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5,573 results for “Optimality”
Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018
<p>Dataset supplementing H. Kohler, B. Ojha, N. Illyaskutty, I. Hartmann, C. Thiel, K. Eisinger, M. Dambacher: In situ high-temperature gas sensors: continuous monitoring of the combustion quality of different wood combustion systems and optimization of combustion process, Journal of Sensors and Sensor Systems (JSSS), 2018</p>
USP5 Zf-UBD Differential Scanning Fluorimetry Assay Optimization
<p>Optimization of a differential scanning fluorimetry (DSF) assay for USP5 zinc finger ubiquitin binding domain (Zf-UBD) to determine if the presence of detergent in buffer conditions results in significant difference in melting temperatures of USP5 Zf-UBD and if the addition of increasing concentrations of ubiquitin RLRGG peptide results in thermal shift of USP5 Zf-UBD. </p>
Experimental data from the PhD thesis "Counterexample-guided Cartesian Abstraction Refinement and Saturated Cost Partitioning for Optimal Classical Planning"
<p>The three data sets contain the raw experiment data, parsed values and basic reports for the three parts of the thesis. For each experiment there are two directories. The first directory contains the raw data of all experiment runs. The code directories and benchmark files have been removed to avoid duplication and save space. The second directory (*-eval) contains "properties" file with all parsed values and an HTML report.</p>
Data from: A landscape approach for optimizing the cost-effectiveness of large-scale forest restoration
<p>This is a complete dataset for achieving results of the manuscript "<strong>A landscape approach for optimizing the cost-effectiveness of large-scale forest restoration</strong>". </p> <p> </p> <p><strong>Abstract: </strong></p> <ol> <li>Achieving global targets for forest restoration will require cost-effective strategies to return agricultural land to forest, while minimizing implementation costs and negative outcomes for agricultural production.</li> <li>We present a landscape approach for optimizing the cost-effectiveness of large-scale forest restoration. Across three different landscapes within Brazil's Atlantic Forest biodiversity hotspot, we modelled landscape scenarios based on spatially-explicit data on the probability of natural regeneration, restoration costs, land opportunity costs, and forest restoration outcomes for increasing carbon stocking and landscape connectivity<em>.</em> We compare benefits of our cost-reduction approach to the legally mandated riparian restoration and randomly distributed approaches.</li> <li>Compared with riparian prioritization and considering both implementation and opportunity costs, our cost-reduction scenario produced the greatest savings (20.9%) in mechanized agricultural landscapes.</li> <li>When only considering implementation costs, our cost-reduction scenario led to the highest savings (38.4%) in the landscape with highest forest cover where natural regeneration potential is highest and enables cost-effective carbon stocking and connectivity.</li> </ol> <p><em>Synthesis and applications.</em> We present a guide for forest restoration planning that maximizes specific outcomes with minimal costs and reduction of agricultural production. Furthermore, we show how policies could encourage prioritization of low-cost restoration via natural regeneration, increasing cost-effectiveness. While our study focuses on Brazil’s Atlantic Forest, the approach can be parameterized for other regions.</p> <p><strong>Resumo:</strong></p> <ol> <li>Atingir metas globais para a restauração florestal exigirá estratégias economicamente viáveis para transformar terras agrícolas em floresta, minimizando custos de implementação e os resultados negativos para a produção agrícola.</li> <li>Apresentamos uma abordagem de paisagem para otimizar a relação custo-eficácia da restauração florestal em larga escala. Em três diferentes paisagens, no Bioma da Mata Atlântica, modelamos cenários baseados em dados espacialmente explícitos sobre a probabilidade de regeneração natural, custos de restauração, custos de oportunidade da terra e resultados de restauração florestal com o objetivo de aumentar o estoque de carbono e a conectividade da paisagem. Por fim, comparamos os benefícios da nossa abordagem de redução de custos com a tradicional abordagem de restauração da paisagem em zonas ripárias (áreas de preservação permanente) e abordagens de espacialidade aleatoriamente distribuídas.</li> <li>Comparado com a priorização ripária e considerando os custos de implementação e de oportunidade, nosso cenário de redução de custos produziu as maiores economias (20,9%) em paisagens agrícolas mecanizadas.</li> <li>Ao considerar apenas os custos de implementação, nosso cenário de redução de custos levou à maior economia (38,4%) na paisagem com maior cobertura florestal, onde o potencial de regeneração natural é maior e permite uma melhor relação de custo-oportunidade no estoque de carbono e na conectividade da paisagem.</li> </ol> <p><em>Síntese e aplicações.</em> Apresentamos aqui um guia para o planejamento de restauração florestal que maximiza resultados específicos com redução de custos e mínima influência na produção agrícola. Além disso, mostramos como políticas públicas poderiam incentivar a priorização da restauração de baixo custo via regeneração natural, aumentando a relação custo-benefício. Enquanto nosso estudo se concentra na Mata Atlântica do Brasil, a abordagem pode ser parametrizada para outras regiões.</p>
Results for Several Simple Algorithms on the W-Model for Black-Box Discrete Optimization Benchmarking
<p>The W-Model follows a layered approach, where each layer can either be omitted or introduce a different characteristic feature such as neutrality via redundancy, ruggedness and deceptiveness, epistasis, and multi-objectivity, in a tunable way. The model problem is defined over bit string representations, which allows for extracting some of its layers and stacking them on top of existing problems that use this representation, such as OneMax, the Maximum Satisfisiability or the Set Covering tasks, and the NK landscape. The ruggedness and deceptiveness layer can be stacked on top of any problem with integer-valued objectives.</p> <p>Here we provide some experimental results with several simple algorithms on the W-Model along with the algorithm and experiment executor implementation. The most recent version of the code can be found at <a href="http://www.github.com/thomasWeise/BBDOB_W_Model">http://www.github.com/thomasWeise/BBDOB_W_Model</a>, while the program used in the experiment are attached to this dataset. The implemented algorithms are</p> <ul> <li>1-flip hill climber with restarts,</li> <li>2-flip hill climber with restarts,</li> <li>μ+λ Evolutionary Algorithms (for different values of μ, λ, and different crossover rates),</li> <li>μ+λ Evolutionary Algorithms with Frequency Fitness Assignment (FFA) (for different values of μ, λ, and different crossover rates),</li> <li>exhaustive enumerate (EE), and</li> <li>random sampling (RS)</li> </ul> <p>These algorithms are applied to a range of different parametric setups of the W-Model for single-objective optimization and fixed-length bit string representations.</p> <p>The experiments are executed on a HP Z640 Work Station with 32 GB DDR4-2400 RAM and Intel Xeon E5-2609v4 CPU under Ubuntu Server Linux 16.04, Kernel 4.4.0-116-generic, and with Java 1.8.0_151. Each run was granted at most 1048576 function evaluations (FEs). The total amount of data collected is about 45 GB, with tar.xz compression down to about 1.2 GB. The text files containing the algorithm traces are fairly self-describing.</p> <p>Publications that describe the W-Model:</p> <ul> <li>Thomas Weise and Zijun Wu. Difficult Features of Combinatorial Optimization Problems and the Tunable W-Model Benchmark Problem for Simulating them. In Black Box Discrete Optimization Benchmarking (<a href="http://iao.hfuu.edu.cn/bbdob-gecco18">BB-DOB</a>) Workshop of <em>Companion Material Proceedings of the Genetic and Evolutionary Computation Conference (<a href="http://gecco-2018.sigevo.org/">GECCO 2018</a>)</em>, July 15th-19th 2018, Kyoto, Japan, ISBN: 978-1-4503-5764-7. ACM. doi:<a href="http://dx.doi.org/10.1145/3205651.3208240">10.1145/3205651.3208240</a> / <a href="http://github.com/thomasWeise/BBDOB_W_Model">source codes</a> [This paper contains some errata for the one below]</li> <li>Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs. A Tunable Model for Multi-Objective, Epistatic, Rugged, and Neutral Fitness Landscapes. In Maarten Keijzer, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H. Holmes, Gregory S. Hornby, Daniel Howard, James Kennedy, Sanjeev P. Kumar, Fernando G. Lobo, Julian Francis Miller, Jason H. Moore, Frank Neumann, Martin Pelikan, Jordan B. Pollack, Kumara Sastry, Kenneth Owen Stanley, Adrian Stoica, El-Ghazali, and Ingo Wegener, editors, <em>Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO'08)</em>, pages 795-802, July 12-16, 2008, Renaissance Atlanta Hotel Downtown: Atlanta, GA, USA. ISBN: 978-1-60558-130-9, New York, NY, USA: ACM Press. doi:<a href="http://dx.doi.org/10.1145/1389095.1389252">10.1145/1389095.1389252</a> / <a href="http://iao.hfuu.edu.cn/images/publications/WNSRG2008ATMFMOERANFL.pdf">pdf</a> [This paper has some deficits fixed in the paper above]</li> </ul>
A general framework for optimizing arterial spin labeling MRI experiments
<p>This repository contains the simulation data, preprocessed in vivo data, and analysis code used in the Magnetic Resonance in Medicine article titled "A general framework for optimizing arterial spin labeling MRI experiments" (<a href="https://doi.org/10.1002/mrm.27580">https://doi.org/10.1002/mrm.27580</a>). Please cite this article if you use this code or data in your work.</p>
The process of HDAC11 Assay Development: Buffer optimization again
<p>The pH range used earlier was expanded for further optimization to check if the activity could improve.</p> <p> </p> <p><strong>Note: </strong>1. In the assay buffer, BSA conc. is 0.5 mg/ml (instead of 0.5%).</p> <p> 2. In the 7.5 ul developer solution, 40 uM of TSA (Trichostatin A) is also included.</p>
Optimizing Quality-Aware Big Data Applications in the Cloud — Supporting Data
<p>Supporting experimental data for the paper "Optimizing Quality-Aware Big Data Applications in the Cloud", submitted to IEEE TCC.</p>
Data: Optimal trans-saccadic integration relies on visual working memory
<p>Dataset for the published paper: </p> <p>Stewart, E. E. M., & Schütz, A. C. (2018). Optimal Trans-saccadic integration relies on visual working memory. <em>Vision research</em>.</p> <p>DOI: <a href="https://doi.org/10.1016/j.visres.2018.10.002">10.1016/j.visres.2018.10.002</a></p>
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization
<p>Data resulting from the experiments described in "Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization" (https://doi.org/10.1016/j.swevo.2018.12.010). The contents of the files is the following:</p> <ul> <li>CAMO-AIS_SWEVO.zip: All of the data below in a single file</li> <li>CAMO_Iowa.zip: Audio and Data for the orchestrations with the Iowa sound database found at http://theremin.music.uiowa.edu/MIS.html</li> <li>CAMO_Phil.zip: Audio and Data for the orchestrations with the Philharmonia sound database found at https://www.philharmonia.co.uk/explore/sound_samples</li> <li>CAMO_RWC.zip: Audio and Data for the orchestrations with the RWC sound database found at https://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html</li> <li>CAMO_SOL.zip: Audio and Data for the orchestrations with the Studio Online sound database available with Orchids http://forumnet.ircam.fr/product/orchids-en/</li> <li>Listening_Test.zip: Raw data (i.e., perceptual similarity ratings) from the listening test found at http://http://camo.inesctec.pt/. This data is anonymous (each participant is assigned a reference number) so the participants cannot be identified.</li> </ul> <p>See the README.txt file for a detailed description of the contents of each file.</p>
Optimizing seagrass conservation for ecological functions
<p>Data used in the publication of "Optimizing seagrass conservation for ecological functions" in Ecosystems</p>
Raw Experimental Data for work presented in 'Optimally diverse communication channels in disordered environments with tuned randomness'
<p>This is the raw experimental data for the work presented in 'Optimally diverse communication channels in disordered environments with tuned randomness', published in Nature Electronics.</p> <p> </p> <p><a href="https://doi.org/10.1038/s41928-018-0190-1">https://doi.org/10.1038/s41928-018-0190-1</a> </p> <p> </p> <p>See the README file for an explanation of the data.</p>
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms: Virtual Machine ID Dataset
<p>The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively.</p> <p> </p> <p>[1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019).</p> <p>[2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In Proceedings of SOSP’17. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3132747.3132772 Dataset access: https://github.com/Azure/AzurePublicDataset (August 2018)</p>
Planning as Optimization: Online Learning of Situations and Optimal Configurations - SASO 2019 - Accompanying material
<p>These files are accompanying material for our submission "" to SASO 19:</p> <p>Many approaches apply optimization techniques in SASs, mostly within the planning procedure, to generate new system configurations or adaptation plans. We performed an analysis of these techniques based on approaches published during the last ten years in conferences and journals related to self-adaptive systems (SASs), namely ACM Transactions on Autonomous and Adaptive System (TAAS), the International Conference on Autonomic Computing and Communications (ICAC), the International Conferences on Self-Adaptive and Self-Organizing Systems (SASO), the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), and the Symposium on the Foundations of Software Engineering (FSE). We identified the use of 29 different techniques in 51 publications. This list shows that a large set of techniques from different classes such as probabilistic, combinatorial, evolutionary, stochastic, mathematical, and meta-heuristic optimization are applied in SASs.</p> <p> </p> <p>We provide two files:</p> <p>- List of References (SASO - References - Planning_as_Optimization.pdf)</p> <p>- Dataset (SASO - Dataset - Planning_as_Optimization.xlsx)</p>
AEROARMS Control-based local optimization methods for planning
<p>This package allows to perform a Dynamic (Re-)Planning within the Set-Based Task-Priority Inverse-Kinematics (SBTP-IK) framework. It is ROS-based and it is provided to work with the Kinova Jaco2 7DOFs but it is can be easily adapted to another robot system changing the files describing the robot kinematics such as Denvait Hartenberg parameters.</p> <p>The proposed approach is based on merging control-based local optimization methods inside the planning algorithms. For the experiments, a fixed-based 7 DOFs manipulator has been considered in two different scenarios. In the first case, a static environment has been considered. In the second one, the user places an obstacle on the manipulator’s path in real-time. Thus, a re-planning results necessary to manage this change in the environment.</p> <p> </p>
Experimental data of the paper "Counterexample-Guided Abstraction Refinement for Pattern Selection in Optimal Classical Planning"
<p>This dataset contains experimental data used in the paper. They were generated<br> using Downward-Lab (see https://doi.org/10.5281/zenodo.399255). The scripts<br> used to run the experiments can be found in the software bundle for this paper<br> (see https://doi.org/10.5281/zenodo.2628703).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.</p>
Stylized urban landscapes optimized for compactness, climate regulation and vascular plant species richness
<p>The data set provides the output of a genetic algorithm optimizing a stylized urban region with respect to three target functions: urban compactness, climate regulation as an exemplary ecosystem service and vascular plant species richness as a measure of biodiversity.</p> <p>The optimisation varies the spatial allocation of three types of land cover blocks in a stylized urban region: high- and low-density and park blocks which consist of green and/or built-up cells. We systematically vary landscape composition at the block level, but keep city size constant.</p> <p>The data set is related to a publication submitted to Frontiers in Environmental Science.</p>
The optimization of a jet turbojet engine by PSO and searching algorithms
<p>The turbojet engine operates on the ideal Brayton cycle (gas turbine) and consists of six main parts: diffusers, compressors, combustion chambers, turbines, afterburners and nozzles. Using computer code writing in MATLAB software environment, exergy analysis on all selected turbojet engine components, exergy analysis on J85-GE-21 turbojet engine for selective height of 10008000 meters above sea level at speeds of 200 m/s and temperatures of 10, 20 and 40 ° C have been provided and then, according to the system functions, the system is optimized based on the PSO method. For the purpose of optimization, variables of Mach number, efficiency of the compressor, turbine, nozzle and compressor pressure ratio are considered in the range of 0.6 to 1.4, 0.8 to 0.95, 0.8 to 0.95 and 7 to 10, respectively. The highest exergy efficiency of different parts of the engine at sea level with an inlet air velocity of 200 m/s corresponds to a diffuser with 73.1%. Then, the nozzle and combustion chamber are respectively 68.6% and 51.5%. The lowest exergy efficiency is related to compressor with 4%. After that, the afterburner is ranked second with 11.6%. Also, the values of entropy produced and the efficiency of the second law before optimization were 1176.99 and 479 w/k respectively and the same values after optimization were 1129 and 51.4 w/k respectively which is identified. After the optimization process, the amount of entropy produced is reduced and the efficiency of the second law of thermodynamics has increased.<br> </p>
Computer-aided design of optimal environmentally benign solvent-based adhesive products
<p>The files contain all the product design problems implemented in GAMS for this publication. All models were run on a single core of a dual 8 core Intel(R) Xeon(R) CPU E5-2650 machine at 3.52 GHz with 125GB of memory.</p> <p> </p> <p><strong>Abstract</strong></p> <p>In this work, a general systematic methodology for the design of optimal adhesive products with low environmental impact is presented. The proposed approach integrates computer-aided design tools and Generalised Disjunctive Programming to formulate and solve the product design problem. Key design decisions in product design (number of ingredients, identity of compounds and their proportions) are optimised simultaneously. This methodology is applied to the design of solvent-based acrylic adhesives, which are commonly used in construction. First, optimal product formulations are determined with the aim to minimize toxicity. This reveals that that high performance can be achieved by investigating different number of components as well as by optimising all ingredients simultaneously rather than sequentially. The relation between two competing objectives is then explored by obtaining a set of Pareto optimal solutions. This leads to significant trade-offs and large areas of discontinuity driven by discrete changes in the list of optimal product ingredients.</p>
Data and code for: Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages
<p><strong>Data and Code for the article:</strong></p> <p><strong>Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages</strong><br> <br> <em>Jonas O. Wolff, Gustavo B. Paterno, Daniele Liprandi, Martín J. Ramírez, Federico Bosia, Arie van der Meijden, Peter Michalik, Helen M. Smith, Braxton R. Jones, Alexandra M. Ravelo, Nicola Pugno and Marie E. Herberstein</em><br> <br> Journal: <strong>Evolution</strong> <br> DOI: <a href="https://doi.org/10.1111/evo.13834">https://doi.org/10.1111/evo.13834</a> </p> <p>Github repository: https://github.com/paternogbc/Wolff_et_al_Evolution_aerial_spider_webs</p> <p><br> When using the <strong>data available</strong> in this repository, please cite the original publication. </p> <p>Contact jonas.wolff@mq.edu.au for any further information. </p> <p>Wolff, J. O., Paterno, G. B., Liprandi, D. , Ramírez, M. J., Bosia, F. , der Meijden, A. , Michalik, P. , Smith, H. M., Jones, B. R., Ravelo, A. M., Pugno, N. and Herberstein, M. E. (2019), <strong>Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages</strong>. Evolution. Accepted Author Manuscript. doi:10.1111/evo.13834</p>
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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.