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FIG. 3 in Lithophyllum artabricum V.Peña, sp. nov. (Corallinales, Rhodophyta): a cryptic species in the Atlantic Iberian Peninsula hitherto assigned to Lithophyllum stictiforme (Areschoug) Hauck
FIG. 3. — Phylogenetic tree inferred from maximum likelihood (ML) and Bayesian inference of psbA sequences included in the present study. In bold Lithophyllum species reported for the European coasts. Bootstrap ML values>60% and posterior probabilities>0.60 from Bayesian inference shown for each node. Scale bar: 0.03 substitutions per site.
FIG. 2 in Lithophyllum artabricum V.Peña, sp. nov. (Corallinales, Rhodophyta): a cryptic species in the Atlantic Iberian Peninsula hitherto assigned to Lithophyllum stictiforme (Areschoug) Hauck
FIG. 2. — Maximum Likelihood (ML) tree of COI-5P sequences included in the present sudy. In bold Lithophyllum species reported for the European coasts. Bootstrap ML values>60% shown for each node. Scale bar: 0.03 substitutions per site.
FIG. 4 in Lithophyllum artabricum V.Peña, sp. nov. (Corallinales, Rhodophyta): a cryptic species in the Atlantic Iberian Peninsula hitherto assigned to Lithophyllum stictiforme (Areschoug) Hauck
FIG. 4. — Morpho-anatomy of Lithophyllum artabricum V.Peña, sp. nov.: A, B, vertical section of the thallus showing a monomerous thallus construction with non-coaxial medulla; C, vertical section of the thallus showing cortical cells disposed in filaments laterally aligned; D, vertical section of the thallus showing a monomerous thallus construction with coaxial medulla, and a tetra/bisporangial uniporate conceptacle empty and buried; E, F, Vertical section of the thallus showing secondary pit-connections between contiguous filaments of cortical cells (arrows) and epithallial cells flattened disposed in 1-2 layers (arrowheads), G, surface view of the thallus showing polygonal epithallial cells; H, I, surface view of two different development stages of tetra/bisporangial uniporate conceptacles showing the pore flush with thallus surface (arrows). A, H, SANT-Algae 11671; B, C, holotype SANT-Algae 33667; D, E, SANT-Algae 26900; F, SANT-Algae 7046; G, SANT-Algae 11683; I, SANT-Algae 15006. Scale bars: A, B, D, 200 µm; C, G-I, 50 µm; E, F, 20 µm.
APPENDIX 2 in Lithophyllum artabricum V.Peña, sp. nov. (Corallinales, Rhodophyta): a cryptic species in the Atlantic Iberian Peninsula hitherto assigned to Lithophyllum stictiforme (Areschoug) Hauck
APPENDIX 2. — Lithophyllum stictiforme (Areschoug) Hauck in Algarve, South Portugal (SANT-Algae 27308): A, habitat of the specimen collected on bedrock at 17 m depth; B, vertical section of the thallus showing a monomerous thallus construction. Scale bar: 500 µm.
FIG. 1 in Lithophyllum artabricum V.Peña, sp. nov. (Corallinales, Rhodophyta): a cryptic species in the Atlantic Iberian Peninsula hitherto assigned to Lithophyllum stictiforme (Areschoug) Hauck
FIG. 1. — Habitat and habit of Lithophyllum artabricum V.Peña, sp. nov.: A-D, characteristic habitat of this species growing in subtidal bedrock, sometimes sciophilous, in the type locality (A), and other Galician localities (B-D, Cedeira, Cambre and Sisargas, respectively); E, F, gross morphology consisted on foliose lamellae or fan-like thallus, single or superimposed (arrow) observed in the specimen SANT-Algae 15006 (E) and in the holotype (F, SANT-33667); G, lower surface of the specimen showing concentric lines, particularly visible at the margins as the remaining surface is covered by sessile invertebrates (SANT-Algae 11666). Scale bars: E, 2 cm; F, G, 1 cm.
Instances and detailed results for the whole testbed of "The Storage Location Assignment and Picker Routing Problem: A Generic Branch-Cut-and-Price Algorithm"
<p>This repository contains the instances and detailed results used for the computational experiments in the article "The Storage Location Assignment and Picker Routing Problem: A Generic Branch-Cut-and-Price Algorithm". Two sets of instances are used:</p> <p><br> The first set of instances comes from the paper "Integrating storage location and order picking problems in warehouse planning" authored by Allyson Silva, Leandro C. Coelho, Maryzam Darvish and Jacques Renaud.<br> https://doi.org/10.1016/j.tre.2020.102003<br> Their instances are available on the following website: https://www.leandro-coelho.com/slot-assignment-and-order-picking/</p> <p><br> The second set of instances comes from the paper "Storage assignment for newly arrived items in forward picking areas with limited open locations" authored by Xiaolong Guo, Ran Chen, Shaofu Du and Yugang Yu.<br> https://doi.org/10.1016/j.tre.2021.102359<br> The set of small instances is made available on this repository, with the kind permission of the authors.</p>
Supplementary Material for "How Students Plagiarize Modeling Assignments"
<p>Supplementary Material for the Paper "How Students Plagiarize Modeling Assignments" at the Educators Symposium at MODELS'23.</p>
Efficacy and Safety Study of Maribavir Treatment Compared to Investigator-assigned Treatment in Transplant Recipients With Cytomegalovirus (CMV) Infections That Are Refractory or Resistant to Treatmen
ClinicalTrials.gov study NCT02931539. IPD Sharing: YES. Countries: 14. Publications: 6.
Custom made python script using network assignment and scoring to estimate the impact of biological processes.
Open the record for dataset details and reuse information.
Insights into natal origins of migratory Nearctic hover flies (Diptera: Syrphidae): New evidence from stable isotope (δ2H) assignment analyses
Open the record for dataset details and reuse information.
Data from: Dispersal in a house sparrow metapopulation: an integrative case study of genetic assignment calibrated with ecological data and pedigree information
Open the record for dataset details and reuse information.
Towards resolving the complex paramagnetic NMR spectrum of small laccase: Assignments of resonances to residue specific nuclei
<p>NMR dataset as TopSpin (Bruker Corporation) files for the article titled "Towards resolving the complex paramagnetic NMR spectrum of small laccase: Assignments of resonances to residue specific nuclei" (https://doi.org/10.5194/mr-2020-31).</p>
Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
1.Dispersal distances are commonly inferred from occupancy data but have rarely been validated. Estimating dispersal from occupancy data is further complicated by imperfect detection and the presence of unsurveyed patches. 2.We compared dispersal distances inferred from seven years of occupancy data for 212 wetlands in a metapopulation of the secretive and threatened California black rail (Laterallus jamaicensis coturniculus) to distances between parent-offspring dyads identified with 16 microsatellites. 3.We used a novel autoregressive multi-season occupancy model that accounted for both unsurveyed patches and imperfect detection to quantify patch isolation using buffer radius (BRM) and incidence function (IFM) connectivity measures at 15 scales (1–10, 15, 20, 25, and 30 km). Connectivity measures were then fit as colonization covariates in occupancy models to estimate a model-averaged dispersal distance. 4.As predicted, colonization was more strongly related to connectivity at small spatial scales (< 10 km). AIC weights were greatest at 7 km for BRM and at 4 km for IFM. 5.Model-averaged dispersal distances (BRM = 7.46 km; IFM = 5.48 km) showed good agreement with the mean (± SE) dispersal distance from 23 parent-offspring dyads (5.58 ± 1.92 km), indicating reasonably accurate mean dispersal distances can be inferred from occupancy data when isolation strongly affects colonization.
Data from: Population genomics and morphometric assignment of western honey bees (Apis mellifera L.) in the Republic of South Africa
Backgrounds: Apis mellifera scutellata and A.m. capensis (the Cape honey bee) are western honey bee subspecies indigenous to the Republic of South Africa (RSA). Both bees are important for biological and economic reasons. First, A.m. scutellata is the invasive "African honey bee" of the Americas and exhibits a number of traits that beekeepers consider undesirable. They swarm excessively, are prone to absconding (vacating the nest entirely), usurp other honey bee colonies, and exhibit heightened defensiveness. Second, Cape honey bees are socially parasitic bees; the workers can reproduce thelytokously. Both bees are indistinguishable visually. Therefore, we employed Genotyping-by-Sequencing (GBS), wing geometry and standard morphometric approaches to assess the genetic diversity and population structure of these bees to search for diagnostic markers that can be employed to distinguish between the two subspecies. Results: Apis mellifera scutellata possessed the highest mean number of polymorphic SNPs (among 2,449 informative SNPs) with minor allele frequencies >0.05 (Np = 88%). The RSA honey bees generated a high level of expected heterozygosity (Hexp = 0.24). The mean genetic differentiation (FST; 6.5%) among the RSA honey bees revealed that approximately 93% of the genetic variation was accounted for within individuals of these subspecies. Two genetically distinct clusters (K = 2) corresponding to both subspecies were detected by Model-based Bayesian clustering and supported by Principal Coordinates Analysis (PCoA) inferences. Selected highly divergent loci (n = 83) further reinforced a distinctive clustering of two subspecies across geographical origins, accounting for approximately 83% of the total variation in the PCoA plot. The significant correlation of allele frequencies at divergent loci with environmental variables suggested that these populations are adapted to local conditions. Only 17 of 48 wing geometry and standard morphometric parameters were useful for clustering A.m. capensis, A.m. scutellata, and hybrid individuals. Conclusions: We produced a minimal set of 83 SNP loci and 17 wing geometry and standard morphometric parameters useful for identifying the two RSA honey bee subspecies by genotype and phenotype. We found that genes involved in neurology/behavior and development/growth are the most prominent heritable traits evolved in the functional evolution of honey bee populations in RSA.
Sequences, taxonomic assignments, and R script from Comparison of Lava Cave Bacterial Mat Communities to Overlying Surface Soil Bacterial Communities from Lava Beds National Monument, USA
<p>This set of files contain the 16S rDNA, taxonomic assignments from the greengenes database, and the R script for processing the data. </p> <p><strong>Abstract</strong></p> <p>Lava caves around the world often support extensive microbial mats on ceilings and walls in a range of colors. Little is known about lava cave microbial diversity and how these subsurface mats differ from microbial communities in overlying surface soils. We generated and analyzed bacterial 16S rDNA from 454 pyrosequencing from three colors of microbial mats (tan, white, and yellow) from seven lava caves in Lava Beds National Monument, CA, USA, and compared them with surface soils overlying each cave. <em>Actinobacteria</em> dominated in all samples, with 39% (caves) and 21% (surface soils). <em>Proteobacteria</em> made up 30% of phyla from caves and 36% from surface soil with <em>Gamma</em>- 20% and <em>Alpha</em>- 10% in the caves and <em>Gamma</em>- 18% with <em>Alpha</em>-17% in soil. Other major phyla in caves were <em>Nitrospirae</em> (7%) followed by Minor Phyla (7%), compared to surface soils with <em>Bacteriodetes</em> (8%) and Minor phyla (8%). A very high proportion (53.33%) of the most abundant sequences could not be identified to genus, indicating a high degree of novelty. Surface soil samples had more OTUs and greater diversity indices than cave samples. The same phyla were represented in both soils and cave microbial mats, but the overlap was only 11.2% at the operational taxonomic unit (OTU). Although surface soil microbes immigrate into underlying caves, the environment selects for microbes able to live in the cave habitats, resulting in very different cave microbial communities. In terms of species richness, diversity by mat color differed, but not significantly. Number of entrances per cave, distance from an entrance, cave length, and temperature also contributed to observed differences in diversity. With high levels of novel microbes, caves may represent excellent habitats for the isolation of new bioactive compounds. This study is the first comprehensive comparisons of bacterial communities in lava caves with the overlying soil community.</p>
Experiments with Frequency Fitness Assignment based Algorithms on the Traveling Salesperson Problem
<p><strong>1. Introduction</strong></p><p>In this archive, we provide the implementation and experimental results of eight different algorithms to solve Traveling Salesperson Problem (TSP) instances from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>.</p><p>A TSP is defined by a fully-connected weighted graph of n cities. The goal is to find the overall shortest tour that visits each cities exactly once and returns to its starting point. The TSP is NP-hard. We consider 56 symmetric instances from the well-known <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>.</p><p>Solutions in our work are stored in the path representation, where such a tour is encoded as a permutation x of the numbers 1 to n, each identifying a city. If a city appears at index j in the permutation x, then it will be the jth city to be visited. This means that a tour x will pass the following edges: (x[1], x[2]), (x[2], x[3]), (x[3], x[4]), … (x[n-1], x[n]), (x[n], x[1]).</p><p><strong>2. Directory Structure</strong></p><p>This dataset is split into multiple separate <i>tar.xz</i> archives. These can be unpacked in the same folder and will produce the directory structure described below. Each archive contains this note and the license information, but apart from that, there is no redundancy.</p><p>This archive contains the following directories:</p><ul><li>source contains the Python source codes needed to run the experiment.<ul><li>moptipy-main is a local copy of the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package used for our experiment.</li><li>tsplib contains the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> data. This includes the instances used in our experiments as files in text format with suffix .tsp. If an optimal tour is given by TSPLib, it is stored in a text format file with suffix .opt.tour and name prefix identical to the instance file. In other words, the file eil51.tsp contains the TSP instance eil51 and the file eil51.opt.tour contains the corresponding optimal tour. Both the TSP instances and optimal tours can be downloaded from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/</a>. We also include the documentation of TSPLIB in file <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">tsp95.pdf</a> documenting them. We further include the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/TSPFAQ.html">TSPLIB FAQ</a> both as HTML and PDF file (tsplib_faq.html and tsplib_faq.pdf) and the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/STSP.html">list of known optimal tour lengths</a> as HTML and PDF file (optimal_tour_lengths_of_symmetric_tsps.html, optimal_tour_lengths_of_symmetric_tsps.pdf). Notice that, while the TSP instances we used are Euclidean, all distances are converted to integers as prescribed by the <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">documentation</a>.</li></ul></li><li>results is the directory with the log files. Each log file contains information of one run, i.e., one execution of one algorithm on one problem instance. All improving moves of a run as well as the final solution are stored in the log file. The direct sub-folders results represent the algorithms and contain one folder per TSP instance, which, in turn, contain the log files.</li><li>evaluation is a folder with the extracted evaluation and figures</li><li>evaluation_edited is a folder with evaluation figures slightly edited for better visual appeal (but obviously without changing any result / scientific content)</li><li>evaluator is a folder with a Python script main.py that generates all the files in evaluation from the data it finds in results. It requires the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package being installed for running in the version given in requirements.txt.</li></ul><p><strong>3. Algorithms</strong></p><p>The (1+1) EA is the most basic evolutionary algorithm and also be considered as a randomized local search. It starts with one random solution/permutation xc and computes its length yc=f(xc). In each iteration, it applies a unary search operator op to obtain a new tour xn=op(xc) and computes its length yn=f(xn). If yn<=yc, then it will accept the new tour and set xn=xn and yc=yn. The results of this algorithm are given in folder results/ea_revn.</p><p>FFA is a fitness assignment process that takes place before this last step in the EA. We integrate FFA into the (1+1) EA and obtain the (1+1) FEA. This algorithm uses an additional table H which counts, for any tour length y, how often it has been seen during the search so far. After the new tour xn is created and its objective value yn is computed, the (1+1) FEA sets H[yc] = H[yc] + 1 and H[yn] = H[yn] + 1. It will accept xn if and only if H[yn] <= H[yc] and, only in this case, set xn=xn and yc=yn. The results of this algorithm are given in folder results/fea_revn.</p><p>SA is the classical simulated annealing algorithm. In our experiment, it will accept the new solution xn with probability P. If the new solution is better, the acceptance probability P is 1. For worse solutions, the probability is between 0 and 1, i.e., sometimes, worse solution are also accepted. This algorithm has a temperature cooling schedule. It starts at an initial temperature and over time, the temperature decreases. The probability P of accepting the worse solution depends on the temperature and decreases as well. The results of this algorithm are given in folder results/sa_revn.</p><p>An FFA-based version of SA uses the frequency fitness instead of the objective values in all acceptance decisions. The results of this algorithm are given in folder results/fsa_revn.</p><p>EAFEA(A) is a hybrid which alternates between the EA and the FEA and copies a solution from the FEA to the EA if it has an entirely new objective value, i.e., if H[yn] = 1. The results of this algorithm are given in folder results/eafea2_revn.</p><p>SAFEA(A) is a hybrid which alternates between the SA and the FEA and copies a solution from the FEA to the SA if it has an entirely new objective value, i.e., if H[yn] = 1. The results of this algorithm are given in folder results/safea2_revn.</p><p>EAFEA(B) is a hybrid which alternates between the EA and the FEA and copies a solution from the FEA to the EA part if it has a better objective value. The results of this algorithm are given in folder results/eafea_revn.</p><p>SAFEA(B) is a hybrid which alternates between the SA and the FEA and copies a solution from the FEA to the SA part if it has a better objective value. The results of this algorithm are given in folder results/safea_revn.</p><p>We apply all algorithms with the same unary operator reverse, which reverses a randomly chosen subsequence of the tour. This operator is also often called a "2-opt move". It has the advantage that the new objective value of a new solution can be computed in O(1) if the objective value of the solution from which it is derived is known.</p><p><strong>4. How to Run the Experiment</strong></p><p>First, you need to make sure to have all the dependencies installed that this program requires. You can do this by executing the following command in the terminal:</p><p>pip install matplotlib numba numpy psutil scikit-learn moptipy moptipyapps</p><p>Now enter the source directory, i.e., the directory containing the run.py file, in your terminal. Depending on your system configuration and whether you run Windows or Linux, you can start the program with <i>one</i> of the commands below. (If running the first command returns with an error, just try the next one in the list.)</p><ul><li>python3 -m run</li><li>python -m run</li><li>python run.py</li><li>python3 run.py</li></ul><p>Then the experiment will run. It will automatically create a sub-folder results in source and place all log files that are generated into it. Be careful: The experiment will take a long time. However, if you have multiple CPUs, you can simply start several instances of this program in independent terminals. Each instance will then conduct different runs. This also works if this folder is shared over the network, in which case you can run multiple processes on multiple PCs.</p><p>Side note: This experiment uses the <a href="https://thomasweise.github.io/moptipy">moptipy</a> package for implementing its algorithms, running the experiments, and gathering their results. If you want to install moptipy on your system instead of using the version supplied here, you can install it via pip install moptipy. It also uses moptipyapps to load some data.</p><p><strong>5. Literature</strong></p><ul><li>Frequency Fitness Assignment (FFA):<ol><li>Thomas Weise, Zhize Wu, Xinlu Li, Yan Chen, and Jörg Lässig. Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient. IEEE Transactions on Evolutionary Computation (TEVC). 2022. Early Access. doi:<a href="https:doi.org/10.1109/TEVC.2022.3191698">10.1109/TEVC.2022.3191698</a>.</li><li>Thomas Weise, Zhize Wu, Xinlu Li, and Yan Chen. Frequency Fitness Assignment: Making Optimization Algorithms Invariant under Bijective Transformations of the Objective Function Value. <i>IEEE Transactions on Evolutionary Computation</i> 25(2):307–319. April 2021. Preprint available at <a href="http://arxiv.org/abs/2001.01416">arXiv:2001.01416v5</a> [cs.NE] 15 Oct 2020. doi:<a href="http://dx.doi.org/10.1109/TEVC.2020.3032090">10.1109/TEVC.2020.3032090</a>. Experimental results and source code are available at doi:<a href="http://doi.org/10.5281/zenodo.3899474">10.5281/zenodo.3899474</a>.</li><li>Tianyu Liang, Zhize Wu, Jörg Lässig, Daan van den Berg, and Thomas Weise. Solving the Traveling Salesperson Problem using Frequency Fitness Assignment. In Hisao Ishibuchi, Chee-Keong Kwoh, Ah-Hwee Tan, Dipti Srinivasan, Chunyan Miao, Anupam Trivedi, and Keeley A. Crockett, editors, Proceedings of the IEEE Symposium on Foundations of Computational Intelligence (IEEE FOCI'22), part of the IEEE Symposium Series on Computational Intelligence (SSCI 2022). December 4–7, 2022, Singapore, pages 360–367. IEEE. doi:<a href="https://doi.org/10.1109/SSCI51031.2022.10022296">10.1109/SSCI51031.2022.10022296</a>.</li><li>Thomas Weise, Mingxu Wan, Ke Tang, Pu Wang, Alexandre Devert, and Xin Yao. Frequency Fitness Assignment. <i>IEEE Transactions on Evolutionary Computation (IEEE-EC)</i> 18(2):226-243, April 2014. doi:<a href="http://dx.doi.org/10.1109/TEVC.2013.2251885">10.1109/TEVC.2013.2251885</a>.</li><li>Thomas Weise, Xinlu Li, Yan Chen, and Zhize Wu. Solving Job Shop Scheduling Problems Without Using a Bias for Good Solutions. In <i>Genetic and Evolutionary Computation Conference Companion (GECCO'21 Companion),</i> July 10-14, 2021, Lille, France. ACM, New York, NY, USA. ISBN 978-1-4503-8351-6. doi:<a href="http://doi.org/10.1145/3449726.3463124">10.1145/3449726.3463124</a>.</li><li>Thomas Weise, Yan Chen, Xinlu Li, and Zhize Wu. Selecting a diverse set of benchmark instances from a tunable model problem for black-box discrete optimization algorithms. <i>Applied Soft Computing Journal (ASOC)</i>, 92:106269, June 2020. doi:<a href="http://dx.doi.org/10.1016/j.asoc.2020.106269">10.1016/j.asoc.2020.106269</a>.</li><li>Thomas Weise, Mingxu Wan, Ke Tang, and Xin Yao. Evolving Exact Integer Algorithms with Genetic Programming. In <i>Proceedings of the IEEE Congress on Evolutionary Computation (CEC'14), Proceedings of the 2014 World Congress on Computational Intelligence (WCCI'14)</i>, pages 1816-1823, Beijing, China, July 6-11, 2014. Los Alamitos, CA, USA: IEEE Computer Society Press. ISBN: 978-1-4799-1488-3. doi:<a href="http://dx.doi.org/10.1109/CEC.2014.6900292">10.1109/CEC.2014.6900292</a>.</li></ol></li><li>Traveling Salesperson Problem (TSP):<ol><li>Pedro Larrañaga, Cindy M. H. Kuijpers, Roberto H. Murga, I. Inza, and S. Dizdarevic. Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators. <i>Artificial Intelligence Review,</i> 13(2):129–170, April 1999. Kluwer Academic Publishers, The Netherlands. doi:<a href="https://doi.org/10.1023/A:1006529012972">10.1023/A:1006529012972</a>.</li><li>Gerhard Reinelt. TSPLIB — A Traveling Salesman Problem Library. <i>ORSA Journal on Computing</i> 3(4):376-384. 1991. <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/</a>.</li><li>Gerhard Reinelt. TSPLIB95. 1995. Heidelberg, Germany: Universität Heidelberg, Institut für Angewandte Mathematik. <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf">http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp95.pdf</a>.</li><li>Thomas Weise, Raymond Chiong, Ke Tang, Jörg Lässig, Shigeyoshi Tsutsui, Wenxiang Chen, Zbigniew Michalewicz, and Xin Yao. Benchmarking Optimization Algorithms: An Open Source Framework for the Traveling Salesman Problem. <i>IEEE Computational Intelligence Magazine (CIM)</i> 9(3):40-52, August 2014. doi:<a href="http://dx.doi.org/10.1109/MCI.2014.2326101">10.1109/MCI.2014.2326101</a>.</li><li>Eugene Leighton Lawler, Jan Karel Lenstra, Alexander Hendrik George Rinnooy Kan, and David B. Shmoys. <i>The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization.</i> Wiley Interscience. 1985.</li><li>David Lee Applegate, Robert E. Bixby, Vasek Chvatal, and William John Cook. <i>The Traveling Salesman Problem: A Computational Study.</i> Princeton University Press. 2007.</li><li>Gregory Z. Gutin and Abraham P. Punnen, editors. <i>The Traveling Salesman Problem and its Variations.</i> Volume 12 of Combinatorial Optimization. Kluwer Academic Publishers. 2002. doi:<a href="https://dx.doi.org/10.1007/b101971">10.1007/b101971</a>.</li></ol></li><li>Software:<ol><li>The Metaheuristic Optimization in Python Package <a href="https://thomasweise.github.io/moptipy">moptipy</a></li></ol></li></ul><p><strong>6. License</strong></p><p>The files in this repository are under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>, with the exception of the files of <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a> in directory source/tsplib, which are under copyright of their respective owner (we believe that they are in the public domain, as they are provided by many sources, included in many software packages under various open source licenses, and on many websites). The license is contained as file LICENSE.txt in this archive.</p><p><strong>7. Contact</strong></p><p>If you have any questions or suggestions, please contact</p><p>Mr. Tianyu LIANG (梁天宇) of the Institute of Applied Optimization (应用优化研究所, <a href="http://iao.hfuu.edu.cn">IAO</a>) of the School of Artificial Intelligence and Big Data (<a href="http://www.hfuu.edu.cn/aibd/">人工智能与大数据学院</a>) at <a href="http://www.hfuu.edu.cn/english/">Hefei University</a> (<a href="http://www.hfuu.edu.cn/">合肥学院</a>) in Hefei, Anhui, China (中国安徽省合肥市) via email to <a href="mailto:liangty@stu.hfuu.edu.cn">liangty@stu.hfuu.edu.cn</a>.</p>
Assignment 6 | Filipino Christmas Star
Denise Ferioli DIG4780C Final Game Object Filipino-inspired Christmas Star Lantern/Wreath (Parol) Source: Objaverse 1.0 / Sketchfab
Benchmark dataset for the Iterative Rotations and Assignments (IRA) algorithm
<p>In the publication of the Iterative Rotations and Assignments (<a href="https://doi.org/10.1021/acs.jcim.1c00567">IRA</a>) algorithm, a dataset of atomic structures was used for the benchamrking of the algorithm. Two other algorithms were included in the benchmark, <a href="https://doi.org/10.1021/acs.jcim.6b00546">ArbAlign</a> and <a href="https://doi.org/10.1021/acs.jctc.7b00543">fastoverlap</a>. The atomic structures were obtained from several other publications, for details please refer to <a href="https://doi.org/10.1021/acs.jcim.1c00567">IRA</a> publication. </p> <p>The data shared here contains the atomic structures, copies of algorithms, and all scripts used in the benchmark of the reference paper.<br>The source code of IRA algorithm is accessible on <a href="https://github.com/mammasmias/IterativeRotationsAssignments/tree/master">github</a>.</p> <p>Each algorithm and dataset contained in this archive may be subject to its own license, please refer to the README files inside.</p>
HHH, HH, and QCD events for boosted and resolved Higgs boson jet assignment
<h2>Description</h2> <p>This dataset contains HHH, HH, and QCD events for boosted and resolved Higgs boson jet assignment. The datasets contain two sets of input jets: small-radius jets (<code>Jets</code>) reconstructed with the anti-kT algorithm with radius parameter R=0.5 (AK5 jets) and large-radius jets (<code>BoostedJets</code>) reconstructed with the anti-kT algorithm with a radius parameter R=0.8 (AK8 jets). The dataset content is below.</p> <h2>Files</h2> <p>The different files are used to train and evaluate different symmetry-preserving attention networks (SPA-Nets).</p> <ul> <li><code>hhh_mh120-130_training.h5</code>: Contains HHH events with Higgs boson masses of 120, 122.5, 125, 127.5, and 130 GeV. Used to train the boosted+resolved HHH SPA-Net model.</li> <li><code>hhh_mh125_training.h5</code>: Contains HHH events with a Higgs boson mass of 125 GeV. Used to train the resolved HHH SPA-Net model.</li> <li><code>hhh_mh125_testing.h5</code>: Contains HHH events with a Higgs boson mass of 125 GeV. Used to evaluate the resolved and boosted+resolved HHH SPA-Net models.</li> <li><code>hh_mh125_training.h5</code>: Contains HH events with a Higgs boson mass of 125 GeV. Used to train the resolved and boosted+resolved HH SPA-Net models.</li> <li><code>hh_mh125_testing.h5</code>: Contains HH events with a Higgs boson mass of 125 GeV. Used to evaluate the resolved and boosted+resolved HH SPA-Net models.</li> <li><code>qcd_training.h5</code>: Contains QCD multijet events. Used to train the baseline boosted decision tree (BDT) large-radius jet tagger.</li> <li><code>qcd_testing.h5</code>: Contains QCD multijet events. Used to evaluate the mass sculpting.</li> </ul> <h2>Content</h2> <pre><code>INPUTS BoostedJets MASK fj_charge fj_chargedenergyfrac fj_cosphi fj_ehadovereem fj_eta fj_mass fj_ncharged fj_neutralenergyfrac fj_nneutral fj_phi fj_pt fj_sdmass fj_sinphi fj_tau21 fj_tau32 Jets MASK btag cosphi eta flavor mass matchedfj phi pt sinphi TARGETS bh1 bb mask pt bh2 bb mask pt h1 b1 b2 mask pt h2 b1 b2 mask pt</code></pre>
Data for high-temperature assignment paper
<p>Data used in the paper reporting on the assignment of ICCG-S165A:</p> <p>Data used in figure 1 (folder Figure 1): <br>17: TROSY @ 30ºC, 117: TROSY @ 50ºC, 18: HSQC @ 30ºC, 118: HSQC @ 50ºC, 14: HMQC @ 30ºC, 114: HMQC @ 50ºC</p> <p>Data used for ICCG-S165A assignment (folder Assignment_ICCG_S165A): <br>25: HNCO, 35: HNCACB</p> <p>Data used for Mutant2 assignment (folder Mutant2):<br>9: hNcaNNH, 15:HNCO, 17: HNCACO, 19: HNCACB, 21: HncaNNH</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.