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695 results for “topologies”
Weighted Link Schedules in sub-100 node Random Topology Wireless Networks
<p>This is a data repo for the data samples used for learning the link scheduling in a randomly placed networks. This data set contains samples for sub 100-node networks, and the scheduling decisions are made from delayed column generation (DCG) algorithm.</p>
Weighted Link Schedules in sub-100 node Fixed Topology Wireless Networks
<p>This data repo contains the link schedules in a multi-hop wireless networks that aims to maximize the weighted throughput. Each instance is represented by a numpy data file that contains the necessary data fields to reconstruct the original problem instance.</p> <p> </p> <p>Note: 100_7_new.tar.xz should be in the repo https://zenodo.org/deposit/7671940.</p>
Topological characterization of the retinal microvascular network visualized by portable fundus camera- effects of chronic disease (TREND2) database
<p><strong>Introduction</strong></p> <p><strong>T</strong>opological characterization of the <strong>R</strong>etinal microvascular n<strong>E</strong>twork visualized by portable fu<strong>ND</strong>us camera (<strong>TREND 2</strong>) is a database of digital color eye fundus images created as an addition to TREND database (https://zenodo.org/badge/DOI/10.5281/zenodo.4521044.svg).</p> <p>TREND 2 databse was created by medical professionals of the Faculty of Medicine of the University of Montenegro in 2023.</p> <p> </p> <p><strong>Purpose</strong></p> <p>1) to provide a standard that defines normal and abnormal retinal anatomy and microvascular geometry as it appears when visualized by the portable fundus camera</p> <p>2) to help the development of new methods for stratification of the risk for the development of various eye diseases, as well as systemic diseases that affect microvasculature</p> <p>3) to aid the development of biomarkers of accelerated aging</p> <p>4) to provide a standard that can be used to develop software for segmentation of retinal microvasculature, grading the quality of retinal digital images, and computer-aided diagnosis of systemic and chronic diseases.</p> <p>All color digital images were acquired with a hand-held portable, non-mydriatic MiiS HORUS Scope DEC 200 with 45º FOV and 2560 X 1920 pixel resolution.</p> <p> </p> <p><strong>Data</strong></p> <p>The TREND public database contains 28 color fundus images of old subjects (20 images from subjects with one or more chronic diseases such as type 2 diabetes mellitus, hypertension or Alzheimer's dementia- O_CD group, and 8 images from subjects with no chronic diseases- O_NCD group). Each image is associated with a corresponding binarized image of the manually segmented microvascular network.</p> <table> <caption>Inclusion and Exclusion Criteria</caption> <thead> <tr> <th scope="col">O_NCD group</th> <th scope="col">O_CD group</th> </tr> </thead> <tbody> <tr> <td><strong>Inclusion Criteria</strong></td> <td><strong>Inclusion Criteria</strong></td> </tr> <tr> <td>- at least 56 years old</td> <td>- at least 56 years old</td> </tr> <tr> <td> <p>- no current acute disease</p> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> <td> <p>- no current acute disease</p> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> </tr> <tr> <td> <p>- no history of alcohol, or drug abuse, or psychiatric disease</p> </td> <td>- no history of alcohol, or drug abuse, or psychiatric disease</td> </tr> <tr> <td>- negative history of any chronic disease</td> <td> <p>- controlled hypertension (blood pressure<140/90 mmHg), and/or</p> <p>- controlled type 2 diabetes mellitus, and/or</p> <p>- Alzheimer's dementia</p> </td> </tr> <tr> <td><strong>Exclusion Criteria</strong></td> <td><strong>Exclusion Criteria</strong></td> </tr> <tr> <td> <p>- presence of opacities of the transparent media in both eyes affecting</p> <p>- myopia ≥5 diopters</p> </td> <td> <p>- presence of opacities of the transparent media in both eyes affecting</p> <p>- myopia ≥5 diopters</p> </td> </tr> </tbody> </table> <p><strong>Files:</strong></p> <p>1_OLD WITH CHRONIC DISEASE_RAW (20 images in tif format)</p> <p>2_OLD WITH CHRONIC DISEASE_SEGMENTED (20 images in png format)</p> <p>3_OLD WITH NO CHRONIC DISEASE_RAW (8 images in tif format)</p> <p>4_OLD WITH NO CHRONIC DISEASE SEGMENTED (8 images in png format)</p> <p>5_ASSOCIATED DATA (xslx format)</p> <p>6_RETINAL PATHOLOGY (docx format)</p> <p> </p> <p> </p>
Electron beam damage to Topological Insulators
<p>Transport data, point spread functions, and analysis code accompanying "Low-damage electron beam lithography for nanostructures on Bi<sub>2</sub>Te<sub>3</sub>-class topological insulator thin films."</p>
Topology optimization dataset
<p>Dataset of topology optimization problems in 50x140 2D meshes. It was used to train machine learning models to accelerate topology optimization. Each sample contains:</p> <ol> <li>Binary definition of supports</li> <li>Sparse definitions of position and components of two point loads</li> <li>Volume fraction</li> <li>Von Mises and strain energy density fields of initial FEA solution</li> <li>Final topology</li> </ol> <p>Other than the dataset, contains files related to data filtering, and checkpoints of the trained generative adversarial networks.</p>
Data and Code for "Topological atom-optics and beyond with knotted quantum wave functions"
<p>This folder contains data files and Mathematica 12 Student Edition files for processing the data files and generating figures for the paper “<em>Topological atom optics and beyond with knotted quantum wavefunctions</em>”, authored by M. Jayaseelan, J. D. Murphree, J. T. Schultz, J. Ruostekoski, and N. P. Bigelow.</p> <p> </p> <ol> <li>Folder “Data_Only” contains *.csv and *.SPE files for each of the following magnetic phases: <ul> <li> <ol> <li>Polar</li> <li>Cyclic</li> <li>Biaxial Nematic</li> </ol> </li> </ul> </li> <li>Folder Fig2_Polar_code contains <ul> <li> <ol> <li>Data for the Polar magnetic phase (duplicated from Data_Only folder): etau.SPE and e.csv</li> <li>e_imGData, e_imGDataC, e_imLGData, e_imLGDataC: *.csv files that are output as intermediate data processing steps.</li> <li>Fig2_KnotsAtomsPolar_v2.nb: Mathematica file that produces the figures for Fig. 2</li> </ol> </li> </ul> </li> <li>Folder Fig3_Cyclic_code contains <ul> <li> <ol> <li>Data for the Cyclic magnetic phase (duplicated from Data_Only folder): lor_atau.SPE and lor_a_tau.csv</li> <li>lor_a_imGData, lor_a_imG0Data, lor_a_imLGData: *.csv files that are output as intermediate data processing steps.</li> <li>Fig3_KnotsAtomsCyclic_v2.nb: Mathematica file that produces the figures for Fig. 3</li> </ol> </li> </ul> </li> <li>Folder Fig4_Cyclic_code contains <ul> <li> <ol> <li>Fig4_KnotsAtomsCyclic_v2.nb: Mathematica file that produces the figures for Fig. 4</li> </ol> </li> </ul> </li> <li>Folder Fig5_BN_code contains <ul> <li> <ol> <li>Data for the BN magnetic phase (duplicated from Data_Only folder): sk_ltau.SPE and sk_l.csv</li> <li>sk_l_imGData, sk_l_imLGData: *.csv files that are output as intermediate data processing steps.</li> <li>Fig5_KnotsAtomsBN_v2.nb: Mathematica file that produces the figures for Fig. 5</li> </ol> </li> </ul> </li> <li>Folder Fig6_Fig7_BN_code contains <ul> <li> <ol> <li>Fig6_Fig7_KnotsAtomsBN_v2.nb: Mathematica file that produces the figures for Fig. 6 and Fig.7</li> </ol> </li> </ul> </li> </ol>
In-plane growth of topological crystalline insulator Pb1−xSnxTe nanowires
<p>data of '<em>In-plane growth of topological crystalline insulator Pb1−x</em><em>Sn</em><em>x</em><em>Te nanowires'</em></p>
Experimental evidence that network topology can accelerate the spread of beneficial mutations
<p>Whether and how the spatial arrangement of a population influences adaptive evolution has puzzled evolutionary biologists. Theoretical models make conflicting predictions about the probability a beneficial mutation will become fixed in a population for certain topologies like stars, in which "leaf" populations are connected through a central "hub." To date, these predictions have not been evaluated under realistic experimental conditions. Here, we test the prediction that topology can change the dynamics of fixation both in vitro and in silico by tracking the frequency of a beneficial mutant under positive selection as it spreads through networks of different topologies. Our results provide empirical support that metapopulation topology can increase the likelihood that a beneficial mutation spreads, broadens the conditions under which this phenomenon is thought to occur, and points the way towards using network topology to amplify the effects of weakly favored mutations under directed evolution in industrial applications. </p>
Topology of phenomenological experience: leveraging LLMs and feature similarity
<p>08/28/2023 Presented at the Monash Neuroscience of Consciousness (MoNoC) lab</p> <p>Title: Topology of phenomenological experience: leveraging LLMs and feature similarity</p> <p>https://www.youtube.com/watch?v=Xj5Ww3tCfY4</p> <p>See interactive graphs here: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqblRSdVlPeEtOdnhWMVJsaUJCOUJ3cDdMNloyQXxBQ3Jtc0tsSUc4cHJUZ1E2Y19ndmU5Y3VRSFg1Ny1RV3JnQUhUa1Q0QWcwOVFBcmV2R0FFeVdwZ1ZjQ3VlN0tXVVV6OXYyd3JCZDBPRlBOc1VTUk9ib1pNM043S1l3UFgzOHFyalRkX01kNjk1U0d3ZUZpTkVmOA&q=https%3A%2F%2Fyoungzielee.github.io%2F&v=Xj5Ww3tCfY4">https://youngzielee.github.io/</a></p>
Quantized Thermal Hall Conductance and the Topological Phase Diagram of a Superconducting Bismuth Bilayer
<p>Data for the topological phase diagrams and thermal Hall effect for the paper "Quantized Thermal Hall Conductance and the Topological Phase Diagram of a Superconducting Bismuth Bilayer". The thermal Hall conductance data is normalised as in figure 4 of the paper.</p> <p>Parameters in the Chern number file names are {t,Delta,Alpha, tp}. For the thermal conductance data as. function of temperature the Chern number is given. All over information can be found in the paper.</p> <p><a href="https://doi.org/10.48550/arXiv.2308.01021">arXiv:2308.01021</a></p> <p> </p>
Data sets for "Dynamical bulk-boundary correspondence and dynamical quantum phase transitions in higher-order topological insulators"
<p>Data sets for the return rates and Loschmidt eigenvalues for the paper "Dynamical bulk-boundary correspondence and dynamical quantum phase transitions in higher-order topological insulators". Included are data for both bulk calculations and finite open systems labelled OBC. Data files are named with the quench parameters, see the article for details.</p>
Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials
<p><strong>Our paper has been published on Phys. Rev. Res. (doi: 10.1103/PhysRevResearch.6.013259)</strong></p> <p><strong>Our preprint paper is also avilable at arXiv(https://arxiv.org/abs/2310.06406), here is the abstract of our paper:</strong></p> <p>Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed Machine Eye for Defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, Segment Anything Model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of ±1/2 defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay–Berne particles. MED performs faster than conventional defect detection method and can achieve over 90% accuracy on recognizing ±1/2 defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding number. Remarkably, MED can provide correct structural information about ±1 defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.</p> <p> </p> <p><strong>Repository Organization</strong></p> <p><strong>Trained Models.zip</strong></p> <p>This directory is integral for model deployment and houses all relevant pre-trained models.</p> <ul> <li><strong>plus_vit_vecUV.pt</strong>: Pre-trained model for the Plus Transformer variant.</li> <li><strong>minus_vit_theR.pt</strong>: Pre-trained model for the Minus Transformer variant.</li> <li><strong>nanodet-plus-m_416-halfenhance</strong>: A sub-directory containing all files associated with the trained Nanodet-Plus model.</li> <li><strong>configs</strong>: Configuration files for training procedures.</li> </ul> <p><strong>Training Data.zip</strong></p> <p>This directory contains all datasets used for the training of Nanodet-Plus, Plus Transformer, and Minus Transformer models.</p> <p><strong>Code.zip</strong></p> <p>This directory features the implementation details and example use-cases showcased in Figure 2 and Figure 3c of our associated paper. The directory also includes code corresponding to the specific versions of Nanodet-Plus and SAM models cited in our study.</p> <ul> <li><strong>nanodet</strong>: Code in this folder is adapted from <a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet). We have included the exact version used for compatibility.</li> <li><strong>segment_anything</strong>: Code sourced from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything). The specific version used is included for compatibility.</li> <li><strong>Fig2</strong>: Code for predicting topological defects in tissue cell images, citing the following reference: T. B. Saw et al., Nature 544, 212 (2017).</li> <li><strong>Fig3c</strong>: Code for predicting topological defects in microtubules images, citing the following reference: M. Golden et al., Sci. Adv. 9, eabq6120 (2023).</li> </ul> <p><strong>Initialization Steps</strong></p> <p>Before executing any code, please ensure the following:</p> <ul> <li>All files in the <strong>Trained Models</strong> directory must be available.</li> <li>Download the checkpoint <strong>sam_vit_l_0b3195.pth</strong> from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a>. (https://github.com/facebookresearch/segment-anything)</li> </ul> <p><strong>Acknowledgments</strong></p> <ul> <li><a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet)</li> <li><a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything)</li> </ul> <p>For further inquiries or issue reporting, you may contact us via email.</p> <p><strong>Contact Information</strong>: <a href="mailto:hrenae@connect.ust.hk">hrenae@connect.ust.hk</a></p>
Data for: Hidden non-collinear spin-order induced topological surface states
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Data from: Phase transition of topological index driven by dephasing
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Experimental evidence that network topology can accelerate the spread of beneficial mutations
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Topology testing and demographic modeling illuminate a novel speciation pathway in the Greater Caribbean Sea following the formation of the Isthmus of Panama
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Data from: Phylogenomics and topological conflicts in the tribe Anthospermeae (Rubiaceae)
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Data from: Fullerene-like structures of Cretaceous crinoids reveal topologically limited skeletal possibilities
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Data and figures from: Topological nano-rainbow laser
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Data from: Terahertz sensing based on the nonlinear electrodynamics of the two-dimensional correlated topological semimetal TaIrTe4
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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.