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38 results for “Estimation Process”
Data from: Measured perceptual nonlinearities show how ON-OFF asymmetric processing improves motion estimation in natural scenes
Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here we use comprehensive measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we showed that the observed improvement occurs only when light-dark asymmetries mimic natural ones. Thus, fly behavior suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world's light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Data from: Inferring contemporary dispersal processes in plant metapopulations: comparison of direct and indirect estimates of dispersal for the annual species Crepis sancta
Analyzing population dynamics in changing habitats is a prerequisite for population dynamics forecasting. The recent development of metapopulation modeling allows the estimation of dispersal kernels based on the colonization pattern but the accuracy of these estimates compared with direct estimates of the seed dispersal kernel has rarely been assessed. In this study, we used recent genetic methods based on parentage analysis (spatially explicit mating models) to estimate seed and pollen dispersal kernels as well as seed and pollen immigration in fragmented urban populations of the plant species Crepis sancta with contrasting patch dynamics. Using two independent networks, we documented substantial seed immigration and a highly restricted dispersal kernel. Moreover, immigration heterogeneity among networks was consistent with previously reported metapopulation dynamics, showing that colonization was mainly due to external colonization in the first network (propagule rain) and local colonization in the second network. We concluded that the differences in urban patch dynamics are mainly due to seed immigration heterogeneity, highlighting the importance of external population source in the spatio-temporal dynamics of plants in a fragmented landscape. The results show that indirect and direct methods were qualitatively consistent, providing a proper interpretation of indirect estimates. This study provides attempts to link genetic and demographic methods and show that patch occupancy models may provide simple methods for analyzing population dynamics in heterogeneous landscapes in the context of global change.
Decision-making in Emergency Medicine: Estimates of Intuitive and Rational Information Processing
<p>Dummy and target vignettes.</p>
Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture
<p>Soil moisture is a key variable in the water, energy, and carbon cycles. Mapping sub-surface soil moisture with fine spatial resolution requires integrating downscaling approaches and process-based models. However, the effectiveness of hybrid methods, such as regression kriging (RK), in enhancing soil moisture estimates through process-based parameter predictions remains inconclusive. This study aims to integrate infiltration processes into downscaling models to predict 1-km multi-layer soil moisture, while comparing performance of nonlinear and linear models, and evaluating RK improvements. Random forests (RF) and generalized linear model (GLM) were used to downscale surface soil moisture (0–5 cm) from 36-km Soil Moisture Active Passive satellite products to 1 km across the Qinghai-Tibet Plateau. Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. RK variants (RFRK and GLMRK) were applied to jointly predict the spatial distribution of multiple infiltration parameters, which were used in SMAR at 1-km grids to estimate sub-surface soil moisture (5–40 cm). The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2% to 69.8%, from 0.09 to 0.03. RF outperformed GLM across all depth intervals, providing higher prediction accuracy (average RMSE, RF: 0.07; GLM: 0.09). Moreover, RK enhanced the Nash-Sutcliffe efficiency coefficient (RFRK: 0.34; GLMRK: 0.28) and coefficient of determination (RFRK: 0.5; GLMRK: 0.38) by 7.7%–13.3% and 2.2%–2.4%. This study provides a reference for mapping multi-layer soil moisture through the integration of data-driven and knowledge-driven approaches in regional-scale study areas.</p>
Data Processing Pipeline and Products from the NANOGrav 12.5-Year Data Set: Dispersion Measure Mis-Estimation with Varying Bandwidths
<p>This Zenodo dataset contains the data processing pipeline, as well as the data products, corresponding to the scientific journal paper "NANOGrav 12.5-Year Data Set: Dispersion Measure Mis-Estimation with Varying Bandwidths". The processing pipeline is structured as follows:</p> <ol> <li>1_fit_dispersion_3terms.py reads the .tim files (containing the times-of-arrival), creates the broadband and narrowband datasets, and fits a dispersion model to both datasets using three parameters.</li> <li>2_plot_fits_differences.py creates the residual plots showing the differences in the fitted values of the parameters.</li> <li>3_autocovariance.py calculates the autocovariance function of the differences in the fitted values, and creates the corresponding plots.</li> <li>All the files starting with "plot" are convenience scripts for creating the plots presented in the paper.</li> <li>All the files starting with "sophia" are utility functions created by the authors that are used in the main pipeline.</li> <li>The folder "NANOGrav_12yv4" contains the dataset analyzed in this work.</li> <li>The folder "NG_timing_analysis" contains utility functions created by the NANOGrav collaboration that are used in the main pipeline.</li> </ol> <p>Please do not hesitate to send all your questions, concerns, or commentaries to sophia.sosa@nanograv.org</p>
Data from: Inferring contemporary dispersal processes in plant metapopulations: comparison of direct and indirect estimates of dispersal for the annual species Crepis sancta
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Data from: Estimating field capacity from volumetric soil water content time series using automated processing algorithms
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Size group (pico, nano, micro) and group total carbon estimates from cell counts via epifluorescent microscopy (EPI) of heterotrophic and autotrophic plankton from CCE LTER process cruises in the California Current region, 2006 - 2016
Microbial community assemblages of the California Current Ecosystem (CCE) are assessed for biomass of heterotrophic (dinoflagellate and other eukaryotes) and autotrophic (dinoflagellate and other eukaryotes) plankton using high-throughput digital epifluorescence microscopy (EPI). Samples to estimate the nano- and microplankton (0.2-2.0-µm and 2.0-20-µm size, respectively) are collected at various depths, preserved, stained, and filtered onto a membrane filter and mounted on a glass microscope slide aboard the process cruises (since 2006, ongoing). Slides are then frozen at -80°C for subsequent imaging and analysis in the laboratory onshore. Carbon biomass is computed from cell biovolumes.
Picophytoplankton and bacteria total carbon estimates from cell counts analyzed with flow cytometry (FCM) from CCE LTER process cruises in the California Current region, 2006 - 2017.
Picophytoplankton populations and non-pigmented prokaryotes are sampled within the California Current Ecosystem (CCE) for abundances from various depths. Seawater is collected from Niskin bottles and cells are fixed in the field aboard the survey cruises (since 2004, ongoing) with paraformaldehyde, and stained with a DNA-specific dye back in the laboratory. The cells are enumerated by an Altra flow cytometer (with a syringe pump for volumetric sample delivery) simultaniously with argon ion lasers, to distinguish three major populations of photoautotrophs (Prochlorococcus, Synechococcus, and pico-eukaryotes) and the assemblage of heterotrophic prokaryotes collectively referred to as H-Bact. FCM abundance estimates for each are converted to carbon biomass equivalents using mixed-layer estimates.
Process map and time estimation per indication
<p>Process map and time estimation per indication</p>
Regional estimates of gross primary production applying the process-based model 3D-CMCC-FEM vs. multiple datasets
<p>This repository contains the model 3D-CMCC-FEM v5.6 executable (Testolin et al.2023), model inputs and model outputs in the folder RUN_BASILICATA; scripts to prepare model inputs and perform model outputs post-processing in SCRIPTS; remote-sensing based data and forcing in DATA; tables and post-processed files in OUTPUT; figures in FIGURE, related to the manuscript entitled “Regional estimates of gross primary production applying the process-based model 3D-CMCC-FEM vs. multiple datasets”</p>
Data and code for simulation study and case study in "A Bayesian Dirichlet process community occupancy model to estimate community structure and species similarity"
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Data from: The effect of fossil sampling on the estimation of divergence times with the fossilised birth death process
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Nitrate uptake estimates phytoplankton incorporation of dissolved NO3- at selected depths from CCE LTER process cruises in the California Current System, 2011 – 2016 (ongoing).
Nitrate uptake samples (1.1-L) of seawater are taken each day shortly before noon on the CTD rosette up-cast during the CCE Process cruises (since 2011, ongoing). Approximate nitrate concentrations are determined at sea and the samples are spiked with 15NO3- at a concentration equivalent to ~10% ambient nitrate. Samples (one sample per depth) are incubated for 24 hours in polycarbonate bottles held at natural light and temperature conditions on the in situ array. After recovery samples are filtered onto pre-combusted GF/F filters, which are analyzed for particulate nitrogen and δ15N by mass spectrometry in the Scripps Analytical Facility. Accurate nitrate concentrations are determined after the cruise from frozen and filtered samples analyzed by autoanalyzer (see separate Dissolved Inorganic Nutrients dataset by Ralf Goericke). Nitrate uptake is calculated using the equations in Dugdale & Wilkerson (1986). In surface waters where nitrate concentrations are often quite low, we spike with a minimum of 10 nmol L-1 15NO3-. Since this can lead to a bias (high) in calculated nitrate uptake, when post-cruise nitrate analyses showed that our spike concentration was greater than 10% of ambient nitrate, we multiplied by the uptake calculated from the equations of Dugdale & Wilkerson (1986) by (ambient NO3-)/(ambient NO3- + added 15NO3-). Primary production was simultaneously estimated from 14C uptake in triplicate 250-mL bottles (plus a dark bottle) incubated similarly on the in situ array (see separate Primary Production – Particulate dataset by Ralf Goericke). We calculated f-ratios by assuming that phytoplankton uptake occurred at Redfield carbon:nitrogen ratios of 106:16 (mol:mol). To eliminate the impact of phytoplankton that may be engaged in luxury nitrate uptake in the deep euphotic zone, we also report conservative nitrate uptake, which is the minimum of nitrate uptake or primary production divided by the Redfield C:N ratio. This value thus sets a maximum for nitrate
Dataset used in the paper "EEG SIGNAL PROCESSING IN MI-BCI APPLICATIONS WITH IMPROVED COVARIANCE MATRIX ESTIMATORS"
<pre>This material is associated with the PhD Thesis of Javier Olias (which is supervised by Sergio Cruces & Ruben Martin) and the paper: <br>J. Olias, R. Martín-Clemente, M. A. Sarmiento-Vega and S. Cruces, "EEG Signal Processing in MI-BCI Applications With Improved Covariance Matrix Estimators," in <em>IEEE Transactions on Neural Systems and Rehabilitation Engineering</em>, vol. 27, no. 5, pp. 895-904, May 2019, doi: 10.1109/TNSRE.2019.2905894.<br> </pre> <div> <div> <div> <div> <p>The paper can be downloaded in open-access format from the following websites:</p> <p>https://hdl.handle.net/11441/154861</p> </div> </div> </div> </div> <div> <div> <div>https://ieeexplore.ieee.org/document/8688582</div> </div> </div>
Cryo-EM map anisotropy can be attenuated by map post-processing and a new method for its estimation. Example Maps
<p>These are the post-processed maps displayed in the manuscript</p> <p>"Cryo-EM map anisotropy can be attenuated by map post-processing and a new method for its estimation"</p>
Data from: A new framework for analysing automated acoustic species detection data: occupancy estimation and optimization of recordings post-processing
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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.