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315 results for “Zeros”
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>
Full data for 'Experimental phase diagram of zero-bias conductance peaks in superconductor/semiconductor nanowire devices'
<p>This repository contains experimental data for the following paper:<br> Experimental phase diagram of zero-bias conductance peaks in superconductor/semiconductor nanowire devices<br> Authors: Jun Chen, Peng Yu, John Stenger, Moïra Hocevar, Diana Car, Sébastien R. Plissard, Erik P.A.M. Bakkers, Tudor D. Stanescu, Sergey M. Frolov</p> <p>Content of this repository: </p> <p>Readme file. </p> <p>/RawData/<br> Original data obtained at the time of measurement for devices 1014-841 and 1115A4. ZBP phase diagram data was measured on device 1014-841; Hard gap data was measured on device 1115A4</p> <p>/Measurement notes/<br> All the measurement data was summarized in powerpoints, catagorized by the name of the device.</p> <p>/Data of paper figures/<br> All the organized data files for the figures in the main text and supplementary information.</p> <p>Data file types:<br> data_NNN.dat - the original data file obtained at the time of the experiment<br> dataNNN.py - the original QTLab data acquisition script saved with data<br> data_NNN.set - settings of measurement instruments at the time of measurement<br> data_NNN.meta - auxillary file necessary for plotting data using SpyView (see below) <br> data_NNN.MTX - a simple 2D/3D matrix format developed for Spyview</p> <p>NNN stands for dataset number, automatically indexed by QTLab</p> <p>How to plot data:</p> <p>1) Spyview - a free data plotting program written by Gary Steele</p> <p>Data in this repository can be simply dropped into Spyview for plotting. </p> <p>Spyview also produces and can read .mtx files which are available for some of the data in this repository.</p> <p>https://nsweb.tn.tudelft.nl/~gsteele/spyview/</p> <p><br> 2) QTPlot - a Python plotter written by Ruben van Gulik</p> <p>Data in this repository can be directly opened with QTPlot, which will read axis labels.</p> <p>https://github.com/Rubenknex/qtplot</p> <p>Note: requires PyQT4</p>
United Atom Parameters for United Atom Multiscale Modelling Of Bio-Nano Interactions Of Zero-Valent Silver Nanoparticles
<p>Short-range surface adsorption potentials of carbohydrates, lipid fragments, and amino acid side chains in tabulated form.</p> <p>Calculated via AWT-MetaD (Gromacs/Plumed).</p> <p>Force Fields: INTERFACE/CHARMM36.</p> <p>Material: zero-valent silver</p>
Suppressing Andreev bound state zero bias peaks using a strongly dissipative lead
<p>This repository contains the raw data and processing Python scripts corresponding to the paper "Suppressing Andreev bound state zero bias peaks using a strongly dissipative lead"</p>
Data and code for zu Ermgassen et al. "Addressing indirect sourcing in zero deforestation commodity supply chains"
<p>Data and code required to reproduce figures and stats quoted in zu Ermgassen et al. "Addressing indirect sourcing in zero deforestation commodity supply chains"</p>
United Atom Parameters for United Atom Multiscale Modelling Of Bio-Nano Interactions Of Zero-Valent Copper Nanoparticles
<p>Short-range surface adsorption potentials of carbohydrates, lipid fragments, and amino acid side chains in tabulated form.</p> <p>Calculated via AWT-MetaD (Gromacs/Plumed).</p> <p>Force Fields: INTERFACE/CHARMM36.</p> <p>Material: zero-valent copper</p>
United Atom Parameters for United Atom Multiscale Modelling Of Bio-Nano Interactions Of Zero-Valent Gold Nanoparticles
<p>Short-range surface adsorption potentials of carbohydrates, lipid fragments, and amino acid side chains in tabulated form.</p> <p>Calculated via AWT-MetaD (Gromacs/Plumed).</p> <p>Force Fields: INTERFACE/CHARMM36.</p> <p>Material: zero-valent gold</p>
Observation of plateau regions for zero bias peaks within 5% of the quantized conductance value 2e2/h
<p>This repository contains the raw data and processing Python scripts corresponding to the paper "Observation of plateau regions for zero bias peaks within 5% of the quantized conductance value 2e2/h"</p>
Large zero bias peaks and dips in a four-terminal thin InAs-Al nanowire device
<p>This file contains the raw data and processing codes of the paper 'Large zero bias peaks and dips in a four-terminal thin InAs-Al nanowire device'.</p>
Minimizing habitat conflicts in meeting net-zero energy targets in the western United States
<p>This dataset contains spatial and non-spatial input and output data generated for and from the <a href="https://www.nature.org/en-us/what-we-do/our-priorities/tackle-climate-change/climate-change-stories/power-of-place/">Power of Place West</a> study, published in the Proceedings of the National Academy of Science (PNAS). See readme pdf for more details. </p>
Interaction-induced strong zero modes in short quantum dot chains with time-reversal symmetry
<p>The code repository for the manuscript "<span>Interaction-induced strong zero modes in short quantum dot</span><br><span>chains with time-reversal symmetry".</span></p>
Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks
<p>This dataset contains the behavioral choice data obtained from N = 230 participants that played a two-armed bandit task (139 females, age: 34 +/- 10 years) in partial and complete feedback conditions, as described in (Findling, Skvortsova et al., 2019, Nature Neuroscience, https://doi.org/10.1038/s41593-019-0518-9).</p> <div> <div> <div> <p>The experiment was performed on the Prolific platform (prolific.co) and the research was carried out following the principles and guidelines for experiments including human participants provided in the declaration of Helsinki and approved by the relevant authorities (Inserm Ethical Review Committee, IRB #00003888). All participants provided written informed consent prior to their inclusion.</p> </div> </div> </div>
Supplemental data for the paper "Managing CO2 under global and country-specific net-zero emissions targets in Europe"
<p>This repository includes results discussed in the paper "Managing CO2 under global and country-specific net-zero emissions targets in Europe".</p>
Consensus Zero Turbulence Power Curve Generation
<p>Model in Microsoft Excel to generate zero turbulence power curve from reference power curve</p>
Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor
<p>Dataset for the paper "Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor"</p>
Pathways to a net zero building sector in Colombia: insights from a circular economy perspective
<p>This dataset contains the input data and core results of the RECC v2.5 model for the analysis of circular economy strategies in the Colombian residential sector. For details about the RECC model, see DOI <a href="https://doi.org/10.1111/jiec.13023" target="_blank" rel="noopener">https://doi.org/10.1111/jiec.13023</a> and the RECC model landing page: <a href="https://www.industrialecology.uni-freiburg.de/odym-recc" target="_blank" rel="noopener">https://www.industrialecology.uni-freiburg.de/odym-recc</a></p> <p>The following data are included in this dataset:</p> <ul> <li>The entire model input database (120 model parameters)</li> <li>The parameters for the sensitivity analysis (8 parameters)</li> <li>The 70 folders with the core results</li> <li>The master classification file RECC_Classifications_Master_V2.0.xlsx</li> <li>The model config file RECC_Config.xlsx</li> <li>The list of scenario configurations RECC_ModelConfig_List.xlsx</li> <li>The result compilation and exporting configuration file RECCv2.5_EXPORT_Combine_Select.xlsx</li> <li>The main result summary file (extracted from the 70 result folders) Results_Extracted_RECCv2.5_10Regs_sep.xlsx</li> <li>The results of the sensitivity analysis</li> </ul> <p> </p> <p>The model itself is available as Python code from <a href="https://github.com/IndEcol/RECC-ODYM" target="_blank" rel="noopener">https://github.com/IndEcol/RECC-ODYM</a></p>
Properties of intermediate- to high-mass stars in the young cluster M17 Characterizing the (pre-)Zero Age Main Sequence
<p>Data and extra figures of the paper "Properties of intermediate- to high-mass stars in the young cluster M17 -- Characterizing the (pre-)Zero Age Main Sequence" by Frank Backs. Published in Astronomy and Astrophysics.</p> <p>The observed data and the fitted models are included. The fitting algorithm Kiwi-GA is available on Github: https://github.com/sarahbrands/Kiwi-GA</p> <p>Additional figures are available. These figures give an overview of the best fit models for each line like Fig. 1 in the paper. </p>
Gas permeability and mechanical properties of dust grains at hyper- and zero-gravity
<p>Two-phase flow experimental data obtained from the TEMPusVoLA facility over the course of three Zero-gravity parabolic flight campaigns (<strong>PFCs</strong>). The experiment, designated 'System 1' , contains low-pressure gas and analogue dust particles (of diverse compositions, shapes, and sizes) that experience a low-pressure gas cross-wind normal to the gravitational force vector. During steady flight, the particles pack into the lower end of the chamber and the layer of granular media creates a pressure differential, which is used to extract permeability coefficients in both the continuum and molecular-diffusion dominated regimes. We compare the permeability both with and without the effect of gravity and make comparisons with the conditions on low-gravity objects in the solar system, such as planetesimals and the dust particles that comprise them. In particular, we are able to compare and contrast the cohesiveness of the dust-regolith analogue types, which has consequences for formation theories of Solar-system bodies. </p> <p> </p> <p><strong>Data products: </strong></p> <p><strong>Pressure, acceleration</strong></p> <p>Each file corresponds to a particular parabolic flight campaign and day of flight. The 4th Swiss PFC lasted 1 day in 2020. The 75 ESA PFC lasted 3 days in 2021, and the the 78th ESA PFC lasted 3 days in 2022. Data was obtained in System 1 during all flights of all campaigns, and for a set of 12-16 parabolas. On each parabola, we studied the sample in one out of three chambers and changed the gas mass flux rate after cycling through all three samples. The below files were used to correlate changes in sample gas permeability with changes in gravitational loading. </p> <p>-- Raw pressure files output from 6-channel pressure logger measuring pressure as function of time as .csv files: DATALOG_DATE. </p> <p>-- Raw acceleration files output from accelerometer measuring gravitational load on aircraft.</p> <p><strong>Imaging data</strong></p> <p>-- Movie sequences obained with high-resolution thor-labs camera. All three chambers were imaged, despite only one chamber being subject to active gas flow at any given time. movie1 and movie 2 are referenced in the associated publication. </p> <p><strong>Multimeter data</strong></p> <p><strong>-- </strong>Output from photo-diodes measured across 9 channels using a Keithley Multimeter/Multiplexor. Acquisition speed was 27 Hz. </p>
LOLA - Evaluation Tables - Zero-Shot
Open the record for dataset details and reuse information.
Exploring zero-shot structure-based protein fitness prediction
<p>This repository contains data used in Exploring zero-shot structure-based protein fitness<br>prediction.</p> <p>Directions to use this data can be found on <a href="https://github.com/gitter-lab/benchmarking-structure-based-models">our GitHub repository</a>.</p> <ol> <li><code>experimental_struct_artifacts</code> contains the experimentally determined structures for ProteinGym assays used in our analysis along with the reference file needed to generate ESM inverse folding predictions for these structures in ProteinGym.</li> <li><code>results</code> contains the prediction results obtained by running SSEmb on the 216 ProteinGym assays being considered in this study.</li> <li><code>test.tar.gz</code> contains all the structures from ProteinGym as well as MSAs generated using mmseqs2. To use this directory: <ul> <li>Setup SSEmb as directed in its <a href="https://github.com/KULL-Centre/_2023_Blaabjerg_SSEmb">repository</a></li> <li>Download this file and extract it in the data folder.</li> </ul> </li> </ol> <p> </p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.