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45 results for “Model Counting”
Model Counting Competition 2024: Submitted Solvers
<p>The dataset contains the submissions that have been evaluated in the Model Counting Competition 2024 on the tracks:</p> <ul> <li>Track 1 (Model Counting)</li> <li>Track 2 (Weighted Model Counting)</li> <li>Track 2b (Weighted Model Counting Bonus Track)</li> <li>Track 3 (Projected Model Counting)</li> <li>Track 4 (Projected Weighted Model Counting)</li> </ul> <p><br>Details will be made public in an upcoming report.</p> <p>We also refer to the competition website: https://mccompetition.org/</p>
Model Counting Competition 2024: Competition Instances
<div> </div> <div> <p>Instances for the Model Counting Competition 2024</p> <ul> <li>Track 1 (Model Counting)</li> <li>Track 2 (Weighted Model Counting)</li> <li>Track 2b (Weighted Model Counting - bonus track)</li> <li>Track 3 (Projected Model Counting)</li> <li>Track 4 (Projected Weighted Model Counting)</li> </ul> <p><br>The even instances (trackX_000.cnf, trackX_002.cnf, ...) were made public for all participants during the testing phase of the solvers, whereas the private instances (trackX_001.cnf, trackX_003.cnf, ...) were used for the final evaluation and disclosed after the submission. </p> <p>For details, we refer to <a href="https://mccompetition.org/past_iterations">https://mccompetition.org/past_iterations</a>.</p> <p>Instances originate from various publicly available data sets and submissions made after a call for benchmarks. The full instances from which we selected are available on Zenodo under </p> <ul> <li>Model Counting Competition 2020: Full Instance Set</li> <li>Model Counting Competition 2021: Full Instance Set</li> <li>Model Counting Competition 2022: Full Instance Set.</li> <li>Model Counting Competition 2023: Full Instance Set.</li> <li>Model Counting Competition 2024: Full Instance Set.</li> </ul> <p>Details will be made public in the upcoming report.</p> </div>
AN EXTENSION OF N-MIXTURE OCCUPANCY MODELS FOR COUNT DATA
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Model Counting Competition 2024: Full Instance Set
<p>The dataset contains all instances that the organizers of the competition received or collected during the preparation phase of the Model Counting Competition 2024. The dataset includes short benchmark descriptions (00_description.{pdf,txt}) by the submitters/collectors (00_authors.txt).</p> <p>For more details, we refer to the upcoming report.</p> <p>Contributors are listed in the dataset.</p> <p>[Version 2: We accidentally included Track2-4 instances instead of Track1 instances in Track1. We fixed this. Now, <span>mc2024-track1-mc_collected.tar</span> correctly contains the Track1 instances.]</p>
Plots for the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history"
<p>These are the raw plot files from the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history".</p> <p>The files have been created with MATLAB 2019, and labeled according to their corresponding figure number(s) in the publication.</p>
Weighted Model Counting with Twin-Width: Experimental Results
<p>The results of our paper "Weighted Model Counting with Twin-Width" published at SAT 2022</p>
Dataset for Improved differential expression analysis of miRNA-seq data by modeling competition to be counted
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Data for Model Counting in the Wild (KR-24 paper)
<h2>Contents</h2> <ol> <li><strong>Model Counting Benchmarks:</strong> Found in the zip file <code>model_counting_benchmarks.zip</code>.</li> <li><strong>Projected Counting Benchmarks</strong>: Found in the zip file <code>projected_counting_benchmarks.zip</code>.</li> <li><strong>Logfiles of Running counters</strong>: Stored in the <code>logfiles.zip</code>.</li> <li><strong>Binaries for Counters: </strong>Provided in the archive: <code>bins.zip</code>.</li> </ol> <h2>Benchmarks</h2> <p>Each benchmark file is systematically prefixed to indicate its source or category as referenced in the paper. For example, benchmarks from the Network Reliability Benchmark Set are prefixed with <code>netrel.</code></p> <h2>Logfiles</h2> <p>Logfiles are organized into folders corresponding to each counter used in the experiments.</p> <h2>Binaries</h2> <p>Contains binaries for each counter used in the experiments, along with a <code>commands.txt</code> file that provides instructions for running them.</p>
On Lower Bounding Minimal Model Count
<p>It contains the benchmark and experimental log files of our work "On Lower Bounding Minimal Model Count"</p>
Blood cell differential count discretization modeling predicts survival in adults reporting to the emergency room: a retrospective cohort study
<p><strong>Objectives</strong>: to assess survival predictivity of baseline blood cell differential count (BCDC), discretized according to two different methods, in adults visiting the Emergency Room (ER) for illness or trauma over one-year. </p> <p><strong>Design</strong>: Retrospective cohort study of hospital records. </p> <p><strong>Setting</strong>: Tertiary care public hospital in northern Italy. </p> <p><strong>Participants</strong>: 11052 patients aged > 18 years, consecutively admitted to the ER in one year, and for whom BCDC collection was indicated by ER medical staff at first presentation.</p> <p><strong>Primary outcome</strong>: Survival was the referral outcome for explorative model development. Automated BCDC analysis at baseline assessed hemoglobin, red cell mean volume (MCV) and distribution-width (RDW), platelet distribution-width (PDW), plateletcrit (PCT), absolute red blood cells, white blood cells, neutrophils, lymphocytes, monocytes, eosinophils, basophils, and platelets. Discretization cutoffs were defined by Benchmark and Tailored methods. Benchmark cutoffs were stated on laboratory reference values (CLSI). Tailored cutoffs for linear, sigmoid-shaped and for U-shaped distributed variables were discretized by Maximally Selected Rank Statistics and by Optimal-Equal Hazard Ratio respectively. Explanatory variables (age, gender, ER admission during SARS-CoV2 surges, in-hospital admission) were analyzed using Cox multivariable regression. ROC curves were drawn by sum of Cox-significant variables for each method.</p> <p><strong>Results</strong>: Of 11052 patients (median age 67 years, IQR 51–81, 48% female), 59% (n=6489) were discharged and 41% (n=4563) were admitted in hospital. After a 306-day median follow up (IQR 208–417 days), 9455 (86%) patients were alive and 1597 (14%) deceased. Increased HRs were associated with age >73-years (HR=4.6 CI=4.0–5.2), in-hospital admission (HR=2.2 CI=1.9–2.4), ER admission during SARS-CoV2 surges (Wave-I HR=1.7 CI=1.5–1.9); Wave-II HR=1.2 CI=1.0–1.3). Gender, hemoglobin, MCV, RDW, PDW, neutrophils, lymphocytes and eosinophils counts were significant in overall. Benchmark-BCDC model included basophils and platelet count (AUROC 0.74). Tailored-BCDC model included monocyte counts and plateletcrit (AUROC 0.79).</p> <p><strong>Conclusions</strong>: baseline discretized BCDC provides meaningful insight regarding Emergency Room patients survival.</p>
Avian point-counts from Rhode Island and Connecticut used to test species distribution models
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Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models
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Blood cell differential count discretization modeling predicts survival in adults reporting to the emergency room: a retrospective cohort study
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Dataset for Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice
<p>The dataset consists of</p> <p>(1) Benchmark files<br> (2) the logfile (outputs) generated by ApproxMC4 and ApproxMC5 respectively</p> <p>(3) A summary of the results for the files where at least one of ApproxMC4 or ApproxMC5 did not time out and took more than 1 second. </p> <p>The corresponding paper titled "Scalable Approximate Model Counting via Concentrated Hashing]{Sparse Hashing for Scalable Approximate Model Counting: Theory and Practice" will appear in the Proceedings of Logic in Computer science(LICS), 2020.</p>
Data from: Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling
<p>As stated in the Read Me file:</p> <p>These data and resources are associated with the manuscript:</p> <p><em>Learning to count: determining the stoichiometry of bio-molecular complexes using fluorescence microscopy and statistical modelling</em>, Mersmann et. al., as submitted to biorXiv in July 2020.</p> <p>The raw imaging data relates to Figure 5, S1, S2 and Table S1. The images are fluorescent micrographs displaying immobilised adenovirus particles bound to a monoclonal antibody 9C12.</p> <p>Each experiment folder is numbered, as in Table S1, and appended with the mixing proportion (Fl), as defined in the manuscript. Within each folder there are 6 subfolders, representing samples incubated with different concentrations of 9C12 antibody.</p> <p>Each image is a 3 channel 1024x1024 tif. Channel 1 = 9C12 Alexa Fluor 647. Channel 2 = 9C12 Biotin + QDot655. Channel 3 = Adenovirus Alexa Fluor 488. Samples were illuminated in TIRF mode using a 100X objective, images were captured on a Hamamatsu OCRA Flash 4 sCMOS camera. Further details are available in the header of each file.</p> <p>The control samples are labelled with 100% 9C12 Alexa Fluor 647 or 100% 9C12 Biotin, as described in the manuscript.</p> <p>The data analysis script is an imageJ macro. It runs on the FIJI version of ImageJ with the NanoJ package installed (https://github.com/HenriquesLab). It outputs fluorescent measurements for each identified AdV particle. Note that the script rearranges the channel order such that Channel 1 = Adenovirus Alexa Fluor 488, Channel 2 = 9C12 Alexa Fluor 647, Channel 3 = 9C12 Biotin + QDot655. </p> <p>The channels require registration due to chromatic aberration, this is achieved using the Realign Channels function in NanoJ, appropriate translation masks are provided along with the script.</p> <p>Any question about the data or script should be addressed in Joe Grove (j.grove@ucl.ac.uk)</p>
Morphological/WB/ELISA/cell counting data of the paper 'Serotonergic and dopaminergic neurons in the dorsal raphe are differentially altered in a mouse model for parkinsonism'
<p>The files contain the data included in Figure 2B, Figure 2C, Figure 4B, Figure 4C, Figure 6B, Figure 6C, Suppl.Fig.4, Suppl. Figure 5, Suppl. Figure 6I, Suppl. Figure 6J.</p>
Data from: miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs
<p>These datasets can be used to reproduce all analyses from the publication "miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs" in conjunction with codes found at https://github.com/mccall-group/miRglmm_paper. </p> <p>"Monocyte_data_subset.rda", "monocyte_exact_subset_filtered2.rda" and "sims_N100_m2_s1_rtruncnorm.rda" can be used to reproduce the simulation analysis. </p> <p>"panel_B_SE.rda" and "ERCC_filtered.rda" can be used to reproduce the ERCC synthetic data analysis with known ground truth.</p> <p>"study89_data_subset.rda" and "study89_data_subset_filtered2.rda" can be used to reproduce the immune cell-type analysis. </p> <p>"bladder_testes_data_subset.rda" and "bladder_testes_data_subset_filtered2.rda" can be used to reproduce the bladder vs testes tissue analysis.</p>
UMI-count modeling and differential expression analysis for single-cell RNA sequencing
GEO Series GSE113660. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Model Counting Competition 2020: Full Instance Set
<p>The dataset contains all instances that the organizers of the competition received or collected during the preparation phase of the Model Counting Competition 2020. The dataset includes short benchmark descriptions (00_description.{pdf,txt,md}) by the submitters/collectors (00_authors.txt).</p><p>For a more details, we refer to the report<br>Fichte, Hecher, Hamiti: The Model Counting Competition 2020.</p><p>-----<br>Changelog:</p><p>2023-10-17 (v2): We updated the instances to the most recent competition format in preparation for the report on the competitions 2021-2023. Note that the old instance set contained various instances with incorrect headers (less variables or clauses than in the actual data), unterminated lines, or a few broken lines. We corrected these instances by scripts that are available on github (daajoe:mc_format_tools).</p>
Bulk RNA-seq count matrices from manuscript: "Therapeutic efficacy of intracerebral hematopoietic stem cell gene therapy in an Alzheimer's disease mouse model"
<p>Gene expression profile of microglia-like cells in the central nervous system (CNS) after transplantation of hematopoietic stem/progenitor cells (HSPC). </p> <p>Different cell subsets and delivery routes are tested to induce a robust and exclusive engraftment of HSPCs and their progeny in the CNS of mice transplant recipients.</p>
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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)
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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.