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11 results for “MODULO”
Measuring Software Testability Modulo Test Quality - Replication Package
<p>This repository represents the replication package for the paper <em>Measuring Software Testability Modulo Test Quality</em>.</p> <p>It includes the dataset and the Jupyter Notebook we used for the analysis in our paper.</p>
Canonical Decision Diagrams Modulo Theories - Benchmarking
<p>This archive contains all data and results that were used to benchmark the approach Canonical Decision Diagrams Modulo Theories. The archive contains the following files: 3 subfolders, one for each dataset that was used in the benchmarking process, each one containing a "data" subfolder which contains the problems (in SMT/SMT2 format) and some output folders which contain JSON files describing in detail the results of each run on the problems.</p>
Incremental Linearization for Satisfiability and Verification Modulo Nonlinear Arithmetic and Transcendental Functions
<p>The tarball contains Satisfiability Modulo Theories (SMT) and Verification Modulo Theories (VMT) benchmarks for the theories of Nonlinear Real Arithmetic (NRA) and NRA extended with Transcendental Functions (NTA). These benchmarks have been collected in the following works:</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Invariant Checking of NRA Transition Systems via Incremental Reduction to LRA with EUF". In proc. Tools and Algorithms for the Construction and Analysis of Systems, TACAS'17, 2017.</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Satisfiability Modulo Transcendental Functions via Incremental Linearization". In proc. Int. Conference on Automated Deduction, CADE, 2017.</p> <p>Alessandro Cimatti, Alberto Griggio, Ahmed Irfan, Marco Roveri, Roberto Sebastiani. "Incremental Linearization for Satisfiability and Verification Modulo Nonlinear Arithmetic and Transcendental Functions". ACM Transactions on Computational Logics. 2018. To appear.</p>
Allen Mouse CCF v3.1 ASR - Labels Modulo 65000
<p>If you use this dataset, please cite the original Allen Mouse CCF paper (<a href="https://doi.org/10.1016/j.cell.2020.04.007">https://doi.org/10.1016/j.cell.2020.04.007</a>):</p> <blockquote> <p>Quanxin Wang, Song-Lin Ding, Yang Li, Josh Royall, David Feng, Phil Lesnar, Nile Graddis, Maitham Naeemi, Benjamin Facer, Anh Ho, Tim Dolbeare, Brandon Blanchard, Nick Dee, Wayne Wakeman, Karla E. Hirokawa, Aaron Szafer, Susan M. Sunkin, Seung Wook Oh, Amy Bernard, John W. Phillips, Michael Hawrylycz, Christof Koch, Hongkui Zeng, Julie A. Harris, Lydia Ng,<br>The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas,<br>Cell,<br>Volume 181, Issue 4,<br>2020</p> </blockquote> <p> </p> <p>More information is available in</p> <p><a href="https://community.brain-map.org/t/allen-mouse-ccf-accessing-and-using-related-data-and-tools/359">https://community.brain-map.org/t/allen-mouse-ccf-accessing-and-using-related-data-and-tools/359</a></p> <p>This dataset is a 3D mutichannel image which is derived from the original data introduced by the Allen Mouse Common Coordinate Framework v3 (<a href="http://help.brain-map.org/download/attachments/2818171/MouseCCF.pdf">http://help.brain-map.org/download/attachments/2818171/MouseCCF.pdf</a>).</p> <p>The image file format is a xml/hdf5 file used by ImageJ/Fiji's bigdataviewer (<a href="https://docs.openmicroscopy.org/bio-formats/6.1.0/formats/big-data-viewer.html">https://docs.openmicroscopy.org/bio-formats/6.1.0/formats/big-data-viewer.html</a>).</p> <p>All channels are isotropically sampled with a voxel size of 10 micrometers.</p> <p>Channels:</p> <ul> <li>0: NISSL (<a href="http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ara_nissl/ara_nissl_10.nrrd">http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ara_nissl/ara_nissl_10.nrrd</a>)</li> <li>1: LABELS Border - Delimits the borders of the labels</li> <li>2: ARA</li> <li>3: LABELS Mod 65000 : Labels modulo 65000.</li> </ul> <p>The labels modulo 65000 channel is used because the xml/hdf5 file format handles only 16-bits images, while the reference atlas has (sparse) values above 65535. There is no overlap of labels after computing the label value mod 65000. Thus no loss of information in the mod labels channels.</p> <p>The related ontology from the allen brain (file 1.json) is also stored here.</p> <p>The only modification between this version and the previous one is to use an ASR convention for the positioning in 3D. It will match exactly the BrainGlobe allen_mouse atlases.</p> <p>The xml file which prevents the appearance of a fake extra timepoint in the dataset.</p>
citizen-income-LIMO-MODULO
<p>Illustration of :</p> <ul> <li>The complete application process for the citizen's income benefit. The data was collected using the MODULO method. The method is based on the standards of the Federal Information Management (FIM).</li> <li>The logic of an online application for the citizen's income benefit. It shows the sequence and scope of the required data and integrates data dependencies. The model was created using the LIMO method. The method is based on the standards of the Federal Information Management (FIM).</li> </ul>
Modulo De Papelitos Da Peste
Lucas Eliel Costa Soares Curso superior em tecnologia de jogos digitais. Modelagem de objetos. Modulo de medicos da peste, feitos de papelão. Source: Objaverse 1.0 / Sketchfab
Solving Constraint Horn Clauses Modulo Algebraic Data Types and Recursive Functions
<p>This repository contains the benchmark instances used for the evaluation of a new CHC solving algorithm. There are 4 folders:</p> <ol> <li>leon-original. Contains original benchmarks generated by Leon</li> <li>leon-with-rdf. leon-original benchmarks modified by adding RDFs</li> <li>rust-horn-original. Contains original benchmarks generated by RustHorn</li> <li>rust-horn-with-rdf. rust-horn-original benchmarks modified by adding RDFs</li> </ol> <p>The publisher is named anonymous to respect double blind review process</p>
Data on chirotropical Grassmannians Trop^\chi G(3,6), Trop^\chi G(3,7), Trop^\chi G(3,8), modulo lineality
<h1><strong>README</strong></h1> <p>This is the repository of the paper "The Chirotropical Grassmannian", by Dario Antolini and Nick Early.</p> <p>Here is some advice on how to use the data:</p> <ul> <li>files ".sobj": the files of this form are SageMath objects. In order to load the file "example.sobj" and use it with name "example", open SageMath in the folder where the file is contained and type the line: <pre><code>example = load("example.sobj")</code></pre> </li> <li>files ".sage": the files of this form are SageMath files. They contain SageMath objects, such as functions or lists. In order to load all the functions and the other objects defined in the file "example.sage", open SageMath in the folder where the file is contained and type the line: <pre><code>load("example.sage")</code></pre> </li> <li>files ".py": the files of this form are Python files. In order to use the objects in them, do the following: suppose the file "example.py" contains a list "L1". In order to load the list and use it with the name "L2", open SageMath in the folder where the file is contained and type the line: <pre><code>from example.py import L1 as L2</code></pre> <pre>The same can be done with any object defined in "example.py", for example a function or a dictionary or a tuple.<br><br>If you want to import every object of "example.py", you can simply type:</pre> <pre><code>from example.py import *</code></pre> </li> </ul> <h1>What does this repository contain?</h1> <ul> <li>The implementation of Algorithm 2 from the paper, stored in the file: <pre><code>chirotropical_dressian.sage</code></pre> </li> <li>the implementation of a function which generates all Plücker relations in SageMath, stored in the file: <pre><code>pluecker_relations.sage</code></pre> </li> <li>Plücker relations for the cases (3,6), (3,7) and (3,8), stored in the files: <pre><code>pluecker_relations_3_6.sobj</code> <code>pluecker_relations_3_7.sobj</code> <code>pluecker_relations_3_8.sobj</code></pre> </li> <li>3-term Plücker relations, generating the Dressian, for the cases (3,6), (3,7) and (3,8), stored in the files: <pre><code>3_term_pluecker_relations_3_6.sobj</code> <code>3_term_pluecker_relations_3_7.sobj</code> <code>3_term_pluecker_relations_3_8.sobj</code></pre> </li> <li>the list of all chirotopes (using the lexicographical order) for the cases (3,6), (3,7) and (3,8), stored in the file: <pre><code>chirotopes.py</code></pre> </li> <li>the list of the rays of the Dressian Dr(k,n) modulo lineality for the cases (3,6), (3,7) and (3,8), stored in the files: <pre><code>R36.py</code> <code>R37.py</code> <code>R38.sobj</code></pre> </li> <li>all chirotropical Grassmannians (equal to the corresponding chirotropical Dressians) modulo lineality for the cases (3,6), (3,7) and (3,8), stored in files: <pre><code>Trop_chi{i}_3_6.sobj</code> <code>Trop_chi{i}_3_7.sobj</code> <code>Trop_chi{i}_3_8.sobj</code></pre> as a Python dictionary: <pre><code>d</code> such that <code>d[j]</code> is the list of cones in the fan of dimension j<br><br>These SageMath objects are contained in the zipped files:<br><br><code>Trop_chi_3_6.zip</code> <code>Trop_chi_3_7.zip</code> <code>Trop_chi_3_8.zip</code><br><br>In each of these zip files, there is a special file dedicated to the positive part:<br><br><code>Trop_pos_3_6.sobj</code> <code>Trop_pos_3_7.sobj</code> <code>Trop_pos_3_8.sobj</code></pre> </li> <li> for the case (3,8), a list with all the f-vectors of the different chirotropical Dressians/chirotropical moduli spaces Trop^chi X(3,8). This is organized in the list: <pre><code>f_vectors_trop_chi_3_8.sobj</code><br><br>where the $i$-th element of the list corresponds to the f-vector of the <br>chirotropicalization corresponding to the $i$-th chirotope in the list: <br><br><code>chirotope_list_3_8</code> contained in the file <code>chirotopes.py</code> </pre> </li> <li>for the same purpose, there is also a Python dictionary whose keys are chirotopes (written as tuples), such that, when evaluating the dictionary at the chirotope, it gives the f-vector of the corresponding chirotropicalization as an output. This is stored in the file: <pre><code>f_vectors_trop_chi_3_8_from_chi.sobj</code><br><br>For example, the following code:</pre> <pre><code>f = load("f_vectors_trop_chi_3_8_from_chi.sobj") f[tuple([1 for i in range(binomial(8,3))])]</code></pre> <p>returns:</p> <pre><code>[120, 2072, 14088, 48544, 93104, 100852, 57768, 13612]</code></pre> which is the f-vector of the positive tropical Grassmannian Trop^+ G(3,8).</li> </ul> <h1>Acknowledgements</h1> <p>We thank Dominik Bendle, Janko Böhm, Yue Ren, and Benjamin Schröter for making their data on the Dressian Dr(3,8) and the tropical Grassmannian Trop G(3,8) publicly available; we used the ray data for the computations in the case (3,8). Their data can be found at:</p> <pre><a href="https://agag-jboehm.math.rptu.de/~boehm/singulargpispace/tropical.htm">https://agag-jboehm.math.rptu.de/~boehm/singulargpispace/tropical.htm</a></pre>
Total RNA-seq in the modulo mutant reveals broad changes to the transcriptome.
GEO Series GSE214456. Drosophila melanogaster. 18 samples. Type: Expression profiling by high throughput sequencing.
Drosophila Modulo is Essential for Transposon Silencing and Developmental Robustness.
GEO Series GSE240478. Drosophila melanogaster. 31 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Synthetic TEST FLIM 6D Data Adapted to 5D Modulo
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