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13 results for “laplacian”
Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks"
<p>Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks". </p> <p>There are two CSV files: one for each of the two iterated local search confgurations used to construct the networks (low or high). In each file, a row contains information about one QAPLIB instance. Easch row contains all the metrics computed for the associated LON and also algorithm performance data on the instance. </p>
Data for "Laplacian-level meta-GGA for the weakly-nonlocal solid and liquid metals"
<p>This dataset contains all VASP inputs and outputs for the paper "Improved Laplacian-level meta-GGA for the weakly-nonlocal solid and liquid metals." For the preprint, see <a href="https://arxiv.org/abs/2203.09403">arXiv:2203.09403</a>, and for the reference densities, fitting routines, and analysis scripts, see the <a href="https://gitlab.com/dhamil/laplacian-level-meta-gga">Gitlab code repository</a>.</p> <p>Description of individual tarballs:</p> <ul> <li>AE6: 6-molecule set of atomization energies</li> <li>ferro: relaxed geometries and magnetic moments for the ferromagnetic solids Fe, Ni, and Co</li> <li>intermetallics: formation energies of three intermetallic solids, HfOs, ScPt, and VPt<sub>2</sub></li> <li>LC20: relaxed geometries and equilibrium bulk moduli for the LC20 set of cubic solids. Equilibrium geometries by equation of state fit. Bandgaps for select insulators are included here.</li> <li>LC20_stress_tensor: same as LC20, but equilibrium geometries found by minimizing forces on unit cell computed with Laplacian-dependent stress tensor</li> <li>LC23: equilibrium geometries, bulk moduli, and cohesive energies for the LC23 set (LC20 + K, Rb, and Cs) found by equation of state fit. Bandgaps for select insulators are included here.</li> <li>Pt_monovac: monovacancy formation energies for Pt, computed in a few different ways described in the text</li> </ul>
Graph Laplacians used in the article "Numerically Efficient $H_{\infty}$ Analysis of Cooperative Multi-Agent Systems"
<p>The repository contains four graph Laplacians written in the Matlab format. These matrices are used in the section "Numerical Examples" in the article "Numerically Efficient $H_{\infty}$ Analysis of Cooperative Multi-Agent Systems".</p>
TensorBoard runs for reinforcement learning on automated conjectured bounds on Laplacian spectral radius of graphs
<p>The zip archive contains separate folders with TensorBoard event files for the runs of our reinforcement learning implementation on the conjectured upper bounds for the Laplacian spectral radius, which are listed in Appendix B of the forthcoming paper: S. Al-Yakoob, M. Ghebleh, A. Kanso, D. Stevanović, Reinforcement learning for graph theory, I. Reimplementation of Wagner’s approach, Art Discrete Appl. Math. (2024).</p> <p>To view the contained graphs and the evolution of rewards, unzip the archive in a folder of your choice, and run in the terminal the command "tensorboard --logdir runs" from the parent folder of the unzipped "runs" folder.</p>
Data from: Automatic Laplacian-based shape optimization for patient-specific vascular grafts
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Enhancing Facial Emotion Recognition: A Comparative Analysis of Sobel and Laplacian Filters for Computer Vision Applications
<p><span>This project explores the efficacy of integrating Sobel and Laplacian filters to enhance the performance of Convolutional Neural Network (CNN) models for facial emotion recognition. The project was part of our final Mtech in Data Science thesis at the Institute of Defence Institute of Advanced Technology, Pune. The FER2013 dataset was utilized for the research.</span></p>
PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian
<p>Datasets that PHLOWER used:</p> <ol> <li>benchmarking data</li> <li>multiome kidney organoid data</li> <li>xenium kidney orgnoid data</li> </ol>
Data from: Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum
The dissection of the mode and tempo of phenotypic evolution is integral to our understanding of global biodiversity. Our ability to infer patterns of phenotypes across phylogenetic clades is essential to how we infer the macroevolutionary processes governing those patterns. Many methods are already available for fitting models of phenotypic evolution to data. However, there is currently no comprehensive non-parametric framework for characterising and comparing patterns of phenotypic evolution. Here we build on a recently introduced approach for using the phylogenetic spectral density profile to compare and characterize patterns of phylogenetic diversification, in order to provide a framework for non-parametric analysis of phylogenetic trait data. We show how to construct the spectral density profile of trait data on a phylogenetic tree from the normalized graph Laplacian. We demonstrate on simulated data the utility of the spectral density profile to successfully cluster phylogenetic trait data into meaningful groups and to characterise the phenotypic patterning within those groups. We furthermore demonstrate how the spectral density profile is a powerful tool for visualising phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical datasets: a comprehensive dataset of traits involved in song, plumage and resource-use in tanagers, and a high-dimensional dataset of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework.
Data from: Characterizing and comparing phylogenies from their Laplacian spectrum
Phylogenetic trees are central to many areas of biology, ranging from population genetics and epidemiology to microbiology, ecology, and macroevolution. The ability to summarize properties of trees, compare different trees, and identify distinct modes of division within trees is essential to all these research areas. But despite wide-ranging applications, there currently exists no common, comprehensive framework for such analyses. Here we present a graph-theoretical approach that provides such a framework. We show how to construct the spectral density profile of a phylogenetic tree from its Laplacian graph. Using ultrametric simulated trees as well as non-ultrametric empirical trees, we demonstrate that the spectral density successfully identifies various properties of the trees and clusters them into meaningful groups. Finally, we illustrate how the eigengap can identify modes of division within a given tree. As phylogenetic data continue to accumulate and to be integrated into various areas of the life sciences, we expect that this spectral graph-theoretical framework to phylogenetics will have powerful and long-lasting applications.
Data from: Characterizing and comparing phylogenies from their Laplacian spectrum
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Data from: Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum
Open the record for dataset details and reuse information.
PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian [Xenium]
GEO Series GSE302264. Homo sapiens. 4 samples. Type: Other.
PHLOWER - Single cell trajectory analysis using Decomposition of the Hodge Laplacian [multiome]
GEO Series GSE302266. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
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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.