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89 results for “Test optimization”
Data from: Higher rates of pre‐breeding condition gain positively impacts clutch size: a mechanistic test of the condition‐dependent individual optimization model
1. A combination of timing of and body condition (i.e., mass) at arrival on the breeding grounds interact to influence the optimal combination of the timing of reproduction and clutch size in migratory species. This relationship has been formalized by Rowe et al. in a condition-dependent individual optimization model (American Naturalist, 1994, 143, 689-722), which has been empirically tested and validated in avian species with a capital-based breeding strategy. 2. This model makes a key, but currently untested prediction; that variation in the rate of body condition gain will shift the optimal combination of laying date and clutch size. This prediction is essential because it implies that individuals can compensate for the challenges associated with late timing of arrival or poor body condition at arrival on the breeding grounds through adjustment of their life history investment decisions, in an attempt to maximize fitness. 3. Using an 11-year data set in arctic-nesting common eiders (Somateria mollissima), quantification of fattening rates using plasma triglycerides (an energetic metabolite), and a path analysis approach, we test this prediction of this optimization model; controlling for arrival date and body condition, females that fatten more quickly will adjust the optimal combination of lay date and clutch size, in favour of a larger clutch size. 4. As predicted, females fattening at higher rates initiated clutches earlier and produced larger clutch sizes, indicating that fattening rate is an important factor in addition to arrival date and body condition in predicting individual variation in reproductive investment. However, there was no direct effect of fattening rate on clutch size (i.e., birds laying on the same date had similar clutch sizes, independent of their fattening rate). Instead, fattening rate indirectly affected clutch size via earlier lay dates, thus not supporting the original predictions of the optimization model. 5. Our results demonstrate that variation in the rate of condition gain allows individuals to shift flexibly along the seasonal decline in clutch size to presumably optimize the combination of laying date and clutch size.
Xenium Cloud Optimized Geotiff Test
<p>Test dataset for Cloud Optimized Geotiff</p>
A combination technique for optimal control problems constrained by random PDEs - numerical tests
<p>This dataset contains the codes used and data analyzed in the manuscript ``A combination technique for optimal control problems under uncertainty".</p> <p> </p>
A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection - Online Appendix
<p>QAOA-TCS - Quantum Regression Test Case Selection <br>This repository contains all the necessary resources to reproduce the results of the QAOA-TCS method. </p> <p>Dataset Files <br>The "datasets" folder contains: <br>- "sir_programs" </p> <p>SIR Programs <br>The "datasets/sir_programs" folder contains, for each SIR program considered by this project, all the files needed to gather statement coverage, execution costs, and past fault coverage information. </p> <p>For example, in the "flex" program folder: <br>- The file "fault-matrix.txt" contains rows representing flex's test cases. Each row has columns representing different versions of the program. Each cell (i,j) contains a binary value (0 or 1) indicating whether the i-th test case detects a fault in the j-th version. This configuration is called the fault matrix and provides historical fault coverage information. <br>- The folder "json_flex" contains a folder for each test case, with files like "flexi.gcov.json" to recover statement coverage and execution costs. These files detail which basic blocks were executed and how many times, enabling the calculation of total statement coverage and execution costs for each test case. </p> <p>Source Code Files </p> <p>DIVGA.m <br>The "MATLAB/DIVGA.m" file contains the pipeline for the DIVGA algorithm. For simplicity, the statement coverage, execution costs, and fault coverage data already gathered by "Notebook.ipynb" are written into text files, which DIVGA.m reads to bypass the actual datasets. </p> <p>DIVGA.m must be reconfigured for each target program. Update parameters like M, N, and gamultiobj routine settings. Ensure H_size in line 104 is less than max{N, M} + 1. Update the denominator in line 53 based on the total number of code lines in the target program. Adjust the result reporting target files as well. </p> <p>Notebook.ipynb <br>This file includes pipelines for dataset analysis, algorithm execution, and empirical comparisons. </p> <p>It has two main sections: </p> <p>1. QAOA-TCS vs SelectQA and Classical Algorithms <br> - Pipelines analyze SIR programs and compare QAOA-TCS, SelectQA, and classical algorithms. Manual configuration is needed when changing the target program, including updating file paths for Pareto fronts and configuring frontiers to build the reference. <br> - Statistical analysis requires populating the variables "algorithm_nondom_sirprogram" with the number of non-dominated solutions found during each of the 10 runs. </p> <p>Results Files <br>The "results" folder contains the outcomes of QAOA-TCS, DIV-GA, Additional Greedy, and SelectQA after experiment execution. These files enable empirical evaluations and comparisons between the methods. </p>
Test and Train data for the retrieval experiment in "A molecule generation-oriented lead compound optimization architecture: discovery of potent, selective, oral NLRP3 inflammasome inhibitors"
Open the record for dataset details and reuse information.
B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests
<p>We provide the data and online Appendix for the paper "<strong>B4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests</strong>". </p> <p>File list:</p> <ul> <li><strong>Appendix.pdf</strong>: including the proofs for the theorems in our paper.</li> <li><strong>data.zip</strong>: including datasets and generated code solutions/test cases used for our replicated package, which is available at <a href="https://github.com/ZJU-CTAG/B4">https://github.com/ZJU-CTAG/B4</a>. </li> </ul>
Optimal Spectral Sampling Forward Model Test Data
<p>Binary and ASCII files for tests of the OSS forward model application.</p>
Test file for optimization of Zanfona
<p>.</p>
Optimizing HIV Counseling and Testing and Referral Through an Adaptive Drug Use Intervention
ClinicalTrials.gov study NCT02945436. IPD Sharing: NO. Countries: 1. Publications: 3.
Inspire Home Study: Utilization of Home Monitoring During Therapy Optimization in Patients With an Inspire Upper Airway Stimulation System (Comparison of Home Sleep Testing vs. In-lab Polysomnography
ClinicalTrials.gov study NCT04416542. IPD Sharing: NO. Countries: 1. Publications: 1.
Testing the Feasibility of Intervening to Optimize Chiropractic Care for Adults With Neck Pain Disorders
ClinicalTrials.gov study NCT02483091. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Mixed Meal Test in Type 1 Diabetes: Optimization of Artificial Pancreas-Pilot Study
ClinicalTrials.gov study NCT01800734. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Mixed Meal Test in Type 1 Diabetes on Insulin Pump Therapy: Optimization of Artificial Pancreas
ClinicalTrials.gov study NCT02003274. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Community-engaged Optimization of COVID-19 Rapid Evaluation And TEsting Experiences
ClinicalTrials.gov study NCT05894655. IPD Sharing: NO. Countries: 1. Publications: 2.
Development and Initial Testing of a Couple-Based Intervention to Optimize Suicide and Self-Injury Treatment: COMPASS (Connecting, Overcoming, and Moving Past Suicide and Self-Injury)
ClinicalTrials.gov study NCT04737252. IPD Sharing: NO. Countries: 1. Publications: 4.
Pharmacogenomic Testing to Optimize Antidepressant Drug Therapy
ClinicalTrials.gov study NCT03591224. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Safety, Tolerability and Efficacy of Rapid Optimization, Helped by NT-proBNP testinG, of Heart Failure Therapies
ClinicalTrials.gov study NCT03412201. IPD Sharing: UNDECIDED. Countries: 12. Publications: 13.
Optimization of Tuberculosis Intradermal Skin Test
ClinicalTrials.gov study NCT01611844. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Optimal Evaluation to Reduce Cardiovascular Imaging Testing
ClinicalTrials.gov study NCT05640752. IPD Sharing: NO. Countries: 1. Publications: 1.
Using Duke Activity Status Index (DASI) to Select Optimal Cardiac Stress Tests
ClinicalTrials.gov study NCT00907855. IPD Sharing: Not stated. Countries: 1. Publications: 2.
ScienceDex guides
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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)
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.