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Dataset results
91 results for “network performance”
Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset
<p>NXTensor extraction library, experiment code, tropical cyclone and background images and their metadata generated from the meterological reanalysis ERA5 and MERRA-2 according to the HURDAT2 cyclone tracks.</p> <p>Version specifications:</p> <ul> <li>NXTensor: v0.3.3.10</li> <li>Experiment code: v2.0.3</li> <li>Image sets: v1</li> </ul> <p> </p>
Results from network experiments conducted on ULiège testbed with the Network Performance Framework (NPF) tool
<p>This dataset results from network experiments on a router configuration with replayed traffic. The network trace comes from a border router of our campus network. The router is used in both directions, i.e. both for reception and transmission of packets (to and from our campus network). Each experiment was done 3 times (for a total of 11466 experiments).</p> <p> </p>
Baseline Performance and MAS reaction to network Anomalies in Demo 1
<p>Collected Latency data and packet captures for the demo described by the paper 10.5281/zenodo.12820942 "Augmented Reality App with AI-based Pervasive Latency Monitoring of RAN and Programmable Metro Packet-Optical Networks" presented during ICTON24 conference.</p>
Supplementary Data and Software for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance"
<p>The file includes supplementary data for "Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance". If you would like to re-use the software provided please cite the following two items.</p> <p>R. Klus, J. Talvitie, J. Equi, G. Fodor, J. Torsner, and M. Valkama, “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and<br>Performance,” IEEE Transactions on Vehicular Technology, 2024.</p> <p>R Klus et al. (2024). Supplementary materials for “Robust NLoS Localization in 5G mmWave Networks: Data-based Methods and Performance”. version v1, 25.06.2024, [Online]. Available: https://doi.org/10.5281/zenodo.12204892</p> <p>If you have any questions about this package, please do not hesitate to contact Roman Klus (roman.klus@tuni.fi).</p>
Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE
<p><strong>Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a></strong></p> <p>This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE"<br> You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format.</p> <p><strong>Acknowledgments</strong><br> The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.</p>
Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition
ClinicalTrials.gov study NCT03822390. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Carbosiloxane Bottlebrush Networks for Enhanced Performance and Recyclability
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Data from: Advancing mold identification in the routine laboratory: Performance of smartphone-based imaging and a newly developed Convolutional Neural Network
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Data from: Performance-based Egress safety assessment of underground tunnels: Simulation and artificial neural network approaches
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Effect of green infrastructure on restoration of pollination networks and plant performance in semi-natural dry grasslands across Europe
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Data from: Reconfiguration of functional brain networks and metabolic cost converge during task performance
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Experiment Dataset for L. Zhu, G. Casale, I. Perez, Fluid approximation of closed queueing networks with discriminatory processor sharing, Performance Evaluation (2020): 102094
<p>This dataset provides the results of the validation experiments for transient, steady-state and response time distribution<br> analysis published in L. Zhu, G. Casale, I. Perez, Fluid approximation of closed queueing networks with discriminatory processor sharing, Performance Evaluation (2020): 102094.</p>
Effects of Network Topology On the Performance of Consensus and Distributed Learning of SVMs Using ADMM
<p>The Alternating Direction Method Of Multipliers (ADMM) is a popular and promising distributed framework for solving large-scale machine learning problems. We consider decentralized consensus-based ADMM in which nodes may only communicate with one-hop neighbors. This may cause slow convergence. We investigate the impact of network topology on the performance of an ADMM-based learning of Support Vector Machine (SVM) using expander, and mean-degree graphs, and additionally some of the common modern network topologies. In particular, we investigate to which degree the expansion property of the network influences the convergence in terms of iterations, training and communication time. We furthermore suggest which topology is preferable. Additionally, we provide an implementation that makes these theoretical advances easily available. The results show that the performance of decentralized ADMM-based learning of SVMs in terms of convergence is improved using graphs with large spectral gaps, higher and homogeneous degrees.</p>
Raw results of the numerical experiments performed to evaluate different MAC schemes for LoRaWAN networks
<p>This zip file contains the scripts, gnuplot and data files needed to generate the figures showing the numerical results presented in [1].</p> <p>[1] S. Herrería-Alonso, A. Suárez-González, M. Rodríguez-Pérez and C. López-García, "Enhancing LoRaWAN scalability with Longest First Slotted CSMA," in <em>Computer Networks</em>, vol. 216, article number 109252, Oct. 2022, doi: 10.1016/j.comnet.2022.109252.</p>
Incorporating the image formation process into deep learning improves network performance
<p>These are representative source data for the main figures (Figs, 1d, 2a, 2d, 3c, 4b, 5a) in paper "Incorporating the image formation process into deep learning improves network performance".</p>
Fig. 5 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 5. Visualization of a feature with high importance and a feature with low importance from a configuration B. The importance of these features for the identification accuracy was determined using permutation tests (see the methods). For each taxon or group (rows) several randomly selected specimens (columns) are shown. For the two selected Global Average Pooling layer features, the corresponding features of the preceding (Max Pooling) layer are visualized as those show specific image parts that had higher activations.Yellow represents the maximal activation strength; dark blue represents the minimal activation strength.
Fig. 2 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 2. Schematic representation of the networks used. On top are the convolutional layers of VGG16, grouped into five blocks. Output of each Max Pooling layer is fed into Global Average Pooling layer. Numbers near each block name indicate number of features in the Global Average Pooling layer. Height of layers roughly corresponds to resolution (except for Global Average Pooling layer), while width roughly corresponds to the number of feature maps or features produced.Then, in approach A, outputs of five blocks are concatenated and passed to the linear classifier. In approach B, output of only one block (block 3 in the final configuration) is passed to the linear classifier. In approach C CNN outputs are as in approach A, but instead connected to a DNN with two layers of 320 fully connected (FC) neurons followed by a prediction layer (PL), with number of neurons equal to number of species classified. Finally, approach D features CNN as in approach B which is connected to DNN as in approach C.
Fig. 1 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 1. Dorsal habitus photos of males and females of Tuxedo spp., Pygovepres vaccinicola, and Phallospinophylus setosus generated for and used in this study.
Fig. 4 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 4. Validation accuracy and accuracy on test data for SVM linear classifier (A, B) and DNN approaches (C, D) for the three datasets.
Fig. 3 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images
Fig. 3. Identification accuracy for the male (top) and female (bottom) Tuxedo dataset for the five selected resolutions and the individual blocks 1–5 and the concatenated block.
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.