paper, allowing you to reproduce our results for MABe22, hBABEL and Shot7M2 datasets.</p> <p>Note that you can <a href="https://huggingface.co/datasets/amathislab/SHOT7M2">download Shot7M2</a> on HuggingFace and <a href="https://github.com/amathislab/BehaveMAE/tree/main/hBABEL">generate hBABEL</a> by following the instructions on the <a href="https://github.com/amathislab/BehaveMAE">github page.</a></p> <ul> <li><strong>hBehaveMAE_hBABEL.pth </strong>: checkpoint for the hBehaveMAE pre-trained on the hBABEL dataset</li> <li><strong>hBehaveMAE_Shot7M2.pth</strong> : checkpoint for the hBehaveMAE pre-trained on the Shot7M2 dataset</li> <li><strong>hBehaveMAE_MABe22.pth</strong>: checkpoint for the hBehaveMAE pre-trained on the MABe22 dataset</li> </ul> <h2>References</h2> <p>If you find our code, weights or ideas useful, please cite:</p> <table> <tbody> <tr> <td>@article {Stoffl2024hBehaveMAE,<br> author = {Stoffl, Lucas and Bonnetto, Andy and d{\textquoteright}Ascoli, St{\'e}phane and Mathis, Alexander},<br> title = {Elucidating the Hierarchical Nature of Behavior with Masked Autoencoders},<br> elocation-id = {2024.08.06.606796},<br> year = {2024},<br> doi = {10.1101/2024.08.06.606796},<br> publisher = {Cold Spring Harbor Laboratory},<br> URL = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796},<br> eprint = {https://www.biorxiv.org/content/early/2024/08/08/2024.08.06.606796.full.pdf},<br> journal = {bioRxiv}<br>}</td> </tr> </tbody> </table>
Data for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning
<p>The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571. </p> <h2><strong>Experimental Description:</strong></h2> <p>The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6).</p> <h3><strong>Pulse Testing:</strong></h3> <p>The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V.</p> <p>Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses.</p> <h3><strong>Driving Cycle Testing:</strong></h3> <p>The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries.</p> <p>As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations.</p> <h2><strong>File Naming Conventions:</strong></h2> <p>The dataset files are named based on the experimental conditions, as follows:</p> <ul> <li><strong>Pulse_data_tempX_batY</strong>: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li><strong>Driving_cycle_data_tempX_batY</strong>: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>
Global Hierarchical Urban Boundaries (GHUB)
<p>Global Hierarchical Urban Boundaries (GHUB) vector Database 2018.</p> <p>GeoPackage (gpkg) format.</p> <p>Please cite the following article when using this data,</p> <p>Xu, Z., Jiao, L., Lan, T., Zhou, Z., Cui, H., Li, C., Xu, G., & Liu, Y. (2021). Mapping hierarchical urban boundaries for global urban settlements. <em>International Journal of Applied Earth Observation and Geoinformation</em>, <em>103</em>, 102480. https://doi.org/10.1016/j.jag.2021.102480</p> <p> </p>
Experimental verification of isotropic auxetic behaviour of hierarchical samples
<p>Video of experimental test on a hierarchical auxetic and isotropic <a href="https://www.sciencedirect.com/topics/engineering/porous-medium">p</a>orous sample with extremely negative Poisson’s ratio, related to the publication:</p> <p>M. Morvaridi, G. Carta, F. Bosia, A. S. Gliozzi, N. M. Pugno, D. Misseroni, M. Brun, "Hierarchical auxetic and isotropic porous medium with extremely negative Poisson’s ratio", Extreme Mechanics Letters 48, 101405 (2021), https://doi.org/10.1016/j.eml.2021.101405.</p>
Datasets for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants"
<p>All data used for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants", Criswell, A.W., et al. (2022). The code used to create the paper results from this data can be found at <a href="https://github.com/criswellalexander/hbpm_paper">https://github.com/criswellalexander/hbpm_paper</a> and the underlying software package can be found at <a href="https://github.com/criswellalexander/bayestack">https://github.com/criswellalexander/bayestack</a>.</p>
Hierarchical Text Classification corpora
<p>A set of 3 datasets for Hierarchical Text Classification (HTC), with samples divided into training and testing splits. The hierarchies of labels within all datasets have depth 2.</p> <ul> <li>The <strong>Amazon5x5</strong> dataset contains 500,000 user reviews tagged with the reviewed product's categories. There are 5 product categories with 100,000 examples each, and each category has 5 sub-categories.</li> <li>The <strong>Bugs</strong> dataset contains 30,050 bugs of the Linux kernel, labeled with exactly two categories identifying the affected component.</li> <li>Finally, the <strong>Web Of Science</strong> dataset contains 46,960 abstracts of scientific papers, labeled the article's domain (see <a href="https://data.mendeley.com/datasets/9rw3vkcfy4/6">original repo</a> for more details).</li> </ul> <p>Datasets are published in JSONL format, where each line is a string formatted as a JSON, like in the example below.</p> <pre><code>{ "text": <article text>, "labels": [<label1>, <label2>, ...] }</code></pre> <p>The <em>hierarchical structure</em> of labels in each dataset is documented in <a href="https://gitlab.com/distration/dsi-nlp-publib/-/tree/main/htc-survey-24/data/taxonomies">this repository</a>.</p> <p> </p> <p>These datasets have been presented in this paper:</p> <ul> <li>"Hierarchical Text Classification and its Foundations: a Review of Current Research" - DOI: <a href="https://doi.org/10.3390/electronics13071199">10.3390/electronics13071199</a></li> </ul> <p>Some of these datasets have also been used in:</p> <ul> <li>"Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios" - DOI: <a href="https://doi.org/10.1016/j.eswa.2023.119984">10.1016/j.eswa.2023.119984</a></li> <li>"A multi-level approach for hierarchical Ticket Classification", accepted at WNUT 2022 - <a href="https://aclanthology.org/2022.wnut-1.22/">link</a></li> </ul> <p> </p> <p>These datasets are partially derived from previous work, namely:</p> <ul> <li>[Amazon] J. Ni, J. Li, J. McAuley, "Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects", EMNLP 2019, doi: <a href="http://dx.doi.org/10.18653/v1/D19-1018">10.18653/v1/D19-1018</a></li> <li>[WOS] K. Kowsari, D. E. Brown, M. Heidarysafa, K. Jafari Meimandi, M. S. Gerber and L. E. Barnes, "HDLTex: Hierarchical Deep Learning for Text Classification," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), 2017, pp. 364-371, doi: <a href="http://doi.org/10.1109/ICMLA.2017.0-134">10.1109/ICMLA.2017.0-134</a></li> <li>[Linux Bugs] V. Lyubinets, T. Boiko and D. Nicholas, "Automated Labeling of Bugs and Tickets Using Attention-Based Mechanisms in Recurrent Neural Networks," <em>2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP)</em>, 2018, pp. 271-275, doi: <a href="http://doi.org/10.1109/DSMP.2018.8478511">10.1109/DSMP.2018.8478511</a></li> </ul>
Code and data from: A hierarchical approach for estimating state-specific mortality and state transition in dispersing animals with incomplete death records
<p>Unbiased mortality estimates are fundamental for testing ecological and evolutionary theory as well as for developing effective conservation actions. However, mortality estimates are often confounded by dispersal, especially in studies where dead-recovery is not possible. In such instances, missing individuals (i.e. individuals with unobserved time of death) may have died or permanently emigrated from a study area, making inferences about their fate difficult. Mortality before and during dispersal, as well as the decision to disperse, usually depend on a suite of individual, social, and environmental covariates, which in turn can be used to draw conclusions about the fate of missing individuals.<br>Here, we propose a Bayesian hierarchical model that takes into account time-varying covariates to estimate transitions between life-history states and mortality in each state using mark-resighting data with missing individuals. Specifically, our framework estimates mortality rates in two states (resident and dispersing state) by treating the fate of missing individuals as a latent (i.e. unobserved) variable that is statistically inferred based on information from individuals with a known fate and given the individual, social, and environmental conditions at the time of disappearance. Our model also estimates rates of state transition (i.e. emigration) to assess whether a missing individual was more likely to have died or survived due to unobserved emigration from the study area. <br>We used simulations to check the validity of our model and assessed its performance with data of varying degrees of uncertainty. Our modeling framework provided accurate mortality and emigration estimates for simulated data of different sample sizes, proportions of missing individuals, and resighting intervals. Variation in sample size appeared to affect the precision of estimated parameters the most.<br>Our approach offers a solution to estimating unbiased mortality of both resident and dispersing individuals as well as the probability of emigration using mark-resighting data with incomplete death records. Conditional on the availability of data on known-fate individuals and relevant time-varying covariates, our model can reconstruct the fate (death or emigration) of missing individuals. The modularity of our framework allows mortality analyses to be tailored to a variety of species-specific life histories.</p>
Dataset and codes of the article "Neural correlates of hierarchical predictive processes in autistic adults"
<p>Data and code related to the article "Neural correlates of hierarchical predictive processes in autistic adults" by Laurie-Anne Sapey-Triomphe, Lauren Pattyn, Veith Weilnhammer, Philipp Sterzer and Johan Wagemans (Nature Communications):</p> <p>- Behavioral dataset of the 26 neurotypical participants (NT_behavioral_data.zip) and of the 26 autistic participants (ASD_behavioral_data.zip)</p> <p>- Source data of the graphics appearing in the article (Source data.xls)</p> <p>- Matlab codes used to run the experiment (Codes_to_run_experiment.zip)</p> <p>- Matlab codes to perform the main behavioral analyses (Codes_behavioral_analyses.zip) and to analyze the behavioral data with the HGF models (Codes_comput_model_analyses.zip)</p> <p>- Matlab codes to preprocess (Codes_fMRI_preprocessing.zip) and run the main fMRI analyses (Codes_fMRI_analyses.zip)</p>
Hierarchical universal matrices for H(curl) tetrahedral finite elements
<p>The contents of this repository are associated with the paper:</p> <p>[1] L. L. Toth, A. Amor-Martin, and R. Dyczij-Edlinger, "Hierarchical Universal Matrices for Curvilinear Tetrahedral H(curl) Finite Elements," submitted to IEEE Transactions on Antennas and Propagation.</p> <p>The subdirectory UniversalMatrices contains mathematical formulas for hierarchical L2 and H(curl) basis functions, the corresponding universal matrices (UM), and a MATLAB script for generating these UMs. The bases and UMs are given in two different formats:</p> <p>*.mat MATLAB data file with a MATLAB structure,</p> <p>*.xml file with a structure.</p> <p>The subdirectory MATLAB_TestCode contains MATLAB scripts and input data for reproducing the numerical results given in [1].</p> <p>For details, see the README.txt files in the bottom-level directories.</p>
Experimental and Simulation Data for "Hierarchical structure formation by crystal growth-front instabilities during ice templating" (2023) PNAS
<pre>Experimental and Simulation Data for: "Hierarchical structure formation by crystal growth-front instabilities during ice templating" by Kaiyang Yin, Kaihua Ji, Louise Strutzenberg Littles, Rohit Trivedi, Alain Karma, Ulrike G.K. Wegst (2023) PNAS, DOI: 10.1073/pnas.2210242120. </pre>
Numerical study of the one-dimensional Holstein model using the momentum-space hierarchical equations of motion method
<p>Data on the finite-temperature current-current correlation function of the one-dimensional Holstein model. Data are obtained using the newly developed momentum-space hierarchical equations of motion (HEOM) method. Details on the method development, as well as on the model parameters, will be given as a supplementary material to a journal publication that will be deposited on arXiv. Folders Regime* contain temporal evolution of the current-current correlation function (j_j_real_time.txt), diffusion constant (diffusion_constant.txt), diffusion exponent (diffusion_exponent.txt), and the electron's spread (delta_x.txt). They also contain frequency profiles of the Fourier transformed current-current correlation function (j_j_real_frequency.txt) and dynamical mobility (dynamical_mobility.txt).</p>
Illuminating the Hierarchical Segmentation of Faults through an Unsupervised Learning Approach applied to clouds of earthquake hypocenters [Dataset]
<p>Data repository to the preprint “Illuminating the Hierarchical Segmentation of Faults through an Unsupersived Larning Approach applied to clouds of earthquake hypocenters” by Piegari et al. (2023), including the datasets for the three analyzed earthquake catalogs.</p>
Hierarchical heuristic species delimitation under the multispecies coalescent model with migration
<p>The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively, heuristic criteria based on population parameters under the MSC model (such as population/species divergence times, population sizes, and migration rates) estimated from genomic sequence data may be used to delimit species. Here we extend the approach of species delimitation using the genealogical divergence index (𝑔𝑑𝑖) to develop hierarchical merge and split algorithms for heuristic species delimitation and implement them in a python pipeline called hhsd. Applied to data simulated under a model of isolation by distance, the approach was able to recover the correct species delimitation, whereas model comparison by bpp failed. Analyses of empirical datasets suggest that the procedure may be less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the procedure, as well as the challenges of species delimitation based on heuristic criteria.</p>
Hierarchical assemblies ssRNA
<p>Models of single-stranded RNA with atomic detail generated with the hierarchical chain growth; RNA fragment MD library the polymers were grown from.</p>
ScienceDex guides
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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.