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5,573 results for “optimality”
Optimizing Coastal Restoration with the Stress Gradient Hypothesis
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No impact of biocontrol agent’s predation cues on development time or size of surviving Aedes albopictus under optimal nutritional availability
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Data from: Online prenatal trial in mindfulness sleep management (OPTIMISM)
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Optimal foraging can drive emergent initiator-follower dynamics in social groups
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Data for: Optimal inference of molecular interaction dynamics in FRET microscopy
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Data from: Automatic Laplacian-based shape optimization for patient-specific vascular grafts
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The giant clam photosymbiosis is a physically optimized photoconversion system for the most intense sunlight on Earth
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Data for: Combining radio-telemetry and radar measurements to test optimal foraging in an aerial insectivore bird
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Identification and selection of optimal reference genes for qPCR-based gene expression analysis in Fucus distichus under various abiotic stresses
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Seasonally optimized calibrations improve low-cost sensor performance: Long-term field evaluation of PurpleAir sensors in urban and rural India
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Cochlear Implant Compression Optimization for Music Listening - Maplaw and AGC
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Optimizing the mnemonic similarity task for efficient, widespread use
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OPTIM-TUNE: In-flight automatic optimal tuning of UAV controllers for robust operation
<p>Tuning results, interplay between gains and allowable radius of a cylindric force constraint. Use PS-Tricks in LaTeX to obtain the final graphical results. </p>
Data for "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models"
<p>This repository contains all geo-physical catchment properties used in the publication "Function Space Optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models".</p>
A Metric for Optimism: John Ioannidis on Reproducibility, Preregistration, and Data Sharing
<p><strong>Episode Summary:</strong></p> <p>In this episode we are discussing data sharing and Open Science. Our interview guest will be Stanford University Professor of Medicine: John Ioannidis who has now come to the Berlin Institute of Health as an Einstein BIH Visiting Fellow at the BIH QUEST Center to establish the Meta-Research Innovation Center Berlin (METRIC-Berlin), the European “sister” of the Meta-Research Innovation Center at Stanford (METRICS). We will cover his research and opinions on data sharing, reproducibility, and how to improve research.</p> <p><strong>Links: </strong></p> <p><a href="https://profiles.stanford.edu/john-ioannidis">John Ioannidis</a></p> <p><a href="https://www.bihealth.org/en/research/quest-center/mission-approaches/">BIH Quest Centre</a></p> <p><a href="https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124">Why Most Published Research Findings Are False</a></p> <p><strong>Quotes:</strong></p> <p>'I think that scientists, by themselves, are recognizing that it is important to share [data] and in many fields, like in Genetics, they realize that unless they share they cannot really go very far'</p> <p>'Clearly over the years we have seen more scientific sharing of data'</p>
MOTIVE - tiMe-OpTimized contextual Information flow on unmanned VEhicles project experimental results
<p>The experimentation data were collected during the 6th Fed4FIRE+ Open Call (https://www.fed4fire.eu/) using the mobile nodes of the w-iLab.t (link) testbed. During this Open Call we proposed to evaluate the performance of an optimization model for temporal control of the transmission of messages from an IoT mobile device. This mechanism is based on a network condition model that transits from favourable to adverse conditions and vice versa. All these transitions are monitored and validated through our system (change detection and optimal stopping). If a network is performing properly then the transmission control can be relaxed to exploit available resources.The main contribution of MOTIVE is to apply a sequential decision-making process (DMP) on IoT devices that leverages the on-line derived network statistics to efficiently control their telemetry measurements transmission.</p> <p>Our data collected from the experimentation using the testbed's devices consists of network related information, packet error rate and latency. We collected data from a completely functional mobile node that operates in a saturated network and five mobile nodes in the same circumstances. Saturated conditions were generated by data produced by twenty static sensors for the duration of the each experiment. On each run we collected more than 2 * 105 samples. The comparative assessment we did was based on five different policies of decision making: i) no-policy model, ii) the heuristic threshold based model in which the transmission of the messages is paused when a specific threshold of quality network is below a threshold, iii) TOCP model which overviews the quality of network in normal mode and gets in pausing mode when a change is detected; the pausing period lasts for a specific threshold and then it is activated again, iv) TOCP-DRP and the v) fair TOCP-DRP which enters in pausing mode if a change is indicated by TOCP decision making model.</p> <p>The datasets include the packet error rate and the latency of each device, for each experiment, and the critical areas found during the experiments (most saturated areas).</p> <p>The datatset includes for each scenario(experiment) the participating devices and the Latency and Packet Error Rate(PER) per policy( NoPolicy, Heuristic, TOCP, TOCPDRP, TOCPDRPFAIR), apart from scenarioA(single node) where only four policies are present, since the TOCPDRPFAIR applies with more that one participating nodes.</p>
When and How to Transfer Knowledge in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments of the paper below:</p> <p>Ruan, G., Minku, L.L., Menzel, S., Sendhoff, B., Yao, X., "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization," 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 2019, pp. 2034-2041. </p> <p> </p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is able to transfer useful information from one problem to help solving another related problem. This paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective benchmark problems, we show that transfer learning fails on problems with fixed Pareto optimal solution sets and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always adequate. Therefore, transfer learning should be avoided when dealing with problems for which transfer learning fails and other kernel functions should be used when the Gaussian kernel is inadequate. This paper proposes novel strategies and kernel functions that can be used in such cases. Experimental studies have demonstrated the superiority of our proposed techniques to state-of-the-art methods, on a number of dynamic bi- and tri-objective test problems.</p>
Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization
<p>This file is the output data obtained when running the experiments from the paper below:</p> <p>Ruan, G., Minku, L., Menzel, S., Sendhoff, B., Yao., “Computational Study on Effectiveness of Knowledge Transfer in Dynamic Multi-objective Optimization” <em>2020 IEEE Congress on Evolutionary Computation</em></p> <p>Transfer learning has been used for solving multiple optimization and dynamic multi-objective optimization problems, since transfer learning is believed to be able to transfer useful information from one problem instance to help solving another related problem instance. This paper aims to study how effective transfer learning is in dynamic multi-objective optimization (DMO). Through computation time analysis of transfer learning, we show that the ‘inner’ optimization problem introduced by transfer learning is very time-consuming. In order to enhance the efficiency, two alternatives are computationally investigated on a number of dynamic bi- and tri-objective test problems. Experimental results have shown that the greatly enhanced efficiency does not result in much degeneration on the performance of transfer learning. Considering the high computational cost of transfer learning, it is likely that the original purpose of using transfer learning in DMO might be negated. In other words, the computation time saved in optimization is eaten up by computationally expensive transfer learning. As a result, there is less gain than expected in the overall computational efficiency. To verify this, experiments have been conducted, regarding using computational cost of transfer learning to optimize randomly generated solutions. The results have demonstrated that the convergence and diversity of final solutions generated from the random solutions are significantly better than those generated from transferred solutions under the same total computational budget.</p>
Data from: Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach
An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. The implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistic constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies. In this study, we computed simulated genomic datasets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographic representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios. Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.
Overcoming information reduced data and experimentally uncertain parameters in ptychography with regularized optimization
<p>These are the datasets used for the publication <a href="https://arxiv.org/abs/2005.01530">https://arxiv.org/abs/2005.01530</a>. The bin files are the actual data, and the other files are the configuration files used to generate the datasets.</p> <p>In more detail, the Forward_Params*.cnf files were used in combination with FDES (<a href="https://doi.org/10.1016/j.ultramic.2015.07.005">https://doi.org/10.1016/j.ultramic.2015.07.005</a>) to generate diffraction patterns.</p> <p>The Reconstruction_Params*.cnf files were used with the code outlined in the main publication to generate the simulated results.</p> <p>The Simulation*.bin files are the raw simulated CBEDs, stored in a binary format. The eCount portion of the filename corresponds to the Poissonian statistics applied to simulate real data. The ground truth file is upsampled, with larger diffraction pattern sizes, while the eCountInf has infinite dose, and therefore no noise.</p> <p>If you want more information, please contact the corresponding author of the publication, at vandenbroek@physik.hu-berlin.de. </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.