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5,155 results for “resolution”

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zenodo32/100

Propositional Resolution Proofs and Raw Data of Experimental Results

<p>This dataset contains propositional resolution proofs generated by the SMT-solver VeriT on problems of the Sat-Race 2010 and on other sat problems.</p> <p>The proofs were used to evaluate two proof compression algorithms: RecyclePivotsWithIntersection and LowerUnits. The experiments were performed in 2010--2011 and raw experimental results are included in the dataset.</p> <p>A paper about this was published in CADE 2011.</p>

opencc-by-nc-sa-4.0Aug 2017View details →

zenodo32/100

Propositional Resolution Proofs

<p>This dataset contains propositional resolution proofs, mostly in the format of the SMT-solver VeriT. There are also a few proofs in the TraceCheck format and one DRUP/DRAT proof.</p> <p>These proofs have been used in various experiments for the evaluation of proof compression algorithms implemented in the Skeptik tool.</p>

opencc-by-nc-sa-4.0Aug 2017View details →

zenodo32/100

Propositional Resolution Proofs - QG-Classification

<p>This dataset contains propositional resolution proofs generated by the SMT-solver VeriT on the QG-Classification problems of the SMT-Lib.</p> <p>These proofs have been used in various experiments for the evaluation of proof compression algorithms implemented in the Skeptik tool.</p>

opencc-by-nc-sa-4.0Aug 2017View details →

zenodo32/100

Research Data for FiHi: Fusion of inertial and high-resolution acoustic data for privacy-preserving human activity recognition

<h1><strong>Description</strong></h1> <div>This dataset contains information on 20 different activities collected from 15 participants (20-55 years old) using Wit-motion smart inertial sensors and Double Acoustics guitar pickups. Each participant performs these daily activities in an unrestricted environment, with each activity lasting at least 60 seconds and repeated twice.</div> <div> </div> <div>If you use the dataset in an academic work, please cite: </div> <div> </div> <div><code>@ARTICLE{10980212,</code><br><code>  author={Yang, Zhe and Zhang, Ying and Li, Yanjun and Huang, Linchong and Hu, Ping and Lin, Yuexiang},</code><br><code>  journal={IEEE Transactions on Instrumentation and Measurement}, </code><br><code>  title={Fusion of Inertial and High-resolution Acoustic Data for Privacy-Preserving Human Activity Recognition}, </code><br><code>  year={2025},</code><br><code>  volume={74},</code><br><code>  number={},</code><br><code>  pages={1-20},</code><br><code>  keywords={Human activity recognition;Sensors;Acoustics;Feature extraction;Privacy;Microphones;Biomedical monitoring;Wireless fidelity;Sensor phenomena and characterization;Sensor fusion;Human activities recognition;inertial sensing;Hi-res audio;attention mechanism},</code><br><code>  doi={10.1109/TIM.2025.3565250}}</code></div> <h1><strong>DataSet Information</strong></h1> <h2><strong>1.Original_data.zip</strong></h2> <div>The data was annotated by manually reviewing the audio clips and assigning appropriate activity labels. Timestamping the inertial sensor data with the start time of the audio device recorded by the experimenter ensured correct segmentation and synchronization of the inertial and acoustic data. The total data length for</div> <div>all participants is over 10 hours.</div> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The inertial data (accelerometer and gyroscope) is sampled at 100 Hz and transmitted by Bluetooth to the host computer. These reviewed and annotated original samples from 15 participants are placed in separate csv files. The arrangement of information in each csv file is:</div> <div>Col 1-3: 3D-acceleration data (g)</div> <div>Col 4-6: 3D-gyroscope data (°/s)</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic data are sampled at 192 kHz by a Steinberg sound card and transmitted by USB cable to the host computer. These original samples are placed in separate wav files, with each file name containing all the necessary information regarding the contents of the file.</div> <div><strong>For example:</strong></div> <div>100801_sitting</div> <div>Participant ID (1-4 digits): 1008, Session ID (5-6 digits): 01, Activity ID: sitting.</div> <div> </div> <h2><strong>2、Processed_data.zip</strong></h2> <div>The last two columns of each file are as follows:</div> <ul> <li> <div>participant_id: such as 100101, 100102, 100201 ...... The last two digits are the Session ID, representing the two sessions from the same participant for the same activity.</div> </li> <li> <div>activity_id: Refer to the ACTIVITY SET below</div> </li> </ul> <h3><strong>(1) </strong><strong>IMU</strong><strong> DATA</strong></h3> <div>The original inertial data is individually aligned with the processed Audio data based on their start times, with any excess data rows at the end being trimmed. Then, these inertial data files are augmented with activity_id and participant_id for identification, and consolidated into a single csv file.</div> <div>The continuous motion signal is segmented into sliding windows, each with a duration of 3 seconds and a step size of 3 seconds. Given the IMU’s sampling rate of 100 Hz, each window of inertial data consists of 300 time steps, with 6 channels of information (3 axes each for accelerometer and gyroscope). Consequently, a single inertial sample is represented by a 300 × 6 dimensional matrix.</div> <h3><strong>(2) Audio DATA</strong></h3> <div>The acoustic signals are processed using the Short Time Fourier Transform (STFT) with a window length of 1024 points and an overlap of 256 points, , which generates n linear spaced frequency bins between n kHz frequency range in the frequency domain. The output contains the estimate of the short-term, time-localized frequency patterns. We examine two levels of privacy protection: 8 &sim; 96 kHz for non-speech sound and 20 &sim; 96 kHz for inaudible sound, with 88 and 76 linear spaced frequency bins, respectively.</div> <div>Under a sample rate of 192 kHz for the original acoustic signal, there are (192000 - 256)/(1024 - 256) ≈ 250 frequency features within a second, while each feature has 88 and 76 dimensions for non-speech (8&sim;96 kHz) and inaudible (20&sim;96 kHz) feature, respectively.</div> <h1><strong>ACTIVITY </strong><strong>SET</strong></h1> <div>The activityIDs and corresponding activities are listed in the following:</div> <div>0: use microwave</div> <div>1: brush teeth</div> <div>2: browse video</div> <div>3: drink water</div> <div>4: fry</div> <div>5: lie down</div> <div>6: flush</div> <div>7: go downstairs</div> <div>8: go upstairs</div> <div>9: sit</div> <div>10: stand</div> <div>11: manipulate door</div> <div>12: type</div> <div>13: jump</div> <div>14: run</div> <div>15: walk</div> <div>16: wash hands</div> <div>17: write</div> <div>18: operate light</div> <div>19: eat</div>

opencc-by-4.0Aug 2024View details →

zenodo32/100

scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution - Supplementary data and code

<p>Data and code to reproduce the analyses from the study: "scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution". </p>

opencc-by-4.0Oct 2024View details →

zenodo32/100

Organocatalyzed Atroposelective Dynamic Kinetic Resolutions via Transient Seven-Membered Cyclic Hemiacetals

<p>DFT_optimized structures accompanying the study "Beyond Five- and Six-Membered Ring Intermediates: Transient Seven-Membered Cyclic Hemiacetals for Organocatalyzed Atroposelective Dynamic Kinetic Resolutions", where conformers occur, they are always named from the lowest Gibbs energy to the highest in ascending order from c1 (sometimes omitted), c2, c3, ...</p> <p>Note: R-3a.xyz corresponds to R-3c.xyz in the manuscript.</p>

opencc-by-4.0May 2024View details →

zenodo32/100

High resolution, interactive, or animated versions of illustrations used in the paper "Quantifying the Dunkelflaute: An analysis of variable renewable energy droughts in Europe"

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

zenodo32/100

Spatially Variant Super-Resolution (SVSR) benchmarking dataset

<p>The Spatially Variant Super-Resolution (SVSR) benchmarking dataset contains 1119 real low-resolution images that are degraded by complex noise of varying intensity and type and their corresponding real noise free X2 and X4 high-resolution counterparts, for evaluation of the robustness of real-world super-resolution methods. Additionally, the dataset is also suitable for evaluation of denoisers. The associated paper can be found here: <a title="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf" href="https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf">https://openaccess.thecvf.com/content/WACV2024/papers/Aakerberg_PDA-RWSR_Pixel-Wise_Degradation_Adaptive_Real-World_Super-Resolution_WACV_2024_paper.pdf</a></p>

opencc-by-nc-sa-4.0Oct 2023View details →

zenodo32/100

DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data (minimal)

<p>This is a minimal version of the DynaBench dataset, containing the first 5% of the data. The full dataset is available at <a href="https://professor-x.de/dynabench">https://professor-x.de/dynabench</a></p><p><strong>Abstract:</strong></p><p>Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at <a href="https://professor-x.de/dynabench">https://professor-x.de/dynabench</a>.</p><p><strong>Technical Info</strong></p><p>The dataset is split into 42 parts (6 equations x 7 combinations of resolution/structure). Each part can be downloaded separately and contains 7000 simulations of the given equation at the given resolution and structure. The simulations are grouped into chunks of 500 simulations saved in the hdf5 file format. Each chunk contains the variable "data", where the values of the simulated system are stored, as well as the variable "points", where the coordinates at which the system has been observed are stored. For more details visit the DynaBench website at <a href="https://professor-x.de/dynabench/">https://professor-x.de/dynabench/</a>. The dataset is best used as part of the dynabench python package available at <a href="https://pypi.org/project/dynabench/">https://pypi.org/project/dynabench/</a>.</p>

opencc-by-sa-4.0Sep 2023View details →

zenodo32/100

Dataset for paper "Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy"

<p>This dataset collects all the material that was used to create the personalised musculoskeletal model employed in the publication by Caillet et al. "Motoneuron-driven computational muscle modelling with motor unit resolution and subject-specific musculoskeletal anatomy" published in PLOS Computational Biology in 2023. The aim of sharing this material is to allow reproducibility of the results and increase adoption of the semi-automatic techniques for musculoskeletal modelling that were used in the publication.</p>

opencc-by-4.0Nov 2023View details →

zenodo32/100

Annual-resolution Sr/Ca records of fossil Porites corals from the northern South China Sea

<p>This dataset reports thirteen annual-resolution Sr/Ca records and seven 10-yr resolution Sr/Ca records of a total of 20 fossil <i>Porites </i>corals collected from the northern South China Sea. The ages of the fossil corals were determined by U-Th geochemistry. The Sr/Ca measurements were carried out using an Inductively Coupled Plasma-optical emission spectroscopy (ICP-OES; Agilent 5110). The international coral standard JCp-1 was measured along with the samples, yielding an average value of 8.839 ± 0.017 mmol/mol (1 SD, n = 36).</p>

opencc-by-4.0Nov 2023View details →

zenodo32/100

Data set for "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation"

<p>Data set for "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation". And This database contains GNSS velocity and strain rate field data for mainland China obtained by the spherical spline method. Also, we include the sub-graph data for each graph of the manuscript "Reconciling High-resolution Strain Rate of Continental China from GNSS Data with the Spherical Spline Interpolation". We have detailed the meaning and format of each data in the "readme.txt", and a separate "readme.txt" is included in each zip.</p>

opencc-by-4.0Mar 2023View details →

zenodo32/100