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171 results for “performance prediction”
Predicting Performance and Power Consumption of Parallel Applications
<p><em><strong>Abstract: </strong>Current architectures provide many control knobs for the reduction of power consumption of applications, like reducing the number of used cores or scaling down their frequency. However, choosing the right values for these knobs in order to satisfy requirements on performance and/or power consumption is a complex task and trying all the possible combinations of these values is an unfeasible solution since it would require too much time. For this reasons, there is the need for techniques that allow an accurate estimation of the performance and power consumption of an application when a specific configuration of the control knobs values is used. Usually, this is done by executing the application with different configurations and by using these information to predict its behaviour when the values of the knobs are changed. However, since this is a time consuming process, we would like to execute the application in the fewest number of configurations possible. In this work, we consider as control knobs the number of cores used by the application and the frequency of these cores. We show that on most Parsec benchmark programs, by executing the application in 1% of the total possible configurations and by applying a multiple linear regression model we are able to achieve an average accuracy of 96% in predicting its execution time and power consumption in all the other possible knobs combinations.</em></p> <p>This dataset includes the raw data of the experiments as well as the scripts used to plot them.</p>
Data from: Usefulness and limitations of thermal performance curves in predicting ectotherm development under global change
1. Thermal performance curves (TPCs) have been estimated in multiple temperate ectotherm species and used to predict the effect of global warming. However, TPCs are typically assessed under constant temperature regimes, so their reliability for predicting thermal responses in the wild where temperature fluctuates diurnally and seasonally remains poorly documented. 2. Here we use distant latitudinal populations of five species of sepsid flies (Diptera: Sepsidae) from the temperate region (Europe, North Africa, North America) to compare estimates derived from constant TPCs with observed development rate under fluctuating temperatures in laboratory and field conditions. 3. TPCs changed across gradients in that flies originating from higher latitudes or altitudes showed accelerated development, an adaptive response. TPCs were then used to predict development rates observed under fluctuating temperatures; these predictions were relatively accurate in the laboratory but not in the field. Interestingly, the precision of TPC-predictions depended not only on the resolution of temperature data, with diurnal and overall temperature summing performing better than hourly temperature summing, but also on the frequency of temperatures falling below the estimated critical minimum temperature. Hourly temperature resolution most strongly underestimated actual development rates, because flies apparently either did not stop growing when temperatures dropped below this threshold, or they speed up their growth when the temperature rises again, thus most severely reflecting this error. 4. We conclude that when flies do not encounter cold temperatures, TPC-predictions based on constant temperatures can accurately reflect performance under fluctuating temperatures if adequately adjusted for non-linearities, but when they encounter cold temperatures this method is more error-prone. 5. Our study emphasizes the importance of the resolution of temperature data and cold temperatures in shaping thermal reaction norms, thus improving predictions of the responses of ectotherms to climate change in the age of big data and citizen science.
The telomere regulatory gene POT1 responds to stress and predicts performance in nature: implications for telomeres and life history evolution
<p>Telomeres are emerging as correlates of fitness-related traits and may be important mediators of ecologically relevant variation in life history strategies. Growing evidence suggests that telomere dynamics can be more predictive of performance than length itself, but very little work considers how telomere regulatory mechanisms respond to environmental challenges or influence performance in nature. Here, we combine observational and experimental datasets from free-living tree swallows (<i>Tachycineta bicolor</i>) to assess how performance is predicted by the telomere regulatory gene POT1, which encodes a shelterin protein that sterically blocks telomerase from repairing the telomere. First, we show that lower POT1 gene expression was associated with higher female quality, <i>i.e.</i> earlier breeding and heavier body mass. We next challenged mothers with an immune stressor (lipopolysaccharide injection) that led to 'sickness' in mothers and 24h of food restriction in their offspring. While POT1 did not respond to maternal injection, females with lower constitutive POT1 gene expression were better able to maintain feeding rates following treatment. Maternal injection also generated a one-day stressor for chicks, which responded with lower POT1 gene expression and elongated telomeres. Other putatively stress-responsive mechanisms (i.e. glucocorticoids, antioxidants) showed marginal responses in stress-exposed chicks. Model comparisons indicated that POT1 mRNA abundance was a largely better predictor of performance than telomere dynamics, indicating that telomere regulators may be powerful modulators of variation in life history strategies.</p>
Plant virus SNP prediction artificial dataset Performance Study
<p>Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research on viral pathogens, especially for virus discovery and monitoring. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS technologies have greatly facilitated the generation of new genomes of the detected viruses and their comparison. Nevertheless, the bioinformatics analyses allowing the reconstruction of genomes and the detection of Single Nucleotide Polymorphisms (SNPs) can potentially create bias, although it has not been widely addressed so far. </p> <p>Therefore, more knowledge is required on the limitation and possibility of predicting SNPs based on HTS-generated sequence datasets. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) through large-scale Performance Testing (PT) using three artificially designed datasets. The bioinformatics analyses were divided into three key steps: reads pre-processing (quality trimming, merging …), virus identification (assembly, alignment, mapping …) and variant calling. Each step was evaluated independently through an original, step-by-step PT design with iteration between participants. </p> <p>Overall, this work underlines key parameters in SNP detection and proposes recommendations for reliable variant calling for plant viruses. The identification of the closest reference, mapping parameters and manual validation of the prediction were the most impactful analysis step for the success or failure of the predictions. Strategies to improve SNPs prediction are also discussed. </p>
Data Set for Predicting the Performance of ATL Model Transformations
<p>Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained.</p> <p>Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models that were transformed. We provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Additionally, we calculated the mean absolute percentage error in % and the 95th percentile of the absolute percentage error in % for each experiment and provide these results. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models, the Java projects used to measure the execution time of the transformations, and other supporting scripts, e.g. for the analysis of the results.</p> <p>A short introduction with a quick start guide can be found in README.md and a detailed documentation in documentaion.pdf.</p>
Does a history of population co-occurrence predict plant performance, community productivity, or invasion resistance?
<p>A history of species co-occurrence in plant communities is hypothesized to lead to greater niche differentiation, more efficient resource partitioning, and more productive, resistant communities as a result of evolution in response to biotic interactions. A similar question can be asked of co-occurring populations: do individual species or community responses differ when communities are founded with plants sharing a history of population co-occurrence (sympatric) or originating from different locations (allopatric)? Using shrub, grass, and forb species from six locations in the western Great Basin, USA, we compared establishment, productivity, reproduction, phenology, and resistance to invaders for experimental communities with either sympatric or allopatric population associations. Each community type was planted with six taxa in outdoor mesocosms, measured over three growing seasons, and invaded with the annual grass <em>Bromus tectorum</em> in the final season. For most populations, the allopatric or sympatric status of neighbors was not important. However, in some cases, it was beneficial for some species from some locations to be planted with allopatric neighbors, while others benefited from sympatric neighbors, and some of these responses had large effects. For instance, the <em>Elymus</em> population that benefited the most from allopatry grew 50% larger with allopatric neighbors than in single origin mesocosms. This response affected invasion resistance, as <em>B. tectorum</em> biomass was strongly affected by productivity and phenology of <em>Elymus</em> spp., as well as <em>Poa secunda</em>. Our results demonstrate that while community composition can affect plant performance in semi-arid plant communities, assembling communities from sympatric populations is not sufficient to ensure high productivity and invasion resistance. Instead, we observed an idiosyncratic interaction between sampling effects and evolutionary history, with the potential for seed source of individual populations to have community-level effects.</p>
Data Set for Enhanced Performance Prediction of ATL Model Transformations
<p>Model transformation languages are domain-specific languages, which are designed to comfortably define transformations. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. In particular, we focused on how to predict the performance of transformations that also transform attributes whose values can have arbitrary size. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained.</p> <p>Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models we used. In this data set, we provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models.</p> <p>A detailed documentation is available in documentaion.pdf.</p>
Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
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Data from: Agent-based versus correlative models of species distributions: Evaluation of predictive performance with real and simulated data
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Data from: Does the number of functional olfactory receptor genes predict olfactory sensitivity and discrimination performance in mammals?
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Does a history of population co-occurrence predict plant performance, community productivity, or invasion resistance?
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Trait means predict performance under water limitation better than plasticity for seedlings of Poaceae species on the eastern Tibetan Plateau
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Data from: Usefulness and limitations of thermal performance curves in predicting ectotherm development under global change
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Thermal performance under constant temperatures can accurately predict insect development times across naturally variable microclimates
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Sample data: Performance prediction of hub-based swarms
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Craniodental traits predict feeding performance and dietary hardness in a community of Neotropical free-tailed bats (Chiroptera: Molossidae)
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Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models
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The telomere regulatory gene POT1 responds to stress and predicts performance in nature: implications for telomeres and life history evolution
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Genomic prediction enables rapid selection of high-performing genets in an intermediate wheatgrass (Thinopyrum intermedium) breeding program
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Data set for the manuscript "Reproductive physiology corresponds to adult nutrition and task performance in a Neotropical paper wasp: a test of dominance-nutrition hypothesis predictions"
<p>Data set for the manuscript "Reproductive physiology corresponds to adult nutrition and task performance in a Neotropical paper wasp: a test of dominance-nutrition hypothesis predictions"</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.