1,721,004 research outputs found
A First Journey into the Complexity of Statistical Statements in Probabilistic Answer Set Programming
No full textProbabilistic Answer Set Programming is an efficient formalism to express uncertain information with an answer
set program (PASP). Recently, this formalism has been extended with statistical statements, i.e., statements that
can encode a certain property of the considered domain, within the PASTA framework. To perform inference,
these statements are converted into answer set rules and constraints with aggregates. The complexity of PASP
has been studied in depth, with results regarding both membership and completeness. However, a complexity
analysis of programs with statements is missing. In this paper, we close this gap by studying the complexity of
PASTA statements
Analyzed Benchmarks on Experiments for frasmt v2.0.0
No full textFor details see:
Johannes K. Fichte, Markus Hecher, Stefan Szeider: Breaking Symmetries with RootClique and LexTopSort, Proceedings of the 26th International Conference on Principles and Practice of Constraint Programming (CP'2020).
For frasmt we refer to https://github.com/daajoe/frasmt/releases/tag/v2.0.
Analyzed Benchmarks and Raw Data on Experiments for gpusat
No full text<p>For details see: </p>
<p>Johannes K. Fichte, Markus Hecher, Stefan Woltran, Markus Hecher: Weighted Model Counting on the GPU by Exploiting Small Treewidth, Proceedings of the 26th Annual European Symposium on Algorithms (ESA'2018).</p>
<p>For benchmarking we used a benchmark-tool. See https://github.com/daajoe/benchmark-tool for details.</p>
<p> </p>
Analyzed Benchmarks on Experiments for gpusat2
No full text<p>For details see: </p>
<p>Johannes K. Fichte, Markus Hecher, Markus Hecher: An Improved GPU-based SAT Model Counter, Proceedings of the 25th International Conference on Principles and Practice of Constraint Programming (CP'2019).</p>
<p>For benchmarking we used a benchmark-tool. See https://github.com/daajoe/benchmark-tool for details.</p>
<p>See also: <a href="https://github.com/daajoe/GPUSAT">https://github.com/daajoe/GPUSAT</a> and <a href="https://github.com/daajoe/gpusat_experiments">https://github.com/daajoe/gpusat_experiments</a></p>
Analyzed Benchmarks and Raw Data on Experiments for FraSMT
No full textFor details see:
Johannes K. Fichte, Markus Hecher, Neha Lodha, Stefan Szeider: An SMT Approach to Fractional Hypertree Width, Proceedings of the 24th International Conference on Principles and Practice of Constraint Programming (CP'2018)
Analyzed Benchmarks on Experiments for a SAT Time Leap Challenge
No full textFor details see:
Johannes K. Fichte, Markus Hecher, Stefan Szeider: A Time Leap Challenge for SAT-Solving, Proceedings of the 26th International Conference on Principles and Practice of Constraint Programming (CP'2020).
We include the sources from various authors.
zchaff is available at: https://www.princeton.edu/~chaff/ zchaff.html
For the benchmark set, we refer to https://www.cs.uni-potsdam.de/wv/projects/sets/set-industrial-09-12.tar.xz or https://www.cs.uni-potsdam.de/wv/projects/sets. The instances are also available on Zenodo at: https://doi.org/10.5281/zenodo.398907
A Benchmark Collection of #SAT Instances and Tree Decompositions
No full text<p>Benchmarks used for the paper</p>
<p>Johannes K. Fichte, Markus Hecher, Stefan Woltran, Markus Hecher: Weighted Model Counting on the GPU by Exploiting Small Treewidth, Proceedings of the 26th Annual European Symposium on Algorithms (ESA'2018).</p>
<p>For details we refer to the original sources of the benchmarks (https://tinyurl.com/countingbenchmarks), which contains benchmarks from the following sources:</p>
<ul>
<li><strong>ApproxMC</strong>(165 Instances): <a href="https://www.cs.rice.edu/CS/Verification/Projects/ApproxMC/">https://www.cs.rice.edu/CS/Verification/Projects/ApproxMC/</a></li>
<li><strong>C2D</strong>(14 Instances): <a href="http://reasoning.cs.ucla.edu/c2d/results.html">http://reasoning.cs.ucla.edu/c2d/results.html</a></li>
<li><strong>Cachet</strong>(1090 Instances): <a href="https://www.cs.rochester.edu/u/kautz/Cachet/Model_Counting_Benchmarks/index.html">https://www.cs.rochester.edu/u/kautz/Cachet/Model<em>Counting</em>Benchmarks/index.html</a></li>
<li><strong>counting-benchmarks</strong>(1451 Instances): <a href="https://github.com/dfremont/counting-benchmarks">https://github.com/dfremont/counting-benchmarks</a></li>
</ul>
A Benchmark Collection of Hypergraphs
No full text<p>This benchmark currently contains 2191 hypergraph instances that originate from CQs and CSPs instances from various sources. All hypergraphs have been generated and published by W. Fischl, G. Gottlob, D. M. Longo, and R. Pichler (2017) at <a href="http://hyperbench.dbai.tuwien.ac.at">http://hyperbench.dbai.tuwien.ac.at</a> together with different hypergraph properties including various notions of width.</p>
<p>See Johannes K. Fichte, Markus Hecher, Neha Lodha, and Stefan Szeider: An SMT Approach to Fractional Hypertree Width, Proceedings of the 24th International Conference on Principles and Practice of Constraint Programming (CP2018) for details on the original sources of the benchmarks.</p>
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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