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5,573 results for “optimality”
Data for "Reducing operational costs of offshore HVDC energy export systems through optimized maintenance"
<p>The files in this record contain the scripts, input data and results for the maintenance model described in the above publication (will be added later).</p> <p>To use the scripts, you need the software MATLAB (min. version 2016b).</p> <p>Steps to run the model:</p> <p>1. run "ini.m"</p> <p>2. run "szenarien_~"</p> <p>3. save the results in the directory "Ergebnisse"</p> <p>4. run scripts located in the directory "Auswertungen"</p>
Online companion - A New Cooperative Framework for a Fair and Cost-Optimal Allocation of Resources within a Low Voltage Electricity Community
<p>Simulation parameters used for the article "A New Cooperative Framework for a Fair and Cost-Optimal Allocation of Resources within a Low Voltage Electricity Community" submitted for review in IEEE Transactions on Smart grid</p>
Fig. 2 in Optimal fishing samplers to reveal the morphological structure of a fish assemblage in a subtropical tidal flat
Fig. 2. Landmarks and semilandmarks used in the geometric morphometric analysis (scale bar= 1 cm).
Optimization of the Mainzelliste Software for Fast Privacy-preserving Record Linkage
<p>Synthetically generated person related datasets used in the evaluation of linkage quality and runtime of the Mainzelliste. To generate person records we used the established GeCo data generator modified with small extensions such as including look-up files for German names in addition to English names. A generated dataset consists of two subsets, org and dup, to be compared with each other. The duplicate records can contain data errors (e.g., different<br> but similarly sounding letters, OCR errors or typos) to simulate reduced data quality, making matching more<br> challenging.</p>
Tagged formatted datasets for 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems'
<p>Tagged formatted datasets used in 'Genetically Optimized Massively Parallel Binary Neural Networks for Intrusion Detection Systems' T.Murovič, A.Trost.</p>
Results of experiments in paper Simulated Hyperparameter Optimization for Statistical Tests in Machine Learning Benchmarks
<p>Results of the experiments in paper Simulated Hyperparameter Optimization for Statistical Tests in Machine Learning Benchmarks</p>
Data for "Optimal Mutation Rates for the (1+λ) EA on OneMax"
<p>This dataset contains optimal mutation rates for the few variations of the (1+λ) evolutionary algorithm on the OneMax problem. The OneMax problem is a famous benchmark problem which is defined on bit strings, with the maximized function being the number of bits set to 1. The optimal mutation rates depend on the Hamming distance to the optimum (or, put simple, on the current fitness). We study three flavours of the algorithm:</p> <ul> <li>the algorithm with the standard bit mutation - where each bit is flipped independently of others with the probability <em>p</em>, which is the sought <em>mutation rate</em> - nicknamed "<strong>sbm</strong>" in the file names;</li> <li>the algorithm with the so-called "shift" bit mutation - which is similar to the one above, except that when none bits were flipped, one randomly chosen bit is forcefully flipped - nicknamed "<strong>shf</strong>" in the file names;</li> <li>the algorithm which always flips <em>k</em> bits, which is the mutation rate for this algorithm - nicknamed "<strong>rls</strong>" in the file names, because it is a generalization of the famous randomized local search (RLS) that always flips one randomly chosen bit.</li> </ul> <p>All these variations have one parent individual, produce λ offspring on each iteration and choose the best of both the parent and the offspring to be the parent in the next iteration (hence the (1+λ) in the notation). As a result, λ is often called the <em>popiulation size</em>. We study the values of λ to be powers of two ranging from 2<sup>0</sup>=1 to 2<sup>18</sup>=262144, and the problem sizes <em>n</em>=1000, 2000, 5000.</p> <p>For all the variations and all population sizes, we compute the optimal mutation rates (in the corresponding sense) as functions of the distance to the optimum, where "optimal" mean those which minimize the <strong>expected running time</strong>. For the "rls" variation we also compute the "drift-maximizing" mutation rates, which maximize the <strong>expected fitness increase</strong> (and which is, in fact, different from minimizing the expected running time).</p> <p>The files in the dataset are as follows:</p> <ul> <li><strong>data-expectations-<problem size>.csv</strong>: the summary of the expected running times of all these algorithms for all population sizes, where <problem size> is the value of the problem size.</li> <li><strong>data-optimal-<algo>-<problem size>.csv</strong>: the mutation rates optimal for the given algorithm and the given problem size for all population sizes. Note that for "<strong>rls</strong>" the mutation rate is an integer from 1 to <em>n</em>, whereas for other algorithms the mutation rate is the probability, so a floating-point number from [0;1].</li> <li><strong>data-drift-optimal-rls-<problem size>.csv</strong>: same thing from drift-optimal mutation rates.</li> <li><strong>img-optimal-<algo>-<problem size>-<population size>.png</strong>: the pictures that display how good each mutation rate is for each distance to the optimum. Please check the exact semantics in the paper, but the overall idea is that yellow is better.</li> </ul>
Data from: Individual- versus group-optimality in the production of secreted bacterial compounds
How unicellular organisms optimize the production of compounds is a fundamental biological question. While it is typically thought that production is optimized at the individual-cell level, secreted compounds could also allow for optimization at the group level, leading to a division of labor where a subset of cells produces and shares the compound with everyone. Using mathematical modelling, we show that the evolution of such division of labor depends on the cost function of compound production. Specifically, for any trait with saturating benefits, linear costs promote the evolution of uniform production levels across cells. Conversely, production costs that diminish with higher output levels favor the evolution of specialization – especially when compound shareability is high. When experimentally testing these predictions with pyoverdine, a secreted iron-scavenging compound produced by Pseudomonas aeruginosa, we found linear costs and, consistent with our model, detected uniform pyoverdine production levels across cells. We conclude that for shared compounds with saturating benefits, the evolution of division of labor is facilitated by a diminishing cost function. More generally, we note that shifts in the level of selection from individuals to groups do not solely require cooperation, but critically depend on mechanistic factors, including the distribution of compound synthesis costs.
Optimization of Convolution Reverberation (Sound Samples)
<p>Sound samples of the paper "Optimization of Convolution Reverberation" by Sadjad Siddiq. To be published in the proceedings of DAFx20</p>
Figures and graphics used in "Optimizing Strength of Shared Anchors in an Array of Floating Offshore Wind Turbines"
<p>All figures and graphics used in tables are included in PDF format here. Instructions on how to reproduce the MATLAB-generated plots (or similar plots) are included in the README of the software package, also cited in the paper.</p>
Data file for paper: Xiaoqian Lin, Chris Zalitis, Jonathan Sharman and Anthony Kucernak, "Electrocatalyst performance at the gas/electrolyte interface under high mass transport conditions: optimization of the "floating electrode" method!,
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Authors:Xiaoqian Lin, Chris Zalitis, Jonathan Sharman and Anthony Kucernak*</p> <p>Title:Electrocatalyst performance at the gas/electrolyte interface under high mass transport conditions: optimization of the “floating electrode” method</p> <p>Journal:ACS Applied Materials & Interfaces</p> <p>DOI: 10.1021/acsami.0c12718</p> <p>Please cite the above reference if you wish to use this data<br> <br> DOI of data:10.5281/zenodo.4074860</p>
Multi-objective Optimization of Long-run Average and Total Rewards: Supplemental Material
<p>Supplemental Material for the Paper:<br> Multi-objective Optimization of Long-run Average and Total Rewards<br> by Tim Quatmann and Joost-Pieter Katoen.</p> <p>This artifact contains our implementation for the paper "Multi-objective Optimization of Long-run Average and Total Rewards". The implementation is fully integrated into the model checker <a href="http://stormchecker.org">Storm</a>.<br> We include the sources and dependencies of Storm as well as the related tools <a href="http://qav.cs.ox.ac.uk/multigain">MultiGain</a> and <a href="http://www.prismmodelchecker.org/games">PRISM-games</a>.<br> Moreover, we include model files and scripts for replicating *all* experiments as conducted for the paper.<br> The original logfiles obtained during our experiments are included as well.</p> <p>The artifact has been tested to work with the <a href="https://zenodo.org/record/4041464">TACAS 21 Artifact Evaluation VM</a> running Ubuntu 20.04 LTS</p>
Suppelementary Table S1. List of heat-affected proteins in buds, open flowers, wilted flowers, and donor leaves of common buckwheat accessions 'Panda' and PA15. Comparison of protein abundance between heat-treated plants and those growing in optimal conditions.
<p>Suppelementary Table S1. List of heat-affected proteins in buds, open flowers, wilted flowers, and donor leaves of common buckwheat accessions ‘Panda’ and PA15. Comparison of protein abundance between heat-treated plants and those growing in optimal conditions. </p>
Improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients for transient electromagnetic inversion(2)
<p>The datas are gained in<Nonlinear programming genetic algorithm in transient electromagnetic inversion>. Li F P, Yang H Y, Liu X H, et al.</p> <p>Geophysical and Geochemical Exploration,2017,41(2) :347-353.</p> <p>http://doi.org /10.11720/wtyht.2017.2.24</p>
Singularity container for CGO'21 artifact description: YaskSite -- Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures
<p>Singularity container for CGO'21 artifact description: YaskSite -- Stencil Optimization Techniques Applied to Explicit ODE Methods on Modern Architectures</p>
Raw datasets for publication: The new Mountain Observatory of the Project "Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification (OCAL)" in the United Arab Emirates: First results on Convection Initiation" - Case studies only (5 and 6 Sept 2018)
<p>Here are zip files containing the raw data sets collected from the Halo Doppler lidar and the Mira Doppler cloud radar from the OCAL Observatory, UAE, on the 5th and 6th September 2018. All RHI and PPI scans at all angles and all time steps from these days are included.</p>
Optimal Resilience in Systems that Mix Shared Memory and Message Passing (video)
Full video presentation of the paper: Optimal Resilience in Systems that Mix Shared Memory and Message Passing.<br><br>Appears in Session 3 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Data from: A unified model for optimizing riverscape conservation
1.Spatial prioritization tools provide a means of finding efficient trade-offs between biodiversity protection and the delivery of ecosystem services. Although a large number of prioritization approaches have been proposed in the literature, most are specifically designed for terrestrial systems. When applied to river ecosystems, they often fail to adequately account for the essential role that landscape connectivity plays in maintaining both biodiversity and ecosystem services. This is particularly true of longitudinal connectivity, which in many river catchments is highly altered by the presence of dams, stream-road crossings, and other artificial structures. 2.We propose a novel framework for coordinating river conservation and connectivity restoration. As part of this, we formulate an optimization model for deciding which subcatchments to designate for ecosystem services and which to include in a river protected area (RPA) network, while also deciding which existing river barriers to remove in order to maximize longitudinal connectivity within the RPA network. In addition to constraints on the size and makeup of the RPA network, the model also considers the suitability of sites for conservation, based on a biological integrity index, and connectivity to multiple habitat types. We demonstrate the usefulness of our approach using a case study involving four managed river catchments located in Hungary. 3.Results show that large increases in connectivity-weighted habitat can be achieved through targeted selection of barrier removals and that the benefits of barrier removal are strongly depend on RPA network size. We find that (i) highly suboptimal solutions are produced if habitat conservation planning and connectivity restoration are done separately and (ii) RPA acquisition provides substantially greater marginal benefits than barrier removal given limited resources. 4.Synthesis and applications. Finding a balance between conservation and ecosystem services provision should give more consideration to connectivity restoration planning, especially in multi-use riverscapes. We present the first modelling framework to directly integrate and optimize river conservation and connectivity restoration planning. This framework can help conservation managers to account better for connectivity, resulting in more effective catchment scale maintenance of biological integrity and ecosystem services delivery.
Data from: Towards improving searches for optimal phylogenies
Finding the optimal evolutionary history for a set of taxa is a challenging computational problem, even when restricting possible solutions to be "tree-like" and focusing on the maximum-parsimony optimality criterion. This has led to much work on using heuristic tree searches to find approximate solutions. We present an approach for finding exact optimal solutions that employs and complements the current heuristic methods for finding optimal trees. Given a set of taxa and a set of aligned sequences of characters, there may be subsets of characters that are compatible, and for each such subset there is an associated (possibly partially resolved) phylogeny with edges corresponding to each character state change. These perfect phylogenies serve as anchor trees for our constrained search space. We show that, for sequences with compatible sites, the parsimony score of any tree T is at least the parsimony score of the anchor trees plus the number of inferred changes between T and the anchor trees. As the maximum-parsimony optimality score is additive, the sum of the lower bounds on compatible character partitions provides a lower bound on the complete alignment of characters. This yields a region in the space of trees within which the best tree is guaranteed to be found; limiting the search for the optimal tree to this region can significantly reduce the number of trees that must be examined in a search of the space of trees. We analyze this method empirically using four different biological data sets as well as surveying 400 data sets from the TreeBASE repository, demonstrating the effectiveness of our technique in reducing the number of steps in exact heuristic searches for trees under the maximum-parsimony optimality criterion.
Data from: Diversity in Müllerian mimicry: the optimal predator sampling strategy explains both local and regional polymorphism in prey
The convergent evolution of warning signals in unpalatable species, known as Müllerian mimicry, has been observed in a wide variety of taxonomic groups. This form of mimicry is generally thought to have arisen as a consequence of local frequency-dependent selection imposed by sampling predators. However, despite clear evidence for local selection against rare warning signals, there appears an almost embarrassing amount of polymorphism in natural warning colors, both within and among populations. Because the model of predator cognition widely invoked to explain Müllerian mimicry (Müller's "fixed nk" model) is highly simplified and has not been empirically supported; here, we explore the dynamical consequences of the optimal strategy for sampling unfamiliar prey. This strategy, based on a classical exploration–exploitation trade-off, not only allows for a variable number of prey sampled, but also accounts for predator neophobia under some conditions. In contrast to Müller's "fixed nk" sampling rule, the optimal sampling strategy is capable of generating a variety of dynamical outcomes, including mimicry but also regional and local polymorphism. Moreover, the heterogeneity of predator behavior across space and time that a more nuanced foraging strategy allows, can even further facilitate the emergence of both local and regional polymorphism in prey warning color.
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.