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2,194 results for “Diffusers”
Confined diffusion of nanoparticles inserted into a diblock polymer membrane captured by fluorescence microscopy
<p>The membrane average pore size was 1µm with a standard deviation of 0.25µm. The video was record by 50 FPS. For more information please see </p> <p>Haramagatti CR, Schacher FH, Müller AHE, Köhler J. Diblock copolymer<br> membranes investigated by single-particle tracking. Phys Chem Chem Phys.<br> 2011;13(6):2278–2284. doi:10.1039/c0cp01658f</p>
Isogeometric Analysis for the Multigroup Neutron Diffusion Equation with Applications in Reactor Physics
<p>Dataset for a comparison between Isogeometric Analysis and Finite Element Analysis applied to the Neutron Diffusion Equation.</p>
Hybrid Multilevel Solvers for Discontinuous Galerkin Finite Element Discrete Ordinate (DG-FEM-SN) Diffusion Synthetic Acceleration (DSA) of Radiation Transport Algorithms
<p>In accordance with EPSRC funding requirements this folder contains all raw data relevant to the named paper: </p> <p>Hybrid Multilevel Solvers for Discontinuous Galerkin Finite Element Discrete Ordinate (DG-FEM-SN) Diffusion Synthetic Acceleration (DSA) of Radiation Transport Algorithms</p> <p><br> Journal: Annals of Nuclear Energy</p>
Freely diffusing 100 nm polystyrene nanoparticles captured with nanoparticle tracking analysis
<p>100nm polystyrene NP (Kisker-Biotech, PPs-0.1) which were collected with a NanoSight<sup>TM</sup> LM10 system equipped with a LM14 green (535 nm) laser module and a cooled Andor camera (Andor-DL-658-OEM).</p> <p>FPS: 30</p> <p>Pixelsize: 166</p>
Extracted dataset for: "Experiment and modeling of concentration-dependent diffusion in a solution with dual transport-phase flow for paper-based sensing"
Open the record for dataset details and reuse information.
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>
Data associated to the manuscript "Enhanced diffusion of tracer particles in nonreciprocal mixtures"
<p>Contains an example of LAMMPS input file and data used to generate the figures of the article:</p><p><strong>Enhanced diffusion of tracer particles in nonreciprocal mixtures</strong></p><p>Anthony Benois, Marie Jardat, Vincent Dahirel, Vincent Démery, Jaime Agudo-Canalejo, Ramin Golestanian, and Pierre Illien</p><p>https://arxiv.org/abs/2307.05408</p><p>Article published in Physical Review E (2023).</p>
Synthetic IMGUR5k dataset with latent diffusion models
<p>SyntheticHTR: Handwritten Text Image Synthesis based on Latent Diffusion Models</p>
SCM files for "Representing Effects of Surface Heterogeneity in a Multi-Plume Eddy Diffusivity Mass Flux Boundary Layer Parameterization"
<p>Supporting files for manuscript "Representing Effects of Surface Heterogeneity in a Multi-Plume Eddy Diffusivity Mass Flux Boundary Layer Parameterization" by N.P. Arnold.</p> <p>The data consists of source code modifications relative to v11.2.0 of the GEOS GCM, modified boundary conditions for running single column model experiments, and netcdf model output from those experiments.</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>
Raw k-space data for free-breathing diffusion weighted sequence
Open the record for dataset details and reuse information.
Quantitative Study Group: Diffusion
<p>In this directory there are diffusion datasets recorded in magnitude mode with 4 b values using a Siemens Prisma 3T MRI scanner. Each image contains a phantom with 5 vials, each with a different SI traceable amount of PVP.</p> <p>The b values are in DICOM tag 0019,100C, and are oriented along 0019x100E. The files are as follows:</p> <p>0-10: b=0, slices.<br>11-40: b=500 s mm^-2, slices. 11-20: [1,0,0], 21-30: [0,0,-1], 31-40: [0,1,0]<br>41-70: b=1000 s mm^-2, slices. 11-20: [1,0,0], 21-30: [0,0,-1], 31-40: [0,1,0]<br>71-100: b=2000 s mm^-2, slices. 11-20: [1,0,0], 21-30: [0,0,-1], 31-40: [0,1,0]</p>
Sodium enrichment of Mercury's subsurface through diffusion
<p>The data contains the temporal evolution of the sodium density in Mercury's subsurface. The four files correspond to the the sodium density evolution at the cold and hot longitudes, and for two different surface binding energy distributions.</p> <h2>Description of the data and file structure</h2> <p>Each file is structured as follow :</p> <p>Line 1 : Thickness of each layer of regolith starting from the surface</p> <p>Line 2 : Porosity of each layer starting from the surface</p> <p>Each following 5 lines correspond to the evolution over 3000 seconds with :</p> <p>Line 3 + (5 n) : Time</p> <p>Line 4 + (5 n) : Mean surface binding energy</p> <p>Line 5 + (5 n) : Density of adsorbates per layer starting from the surface</p> <p>Line 6 + (5 n) : Density of gaseous sodium per layer starting from the surface</p> <p>Line 7 + (5 n) : Density of sodium (adsorbate and gas) per layer starting from the surface</p> <p>The figures in the letter use only the density of adsorbates</p> <h2>Sharing/Access information</h2> <p>The data and the software can also be asked directly to the author of the letter.</p> <h2>Code/Software</h2> <p>The software is detailed in the letter.</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>
Data deposit for those used in the figures of publication, named 'Effect of iron content on the thermal conductivity and thermal diffusivity of orthopyroxene', in G3
<p>Data deposit for those used in the figures of publication, named 'Effect of iron content on the thermal conductivity and thermal diffusivity of orthopyroxene', in G3.</p>
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error (Dataset)
<p>This upload contains the dataset used in <a href="https://arxiv.org/abs/2401.17879" target="_blank" rel="noopener">AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error</a>.</p> <p>Further details and instructions are provided in the <a href="https://github.com/jonasricker/aeroblade" target="_blank" rel="noopener">project's Github repository</a>.</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>
DATA SET: Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies
<p>This repository contains the data sets of the article:</p> <p>P. Fernández Esteberena et al. (2024). Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies. <em>Biomedical Optics Express</em>. <a href="https://doi.org/10.1364/BOE.538141">https://doi.org/10.1364/BOE.538141</a></p>
Sampled Structures from Diffusion Models for Protein SHAPES
<p>Data associated with SHAPES (Structural and Hierarchical Assessment of Proteins with Embedding Similarity)</p> <ul> <li><code>rmsd.tar.gz</code> : RMSD and TM scores of ESMFold predicted structures with original designed backbones.</li> <li><code>mpnn.tar.gz</code> : ProteinMPNN designed sequences.</li> <li><code>raster.tar.gz</code> : CATH and sampled structures annotated with the grid square label after rasterization. Also includes the specific structures which are displayed in the raster plots.</li> <li><code>dssp.tar.gz</code> : Secondary structure frequences computed by DSSP.</li> <li><code>samples.tar.gz</code> : All sampled structures.</li> </ul> <p>Due to size, the ESMFold predicted structures for eight ProteinMPNN-designed sequences per sampled backbone are not included.</p> <p> </p> <p>Code: <code>proteinshapes-main.zip</code></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.