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6 results for βInstance setβ
quapps Instance Data Set
<h1>quapps Instance Database</h1> <p dir="auto">In the quapps package, concrete problem types for quantum optimization are specified. A detailed description of each of the problem types can be found in the corresponding Gitlab repository (<a href="https://gitlab.com/quantum-computing-software/quapps" target="_blank" rel="nofollow noreferrer noopener">https://gitlab.com/quantum-computing-software/quapps</a>). We will therefore not explain the problems in more detail here.</p> <p dir="auto">The instantiated optimization problems of this database are based on the quapps package and were created with the implemented random generators. In view of the qubit numbers available in the near future, the number of instances was limited to instances that comprise a maximum of 128 variables. This resulted in different different combinations of the defining parameters, such as the size of the graphs or their density:</p> <ul> <li> <p>Maximum Cut:</p> <ul> <li>numbers of nodes π ∈ {8, 16, 32, 64, 128, 175},</li> <li>densities π ∈ {0.4, 0.6, 0.8} (too low densities lead to non-connected graphs, whereas graphs, whereas a density of 1.0 results in a complete graph for which the maximum cut problem for which the maximum cut problem is trivial), and</li> <li>a randomly chosen integer edge weighting between 1 and 5 or no weighting at all,</li> </ul> </li> <li> <p>Maximum Colorable Subgraph:</p> <ul> <li>node numbers π ∈ {8, 12, 16},</li> <li>densities π ∈ {0.4, 0.6, 0.8} and</li> <li>a number of colors from 3 to the maximum possible for the respective graph such that the number of variables does not exceed 175,</li> </ul> </li> <li> <p>Ising model:</p> <ul> <li>qubit numbers π ∈ {8, 16, 32, 64, 128, 175} and</li> <li>coupling densities π ∈ {0.2, 0.4, 0.6, 0.8, 1.0}</li> <li>with an accuracy of 2 decimal places,</li> </ul> </li> <li> <p>Prime Factorization:</p> <ul> <li>two random prime numbers</li> <li>with a number of 3 to 11 bits per prime number</li> <li>which result in a non-trivial optimization problem,</li> </ul> </li> <li> <p>Traveling Salesperson:</p> <ul> <li>node numbers from 8 to 13.</li> </ul> </li> <li> <p>Graph Partitioning:</p> <ul> <li>number of nodes N ∈ {8, 15, 35} (limited since number of variables is product of N and k and shall not surpass 175)</li> <li>densities d ∈ {0.4, 0.6, 0.8} (densities can not be too small so a valid graph can be generated)</li> <li>number of subgraphs k ∈ {2, 3, 5} (chosen such that the highest number of variables is 35*5 = 175)</li> <li>weights chosen randomly (uniformly) between 0 and 10 with 2 decimal places</li> </ul> </li> <li> <p>Knapsack:</p> <ul> <li>number of items I ∈ {32, 100, 175} (cant go higher because number of variables = number of items)</li> <li>maximum value v ∈ {6, 12, 24}</li> <li>maximum weight w ∈ {6, 12, 24}</li> <li>maxim weight limit W ∈ {20, 40, 80} (range chosen in accordance with maximum weights)</li> </ul> </li> <li> <p>Minimum k-Union:</p> <ul> <li>number of elements E ∈ {40, 80, 120} (limited since number of variables = number of elements + number of subsets)</li> <li>number of subsets S ∈ {40, 45, 50} (chosen such that the highest number of variables is 125 + 50 = 175)</li> <li>choices of k ∈ {12, 16, 20} (must not exceed number of subsets but needs to be big enough so that at least one valid covering exists)</li> <li>biggest subset sizes s ∈ {28, 34, 40} (must be big enough to ensure existence of valid covering)</li> </ul> </li> <li> <p>Subset Sum:</p> <ul> <li>number of different numbers N ∈ {40, 80, 120, 175} (decides number of variables so limited to 175)</li> <li>maximum number M ∈ {30, 60, 150, 300} (chosen such that for all N, there exists one M thats smaller and one M thats bigger (to allow for both instances with duplicate numbers and instances with unique numbers))</li> <li>maximum target sum T ∈ {500, 5000, 10000} (big enough so that instances with low N and M can still find a valid sum)</li> </ul> </li> </ul> <p dir="auto">We created 5 different instances for each parameter configuration. This results in a total of 125 Ising, 244 Maximum Colorable Subgraph, 150 Maximum Cut, 124 Prime Factorization, 25 Traveling Salesperson instances, 135 Graph Partitioning, 405 Knapsack, 405 Minimum k-Union and 240 Subset Sum Instances.</p> <p dir="auto">Additionally we added 96 Flight-Gate Assignment instances from our publication (<a href="https://doi.org/10.1007/978-3-030-14082-3_9" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1007/978-3-030-14082-3_9</a>):</p> <ul> <li>Flight-Gate Assignment: <ul> <li>with 3 to 17 flights and</li> <li>correspondingly 3 to a maximum of 17 gates.</li> </ul> </li> </ul> <pre> </pre>
Figure 2. Correct Classified Instances for different data sets-Intelligent System for Diagnosis of a Three-Phase Separator
<p>The data mining models may be considered a superior</p> <p>technique that may be successful</p> <p>applied in diagnosis and may be develop in the futu</p> <p>re on the base of more training data to increase</p> <p>the accuracy of results.</p> <p>Industrial processes are dynamic processes with ran</p> <p>dom behavior and whose evolution over</p> <p>time cannot be predicted unless it is well known th</p> <p>e process model and use advanced predictive</p> <p>techniques. Consequently, design and implement an a</p> <p>utomated online monitoring and diagnosis</p> <p>three-phase separator remains a future direction of</p> <p>research conducted so far.</p> <p>Conceptually, this system should have permanent acc</p> <p>ess to data collected from field</p> <p>transducers, to be able to identify the type of fau</p> <p>lt occurred, to locate the fault and provide</p> <p>recommendations to remedy abnormal operating condit</p> <p>ion. Also, updating the database defects with</p> <p>new types of defects occurred and the adequate solu</p> <p>tions adopted for eliminating errors in the</p> <p>operating mode is an important feature to be consid</p> <p>ered during the design of the online diagnosis</p> <p>system. This is possible if the system would have s</p> <p>elf-learning capabilities. To acquire this "skill",</p> <p>the automatic online diagnosis system may contain a</p> <p>diagnosis module based on artificial neural</p> <p>networks.</p>
Set of instances used in article "Medical staff planning for field hospital deployments: the START hospital"
<p>The dataset presented is used in the article "Medical staff planning for field hospital deployments: the START hospital" by F. Javier Martín-Campo, M. Teresa Ortuño and Berta Ruiz-González, submitted for publication (2024).</p> <p>This paper proposes a mathematical optimisation model to deal with the management of emergency staff to a field hospital from a roster of volunteers with different characteristics.</p> <p>A set of Excel files is available being:</p> <ul> <li>Readme: It contains the general information of the files.</li> <li>Data: It contains the data (general parameters, demand, prices, charter flights, health profiles, availability and grades).</li> <li>8 files with the solution for: Infeasibility test, Cost, Availability, Grades, Monoobjective, Goal Programming 1, Goal Programming 2 and Compromise Programming.</li> </ul>
SNDlib-MIPs: A new set of homogeneous MILP instances
<p>We constructed 289 MILP instances based on the Survivable Network Design Library (SNDLib) <a href="http://sndlib.zib.de/home.action">http://sndlib.zib.de/home.action</a> and the models presented within. Our models differ from the formulations provided in that SOS constraints are used in place of big-M constraints, a set of edge disjoint paths are used in place of all possible paths, pre-installed capacity is only available if links are included, and in the bidirectional case capacities are the sum of both capacities instead of the maximum. The models were constructed using SCIP 8.0.3, with the code available at <a href="https://github.com/Opt-Mucca/branching-via-cut-selection">https://github.com/Opt-Mucca/branching-via-cut-selection</a>. Please see the attached PDF for a complete model description.</p>
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>
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>
ScienceDex guides
Understand access before you commit
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.