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8 results for “branch and bound”
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50). in A New Thaumastocyoninae (Amphicyonidae, Carnivora) From The Early Miocene Of Tuchořice, The Czech Republic
Text-fig. 3. Phylogenetic relationship of Peignecyon felinoides n. gen. et n. sp., within some selected Amphicyonidae, and some extinct caniform carnivorans. Paramiacis exilis is the outgroup. Searches were performed by means of the Branch and Bound and a Bootstrap analysis through 1,000 replicates. One tree is obtained (length 73 steps, consistency index (CI) = 0.6301, retention index (RI) = 0.7000). The numbers below nodes are Bremer indices, and the numbers above nodes are Bootstrap support percentages (only shown ≥ 50).
Figure 34. Interrelationships among basal actinopterygians including the new data from Stegotrachelus finlayi. A branch and bound search reveals a in Devonian actinopterygian phylogeny and evolution based on a redescription of Stegotrachelus finlayi
Figure 34. Interrelationships among basal actinopterygians including the new data from Stegotrachelus finlayi. A branch and bound search reveals a single most parsimonious tree (length = 145; consistency index = 0.57; retention index = 0.75; rescaled consistency index = 0.43). Values immediately below the nodes are Bremer decay indices. Values above Bremer decay indices represent bootstrap support values based on a heuristic search algorithm with 10 000 pseudoreplicates and ten addition-sequence replicates. Bootstrap values in excess of 40% are included. Letters at nodes are keyed to the apomorphy list provided in Appendix S3. Line drawings adapted from: Onychodus jandemarrai, Andrews et al. (2006), with body adapted from Strunius, Jessen (1966); Miguashaia bureaui, Cloutier (1996); Uranolophus wyomingensis, Long (1993); Osteolepis macrolepidotus, Jarvik (1948); Cheirolepis canadensis, Arratia & Cloutier (1996); Cheirolepis schultzei, Arratia & Cloutier (2004); Cheirolepis tralli, Pearson & Westoll (1979); Osorioichthys marginis, Taverne (1997); Donnrosenia schaefferi, Long et al. (2008); Tegeolepis clarki, Friedman & Blom (2006); Howqualepis rostridens, Long (1988); 'Mimia' toombsi, Gardiner (1984); Krasnoyarichthys jesseni, Prokofiev (2002); S. finlayi is original; Moythomasia dugaringa, Gardiner (1984) and Brian Choo (pers. comm., 2008); Moythomasia nitida, Jessen (1968); Limnomis delaneyi, Daeschler (2000); Cuneognathus gardineri and Kentuckia hlavini, Friedman & Blom (2006); Melanecta anneae and Woodichthys bearsdeni, Coates (1998); Wendyichthys dicksoni, Lund & Poplin (1997).
Tip of the Red Giant Branch Bounds on the Axion-Electron Coupling Revisited
<p># Reproduction package for the Paper "Tip of the Red Giant Branch Bounds on the Axion-Electron Coupling Revisited"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 12778 (http://mesa.sourceforge.net/) <br> MESASDK version 20200325 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> Python 3.8.10<br> TensorFlow 2.4.1<br> ## Citation Policy<br> If you use any part of this reproduction package for independent work, we recommend you cite:<br> * This paper<br> If you use any of the MESA outputs found in the LOGS folder, we recommend you cite the MESA papers<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019)</p> <p> </p> <p>For more information, see the Readme.md</p>
Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited
<p>Reproduction Package for the Paper "Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited".</p> <p><strong>File Organization</strong></p> <ul> <li>MESA: MESA modifications to include losses due to the neutrino magnetic dipole moment, scripts to run the grid of models, and post processing pipeline scripts including the Worthey \& Lee bolometric correction code.</li> <li>ML_models: Machine learning code to train and use the models as well as the models themselves.</li> <li>analysis: Plotting code to create figures for papers and presentations.</li> <li>makeGrids: Scripts to create the different input grid files to run MESA on.</li> <li>mcmc: Scripts and plots for the MCMC analysis.</li> <li>mesa_data: All MESA models generated in this project.</li> <li>environment.yml: Conda environment for analysis and the mcmc. </li> </ul> <p>More details can be found in the README files within each directory.</p> <p><strong>Citation Policy</strong><br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers:</p> <ul> <li>This paper</li> <li>https://arxiv.org/abs/2303.12069</li> <li>https://arxiv.org/abs/2305.03113</li> <li>Astrophys. J. Suppl. 192, 3 (2011)</li> <li>Astrophys. J. Suppl. 208, 4 (2013)</li> <li>Astrophys. J. Suppl. 234, 34 (2018)</li> <li>Astrophys. J. Suppl. 243, 10 (2019)</li> </ul> <p><strong>Software</strong></p> <p>Python version 3.8, NumPy version 1.22.3, Pandas version 1.4.3, Matplotlib version 3.5.1, Seaborn version 0.11.2, Tensorflow version 2.4.1, corner version 2.2.1, emcee version 3.1.2, MESA version 12778, MESASDK version x86_64-linux-20.3.2.</p>
Data from: Using branch-and-bound algorithms to optimize selection of a fixed-size breeding population under a relatedness constraint
Tree breeders often face the challenge of conserving genetic diversity, while at the same time maximizing response to selection. When selecting advanced-generation breeding populations, the best-performing candidates will quite often be closely related and selecting them without consideration of their relatedness will very quickly erode genetic diversity. Optimal selection will not completely avoid kinship, but rather maximize gain while imposing a constraint on average relatedness. Genetic contributions are most easily optimized if breeders can manage a real, continuous distribution of contributions from parents. While generally possible when establishing seed orchards, unequal contributions to a breeding population may present difficult and time-consuming operational constraints. In these situations, a specified number of parents contributing equally may be a preferred configuration for the breeding population. Here we formulate the selection of a fixed-size breeding population while imposing a constraint on relatedness of the population members. The problem is expressed as a Mixed Integer Quadratically Constrained Optimization (MIQCO) and solved using branch-and-bound techniques (BB). An open-source solver, dsOpt, was developed and embedded into a user-friendly tool, OPSEL, designed to simplify the process of optimizing selection of breeding populations. Case studies optimizing selection of breeding populations for Scots pine and loblolly pine illustrate the superiority of the BB solution compared with selection from ranked lists with restrictions on numbers of genotypes contributed by each full-sib family, and with solutions from GENCONT, a publically available optimum selection program using an algorithm with Lagrangian multipliers. The case studies also illustrate the extreme differences that can occur with respect to time required to confirm the optimality of solutions found by BB.
Twin-Width Benchmark Results for Branch & Bound and SAT Encodings
<p>Benchmark results on TWLIB and PACE 2023 benchmarks for different twin-width algorithms.</p>
Computing twin-width with SAT and branch & bound - Results
<p>The experimental results, including instances.</p>
Data from: Using branch-and-bound algorithms to optimize selection of a fixed-size breeding population under a relatedness constraint
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