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1,782 results for “algorithms”
Data from: Validation of an algorithm for identifying MS cases in administrative health claims datasets
Objective: To develop a valid algorithm for identifying multiple sclerosis (MS) cases in administrative health claims (AHC) datasets. Methods: We used 4 AHC datasets from the Veterans Administration (VA), Kaiser Permanente Southern California (KPSC), Manitoba (Canada), and Saskatchewan (Canada). In the VA, KPSC, and Manitoba, we tested the performance of candidate algorithms based on inpatient, outpatient, and disease-modifying therapy (DMT) claims compared to medical records review using sensitivity, specificity, positive and negative predictive values, and interrater reliability (Youden J statistic) both overall and stratified by sex and age. In Saskatchewan, we tested the algorithms in a cohort randomly selected from the general population. Results: The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT claims within a 1-year time period; a 2-year time period provided little gain in performance. Algorithms including DMT claims performed better than those that did not. Sensitivity (86.6%–96.0%), specificity (66.7%–99.0%), positive predictive value (95.4%–99.0%), and interrater reliability (Youden J = 0.60–0.92) were generally stable across datasets and across strata. Some variation in performance in the stratified analyses was observed but largely reflected changes in the composition of the strata. In Saskatchewan, the preferred algorithm had a sensitivity of 96%, specificity of 99%, positive predictive value of 99%, and negative predictive value of 96%. Conclusions: The performance of each algorithm was remarkably consistent across datasets. The preferred algorithm required ≥3 MS-related claims from any combination of inpatient, outpatient, or DMT use within 1 year. We recommend this algorithm as the standard AHC case definition for MS.
Data from: An SSR based approach incorporating a novel algorithm for identification of rare maize genotypes facilitates criteria for landrace conservation in Mexico
Since maize was domesticated in Mexico around 9000 years ago, local farmers have selected and maintained seed stocks with particular traits and adapted to local conditions. In the present day many of these landraces are still cultivated, however increased urbanization and migration from rural areas implies a risk that this invaluable maize germplasm may be lost. In order to implement an efficient mechanism of conservation in situ, the diversity of these landrace populations must be estimated. Development of a method to select the minimum number of samples that would include the maximum number of alleles and identify germplasm harboring rare combinations of particular alleles will also safeguard the efficient ex-situ conservation of this germplasm. To reach this goal a strategy based on SSR analysis and a novel algorithm to define a minimum collection and rare genotypes using landrace populations from Puebla State, Mexico was developed as a "proof of concept" for methodology that could be extended to all maize landrace populations in Mexico and eventually to other native crops. The SSR based strategy using bulked DNA samples allows rapid processing of large numbers of samples and can be set up in most laboratories equipped for basic molecular biology. Therefore, continuous monitoring of landrace populations locally could easily be carried out. This methodology can now be applied to support incentives for small farmers for the in situ conservation of these traditional cultivars.
Evaluating the performance of probabilistic algorithms for phylogenetic analysis of big morphological datasets: a simulation study
<p>Reconstructing the tree of life is an essential task in evolutionary biology. It demands accurate phylogenetic inference for both extant and extinct organisms, the latter being almost entirely dependent on morphological data. While parsimony methods have traditionally dominated the field of morphological phylogenetics, a rapidly growing number of studies are now employing probabilistic methods (maximum likelihood and Bayesian inference). The present-day toolkit of probabilistic methods offers varied software with distinct algorithms and assumptions for reaching global optimality. However, benchmark performance assessments of different software packages for the analyses of morphological data, particularly in the era of big data, are still lacking. Here, we test the performance of four major probabilistic software under variable taxonomic sampling and missing data conditions: the Bayesian inference-based programs MrBayes and RevBayes, and the maximum likelihood-based IQ-TREE and RAxML. We evaluated software performance by calculating the distance between inferred and true trees using a variety of metrics, including Robinson-Foulds (RF), Matching Splits (MS), and Kuhner-Felsenstein (KF) distances. Our results show that increased taxonomic sampling improves accuracy, precision, and resolution of reconstructed topologies across all tested probabilistic software applications and all levels of missing data. Under the RF metric, Bayesian inference applications were the most consistent, accurate, and robust to variation in taxonomic sampling in all tested conditions, especially at high levels of missing data, with little difference in performance between the two tested programs. The MS metric favored more resolved topologies that were generally produced by IQ-TREE. Adding more taxa dramatically reduced performance disparities between programs. Importantly, our results suggest that the RF metric penalizes incorrectly resolved nodes (false positives) more severely than the MS metric, which instead tends to penalize polytomies. If false positives are to be avoided in systematics, Bayesian inference should be preferred over maximum likelihood for the analysis of morphological data.</p>
Data from: Spatially sparse emitters localization with QVBEM algorithm
<p>We study the estimation of the spatially sparse radio emitter locations from space, via the proposed Quad-tree variational Bayesian expectation maximization (QVBEM) algorithm. Firstly, we assume that the emitters are approximately lie on a uniform grid points in the region under surveillance. The VBEM algorithm is applied and the points exceeding the threshold level are considered as potential targets. Then, the grids are refined around the potential targets via the Quad-tree algorithm and the process is iterated. It allows us to find the location of sparse emitters with much less computational complexity due to the use of fewer grid points. </p>
Software for control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms
<p>This software implements the autonomous control of a robot by using a fuzzy logic controller tuned by a genetic algorithm. The software was written in C programming language for Windows (SDK). A description of the software can be found in the research publication "Arsene, C.T.C., & Zalzala, A.M.S., "Control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms", In Proc Congress on Evolutionary Computation, Vol. 1, pp. 428-35, Washington DC, 1999, IEEE Computer Science Press, ISBN 0-7803-5536-9". Possibly the software to be used also for simulation of Nano-robots.</p>
Data from: FEATHER: automated analysis of force spectroscopy unbinding and unfolding data via a Bayesian algorithm
Single-molecule force spectroscopy (SMFS) provides a powerful tool to explore the dynamics and energetics of individual proteins, protein-ligand interactions, and nucleic acid structures. In the canonical assay, a force probe is retracted at constant velocity to induce a mechanical unfolding/unbinding event. Next, two energy landscape parameters, the zero-force dissociation rate constant (ko) and the distance to the transition state (Δx‡), are deduced by analyzing the most probable rupture force as a function of the loading rate, the rate of change in force. Analyzing the shape of the rupture force distribution reveals additional biophysical information, such as the height of the energy barrier (ΔG‡). Accurately quantifying such distributions requires high-precision characterization of the unfolding events and significantly larger data sets. Yet, identifying events in SMFS data is often done in a manual or semiautomated manner and is obscured by the presence of noise. Here, we introduce, to our knowledge, a new algorithm, FEATHER (force extension analysis using a testable hypothesis for event recognition), to automatically identify the locations of unfolding/unbinding events in SMFS records and thereby deduce the corresponding rupture force and loading rate. FEATHER requires no knowledge of the system under study, does not bias data interpretation toward the dominant behavior of the data, and has two easy-to-interpret, user-defined parameters. Moreover, it is a linear algorithm, so it scales well for large data sets. When analyzing a data set from a polyprotein containing both mechanically labile and robust domains, FEATHER featured a 30-fold improvement in event location precision, an eightfold improvement in a measure of the accuracy of the loading rate and rupture force distributions, and a threefold reduction of false positives in comparison to two representative reference algorithms. We anticipate FEATHER being leveraged in more complex analysis schemes, such as the segmentation of complex force-extension curves for fitting to worm-like chain models and extended in future work to data sets containing both unfolding and refolding transitions.
DATA of Effect of different iterative reconstruction algorithms on ultra-low dose CT of inflammatory bowel disease in a rabbit model
<p>This file contains the experimental data of the scientific paper titled: " <strong>Effect of different iterative reconstruction algorithms on ultra-low dose CT of inflammatory bowel disease in a rabbit model"</strong></p>
Hybrid Multilevel Solvers for Discontinuous Galerkin Finite Element Discrete Ordinate (DG-FEM-SN) Diffusion Synthetic Acceleration (DSA) of Radiation Transport Algorithms
<p>In accordance with EPSRC funding requirements this folder contains all raw data relevant to the named paper: </p> <p>Hybrid Multilevel Solvers for Discontinuous Galerkin Finite Element Discrete Ordinate (DG-FEM-SN) Diffusion Synthetic Acceleration (DSA) of Radiation Transport Algorithms</p> <p><br> Journal: Annals of Nuclear Energy</p>
When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics - datasets and results
<p>This directory contains the data and results that were used and obtained during the realization of the following paper:</p> <blockquote> <p>When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics. Rinde R.S. van Lon and Tom Holvoet. Journal of Autonomous Agents and Multi-Agent Systems (2017).</p> </blockquote> <p>At the time of writing the paper is not yet accepted, some of the details (e.g. title, date of publication) are subject to change. The code that has been used can be found in this repository: https://github.com/rinde/vanLon17-JAAMAS-code</p> <p>Note that unpacked, all files are about 5 GB in total.</p> <p>This directory has the following structure:</p> <p>- makefile - script that can be used to run the analyses. The most important commands are:<br> - all - runs all analyses.<br> - paper-deps - runs only the analyses that were directly used in the paper.<br> - additional-analysis - runs several additional analyses.</p> <p>- readme.txt - this file.</p> <p>- results - contains all R-scripts used to analyse the data<br> - data/main - contains the results of the main result.<br> - data/mas-tuning - contains the results of the MAS tuning experiments.<br> - data/optaplanner-tuning-gendreau - contains the results of the OptaPlanner tuning experiments.<br> - data/sensitivity - contains the results of the simulator sensitivity experiments (using OptaPlanner algorithms).</p> <p>- scenarios<br> - gendreau2006 - contains the dataset from the Gendreau et al. [1] paper, used for the OptaPlanner tuning experiments.<br> - mas-tuning-dataset - contains the dataset that was used for tuning the MAS. This was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code<br> - vanLonHolvoet15-adapted-to-4-hours - contains the dataset that was used for the main experiment. This is an adapted version of the dataset from [2]. This version has a duration of 4 hours and requires a real-time simulator. It was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code </p> <ul> <li>[1] Michel Gendreau, Francois Guertin, Jean-Yves Potvin, and René Séguin. Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick- ups and deliveries. Transportation Research Part C: Emerging Technologies, 14 (3):157–174, 2006. ISSN 0968090X. doi:10.1016/j.trc.2006.03.002.</li> <li>[2] Rinde R. S. van Lon and Tom Holvoet. Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics. In Qingliang Chen, Paolo Torroni, Serena Villata, Jane Hsu, and Andrea Omicini, editors, PRIMA 2015: Principles and Practice of Multi-Agent Systems: 18th International Conference, Bertinoro, Italy, October 26-30, 2015, Proceedings, pages 248–264. Springer In- ternational Publishing, Cham, 2015. ISBN 978-3-319-25524-8. doi:10.1007/978- 3-319-25524-8 16.</li> </ul>
FIGURE 2 in VARSEDIG: an algorithm for morphometric characters selection and statistical validation in morphological taxonomy
FIGURE 2. Discriminant analysis performed on Moenkhausia dichroura and M. oligolepis using all morphometric variables. A longer arrow in the discriminant analysis means a higher contribution of the variable to discriminating the two species.
FIGURE 1 in VARSEDIG: an algorithm for morphometric characters selection and statistical validation in morphological taxonomy
FIGURE 1. Application of VARSEDIG to compare Moenkhausia dichroura and M. oligolepis with the overlap method and the argument minimum=TRUE, as default options of VARSEDIG. (A) Density plot with the overlap of the variable that best discriminates the two species, in this case variable M26. (B) Scatterplot of the polar coordinates obtained for both species using variables M26 and M11. The ellipses show the levels of significance to the 0.5 (inner ellipse) and 0.95 (outer ellipse) of each of the regions. (C) Bivariate randomisation test. The x-axis corresponds to the X polar coordinates and the y-axis corresponds to Y polar coordinates. Kernel density is estimated to indicate the contours of the distribution of randomized values (blue line). The two marginal histograms correspond to the univariate tests on each axis for which the p-values (one-sided tests) are computed. The figure shows the individual (red point) with a higher probability of belonging to Moenkhausia oligolepis among all included individuals identified as M. dichroura. (D) As in figure C, this shows the individual (red point) with a higher probability of belonging to M. dichroura among all individuals identified as M. oligolepis.
Supplemental Information: Dynamic Team Heterogeneity in Cooperative Coevolutionary Algorithms
<p>Videos of some of the solutions evolved with Hyb-CCEA and a standard CCEA, for the Soccer and Multi-rover tasks.</p>
When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics - datasets and results v1.1.0
<p>This directory contains the data and results that were used and obtained during the realization of the following paper:</p> <p>When do agents outperform centralized algorithms? - A systematic empirical evaluation in logistics. Rinde R.S. van Lon and Tom Holvoet. Journal of Autonomous Agents and Multi-Agent Systems (2017).</p> <p>This is version v1.1.0. At the time of writing the paper is not yet accepted, some of the details (e.g. title, date of publication) are subject to change. The code that has been used can be found in this repository: https://github.com/rinde/vanLon17-JAAMAS-code </p> <p>This directory has the following structure:</p> <p>- makefile - script that can be used to run the analyses. The most important commands are:<br> - all - runs all analyses.<br> - paper-deps - runs only the analyses that were directly used in the paper.<br> - additional-analysis - runs several additional analyses.</p> <p>- readme.txt - this file.</p> <p>- results - contains all R-scripts used to analyse the data<br> - data/main - contains the results of the main result.<br> - data/mas-tuning - contains the results of the MAS tuning experiments.<br> - data/optaplanner-tuning-gendreau - contains the results of the OptaPlanner tuning experiments.<br> - data/sensitivity - contains the results of the simulator sensitivity experiments (using OptaPlanner algorithms).</p> <p>- scenarios<br> - gendreau2006 - contains the dataset from the Gendreau et al. [1] paper, used for the OptaPlanner tuning experiments.<br> - mas-tuning-dataset - contains the dataset that was used for tuning the MAS. This was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code<br> - vanLonHolvoet15-adapted-to-4-hours - contains the dataset that was used for the main experiment. This is an adapted version of the dataset from [2]. This version has a duration of 4 hours and requires a real-time simulator. It was generated using the code from https://github.com/rinde/vanLon17-JAAMAS-code </p> <p>[1] Michel Gendreau, Francois Guertin, Jean-Yves Potvin, and René Séguin. Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick- ups and deliveries. Transportation Research Part C: Emerging Technologies, 14 (3):157–174, 2006. ISSN 0968090X. doi:10.1016/j.trc.2006.03.002.</p> <p>[2] Rinde R. S. van Lon and Tom Holvoet. Towards systematic evaluation of multi-agent systems in large scale and dynamic logistics. In Qingliang Chen, Paolo Torroni, Serena Villata, Jane Hsu, and Andrea Omicini, editors, PRIMA 2015: Principles and Practice of Multi-Agent Systems: 18th International Conference, Bertinoro, Italy, October 26-30, 2015, Proceedings, pages 248–264. Springer In- ternational Publishing, Cham, 2015. ISBN 978-3-319-25524-8. doi:10.1007/978- 3-319-25524-8 16.</p>
An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy
<p>Data related to the article:</p> <p>Front. Oncol.</p> <p>Sec. Radiation Oncology</p> <p>Volume 13 - 2023 | doi: 10.3389/fonc.2023.1285725</p> <p> </p> <p>An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy</p> <p>Ebbe Laugaard Lorenzen 1,2*, Bahar Celik 1, Nis Sarup1, Lars Dysager3, Rasmus Lübeck Christiansen1, Anders Smedegaard Bertelsen1, Uffe Bernchou1,2, Søren Nielsen Agergaard1, Maximilian Lukas Konrad1, Carsten Brink1,2*, Faisal Mahmood1,2, Tine Schytte2,3, Christina Junker Nyborg3</p> <p>1 Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark </p> <p>2 Department of Clinical Research, University of Southern Denmark, J.B. Winsløws Vej 19 3., 5000 Odense C, Denmark</p> <p>3 Department of Oncology, Odense University Hospital, J. B. Winsløws Vej 4, 5000 Odense C, Denmark</p> <p>* Correspondence: </p> <p>Ebbe Laugaard Lorenzen</p> <p>ebbe.lorenzen@rsyd.dk</p> <p>Carsten Brink </p> <p>carsten.brink@rsyd.dk</p> <p> </p> <p>NOTE: Version 2 of this repository contains the nnU-Net v2 model, while version 1 contains the original, nnU-Net v1 model</p> <p> </p>
Envelopes for the article "Realtime Selection of Optimal Source Parameters Using Ground Motion Envelopes" and the python notebook that shows the algorithm usage.
<p>The github repository is available at https://github.com/djozinovi/goodnessOfFitEnv</p>
Datafiles for: "Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets"
<p>Datafiles for results of the experiments reported in the paper "Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets"</p>
Illustration of IMTs Behavior in Recurrent Expansion Algorithm for Complex Classification Problems
<p>This video provides a visual representation of the Inputs Mappings and estimated Targets (IMTs) behavior within the Recurrent Expansion Algorithm when addressing highly complex classification problems. Specific details about this experiment and the dataset used can be downloaded from [1]. The video serves as a valuable illustrative resource for understanding the algorithm's performance and its approach to complex classification challenges. It also serves as supplementary material aiding in the understanding of Figure 9 from [1]. For more detailed information, please refer to the accompanying references. Please cite our paper.</p> <p>[1] Berghout, T. & Benbouzid, M. (2024). Empirical Analysis of Aeroengine Inter-Shaft Bearing Faults: Multiverse Recurrent Expansion and Data Quality Enhancement Strategies. 1–21. https://doi.org/http://dx.doi.org/10.2139/ssrn.4833247</p>
Data for the CBM Algorithm associated with the WASPAA23 publication.
<p>Data associated with the publication "Marmoret, Axel, Jérémy E. Cohen, and Frédéric Bimbot. "Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis." <em>2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)</em>. IEEE, 2023." https://arxiv.org/abs/2210.15356.</p> <p>Initially released on https://gitlab.inria.fr/amarmore/autosimilarity_segmentation/-/tree/WASPAA23, but moved because the git folder was too big.</p> <p>If you are willing to reproduce experiments, you should download these files and place them in the "data" folder, at root.</p>
ASYMPTOTIC PERFORMANCES OF POPULAR PROGRAMMING LANGUAGES FOR POPULAR SORTING ALGORITHMS
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Robust candidate solutions derived by our explicit ROPAR algorithm
<p># sset25 represents the optimal robust candidate solutions with expected hydrological laternatin degree being 0.25.</p> <p> </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.