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1,019 results for “Assignment”
Experimental Data for the Paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient"
<p><strong><em>The data for the paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient"</em></strong></p> <p>This is the data set with the experimental results for our paper "Frequency Fitness Assignment: Optimization without a Bias for Good Solutions can be Efficient." We conduct more than 56 million runs, consuming more than 6.5*10<sup>14</sup> FEs as well as 150 processor years, ensuring that our results are statistically sound and rigorous. Here, you can find all the results, all the program codes used for obtaining the results, and all the tables and figures produced from the results, and the program codes used to produce them.</p> <p><strong><em>Included Files</em></strong></p> <ul> <li><code>ffa-empirical-complexity_results.tar.xz</code> (size packed 11.7 GiB, unpacked 335.2 GiB): The complete set of log files. For each run of each experiment, one distinct text-based log file is created. The log file contains every improving step of the algorithm, the final result, and the system configuration. This archive is very large and unpacked it will occupy more than 335 GiB of hard disk space.</li> <li><code>ffa-empirical-complexity_end_of_run_results_and_stats.tar.xz</code> (size packed 861.4 MiB, unpacked 6,295.9 MiB): The end-of-run result qualities and consumed runtime as well as statistics thereof. These information have been extracted from the log files and are provided in form of semicolon-separated values text files. These files are much easier to consume. They do not reflect the progress of the single runs, but only their end results.</li> <li><code>ffa-empirical-complexity_sources.tar.xz</code> (size packed 65.5 MiB, unpacked 173.6 MiB): The complete set of <code>Java</code> sources that was used to perform the experiments. Since the random seeds of the random number generators are created in a deterministic way, you could execute this code and obtain the exactly same log files in terms of consumed FEs, improving steps, and end results as we provide in <code>ffa-empirical-complexity_results.tar.xz</code>. (Of course, your systems configuration and measured runtime in milliseconds would probably be different.)</li> <li><code>ffa-empirical-complexity_evaluator.tar.xz</code> (size packed 4,015.8 KiB, unpacked 4,510 KiB): The <code>Java</code> and <code>R</code> source codes that are used to extract the end results from the log files, compute all relevant statistics, and produce the graphics in our article.</li> <li><code>ffa-empirical-complexity_evaluation.tar.xz</code> (size packed 417.3 KiB, unpacked 590.0 KiB): The high-level conclusions produced by the evaluator from the raw data, including tables and figures.</li> <li><code>ffa-empirical-complexity_saga_ffa_on_plateau.tar.xz</code> (size packed 7,238.9 KiB, unpacked 26.8 MiB): We also conducted an additional experiment to better understand the behavior of the SAGA algorithm variants using FFA on the Plateau problem. Here we provide source codes and result log files of this experiment. The result log files are much more comprehensive, as we tried to figure out why these algortihms were able to solve the Plateau problems (and they ultimately helped us to successfully do so).</li> </ul> <p><strong><em>License</em></strong></p> <p>The copyright holder of this dataset is Prof. Dr. Thomas Weise (see <a href="#contact">Contact</a>). The dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0/en/legalcode">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong><em>Contact</em></strong></p> <p>If you have any questions or suggestions, please contact the corresponding author of this dataset, Prof. Dr. <a href="http://iao.hfuu.edu.cn/team/director">Thomas Weise</a> of the Institute of Applied Optimization (<a href="http://iao.hfuu.edu.cn/">IAO</a>) at <a href="http://www.hfuu.edu.cn/english/main.htm">Hefei University</a> [<a href="http://www.hfuu.edu.cn">合肥学院</a>] in Hefei, Anhui, China via email to <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a> with CC to <a href="mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a>.</p>
NMR assignment of methyl groups in solid-state using 1H-detection and fast MAS - NMR raw and processed data
<p>This data set contains raw NMR data in Bruker format for experimental series on (1) 2,3-13C-labelled microcrystalline alanine, (2) U-13C,15N-labelled N-fomylated microcrystalline tripeptide Met-Leu-Phe, and (3,4) two differently labelled (ILV-C4 and ILV-C5) microcrystalline chicken-alpha-spectrin SH3 domain. Measurements were performed at 14.4 T and 55.5 kHz MAS (alanine), 18.8 T and 55.5 and 98 kHz MAS (fMLF), 23.5 T and 55.5 kHz (SH3 C5), 18.8T and 55.5 and 94.5 kHz MAS (SH3 C5) and 18.8T and 55.5 and 94.5 kHz MAS (SH3 C4). The data set also contains Fourier processed data (spectra) in UCSF format, Sparky project, save and peak list files. Pulse programs for Bruker spectrometers are provided. The data set is complemented with SIMPSON scripts for simulation of spin dynamics under aformentioned conditions.</p>
Assignment feedback in school choice mechanisms
<p>This paper experimentally investigates the provision of real-time feedback about school assignments during the preference reporting period in three widely employed mechanisms: deferred acceptance, top trading cycles, and the Boston mechanism. Adaptive models predict that greater sensitivity to tentative assignments during the reporting period will produce more equilibrium assignments in all three mechanisms. Consistent with adaptive predictions, real-time assignment feedback consistently increased equilibrium assignments but did not increase truthful reporting. These findings suggest that providing feedback about assignments during the preference reporting period could help student assignment mechanisms more reliably achieve policy goals.</p>
Experiments with Frequency Fitness Assignment in a (1+1) EA on the Traveling Salesperson Problem
<p><strong>1. Introduction</strong></p> <p>The implementation and experimental results of the (1+1) EA with and without Frequency Fitness Assignment (FFA) to solve the <code>EUC_2D</code> 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 <code>n</code> 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 18 symmetric Euclidean 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 <code>x</code> of the numbers <code>1</code> to <code>n</code>, each identifying a city. If a city appears at index <code>j</code> in the permutation <code>x</code>, then it will be the <code>j</code><sup>th</sup> city to be visited. This means that a tour <code>x</code> will pass the following edges: <code>(x[1], x[2])</code>, <code>(x[2], x[3])</code>, <code>(x[3], x[4])</code>, … <code>(x[n-1], x[n])</code>, <code>(x[n], x[1])</code>.</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 <code>xc</code> and computes its length <code>yc=f(xc)</code>. In each iteration, it applies a unary search operator <code>op</code> to obtain a new tour <code>xn=op(xc)</code> and computes its length <code>yn=f(xn)</code>. If <code>yn<=yc</code>, then it will accept the new tour and set <code>xn=xn</code> and <code>yc=yn</code>.</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 <code>H</code> which counts, for any tour length <code>y</code>, how often it has been seen during the search so far. After the new tour <code>xn</code> is created and its objective value <code>yn</code> is computed, the (1+1) FEA sets <code>H[yc] = H[yc] + 1</code> and <code>H[yn] = H[yn] + 1</code>. It will accept <code>xn</code> if and only if <code>H[yn] <= H[yc]</code> and, only in this case, set <code>xn=xn</code> and <code>yc=yn</code>.</p> <p>We apply both EAs with two operators. <code>swap</code> exchanges two randomly chosen cities in the permutation. <code>reverse</code> reverses a randomly chosen subsequence of the tour.</p> <p><strong>2. Directory Structure</strong></p> <p>This archive contains the following directories:</p> <ul> <li><code>results_and_evaluation</code> contain the results of two experiments as well as their evaluation. <ul> <li><code>performance</code> contains the results and evaluation of the main experiment, namely the 21 runs on 18 <code>EUC_2D</code> Traveling Salesperson Problem (TSP) instances from <a href="http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/">TSPLIB</a>. <ul> <li><code>results</code> is the directory with the log files</li> <li><code>evaluation</code> is a folder with the extracted evaluation and figures</li> <li><code>evaluation.py</code> is a Python script that generates all the files in <code>evaluation</code> from the data it finds in <code>results</code>. It requires the <a href="https://thomasweise.github.io/moptipy"><code>moptipy</code></a> package being installed for running.</li> </ul> </li> <li><code>H</code> contains the results of the experiment conducting single runs on the instances and gathering the data of the frequency table <code>H</code> at different objective function evaluations in the log files. <ul> <li><code>results</code> is the directory with the log files</li> <li><code>evaluation</code> is a folder with the extracted evaluation and figures</li> <li><code>evaluation.py</code> is a Python script that generates all the files in <code>evaluation</code> from the data it finds in <code>results</code>. It requires the <a href="https://thomasweise.github.io/moptipy"><code>moptipy</code></a> package being installed for running.</li> </ul> </li> </ul> </li> <li><code>source</code> contains the Python source codes needed to run the <code>performance</code> experiment. <ul> <li><code>moptipy</code> is a local copy of the <a href="https://thomasweise.github.io/moptipy"><code>moptipy</code></a> package used for our experiment.</li> <li><code>tsplib</code> 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 <code>.tsp</code>. If an optimal tour is given, it is stored in a text format file with suffix <code>.opt.tour</code> and name prefix identical to the instance file. In other words, the file <code>eil51.tsp</code> contains the TSP instance <code>eil51</code> and the file <code>eil51.opt.tour</code> 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"><code>tsp95.pdf</code></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 (<code>tsplib_faq.html</code> and <code>tsplib_faq.pdf</code>) 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 (<code>optimal_tour_lengths_of_symmetric_tsps.html</code>, <code>optimal_tour_lengths_of_symmetric_tsps.pdf</code>). 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> </ul> <p><strong>3. What algorithms are included in this experiment?</strong></p> <p>We implement the simple local search with (1+1) EA with and without Frequency Fitness Assignment. We use the common path representation for the TSP with <code>n</code> cities, which encodes each solution as a permutation of the numbers <code>1..n</code>. The value <code>i</code> at position <code>j</code>, i.e., <code>x[j] = i</code>, in such a permutation indicates that the <code>j</code><sup>th</sup> city to be visited by <code>i</code>. We implement two search operators, namely <code>reverse</code>, which reverses a subsequence of the tour, and <code>swap</code> which swaps two cities.</p> <ul> <li>(1+1) EA <ul> <li>with <code>swap</code> operator</li> <li>with <code>reverse</code> operator</li> </ul> </li> <li>(1+1) FEA, i.e., the (1+1) EA with Frequency Fitness Assignment (FFA) <ul> <li>with <code>swap</code> operator</li> <li>with <code>reverse</code> operator</li> </ul> </li> </ul> <p><strong>4. How to Run the Experiment</strong></p> <p>First, you must 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> <pre><code>pip install matplotlib numba numpy pandas psutil scikit-learn</code></pre> <p>Now enter the <code>source</code> directory, i.e., the directory containing the <code>run.py</code> file, in your terminal. Depending on your system configuration and whether you run Windows or Linux, you can start the program with <em>one</em> of the commands below. (If running the first command returns with an error, just try the next one in the list.)</p> <ul> <li><code>python3 -m run</code></li> <li><code>python -m run</code></li> <li><code>python run.py</code></li> <li><code>python3 run.py</code></li> </ul> <p>Then the experiment will run. It will automatically create a sub-folder <code>results</code> in <code>source</code> 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"><code>moptipy</code></a> package for implementing its algorithms, running the experiments, and gathering their results. If you want to install <code>moptipy</code> on your system instead of using the version supplied here, you can install it via <code>pip install moptipy</code>.</p> <p><strong>5. Literature</strong></p> <ul> <li>Frequency Fitness Assignment (FFA): <ol> <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. <em>IEEE Transactions on Evolutionary Computation</em> 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>Thomas Weise, Zhize Wu, Xinlu Li, Yan Chen, and Jörg Lässig. Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient. <a href="https://arxiv.org/abs/2112.00229v4">arXiv:2112.00229v4</a> [cs.NE] 25 May 2022.</li> <li>Thomas Weise, Mingxu Wan, Ke Tang, Pu Wang, Alexandre Devert, and Xin Yao. Frequency Fitness Assignment. <em>IEEE Transactions on Evolutionary Computation (IEEE-EC)</em> 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 <em>Genetic and Evolutionary Computation Conference Companion (GECCO’21 Companion),</em> 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. <em>Applied Soft Computing Journal (ASOC)</em>, 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 <em>Proceedings of the IEEE Congress on Evolutionary Computation (CEC’14), Proceedings of the 2014 World Congress on Computational Intelligence (WCCI’14)</em>, 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. <em>Artificial Intelligence Review,</em> 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. <em>ORSA Journal on Computing</em> 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. <em>IEEE Computational Intelligence Magazine (CIM)</em> 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. <em>The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization.</em> Wiley Interscience. 1985.</li> <li>David Lee Applegate, Robert E. Bixby, Vasek Chvatal, and William John Cook. <em>The Traveling Salesman Problem: A Computational Study.</em> Princeton University Press. 2007.</li> <li>Gregory Z. Gutin and Abraham P. Punnen, editors. <em>The Traveling Salesman Problem and its Variations.</em> 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"><code>moptipy</code></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 <code>source/tsplib</code>, 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 <code>LICENSE</code> in this archive.</p>
Assignment Feedback in School Choice Mechanisms
<p>Data and supplementary materials for the manuscript entitled "Assignment Feedback in School Choice Mechanisms." </p>
Database of 16S sequences from SILVA (r114), filtered, curated and annotated to be used easily by programs of taxonomic assignments
<p>The database used for the taxonomic assignment of reads generally comes from the SILVA database (http://www.arb-silva.de/). The logic behind this database is to use the information from the best one to the worst one. This is why the curated database was splitted in two parts : the [C] sequences for Complete sequences in terms of taxonomy, and the [I] and [E] sequences, for Incomplete and Environmental sequences.</p> <p>Each sequence included into the database must have a specific format summarizing all needed information (example below):<br> >[I]AACY020336309;Archaea(superkingdom);Euryarchaeota(phylum);Thermoplasmata(class);Thermoplasmatales(order);Marine_Group_II(no_rank);;marine_metagenome</p> <p>This sequence is an incomplete one ([I]), with a specific accession number from NCBI or SILVA, or another database (AACY020336309). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, <br> class, order, family, and genus). The species name is the last one and separated by two ';' characters from the rest of the descriptive line. Finally, the descriptive line must not contain specific characters like spaces. If one or several levels are unknown, this is indicated by 'no_rank'.</p> <p>Another example here for [C] sequences:<br> >[C]AAAK03000010;Bacteria(superkingdom);Firmicutes(phylum);Bacilli(class);Lactobacillales(order);Enterococcaceae(family);Enterococcus(genus);;Enterococcus_faecium_DO<br> This sequence is a complete one ([C]), with a specific accession number from NCBI or SILVA, or another database (AACY020187844). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, <br> class, order, family, and genus). The species is the last one and separated by two ';' characters from the rest of the descriptive line. Complete sequences must have six levels of information (superkingdom, phylum, class, order, family, and genus). If it is not the case, the sequence will be considered as Incomplete ([I]) (between three and five levels), or Environmental ([E]) (with only the superkingdom and the phylum levels).</p> <p>Another example here for [E] sequences:<br> >[E]U59968;Archaea(superkingdom);Thaumarchaeota(phylum);Soil_Crenarchaeotic_Group(SCG)(no_rank);;uncultured_crenarchaeote<br> This sequence is a environmental one ([E]), with a specific accession number from NCBI or SILVA, or another database (U59968). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, class, order, family, and genus). The species is the last one and separated by two ';' characters from the rest of the descriptive line. Complete sequences <br> must have six levels of information (superkingdom, phylum, class, order, family, and genus). If it is not the case, the sequence will be considered as Incomplete ([I]) (between three and five levels), or Environmental ([E]) (with only the superkingdom and the phylum levels).</p> <p>More details on the steps defined to clean and define this new database can be available on demand (sebastien.terrat@inra.fr).</p>
The TCR assigns naive T cells to a preferred lymph node
<p>Naive T cells recirculate between the spleen and lymph nodes where they mount immune responses when meeting dendritic cells presenting foreign antigen. As this may happen anywhere, naive T cells ought to visit all lymph nodes. Here, deep sequencing almost-complete TCR-repertoires led to a comparison of different lymph nodes within and between individual mice. We find strong evidence for a deterministic CD4/CD8 lineage choice and a consistent spatial structure. Specifically, some T cells show a preference for one or multiple lymph nodes, suggesting that their TCR interacts with locally presented (self-)peptides. These findings are mirrored in TCR-transgenic mice showing localized CD69-expression, retention, and cell division. Thus, naïve T cells intermittently sense antigenically dissimilar niches, which is expected to affect their homeostatic competition.</p>
GTDB and RefSeq-RDP databases parsed for species assignment
<p>GTDB and RefSeq-RDP databases parsed for the <em>addSpecies()</em> dada2 function. Both databases were parsed with `<a href="https://github.com/antonioggsousa/GTDB-RefSeq-RDP-assign-spp-dada2/blob/master/script/parsing_DB_dada2_spp_assign.py">parsing_DB_dada2_spp_assign.py</a>` python script using as input both databases downloaded at: <a href="https://zenodo.org/record/2541239#.XM2UgCOZPOQ">https://zenodo.org/record/2541239#.XM2UgCOZPOQ</a>. The python script and a detailed description explaining the code and databases versions can be found at: <a href="https://github.com/antonioggsousa/GTDB-RefSeq-RDP-assign-spp-dada2">https://github.com/antonioggsousa/GTDB-RefSeq-RDP-assign-spp-dada2</a></p> <p> </p>
walterASEL/Miller-et-al.---assignment: Initial
<p>Microsatellite genotypes for 1912 white-tailed deer used to assess ancestry and assignment to captive and wild populations in the Mid-Atlantic region of the United States.</p>
all SNPs and genes assigned to each LD block
<p><strong>Downloadable data files</strong></p> <p>The DistiLD resource integrates information on:</p> <ul> <li>Associations between Single Nucleotide Polymorphisms (SNPs) and diseases from genome-wide association studies (GWAS)</li> <li>Links between SNPs and genes based on linkage disequilibrium (LD) data from <a href="http://www.hapmap.org/">HapMap</a></li> </ul> <p>...</p> <p> </p> <p>The second file contains <a href="http://distild.jensenlab.org/lds.tsv.gz">all SNPs and genes assigned to each LD block</a> [114MB] in the following format:</p> <ol> <li>Linkage disequilibrium (LD) block</li> <li>Reference SNP (rs) number of SNPs in LD block</li> <li>Ensembl genes in LD block</li> </ol>
Figure 1 in Microhabitat partitioning of closely related Sarawak (Malaysian Borneo) frog species previously assigned to the genus Hylarana (Amphibia: Anura)
Figure 1. Bayesian inference of 16S mtDNA Sarawak frogs (* represents 95% BPP).
Fig. 1 in A New Species Of The Freshwater Crab, Previously Assigned To Geothelphusa Miyazakii (Miyake & Chiu, 1965) (Crustacea: Decapoda: Potamidae), From Yaeyama Group, Southern Ryukyus, Japan
Fig. 1. The location of Ishigaki and Iriomote Islands in Southern Ryukyus and East Asia.
Fig. 2 in A New Species Of The Freshwater Crab, Previously Assigned To Geothelphusa Miyazakii (Miyake & Chiu, 1965) (Crustacea: Decapoda: Potamidae), From Yaeyama Group, Southern Ryukyus, Japan
Fig. 2. Colouration of Geothelphusa marginata marginata, G. m. fulva, and G. miyazakii.
Production of Alternate Realizations of DESI Fiber Assignment for Unbiased Clustering Measurement in Data and Simulations
<p>A critical requirement of spectroscopic large scale structure analyses is correcting for selection of which galaxies to observe from an isotropic target list. This selection is often limited by the hardware used to perform the survey which will impose angular constraints of simultaneously observable targets, requiring multiple passes to observe all of them. In SDSS this manifested solely as the collision of physical fibers and plugs placed in plates. In DESI, there is the additional constraint of the robotic positioner which controls each fiber being limited to a finite patrol radius. A number of approximate methods have previously been proposed to correct the galaxy clustering statistics for these effects, but these generally fail on small scales. To accurately correct the clustering we need to upweight pairs of galaxies based on the inverse probability that those pairs would be observed (Bianchi & Percival 2017). This paper details an implementation of that method to correct the Dark Energy Spectroscopic Instrument (DESI) survey for incompleteness. To calculate the required probabilities, we need a set of alternate realizations of DESI where we vary the relative priority of otherwise identical targets. These realizations take the form of alternate Merged Target Ledgers (AMTL), the files that link DESI observations and targets. We present the method used to generate these alternate realizations and how they are tracked forward in time using the real observational record and hardware status, propagating the survey as though the alternate orderings had been adopted. We detail the first applications of this method to the DESI One-Percent Survey (SV3) and the DESI year 1 data. We include evaluations of the pipeline outputs, estimation of survey completeness from this and other methods, and validation of the method using mock galaxy catalogs. </p>
ML-Optimized QKD Frequency Assignment for Efficient Quantum-Classical Coexistence in Multi-Band EONs
<p>Abstract: Quantum key distribution (QKD) represents a cutting-edge technology that ensures unbreakable security. Coexisting quantum and classical signals on a multi-band (O+E+S+C+L-band) system offer a viable solution for secure, high-rate networks amidst growing classical traffic and address quantum signal sensitivity. In this study, we assume a dynamic classical traffic load and varying configurations of classical channels (CChs). Considering the varying behavior of Secure Key Rate (SKR) under different classical conditions, solving the integral noise equations are crucial for optimizing QKD implementation and enhancing resource efficiency. The complexity and time-consuming nature of this process challenge infrastructure providers in determining the optimal quantum channel (QCh) frequency in real time. To tackle these challenges, we propose a machine learning (ML) algorithm. By leveraging ML, QKD can be implemented efficiently, optimizing resource utilization while significantly reducing computation and processing time in dynamic classical traffic. We implement three ML algorithms at various fiber intervals, all of which estimate the optimal frequency for QCh with 99\% accuracy and perform computations on average in 0.09 seconds, which is significantly faster compared to integral computational methods that have a mean time of 637 seconds.<br><br>Information: In this file, the Excel sheet contains data for each fiber interval, including inputs such as fiber length in each interval, the overall classical loading factor percentage, the C-band loading factor percentage, the L-band loading factor percentage, the highest active classical frequency (which serves as input to the machine learning model), and the QCh frequency that resulted in the highest SKR.</p>
Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-regional Sustainability Analysis
<p>This dataset provides the MOVES-Matrix emission and energy use rates for the NCST project "Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-regional Sustainability Analysis" by the Georgia Tech research team.</p> <p> </p> <p>The abstract of the project is as follows.</p> <p><span>In this study, a modeling framework was developed to generate high-resolution synthetic fleets, for use with synthetic household modeling in activity-based travel models, by integrating various data sources. The synthetic households were generated by pairing household locations and demographic attributes, and synthetic fleets were assigned to the households so that travel demand model outputs would have vehicles associated with each model-predicted tour for energy and emissions analysis. The CO emissions were modeled for each vehicle and each link traversed by vehicles as predicted by the travel demand model, and the results of the synthetic fleet (by employing Monte Carlo simulations and Bootstrap techniques) were compared with those from standard regional and sub-regional fleet configurations. The results demonstrated that using a traditional sub-regional fleet scenario produced 30% higher predicted emissions than when the synthetic fleet was employed with predicted vehicle trips, and that using a regional average fleet (applied throughout the region) produced emissions that were more than 50% higher than synthetic fleet emissions. Lowest household emissions were associated with low-income and non-working households, and highest emissions were associated with moderate-income households and one-person high-income household groups. The results presented in the research are not necessarily conclusive, because the licensed vehicle data procured for Atlanta appear to be biased toward older vehicles. Model year penetration rates are accounted for in these analyses, but the authors believe that the variability in the registration mix for newer vehicles is likely underestimated in the data procured for these analyses. The authors conclude that access to statewide registration data will be required to remove potential biases that exist in licensed private data sets. Nevertheless, the study does demonstrate that properly pairing vehicle model years with the most active households (and their daily trips) significantly impacts energy and emissions analysis.</span></p> <p> </p>
Fig. 5 in Copelatus sibelaemontis sp. nov. (Coleoptera: Dytiscidae) from the Moluccas with generic assignment based on morphology and DNA sequence data
Fig. 5. Distribution of Copelatus sibelaemontis sp. nov.
Population assignment tests uncover rare long-distance larval dispersal events
<p>Long-distance dispersal (LDD) is consequential to metapopulation ecology and evolution. In systems where dispersal is undertaken by small propagules, such as larvae in the ocean, documenting LDD is especially challenging. Genetic parentage analysis has gained traction as a method for measuring larval dispersal, but such studies are generally spatially limited, leaving LDD understudied in marine species. We addressed this knowledge gap by uncovering LDD with population assignment tests in the coral reef fish <i>Elacatinus lori</i>—a species whose short-distance dispersal has been well-characterized by parentage analysis. When adults (<i>n</i> = 931) collected throughout the species' range were categorized into three source populations, assignment accuracy exceeded 99%, demonstrating low rates of connectivity between populations in the adult generation. After establishing high assignment confidence, we assigned settlers (<i>n</i> = 3,828) to source populations. Within the settler cohort, < 0.1% of individuals were identified as long-distance dispersers from other populations. These results demonstrate an exceptionally low level of connectivity between <i>E. lori</i> populations, despite the potential for ocean currents to facilitate LDD. More broadly, these findings illustrate the value of combining genetic parentage analysis and population assignment tests to uncover short- and long-distance dispersal, respectively.</p>
Assignment & prediction of 2-propanimine (CH3)2C=NH
<p>The measure frequency line list, the prediction, and the XIAM and ERHAM fit input and output file of 2-propanimine [(CH3)_2C=NH] of the ground state. File description and format see ReadMe.</p>
General principles for assignments of communities from eDNA: Open versus closed taxonomic databases
<p><span>Metabarcoding of environmental DNA (eDNA) is a powerful tool for describing biodiversity, such as finding keystone species or detecting invasive species in environmental samples. Continuous improvements in the method and the advances in sequencing platforms over the last decade have meant this approach is now widely used in biodiversity sciences and biomonitoring. For its general use, the method hinges on a correct identification of taxa. However, past studies have shown how this crucially depends on important decisions during sampling, sample processing, and subsequent handling of sequencing data. With no clear consensus as to the best practice, particularly the latter has led to varied bioinformatic approaches and recommendations for data preparation and taxonomic identification. </span><span>In this study, using a large freshwater fish eDNA sequence dataset, we compared the frequently used zero-radius Operational Taxonomic Unit (zOTUs) approach of our raw reads and assigned it taxonomically i) in combination with publicly available reference sequences (open databases) or ii) with an OSU (Operational Sequence Units) database approach, using a curated database of reference sequences generated from specimen barcoding (closed database). </span><span>We show both approaches gave comparable results for common species. However, the commonalities between the approaches decreased with read abundance and were thus less reliable and not comparable for rare species. The success of the </span><span>zOTU</span><span> approach depended on the suitability, rather than the size, of a reference database. Contrastingly, the OSU approach used reliable DNA sequences and thus often enabled species-level identifications, yet this resolution decreased with the recent phylogenetic age of the species. We show the need to include target group coverage, outgroups and full taxonomic annotation in reference databases to avoid misleading annotations that can occur when using short amplicon sizes as commonly used in eDNA metabarcoding studies. Finally, we make general suggestions to improve the construction and use of reference databases for metabarcoding studies in the future.</span></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.