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309 results for “swarm”
Data from: Patterns of mating and generation of diversity in a Geum hybrid swarm
To understand the evolutionary consequences of hybridization between the outcrossing plant Geum rivale and the selfer G. urbanum we tested the predictions of two simple models which assume either A). low or B). high pollen fitness in hybrids. Model A predicts only four genotypic classes (G. rivale, G. rivale backcross (BCR), F1 and G. urbanum) and asymmetric introgression from inbreeding to outbreeding species. Model B predicts additional genotypic classes and potential generation of novel inbreeding lines in the hybrid swarm. AFLP analysis of adults revealed only the four genotypes predicted by model A. However, microsatellite analysis of parent-progeny arrays demonstrated production of selfed offspring by F1 and BCR maternal parents and contribution of these genotypes to outcross pollen pools, as predicted by model B. Moreover AFLP and morphological analysis showed that the offspring generation comprised genotypes and phenotypes covering the entire spectrum of variation between the two parental species, in line with model B. A common garden experiment indicated no systematic reduction in fitness of offspring derived from hybrid parents. The genetic structure of the adults in the Geum hybrid swarm cannot be explained by restricted mating patterns but may result from ecological selection acting on a diverse offspring population.
Data from: Admixture of hybrid swarms of native and introduced lizards in cities is determined by the cityscape structure and invasion history
Introductions of non-native lineages increase opportunities for hybridization. Non-native lineages of the common wall lizard, Podarcis muralis, are frequently introduced in cities where they hybridize with native populations. We aimed at unravelling the invasion history and admixture of native and non-native wall lizards in four German cities using citywide, comprehensive sampling. We barcoded and genotyped 826 lizards and tested if gene flow in populations composed of admixed native and introduced lineages is facilitated by similar environmental factors as in native populations by comparing fine-scale landscape genetic patterns. In cities with non-native lineages, lizards commonly occurred in numerous clusters of hybrid swarms, which showed variable lineage composition, consisting of up to four distinct evolutionary lineages. Hybrid swarms held vast genetic diversity and showed recent admixture with other hybrid swarms. Landscape genetic analyses showed differential effects of cityscape structures across cities, but identified water bodies as strong barriers to gene flow in both native and admixed populations. In contrast, railway tracks facilitated gene flow of admixed populations only. Our study shows that cities represent unique settings for hybridization, caused by multiple introductions of non-native taxa. Cityscape structure and invasion histories of cities will determine future evolutionary pathways at these novel hybrid zones.
Data from: Host plants of the non-swarming edible bush cricket Ruspolia differens
The edible Ruspolia differens (Orthoptera: Tettigoniidae) is a widely-consumed insect in East Africa but surprisingly little is known of its host plant use in the field. We studied host plants used by non-swarming R. differens for 15 months, in central Uganda. In particular, we assessed the use of host plant species with respect to host cover in the field and host parts used by R. differens, also recording their sex, developmental stages, and colour morph. Ruspolia differens were found on 19 grass and two sedge species and they were observed predominantly (99% of 20,915 observations) on seven grasses (namely, Panicum maximum, Brachiaria ruziziensis, Chloris gayana, Hyparrhenia rufa, Cynodon dactylon, Sporobolus pyramidalis, and Pennisetum purpureum). Ruspolia differens was most frequently observed on the most common grass of each study site but P. maximum, and S. pyramidalis were used more frequently than expected from their cover in the field. Furthermore, R. differens were observed predominantly on inflorescences (97% of feeding observations) and much less frequently on the leaves (3.0%), stems (0.1%), and inflorescence stalks (0.1%) of grasses and sedges. Host use was not independent of sex, developmental stage, or colour morph. Panicum maximum was the preferred host of the youngest nymphs of R. differens. Overall, our findings indicate that a continuous supply of diverse grass resources with inflorescences is necessary for the management and conservation of wild populations of R. differens.
FIGURES 9–14. Cylindroiulus oromii n in Insular species swarm goes underground: two new troglobiont Cylindroiulus millipedes from Madeira (Diplopoda: Julidae)
FIGURES 9–14. Cylindroiulus oromii n. sp. male gonopod. 9–10: mesal view, 11: pro- & mesomerite, anterior view, 12–13: lateral view, 14: opisthomerite, posterior view. afl: anterior flagellum-conducting lamella, f: flagellum, fl: flagelliferous lobe of promerite, fp: finger-shaped projection of promerite, m: mesomerite, p: promerite, pc: lateral rim of paracoxite, pfl: posterior flagellum-conducting lamella, pp: paracoxal process, s: solenomerite, sc: sperm channel, t: apical fringe. Scale bar: 100 µm.
FIGURE 15. Cylindroiulus oromii n in Insular species swarm goes underground: two new troglobiont Cylindroiulus millipedes from Madeira (Diplopoda: Julidae)
FIGURE 15. Cylindroiulus oromii n. sp. vulva. bu: bursa, op: operculum, rs: receptaculum seminis,. Scale bar: 100 µm.
FIGURE 8. Cylindroiulus oromii n in Insular species swarm goes underground: two new troglobiont Cylindroiulus millipedes from Madeira (Diplopoda: Julidae)
FIGURE 8. Cylindroiulus oromii n. sp. habitus of adult female in situ and preserved. Scale bar: 2 mm. (Inset: photo of live specimen, courtesy of É. Nunes).
FIGURES 2–7. Cylindroiulus julesverni n in Insular species swarm goes underground: two new troglobiont Cylindroiulus millipedes from Madeira (Diplopoda: Julidae)
FIGURES 2–7. Cylindroiulus julesverni n. sp. male gonopod. 2–3: mesal view, 4: pro- & mesomerite, anterior view, 5–6: lateral view, 7: opisthomerite, posterior view. afl: anterior flagellum-conducting lamella, f: flagellum, fl: flagelliferous lobe of promerite, fp: finger-shaped projection of promerite, m: mesomerite, p: promerite, pc: lateral rim of paracoxite, pfl: posterior flagellum-conducting lamella, pp: paracoxal process, s: solenomerite, sc: sperm channel. Scale bar: 100 µm.
Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
Multi-objective particle swarm optimization and training of datasheet-based load dependent lithium-ion voltage models
Open the record for dataset details and reuse information.
Self-organization and information transfer in Antarctic krill swarms
<p>Antarctic krill swarms are one of the largest known animal aggregations, and yet, despite being the keystone species of the Southern Ocean, little is known about how swarms are formed and maintained. Understanding the local interactions between individuals that provide the basis for these swarms is fundamental to knowing how swarms arise in nature, and what potential factors might lead to their breakdown. Here we analyzed the trajectories of captive, wild-caught krill in 3D to determine individual level interaction rules and quantify patterns of information flow. Our results demonstrate that krill align with near neighbors and that they regulate both their direction and speed relative to the positions of groupmates. These results suggest social factors are vital to the formation and maintenance of swarms. Further, krill operate a novel form of collective organization, with measures of information flow and individual movement adjustments expressed most strongly in the vertical dimension, a finding not seen in other swarming species. This research represents a vital step in understanding the fundamentally important swarming behavior of krill.</p>
Swarm behavior simulation of plateau pika
<p> As an important species on the Qinghai-Tibet Plateau, the pika has great controversy in grassland protection and ecological function service. The population change of pika is related to the fragile and sensitive ecological chain of Qinghai-Tibet Plateau. The traditional methods of population density survey include sampling method, marker recapture method, removal sampling method, etc., which are cumbersome to operate and consume a lot of manpower and material resources. In recent years, more and more scholars use camera capture method to characterize or calculate population density. This method is simple to operate and widely applicable, but how to establish the relationship between actual population density and monitoring data under the condition that individual identification cannot be carried out is a big challenge faced by this method. In order to solve this problem, the density of pika was estimated by two methods. First, random encounter model (SEM) was used to estimate the density of pika based on actual field observation data. Secondly, a Monte Carlo model is established to describe the behavior of pika and the practical problems are simulated by using probability statistics. The results obtained by the two methods are compared and mutually verified, and it is found that the two model results are in good agreement, and the probabilistic model can effectively establish the relationship between the ecological physical parameters of the population and the monitoring space. </p>
Enhanced Weighted Quantum Particles Swarm Optimization in Electromagnetic Contenst
<p>This article describes how the EWQPSO (Enhanced Weigthed Quantum Particle Swarm Optimization) optimization algorithm can also be used in an electromagnetic context for the circuit synthesis of an antenna (in this case of a sinuous antenna) and how, this version of the algorithm , is better than its previous versions (WQPSO, QPSO and PSO).</p>
Figure 1 in Swarms of the hyperiid amphipod Themisto gaudichaudii along the False Bay (Western Cape, South Africa) coastline
Figure 1. Tornado shaped swarms of Themisto guadichaudii. Footage supplied by Riaan de Villiers of Pisces Divers. The footage can be viewed at: https://fb.watch/b4Ykmm9mck/.
Tidal triggering of seismic swarm associated with hydrothermal circulation at Blanco Ridge Transform Fault Zone in northeast Pacific
<p>Supplementary Information</p> <p>for</p> <p>Tidal triggering of seismic swarm associated with hydrothermal circulation at Blanco Ridge Transform Fault Zone in northeast Pacific</p>
Change Detection in Dynamic Environments by Means of a Robotic Swarm - Digital Appendix
<p>Digital Appendix for my thesis Change Detection in Dynamic Environments by Means of a Robotic Swarm.</p> <p>In the zip-file you find a README that guides you through the structure and where to find the plots for the respective experiments.</p> <p><strong>Change Detection of Dynamic Environments by Means of a Robotic Swarm - Digital Appendix</strong></p> <p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (BOCPD, PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folders of BOCPD and PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Audio Swarm supplementary material - audio/video recordings
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Bug Report Analytics for Software Reliability Assessment using Hybrid Swarm-Evolutionary Algorithm
<p><span>There are in total 6 files.</span></p> <p><span><span>1.<span> </span></span></span><span>Out of these files three documents are related to datasets. Two are related to unrefined Eclipse and JDT files and third is refined data of Eclipse and JDT Project Failure Datasets which has been used for experimentation purpose.</span></p> <p><span><span>2.<span> </span></span></span><span>This package also includes code for all the models version wise for all versions of Eclipse and JDT projects.</span></p> <p><span><span>3.<span> </span></span></span><span>Sample Code has also been given for version 4.3 and 4.10. </span></p> <p><span>Steps to run </span></p> <p><span><span>a)<span> </span></span></span><span>In this code, Main ABCDE file needs to be run and different datasets could be executed on this file. This is for one type of datasets that is time domain dataset only. </span></p> <p><span><span>b)<span> </span></span></span><span>If anyone is interested in getting separate results for cumulative sum and failure intensity, separate file has been given. </span></p> <p><span><span>c)<span> </span></span></span><span>Code for ABCDE algorithm that is Swarm Evolutionary algorithm used in the paper has also been given in these files.</span></p>
Adaptive Online Fault Diagnosis in Robot Swarms
<p>Contained are the data sets generated and analysed for the paper 'Adaptive Online Fault Diagnosis in Robot Swarms' by James O'Keeffe, Danesh Tarapore, Alan G. Millard and Jon Timmis</p>
An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation
<p><strong>Abstract: </strong></p> <p>Swarms of drones offer an increased sensing aperture, and having them mimic behaviors of natural swarms enhances sampling by adapting the aperture to local conditions. We demonstrate that such an approach makes detecting and tracking heavily occluded targets practically feasible. While object classification applied to conventional aerial images generalizes poorly the randomness of occlusion and is therefore inefficient even under lightly occluded conditions, anomaly detection applied to synthetic aperture integral images is robust for dense vegetation, such as forests, and is independent of pre-trained classes. Our autonomous swarm searches the environment for occurrences of the unknown or unexpected, tracking them while continuously adapting its sampling pattern to optimize for local viewing conditions. We achieved an average positional accuracy of 0.39 m with an average precision of 93.2% and an average recall of 95.9%. Here, adapted particle swarm optimization considers detection confidences and predicted target appearance. We show that sensor noise can effectively be included in the synthetic aperture image integration process, removing the need for a computationally costly optimization of high-dimensional parameter spaces. Finally, we present a complete hard- and software framework that supports low-latency transmission and fast processing of extensive video and telemetry data.</p>
Source files for training the NN-based calibration model of Swarm LP ion densities
<p>Data files used for training the NN-based calibration model for Swarm Langmuir Probe ion densities.</p>
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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.