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2,194 results for “Diffusers”
Dataset for "Investigation of convective transport in the gas diffusion layer used in polymer electrolyte fuel cell"
<p>Dataset to all the figures in the paper (including Supplementary Material):</p> <p>Investigation of convective transport in the gas diffusion layer used in polymer electrolyte fuel cell</p> <p>Beruski, O., Lopes, T., Kucernak, A. R., Perez, J.</p> <p>Phys. Rev. Fluids, 2, 103501, 2017.</p> <p> </p>
Diffusion rates for GRL manuscript "Red line diffusion-like auroras driven by time domain structures associated with braking magnetotail flow bursts"
<p>This dataset in MATLAB files provide electron bounce-averaged pitch-angle diffusion rates due to scattering by time domain structures and whistler-mode waves, as pertinent to the manuscript submission to the Geophysical Research Letters (doi: 10.1029/2024GL109000), titled "Red line diffusion-like aurora driven by time domain structures associated with braking magnetotail flow bursts". </p> <p>Please cite the original GRL manuscript when using TDS-driven diffusion rates from this dataset for your research.</p>
Data for "Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations"
Open the record for dataset details and reuse information.
Capturing alterations of intracellular-extracellular lactate distribution in the brain using diffusion-weighted MR spectroscopy in vivo
Open the record for dataset details and reuse information.
Fairness in Diffusion Recommender Models: Insights from an Empirical Study of DiffRec
<p>Dataset for the experiments of the paper submission `Fairness in Diffusion Recommender Models: Insights from an Empirical Study of DiffRec`. The included datasets are Foursquare Tokyo (FKTY), MovieLens 1M with all attributes and users (ML1M).</p>
Gravitational Asymmetry and the Arrow of Time: The Role of Spacetime Energy Diffusion
<p>The asymmetry of gravity and its relationship to the arrow of time is a fundamental question in physics. This paper explores this connection from the perspective of spacetime energy diffusion, proposing a mechanism by which the asymmetry of the gravitational field induces energy diffusion, leading to the emergence of irreversibility of time on macroscopic scales. The theory is developed through a detailed derivation of the energy diffusion equation, incorporating the effects of spacetime curvature via the connection coefficients. A curvature-dependent diffusion coefficient is introduced to quantify the effect of curvature on energy diffusion rates. The entropy production rate is separated into thermal and gravitational contributions, providing a new perspective on the thermodynamic arrow of time. The potential impact of quantum gravity effects in regions of extreme spacetime curvature is also considered, suggesting possible modifications to the classical energy diffusion equation. The theory is consistent with the thermodynamic arrow of time and opens up new avenues for exploring the deep connections between gravity, quantum mechanics, and the nature of time. Future research directions include connecting this theory to fundamental physics, exploring applications to the early universe, and pursuing experimental and observational verification.</p>
A Unified Theoretical Framework for the Synergistic Integration of Transformers and Diffusion Models
<p>This paper introduces a novel, comprehensive theoretical framework for the synergistic integration of Transformer and Diffusion models, two paradigms that have independently revolutionized machine learning. We establish a fundamental correspondence between these models through a unified representation and a generalized dynamics equation, bridging the gap between their seemingly disparate architectures. Our key contributions include:</p> <p>(1) A unified mathematical formulation that encapsulates both Transformer and Diffusion processes;</p> <p>(2) A novel Diffusion-Enhanced Attention mechanism that incorporates Diffusion dynamics into Transformer attention;</p> <p>(3) Rigorous theoretical analyses including convergence guarantees, generalization bounds, and sample efficiency proofs for the integrated model.</p> <p>We provide detailed mathematical derivations and empirical validations across various tasks, demonstrating significant improvements over standalone models and existing hybrid approaches. This work lays the foundation for a new class of AI models that leverage the strengths of both paradigms, potentially leading to more powerful, efficient, and versatile AI systems. Our framework opens up new avenues for research in areas such as enhanced language modeling, advanced image generation, and multi-modal learning, paving the way for the next generation of AI technologies.</p>
MRI Dataset: Diffusion MRI as a potential non-invasive biomarker of neuroinflammation following intracerebral haemorrhage
<p>Raw MRI dataset of T2 and NODDI scans.</p> <p>Subjects: Adult male Sprague-Dawley rats, aged 10-12 weeks, 300-450g. Subjected to intracerebral haemorrhage induction by intracranial injection of 0.2 Type VII collagenase, recovered for 7 days prior to these MRI scans being performed. Further methodological information available upon request.</p>
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>partab of His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models</p>
Diffusion map objects of FACS-sorted (CD31+/CD45-) endothelial cells
<p><strong>Diffusion map objects of FACS-sorted (CD31+/CD45-) endothelial cells<br></strong></p> <p><em><span>-> part of the manuscript: Single-cell atlas of the human brain vasculature across development, adulthood and disease</span></em><span><br><em><span>https://www.nature.com/articles/s41586-024-07493-y</span></em></span></p> <p><strong> </strong></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p> </p> <p><em>-i) Fetal CNS sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from fetal brain (Fetal CNS).<br><br><em>- ii) Adult control brain (temporal lobe) sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from adult/control brains (temporal lobes).<br><br><em>- iii) AVM sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain arteriovenous malformations (AVM) (a brain vascular malformation).<br><br><em>- iv) LGG sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from lower-grade gliomas (LGG) (a brain tumor)<br><br><em>- v) GBM sorted endothelial cells_diffusionmap.rds:</em> <br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from high-grade gliomas (glioblastoma (GBM)) (a brain tumor).<br><br><em>- vi) MET sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain metastasis (MET) (a brain tumor).<br><br><em>- vii) MEN sorted endothelial cells_diffusionmap.rds: </em><br> -> this is a diffusion map object of FACS-sorted (CD31+/CD45-) endothelial cells isolated from brain meningioma (MEN) (a brain tumor).</p>
Data for Hydrogen Diffusion in Hybrid Perovskites from Exchange NMR
<p>Supporting data for Hydrogen Diffusion in Hybrid Perovskites from Exchange NMR<br>https://doi.org/10.1021/acs.chemmater.4c01498</p> <p>Raw and processed 2H, 1H, and 15N NMR data for MAPbI3 and FA0.7MA0.3PbI3. In particular, variable temperature 2H EXSY spectra, and the measured 2H exchange rates as a function of temperature. MA = methylammonium, FA = formamidinium. </p> <p>See README.txt for full details</p>
2000-2002 Dataset [1/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 1/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2000-2002. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This part 1/7 of the dataset also includes files related to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2009-2011 Dataset [4/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 4/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2009-2011. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2012-2014 Dataset [5/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 5/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2012-2014. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2015-2017 Dataset [6/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 6/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2015-2017. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2006-2008 Dataset [3/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 3/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2006-2008. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
2003-2005 Dataset [2/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 2/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2003-2005. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Perturbing the travelling pulse in a three-species competition-diffusion system
<p>We consider the situation where an exotic species <em>w</em> invades an ecosystem inhabited by two native species <em>u</em> and <em>v</em>. All species are competing for the same limited resource. Supposing that <em>u</em> and <em>v</em> are not able to coexist in the absence of the invader, we want to determine whether a successful invasion by <em>w</em> may allow all species to coexist (competitor-mediated coexistence). Mathematically, this problem can be modelled by the following three-species competition-diffusion system<br> <span class="math-tex">\( \left\{ \begin{alignedat}{6} u_t &= d_1 \, \Delta u &&+ (r_1 &&- u &&- b_{12} \, v &&- b_{13} \, w &&)\,u, \\ v_t &= d_2 \, \Delta v &&+ (r_2 &&- v &&- b_{21} \, u &&- b_{23} \, w &&)\,v, \\ w_t &= d_3 \, \Delta w &&+ (r_3 &&- w &&- b_{31} \, u &&- b_{32} \, v &&)\,w, \end{alignedat} \right.\)</span><br> where all parameters are positive constants.</p> <p>We are interested in the case in which the invading species is weaker than the native ones, i.e., it is not able to survive in the diffusion-free system obtained by setting <em>d</em><sub>1</sub> = <em>d</em><sub>2</sub> = <em>d</em><sub>3</sub> = 0.<br> We fix all parameters as<br> <span class="math-tex">\( \begin{aligned} & d_1 = d_2 = d_3 = 1, \\ & r_1 = r_2 = 28, \\ & \begin{aligned} b_{12} &= 22/21, & b_{13} &= 4, \\ b_{21} &= 1.87, & b_{23} &= 3/4, \\ b_{31} &= 26/21, & b_{32} &= 22/21, \\ \end{aligned} \end{aligned}\)</span><br> and leave <em>r</em><sub>3</sub>, which measures the strength of the exotic species, as a free parameter. Depending on the value of <em>r</em><sub>3</sub>, the invasion can be either successful or not and competitor-mediated coexistence may or may not occur.</p> <p>It turns out that if <em>r</em><sub>3</sub> lies in a certain range of values, the three-species competition-diffusion system admits several types of travelling wave solutions. In particular, there exists a travelling pulse which is stable for relatively high values of <em>r</em><sub>3</sub> and then becomes unstable when <em>r</em><sub>3</sub> decreases. In the movie here presented, we show the outcome of perturbing the unstable travelling pulse for <em>r</em><sub>3</sub> = 26.75. In this case, the pulse splits in two three-species waves moving in opposite directions.</p>
Breathing travelling wave solutions in a three-species competition-diffusion system
<p>We consider the situation where an exotic species <em>w</em> invades an ecosystem inhabited by two native species <em>u</em> and <em>v</em>. All species are competing for the same limited resource. Supposing that <em>u</em> and <em>v</em> are not able to coexist in the absence of the invader, we want to determine whether a successful invasion by <em>w</em> may allow all species to coexist (competitor-mediated coexistence). Mathematically, this problem can be modelled by the following three-species competition-diffusion system<br> <span class="math-tex">\( \left\{ \begin{alignedat}{6} u_t &= d_1 \, \Delta u &&+ (r_1 &&- u &&- b_{12} \, v &&- b_{13} \, w &&)\,u, \\ v_t &= d_2 \, \Delta v &&+ (r_2 &&- v &&- b_{21} \, u &&- b_{23} \, w &&)\,v, \\ w_t &= d_3 \, \Delta w &&+ (r_3 &&- w &&- b_{31} \, u &&- b_{32} \, v &&)\,w, \end{alignedat} \right.\)</span><br> where all parameters are positive constants.</p> <p>We are interested in the case in which the invading species is weaker than the native ones, i.e., it is not able to survive in the diffusion-free system obtained by setting <em>d</em><sub>1</sub> = <em>d</em><sub>2</sub> = <em>d</em><sub>3</sub> = 0.<br> We fix all parameters as<br> <span class="math-tex">\( \begin{aligned} & d_1 = d_2 = d_3 = 1, \\ & r_1 = r_2 = 28, \\ & \begin{aligned} b_{12} &= 22/21, & b_{13} &= 4, \\ b_{21} &= 1.87, & b_{23} &= 3/4, \\ b_{31} &= 26/21, & b_{32} &= 22/21, \\ \end{aligned} \end{aligned}\)</span><br> and leave <em>r</em><sub>3</sub>, which measures the strength of the exotic species, as a free parameter. Depending on the value of <em>r</em><sub>3</sub>, the invasion can be either successful or not and competitor-mediated coexistence may or may not occur.</p> <p>It turns out that if <em>r</em><sub>3</sub> lies in a certain range of values, the three-species competition-diffusion system admits a breathing travelling wave solution, i.e., a travelling pulse whose width is oscillating. Such a breathing wave is originated from a standard travelling pulse which is destabilized through a Hopf bifurcation. The period <em>T</em> of the breathing wave depends on the free parameter <em>r</em><sub>3</sub> and goes to infinity at one end of the solution branch.</p> <p>The movie "Breathing Travelling Wave Orbits" shows the evolution of the spatial profile of the breathing wave. After one period, the value of the parameter <em>r</em><sub>3</sub> is changed and the next solution on the breathing wave branch is displayed. Please note that the solution is plotted in a reference frame moving at the velocity of the breathing wave.</p> <p>The movie "Breathing Travelling Wave Features" shows several features of the breathing wave as <em>r</em><sub>3</sub> is changed. The first plot in the third row shows the current value of <em>r</em><sub>3</sub> and the position on the solution branch. The first row shows the space-time profiles of the solution. The first plot of the second row shows the evolution of the pulse width during one period. The remaining plots on the second row show the instantaneous velocities of the back and leading fronts of the oscillating pulse, while the plots immediately below show the density of the invading species <em>w</em> in correspondence of those two fronts.</p>
Interaction of one-dimensional trivial and non-trivial travelling waves in a three-species competition-diffusion system
<p>We consider the situation where an exotic species <em>w</em> invades an ecosystem inhabited by two native species <em>u</em> and <em>v</em>. All species are competing for the same limited resource. Supposing that <em>u</em> and <em>v</em> are not able to coexist in the absence of the invader, we want to determine whether a successful invasion by <em>w</em> may allow all species to coexist (competitor-mediated coexistence). Mathematically, this problem can be modelled by the following three-species competition-diffusion system<br> <span class="math-tex">\( \left\{ \begin{alignedat}{6} u_t &= d_1 \, \Delta u &&+ (r_1 &&- u &&- b_{12} \, v &&- b_{13} \, w &&)\,u, \\ v_t &= d_2 \, \Delta v &&+ (r_2 &&- v &&- b_{21} \, u &&- b_{23} \, w &&)\,v, \\ w_t &= d_3 \, \Delta w &&+ (r_3 &&- w &&- b_{31} \, u &&- b_{32} \, v &&)\,w, \end{alignedat} \right.\)</span><br> where all parameters are positive constants.</p> <p>We are interested in the case in which the invading species is weaker than the native ones, i.e., it is not able to survive in the diffusion-free system obtained by setting <em>d</em><sub>1</sub> = <em>d</em><sub>2</sub> = <em>d</em><sub>3</sub> = 0.<br> We fix all parameters as<br> <span class="math-tex">\( \begin{aligned} & d_1 = d_2 = d_3 = 1, \\ & r_1 = r_2 = 28, \\ & \begin{aligned} b_{12} &= 22/21, & b_{13} &= 4, \\ b_{21} &= 1.87, & b_{23} &= 3/4, \\ b_{31} &= 26/21, & b_{32} &= 22/21, \\ \end{aligned} \end{aligned}\)</span><br> and leave <em>r</em><sub>3</sub>, which measures the strength of the exotic species, as a free parameter. Depending on the value of <em>r</em><sub>3</sub>, the invasion can be either successful or not and competitor-mediated coexistence may or may not occur.</p> <p>It turns out that if <em>r</em><sub>3</sub> lies in a certain range of values, the three-species competition-diffusion system admits two planarly stable travelling wave solutions. In the movies here presented, the result of the interaction of these two waves in one spatial dimension is reported for several value of <em>r</em><sub>3</sub>. For relatively higher values of the free parameter, the relative velocity of the interacting waves is small and they merge into a single travelling pulse. As <em>r</em><sub>3</sub> decreases, the relative velocity of the two waves becomes larger and they merge into a breathing wave (a travelling pulse whose width is oscillating). If <em>r</em><sub>3</sub> is even smaller, the trivial wave is reflected as a leftward-moving non-trivial wave.</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.