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1,782 results for “algorithms”
Raw data and scripts for "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation"
<p>This package contains all required scripts to generate the simulations and figures from the study "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation" by Volker Hohmann, published in Acta Acustica:</p> <p>The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation<br>Volker Hohmann<br>Acta Acust. 5 56 (2021)<br>DOI: 10.1051/aacus/2021050</p> <p>When referring to this work, please cite the journal paper.</p> <p>Note that additive noise is generated at random, i.e., small differences in the estimation accuracy occur when repeating a simulation. For further details see the journal paper.</p> <p>Thank you for downloading the package. Your comments are very welcome!</p> <p>Method patented: DE Patent DE102021207339B3</p> <p>Author: Volker Hohmann, Carl von Ossietzky Universität Oldenburg, Germany</p>
The accuracy and efficiency of a reference-based adaptive selection algorithm for comparative judgement
<p>Data and R code with article for Frontiers in Education special issue Validity, Reliability and Efficiency of Comparative Judgement to Assess Student Work. [Submitted]</p> <p>Abstract:</p> <p>Several studies have proven that comparative judgement (CJ) is a reliable and valid assessment method for a variety of competences, expert assessment and peer assessment, and CJ is emerging as a possible approach to help maintain standards over time. For consecutive pairs of student works (representations) assessors are asked to judge which representation is better. It has been shown that random construction of pairs leads to very inefficient assessments, requiring a lot of pairwise comparisons to reach reliable results. Some adaptive selection algorithms using information from previous comparisons were proposed to increase the efficiency of CJ. These adaptive algorithms appear however to artificially inflate the reliability of CJ results through increasing the spread of the results. The current article proposes a new adaptive selection algorithm using a previously calibrated reference set. Using a reference set should eliminate the reliability inflation. In a real assessment, using reference sets of different reliability, and in a simulation study it is proven that this adaptive selection algorithm is more efficient without reducing the accuracy of the results and without increasing the standard deviation of the assessment results. As a consequence, a reference-based adaptive selection algorithm produces high and correct reliability values in an efficient manner.</p>
Performance comparison of optimization methods on variational quantum algorithms
<p>This repository contains the code, data and notebooks to reproduce the plots from the manuscript "Performance comparison of optimization methods on variational quantum algorithms"</p>
Performance breakdown for all algorithms for the matrix raefsky3 (M2)
<p>Runtime of computational kernels for different number of processes on matrix M2 with block size <span class="math-tex">\(k=32\)</span> after 100 iterations. For each kernel, we show the maximum time taken over all processes. The power scheme dominates the runtime in RandQB_EI. QR with column tournament pivoting dominates the runtime in the deterministic methods.</p>
FAW attack algorithm
<p>The algorithm of FAW attack</p>
Sea ice velocity and area flux in the Fram Strait based on an improved algorithm
<p>These data are related to the paper on the sea ice velocity improvement in the Fram Strait. I provide the daily sea ice velocity and area flux in the Fram Strait. Besides, the data used for validation with buoy ice velocity are also included.</p>
Fourier projection algorithm for calculating phonon dispersion relations of crystals
<p>We propose a Fourier projection algorithm and demonstrate its superiority for calculation of the phonon dispersion relations of 2D and 3D crystals based on the atomic coordinate trajectories from supercell molecular dynamics/first-principle molecular dynamics simulations. The lattice vibration states are described in a six-dimensional phase space composed of lattice and wave vectors. Phonon dispersion information in the first Brillouin zone sampled with a k-mesh grid of N×N×N is generated by Fourier projection of the lattice vibration frequencies in the supercell by N×N×N primitive cells, the dispersion relations along high-symmetry paths are retrieved therefrom. Our algorithm is physically intuitive, computationally convenient, and universally applicable. As examples of application, the successful dispersion relation calculations of 2D graphene, 3D BCC-Fe and FCC-Cu are presented. This algorithm shows the correspondence between real and reciprocal lattices, that is, the spatial information in the states of the lattice vibration were preserved. The high-symmetry paths of the first Brillouin zone in the reciprocal lattice primitive cells of the hexagonal, BCC, and FCC lattice were also given in detail. In combination with first-principles molecular dynamics, as long as the interatomic force is accurately known, reliable and accurate dispersion relation can be achieved by this Fourier projection algorithm.</p>
data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease – a comparison of three screening tools used in the GLIM diagnostic algorithm
<p>data set regarding to project Optimal assessment of nutritional status in older subjects with the chronic obstructive pulmonary disease – a comparison of three screening tools used in the GLIM diagnostic algorithm </p>
(D2.1_dataset01) Parameter values used in the optimal loads split algorithm to find the baseload
<p>In Task 2.1 of hybridGEOTABS project, an <em>optimal load splitting algorithm (OLSA) </em>was developed and validated. The OLSA splits the building thermal loads (calculated in T2.2 of the project) to the baseload and residual loads. The baseload is defined as the maximum share of GEOTABS that allows to minimise the energy use of the entire (primary + secondary) heating and cooling system, while maintaining thermal comfort in the building and by using and taking into account the thermal storage in the TABS. The algorithm is documented in detail in D2.1 of the hybridGEOTABS project. This document reports all the parameter values used in the OLSA.</p>
Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms - Dataset
<p>This repository contains the raw data and code nessecary to reproduce the results from the paper "Analyzing the Impact of Undersampling on the Benchmarkingand Configuration of Evolutionary Algorithms"</p> <p>The main file is the python-notebook 'reproducibility.ipynb', which details the full process for reproduction of the results shown in the paper. The two additional .py files are included for computation which takes longer and can be parallelized.</p> <p>The folder 'irace_conf_static_modcma.zip' contains the verification runs: 200 independent runs of each configuration. Indexes are according to 'Irace_confs_static_modcma_v2.csv'</p> <p>The folder 'logs_baseline_cs.zip' contains the raw irace files on which the analysis is based. This data is taken from the following repository:<br> de Nobel, Jacob, Vermetten, Diederick, Wang, Hao, Doerr, Carola, & Bäck, Thomas. (2021). Data and Code from: Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4524959</p>
GECCO2022 Automated Algorithm Selection for Radar Network Configuration
<p>This upload is a dataset companion to a paper in the Real World Application track of the GECCO 2022 conference.</p> <p>The dataset includes a binary container for the objective function used in the paper as well as the corresponding experimental logs.</p>
A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data
<p>Supporting Matlab code and data for the manuscript "A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data"</p>
Smart Battery Management System for Electric Vehicles: Selflearning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection
<p>The project proposes to develop parameter-varying SOH-coupled models for lithium-ion battery and self-learning algorithms to learn the model for simultaneous state and parameter estimation and fault detection. The traditional battery models use constant parameters, limiting their accuracy for predicting the state of the charge and health over the complete life-cycle. In practice, the battery parameters vary with the change in the state of charge and state of health. SOH-coupled models can be used to estimate the state of charge and health accurately. Further, obtaining the model parameters is also a challenging task for designing filters or observers for state estimation. A self-learning algorithm can eliminate the requirement of the model parameters. In this project, three SOH-coupled models are proposed and validated experimentally. The models are also used to design extended Kalman filters (EKF) for the state of charge, state of health, core and surface temperature, and internal resistance estimation. The results showed that the SOHcoupled models are more effective when compared to the uncoupled models in the literature. Further, it was found that EKFs based state estimation errors were within 1%. The self-learning algorithm using a two-layer neural network showed the ability to learn the models in real-time. However, the state estimation errors are higher for the self-learning scheme compared to the EKF based approaches. This is due to the limited measurement and online training schemes utilized to train neural networks. This requires further investigation in hyper-parameter tuning for implementation. Finally, a model-based fault detection scheme was proposed to detect internal thermal fault at its onset. The SOHcoupled model is reformulated to incorporate the internal resistance as a state. The EKF is used as a fault detection observer. The proposed fault detection scheme is validated using numerical simulation. It was observed that the fault detection scheme with SOH coupled electro-thermal-aging model could effectively detect a thermal fault at its incipient state.</p>
OpenML Dataset for Data and Algorithm Recommendations
<p>This dataset contains data obtained from the online platform OpenML [1] and represents interactions between users, algorithms, and datasets. For example, a user could apply an algorithm to a dataset for a classification task. Furthermore, it includes textual descriptions for the datasets and algorithms.</p> <p>* 512 users<br> * 12,344 algorithms<br> * 2,677 datasets<br> * 10,945 interactions</p> <p>The format of interactions.csv is (interaction id, task type, user id, dataset id, algorithm id).<br> The format of datasets.csv and algorithms.csv is (id, name, description).</p> <p>[1] https://www.openml.org/</p>
Antioxidant capacity prediction of Nanoforms using supervised algorithms.
<p>This dataset contains p-chem properties of Nanoforms and experimental conditions for the antioxidant capacity measured with the DPPH assay.</p>
Do expectation-maximization algorithms have to make use of 'background distribution' for de novo motif search?
<p>Data for the article "Do expectation-maximization algorithms have to make use of ‘background distribution’ for de novo motif search?". The preprint is live on Research Square (DOI: <a href="https://www.researchsquare.com/article/rs-1725972/v2">10.21203/rs.3.rs-1725972/v2</a>).</p>
Towards understanding the importance of time-series features in automated algorithm performance prediction
<p><strong>merged_feature_importance.csv</strong> - CSV with feature importance values with different meta-models, forecasting algorithms, and feature importance methods computed on 30 different train/test splits.</p> <p><strong>Catch22.csv</strong> - Catch22 features (raw time-series)</p> <p><strong>Catch22Log.csv</strong> - Catch22 features (log time-series)</p> <p><strong>Catch22Diff.csv</strong> - Catch22 features (differenced time-series)</p> <p><strong>TSFresh.csv</strong> - TSFresh features (raw time-series)</p> <p><strong>TSFreshLog.csv</strong> - TSFresh features (log time-series)</p> <p><strong>TSFreshDiff.csv</strong> - TSFresh features (differenced time-series)</p> <p><strong>mape.csv</strong> - sMAPE performance for all forecasting algoirthms</p>
SMR algorithm, dataset and training loss
<p>这是我们提出的SMR方法的数据,算法和训练损失的概况。数据集来自中国两个致密储集层:松辽盆地三豑凹陷的扶余储集层(SZS数据集)和鄂尔多斯盆地林兴区块的上古生界(实况成套数据集)。</p>
Data from: Optimal mating of Pinus taeda L. under different scenarios using differential evolution algorithm
<p>A newly developed software, AgMate, was used to perform optimized mating for monoecious <em>Pinus taeda L.</em> breeding. Using a computational optimization procedure called differential evolution (DE), AgMate was applied under different breeding population sizes scenarios (50, 100, 150, 200, 250) and candidate contribution scenarios (max use of each candidate was set to 1 or 8), to assess its efficiency in maximizing the genetic gain while controlling inbreeding. Real pedigree data set from North Carolina State University Tree Improvement Co-op with 962 Pinus taeda were used to optimize objective functions accounting for coancestry of parents and expected genetic gain and inbreeding of the future progeny. AgMate results were compared with those from another widely used mating software called MateSel (Kinghorn, 1999). For the proposed mating list for 200 progenies, AgMate resulted in an 83.7% increase in genetic gain compared with the candidate population. There was evidence that AgMate performed similarly to MateSel in managing coancestry and expected genetic gain, but MateSel was superior in avoiding inbreeding in proposed mate pairs. The developed algorithm was computationally efficient in maximizing the objective functions and flexible for practical application in monoecious diploid conifer breeding.</p>
Classification of Eye Images by Personal Details With Transfer Learning Algorithms
<p>During the data collection phase of the research, first of all, a brief information was given to the participants about the study, and how the data would be used and what to do. Photographs of the eye area were collected from participants consisting of a total of 96 different people aged between 3-64. It has been clearly stated that there will be no situations that will define them during the photo shoot. Then, at least ten images of the right eye area of each person were taken. In addition, at least ten photographs of the left eye area were taken. Along with these photographs, no data other than the age and gender of the persons was recorded. Below are images of two people of different genders.</p> <p>A total of 1980 images were obtained from the participants, as in the figure above. More than ten images were obtained from some people. For this reason, there is a difference in the number of photos of people. Care has been taken to use different angles and lights so that each photograph does not form the same frame. Thus, photographs that were not, all the same, were collected. In order for each photograph not to be confused with another photograph, a naming rule has been developed to express the person, age, gender and the number of the photograph taken. An underscore ("_") character is inserted between each expression. Each expression used in the naming convention is given below in order.</p> <ul> <li>Person ID: It is a unique code value for each person photographed. This value ranges from 1 to 100.</li> <li>Age: The age is written directly as a number to express how old the person is. This value varies between 3-64.</li> <li>Gender ID: The value of 1 is expressed if the person photographed is male, and the value of 0 if it is a woman.</li> <li>Photo ID: Due to the fact that more than one photo was taken for each person, each photo was numbered sequentially from 1-10.</li> </ul> <p>If this dataset is used, reference should be made to the article below.</p> <ul> <li>Aktürk, C., Aydemir, E., Hama Rashid, Y. M. 2022. Classification of eye images according to person details with the transfer learning algorithms. Acta Informatica Pragensia, DOI: 10.18267/j.aip.190</li> </ul>
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.