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136 results for “Diffusion models”
Wave-Cascade Nested Model: The Urban-rural Diffusion Model of Suzhou(Essays on Linguistics 2025(1))
<p>This is the high-resolution images (Figure 3) from published paper entitled Wave-Cascade Nested Model: The Urban-rural Diffusion Model of Suzhou (Essays on Linguistics 2025(1)) (in Chinese 波浪-级联嵌套模型”:苏州城乡方言的扩散模式 (《语言学论丛》2025年第1期)) . </p> <p>Given the journal doesn't provide color printing or needs to compress images due to size limitations, it offers readers access to the high-quality and full-color image.</p> <p>If you have any question, please contact me without any hesitation! Thank you very much!</p>
Conditional Neural Field Latent Diffusion Model for Generating Spatiotemporal Turbulence
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Dataset for 'A microscopic diffusion-induced discrete element model for swellable particles'
<p>This dataset contains both numerical simulation data and experimental data describing the swelling behaviour of binary particle systems. Detailed study on the swelling behaviour is presented in a journal paper entitled "A microscopic diffusion-induced discrete element model for swellable particles ". We provide all the essential data in a single excel file with the description below:</p> <p>Dataset 1-Evolution of the radius of core zone obtained from experiments (used in Fig.7c of the associated paper): Experimental data for the measured radius of core zone and associated standard deviation at various time.</p> <p>Dataset 2-Calibration procedures for the microscopic swelling model from DEM simulation (used in Fig8 of the associated paper).</p> <p>Dataset 3 - Evolutions of the normalised average diameter d/d_0 (used in Fig.9) obtained from experiments and DEM simulations.</p> <p>Dataset 4 - Evolutions of normalised water concentration (used in Fig.10a) and Young’s modulus (used in Fig.10b) of the swollen particle obtained with different swelling models.</p> <p>Dataset 5 - Evolution of core zone obtained from DEM simulations with the microscopic swelling model (used in Fig.10b).</p> <p>Dataset 6 - Evolution of the height of the particle bed obtained from experiments and simulations (used in Fig.13 of the associated paper): Experimental data for the measured hight and associated standard deviation at various time, as well as the numerical data.</p> <p>Dataset 7 -Evolutions of (a) The ratio of average normal contact force to the initial gravity of the dry particle (used in Fig.14a of the associated paper) and (b) the ratio of average normal contact force to the instantaneous gravity(used in Fig.14b of the associated paper) obtained from DEM simulations with the microscopic swelling model.</p> <p>Dataset 8 - Evolution of the dimensionless height of the bilayer particle bed and randomly mixed particle bed obtained from experiments and simulations (used in Fig.17 of the associated paper): Experimental data for the measured hight and associated standard deviation at various time, as well as the numerical data.</p> <p>Dataset 9 - Evolution of the dimensionless height of the pure SAP particle bed obtained from simulations with different water concentration in the liquid (used in Fig.18 of the associated paper):</p> <p>Dataset 10 -Evolution of dimensionless heights of SAP particle bed and (b) the dimensionless diameter of the particle obtained from DEM simulations during shrinkage with different water concentrations in the liquid (used in Fig.19 of the associated paper).</p> <p>Dataset 11 -Comparison of the dimensionless height of SAP particle bed, dimensionless diameter and the standard deviation of dimensionless diameter obtained from DEM simulations with different values of diffusivity for particle-particle diffusion (used in Fig.20 of the associated paper).</p>
Ex vivo 100 μm isotropic diffusion MRI‐based tractography of connectivity changes in the end‐stage R6/2 mouse model of Huntington's disease
<div> <div> <div> <div> <p><strong>Background</strong>: Huntington's disease is a progressive neurodegenerative disorder. Brain atrophy, as measured by volumetric magnetic resonance imaging (MRI), is a downstream consequence of neurodegeneration, but microstructural changes within brain tissue are expected to precede this volumetric decline. The tissue microstructure can be assayed non-invasively using diffusion MRI, which also allows a tractographic analysis of brain connectivity.</p> <p><strong>Methods</strong>: We here used ex vivo diffusion MRI (11.7T) to measure microstructural changes in different brain regions of end‐stage (14 weeks of age) wild type and R6/2 mice (male and female) modeling Huntington's disease. To probe the microstructure of different brain regions, reduce partial volume effects and measure connectivity between different regions, a 100 μm isotropic voxel resolution was acquired.</p> <p><strong>Results</strong>: Although fractional anisotropy did not reveal any difference between wild‐type controls and R6/2 mice, mean, axial, and radial diffusivity were increased in female R6/2 mice and decreased in male R6/2 mice. Whole brain streamlines were only reduced in male R6/2 mice, but streamline density was increased. Region‐to‐region tractography indicated reductions in connectivity between the cortex, hippocampus, and thalamus with the striatum, as well as within the basal ganglia (striatum—globus pallidus—subthalamic nucleus—substantia nigra—thalamus).</p> <p><strong>Conclusions</strong>: Biological sex and left/right hemisphere affected tractographic results, potentially reflecting different stages of disease progression. This proof‐of‐principle study indicates that diffusion MRI and tractography potentially provide novel biomarkers that connect volumetric changes across different brain regions. In a translation setting, these measurements constitute a novel tool to assess the therapeutic impact of interventions such as neuroprotective agents in transgenic models, as well as patients with Huntington's disease.</p> </div> </div> </div> </div>
The role of chloroplast movement in C4 photosynthesis: A theoretical analysis using a 3-D reaction-diffusion model for maize
<p>Chloroplast movement within mesophyll (M) cells in C<sub>4</sub> plants is hypothesized to enhance the CO<sub>2</sub> concentrating mechanism (CCM), but this is difficult to verify experimentally. A three-dimensional (3-D) leaf model can help analyze how chloroplast movement influences the operation of CCM. The first volumetric reaction-diffusion model of C<sub>4</sub> photosynthesis that incorporates: detailed 3-D leaf anatomy, light propagation, ATP and NADPH production and CO<sub>2</sub>, O<sub>2</sub> and bicarbonate concentration driven by diffusional and assimilation/emission processes, was developed and implemented for maize leaves to simulate various chloroplast movement scenarios within M cells: the movement of all M chloroplasts towards bundle-sheath (BS) cells (aggregative movement) and movement of only those of interveinal M cells towards BS cells (avoidance movement). Light absorbed by bundle-sheath (BS) chloroplasts relative to M chloroplasts increased in both cases. Avoidance movement decreased light absorption by M chloroplasts considerably. Consequently, total ATP and NADPH production and net photosynthesis rate increased for aggregative movement and decreased for avoidance movement case compared to the default case of no chloroplast movement at high light intensities. Leakiness increased in both chloroplast movement scenarios due to the imbalance in energy production and demand in M and BS cells. These results suggest the need to design strategies for coordinated increases in electron transport and Rubisco activities for an efficient CCM at very high light intensities.</p>
Data set related to the manuscript "Investigating particle size effects on NMR spectra of ions diffusing in porous carbons through a mesoscopic model"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Investigating particle size effects on NMR spectra of ions diffusing in porous carbons through a mesoscopic model". Examples of input files for the lattice simulations are also provided.</p>
Dataset for "DEM-CFD analysis of swelling behaviours of binary particle systems with a microscopic diffusion model"
<p>This dataset contains both numerical simulation data and experimental data describing the swelling behaviour of binary particle systems. Detailed study on the swelling behaviour is presented in a journal paper entitled "DEM-CFD analysis of swelling behaviours of binary particle systems with a microscopic diffusion mode". We provide all the essential data in a single excel file with the description below:</p> <p>Dataset 1- Evolution of the height of the particle bed obtained from experiments and simulations (used in Fig.6 of the associated paper): Experimental data for the measured hight and associated standard deviation at various time, as well as the numerical data.</p> <p>Dataset 2 - Evolutions of the normalised average radius r/r_0 (used in Fig.7a) and the coefficient of variation CV (used in Fig.7b) for SAP particles obtained from DEM-CFD simulations.</p> <p>Dataset 3 - Radial distribution Functions (RDFs) for (Fig.9a) SAP particles and (Fig.9b) POM particles at different time instants.</p> <p>Dataset 4 - Positions of POM particles projected into the x-y plane at different time instants (the red dots are particle centres), (a) t = 0 min; (b) t = 3 min; (c) t = 6 min; (d) t = 10 min.</p> <p>Dataset 5 - Evolution of Lacey mixing index for the binary particles during the swelling process.</p> <p>Dataset 6 - Average normal stress distribution of particles in x-y and x-z planes at t = 2 min.</p> <p>DataSet 7 - Velocity vectors of particles projected in x-z planes at different time instants: (a) t = 1 min; (b) t= 2 min (Data for Fig.13)</p> <p>Dataset 8 - Positions of POM particles projected into x-y plane at time t= 10 min (the red dots are particle centres) with different coefficient of frictions (a) μ=0.1; (b) μ=0.2; (c) μ=0.3; (d) μ=0.4; (e) μ=0.5.(Data for Fig.15)</p> <p>Dataset 9 - RDFs for POM particles at time t= 10 min with different coefficients of friction.(Data for Fig.16)</p>
modeled and measured diffuse and diffuse fraction of PAR range
<p>Csv file with the diffuse fraction of PAR (modeled and measured), diffuse component of PAR (modeled and measured), and clearness index in the PAR and Total ranges.</p>
The role of chloroplast movement in C4 photosynthesis: A theoretical analysis using a 3-D reaction-diffusion model for maize
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Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models
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Ex vivo 100 μm isotropic diffusion MRI‐based tractography of connectivity changes in the end‐stage R6/2 mouse model of Huntington's disease
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A model of spatio-temporal regulation within biomaterials using DNA reaction–diffusion waveguides
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Data from: Sleep, inflammation, and cognitive behavior of aged wild-type mice subjected to diffuse brain injury and aged 3xTg-AD mice as a model of Alzheimer’s disease
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Extracted dataset for: "Experiment and modeling of concentration-dependent diffusion in a solution with dual transport-phase flow for paper-based sensing"
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Controllable velocity synthesis using generative diffusion models
<div> <p><span>We presented a controllable velocity synthesis framework using diffusion models, </span><span>yielding a high-diversity and high-quality velocity synthesis. Here</span> we present the relevant dataset to the paper. </p> </div>
Synthetic IMGUR5k dataset with latent diffusion models
<p>SyntheticHTR: Handwritten Text Image Synthesis based on Latent Diffusion Models</p>
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable Diffusion
<p>In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-ZSC), which refers to training non-specific classification architectures (downstream models) to classify real images without using any real images during training. Recent research has demonstrated that generating synthetic training images using diffusion models provides a potential solution to address MA-ZSC. However, the performance of this approach currently falls short of that achieved by large-scale vision-language models. One possible explanation is a potential significant domain gap between synthetic and real images. Our work offers a fresh perspective on the problem by providing initial insights that MA-ZSC performance can be improved by improving the diversity of images in the generated dataset. We propose a set of modifications to the text-to-image generation process using a pre-trained diffusion model to enhance diversity, which we refer to as our <strong>bag of tricks</strong>. Our approach shows notable improvements in various classification architectures, with results comparable to state-of-the-art models such as CLIP. To validate our approach, we conduct experiments on CIFAR10, CIFAR100, and EuroSAT, which is particularly difficult for zero-shot classification due to its satellite image domain. We evaluate our approach with five classification architectures, including ResNet and ViT. Our findings provide initial insights into the problem of MA-ZSC using diffusion models.</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data
<p>Data accompanying our paper on: <em>Evaluating Text-to-Image Diffusion Models for Texturing Synthetic Data</em></p> <p> </p> <p><a href="https://github.com/tlpss/diffusing-synthetic-data" target="_blank" rel="noopener">github repository</a></p> <p>meshes.zip contains the 3D meshes used to generate the synthetic data for all three object categories</p> <p>real-datasets.zip contains the real-world image datasets gathered to evaluate the synthetic data</p> <p> </p> <p> </p>
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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.