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4,276 results for “Estimation”
Dataset: Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks
<p>Dataset for the paper E. Rinaldi, M. González Lastre, S. García Herreros, S. Ahmed, M. Khanahmadi, F. Nori, and C. Sánchez Muñoz (2023), <a href="https://arxiv.org/abs/2310.02309">¨Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks¨, arxiv: 2310.02309</a></p> <p>This dataset can be used to populate the [datapath] folder in the repository <strong>ParamEst-NN</strong> (<a href="https://github.com/CarlosSMWolff/ParamEst-NN">github.com/CarlosSMWolff/ParamEst-NN</a> ) and reproduce the results shown in the paper.</p> <p>The dataset consist of four folders:</p> <ol> <li><strong>Training trajectories.</strong> Records of quantum-jump trajectories simulated with the Monte-Carlo solver of the <a href="https://qutip.org/">QuTiP</a> library, used to train neural networks for the problem of quantum parameter estimation. The records consist of time delays between quantum jumps.</li> <li><strong>Models. </strong>Models trained with the training trajectories provided, and used to obtain the results shown in the paper.</li> <li><strong>Validation trajectories. </strong>Trajectories used to benchmark the trained models. For the 2D case, the same trajectories are provided as a single .npy file, and split in 10 separated batches inside a ```batches``` folder. These are the batches that we used to generate Bayesian estimations on a cluster using nested sampling (see README file of the <a href="http://github.com/CarlosSMWolff/ParamEst-NN">repository</a>).</li> <li><strong>Cached results. </strong>Here we provide pre-computed Bayesian estimations for the 2D multi-parameter estimation case using nested sampling.</li> </ol> <p> </p> <p><em>E.R. was supported by Nippon Telegraph and Telephone Corporation (NTT) Research during the early stages of this work.<br> C.S.M. acknowledges that the project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.847648, with fellowship code LCF/BQ/PI20/11760026, and financial support from the MCINN project PID2021-126964OB-I00 (QENIGMA) and the Proyecto Sinérgico CAM 2020 Y2020/TCS- 6545 (NanoQuCo-CM).</em></p>
Combining active and passive temperature signals for estimating sediment depths
<p>This dataset contains the results of the experimental campaign and how data were collected on the the <a href="https://co-udlabs.eu/">Co-UDlabs</a> Transnational Access project: <em>Characterization of thermal properties in sediment samples from urban drainage systems with temperature probes</em>, by Langeveld et al. The Transnational Access project was funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626.</p> <p>The experiments were designed to further develop an innovative methodology for measuring sediment bed deposits in UDS based on temperature data analysis (<a href="https://doi.org/10.5281/zenodo.7258998">Anta et al., 2022</a>; <a href="https://doi.org/10.1039/D2EW00820C">Regueiro-Picallo et al., 2023</a>). Particularly, the aim of these experiments was to prove the application of this methodology for measuring sewer sediment accumulation. Therefore, the main tasks of this project were the development of a system based on active temperature sensors and its application in the measurement of sediment thermal properties of UDS. Furthermore, this system included additional temperature sensors installed at different height locations to allow the estimation of the sediment depth. The purpose of these measurements is to deepen the understanding of sedimentation and sediment transport processes.</p> <p>The data are described so that others can use and reproduce.</p>
Figure 6 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 6. – Histological changes in testis development of Engraulis encrasicolus at the different reproductive maturity stages. A: Stage I, represented only by spermatogonia cells, inactive spermatogenesis, the number of the fields concerned in the biometric analysis N = 236. B: Stage II, active spermatogenesis with primary spermatogenium multiplication and tubular size increase, the number of the fields concerned in the biometric analysis N = 241. C: Stage III, active spermatogenesis, spermatozoa are concentrated in the light of the seminiferous tubules, the number of the fields concerned in the biometric analysis N = 125. D: Stage IV, spermiation, the number of the fields concerned in the biometric analysis N = 93. E: Stage V, seminiferous tubules are empty, spermatozoa are eliminated by release or resorption, the number of the fields concerned in the biometric analysis N = 988.
Figure 4 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 4. – Mean values of the gonadosomatic index (GSI, represented in percentage) in Engraulis encrasicolus male collected in the Gulf of Bejaia from October 2007 to May 2008. Data are represented as Mean ± SEM. (values indicates the number of fishes).
Figure 1 in Seminiferous tubule number and surface: validation of objective parameters to estimate reproduction activity of male European anchovy (Engraulis encrasicolus, L.)
Figure 1. – Photomicrograph of male anchovy testicular tissue illustrating the seminiferous tubules in stage I.
Composite activity type and stride-specific energy expenditure estimation model for thigh-worn accelerometry
<p>This repository contains code and data for the research project 'Estimation of activity induced energy expenditure using thigh-worn accelerometry and machine learning approaches'.</p> <ul> <li>The <strong>code </strong>subfolder contains Jupyter Notebooks and a Python file with helper functions. Further, the models subfolder contains the trained models.</li> <li>The <strong>data </strong>subfolder contains the raw accelerometer files (AX) as well as the raw data from the indirect calorimetry (CPET). Further, different processing files can be found here. The file log_master.csv contains the sociodemographic and timestamp data.</li> <li>The <strong>figures</strong> subfolder contains all relevant figures, which are created as part of running the Jupyter Notebooks. These figures are also part of the research publication.</li> </ul>
Validity of an AI-based Program to Identify Foods and Estimate Food Portion Size
ClinicalTrials.gov study NCT05343585. IPD Sharing: NO. Countries: 1. Publications: 1.
Study Estimating the Clinical Difference Between 300 mg and 150 mg of Secukinumab Following Dose Escalation to 300 mg in Patients With Ankylosing Spondylitis
ClinicalTrials.gov study NCT03350815. IPD Sharing: YES. Countries: 1. Publications: 1.
Data For: Herbarium specimens provide reliable estimates of phenological responsiveness to climate at unparalleled taxonomic and spatiotemporal scales
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Distance estimation in the Goldfish (Carassius auratus)
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Estimates of Shark at-vessel, Post-release Mortality, and Retention Ban Effects on Stopping Overfishing
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A novel method for estimating avian roost sizes using passive acoustic recordings
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Data from: New estimates indicate that males are not larger than females in most mammal species
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Evidence of absence regression: a binomial N-mixture model for estimating fatalities at wind power facilities
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Are trapping data suited for home-range estimation?
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SSP: An R package to estimate sampling effort in studies of ecological communities
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Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
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Estimating household preferences for coastal flood risk mitigation policies under ambiguity
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Data from: Estimating a physiologically-based threshold to oxygen and temperature from marine monitoring data reveals challenges and opportunities for forecasting distribution shifts
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Annual occupancy estimates for butterflies, grasshoppers and dragonflies in Bavaria (Germany), 1980-2019
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International Brain Laboratory public data
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OpenNeuro
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