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16 results for “Root Detection”
Figure 1 in Detection of Male Mediterranean Fruit Flies (Diptera: Tephritidae): Performance of Trimedlure Relative to Capilure and Enriched Ginger Root Oil
Figure 1. Capture of C. capitata males in TML- versus CPL-baited Jackson traps for 3 replicates in an Oahu coffee field. Abscissa represents period of lure ageing, where 0 weeks represents fresh lures. Bar heights indicate mean of 20 traps per lure type; error bars represent + 1 SE. Symbols above bars show results of the Tukey HSD test comparing the 2 lures for each ageing category, where an asterisk indicates P <0.001 and ns indicates no significant difference.
Annotated datasets of Scots pine cross-sectional images for root rot detection and resin detection
<p>These are the annotated datasets used in the publication available at</p> <p><a href="https://doi.org/10.1080/14942119.2024.2327247">https://doi.org/10.1080/14942119.2024.2327247</a></p> <p>The first dataset is for root rot detection, the other dataset is for resin detection. Please see the publication for details.</p>
Supplementary Material - Dataset for "Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis"
<h2>README</h2> <p>The dataset consists of the following components:<br> <br>- `<strong>prompts.txt</strong>`: This file contains the prompts used for large language models.<br> <br>- `<strong>Dataset</strong>` directory: includes general information about the dataset. Specifically, the `dataset.xlsx` file lists flaky and non-flaky tests, along with their root causes and fix types.<br> <br>- `<strong>Full</strong>` directory contains two subdirectories: `Flaky` and `Non-flaky`. Each of these directories is organized by individual GitHub organization projects, with each project having its list of repository subdirectories. These subdirectories are further divided into “issues” and “pull requests” (PRs).</p> <p><br>- `<strong>Method</strong>` level subdirectory has a similar structure but contains extracted code snippets at the method level instead of full code listings. The `code.diff` file is copied over and left unaltered. </p> <p><br>- <strong>Issue Directories (IRs):</strong> Named with an `issueID` template, each issue directory contains a `log.issue` file that includes the extracted description, comments, and metadata.<br> <br>- <strong>PR Directories (PRs)</strong>: Named using the `prID` template, each PR directory contains the text, comments, and metadata in the `pr.log` file. The text of the associated issue is stored in the `log.issue` file. Code listings are stored in a file with the `.bug` suffix, while the corresponding fixed version is in a `.fix` file. The `code.diff` file contains the patch that transforms the `.bug` version into the `.fix` version.</p> <p><br><strong>Additional notes:</strong><br>Issues with associated pull requests in `dataset.xlsx` are combined into the pull request directory template. If two pull requests are listed for a row, a PR directory is created for each. Due to updates in the extended dataset, some repositories have been renamed or archived, meaning the current repository directory names in `Dataset` will include both the previous and new names if it has been changed (e.g., a repository previously saved as Qiskit/qiskit-terra may now be saved as Qiskit/qiskit following the renaming from qiskit-terra to qiskit).</p> <h2>Directory Structure:</h2> <p><br>├── prompts.txt<br>├── Dataset/<br> └── dataset.xlsx<br>├── Full/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-Flaky/<br> └── <Organization>/<Repository>/...<br>├── Method/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-flaky/<br> └── <Organization>/<Repository>/...</p> <p> </p>
EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems
<p>Dataset of firmwares used for the experiments of the paper <em>EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems</em></p>
Figure 2 in Detection of Male Mediterranean Fruit Flies (Diptera: Tephritidae): Performance of Trimedlure Relative to Capilure and Enriched Ginger Root Oil
Figure 2. Capture of C. capitata males in TML- versus EGRO-baited Jackson traps for a single replicate in an Oahu coffee field. Abscissa represents period of lure ageing, where 0 weeks represents fresh lures. Bar heights indicate mean of 20 traps per lure type; error bars represent + 1 SE. Symbols above bars show results of the Tukey HSD test comparing the 2 lures for each ageing category, where an asterisk indicates P <0.001 and ns indicates no significant difference.
BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection
<p>Artifacts for the paper titled <strong><em>BARO: Robust Root Cause Analysis for Microservices via Multivariate Bayesian Online Change Point Detection</em></strong>.</p> <p>This artifact repository contains 3 compressed folders, as follows: </p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Benchmark System</strong></td> </tr> <tr> <td>fse-ob.zip</td> <td>Online Boutique</td> </tr> <tr> <td>fse-ss.zip</td> <td>Sock Shop</td> </tr> <tr> <td>fse-tt.zip</td> <td>Train Ticket</td> </tr> </tbody> </table> <p>Each zip file contains the collected data from the corresponding microservice benchmark systems (e.g., fse-ob.zip contains metrics data collected from the Online Boutique system). </p> <p><strong><strong>Data description</strong></strong></p> <p>To collect the metrics data, we deploy three benchmark microservice systems: Online Boutique, Sock Shop, and Train Ticket, on a Kubernetes cluster consisting of one master node and five worker nodes. Then, we deploy a monitoring system to monitor and collect resource-level and service-level metrics. To generate traffic, we use the load generators supplied by these systems and tailor them to explore all services with a load of 40-50 requests per second. Initially, we operate the applications normally to gather metrics data under normal conditions. Then, we inject faults into the running services. We execute into the designated container using kubectl exec. For CPU hog and memory leak, we use stress-ng to stress the container resource. For network delay and packet loss, we use tc (traffic control) to manipulate the traffic of the container. Specifically, we inject faults into five targeted services of Sock Shop (carts, catalogue, orders, payment, and user), five targeted services of Online Boutique (adservice, cartservice, checkoutservice, currencyservice, and productcatalogue), and five targeted services of Train Ticket (ts-auth-service, ts-order-service, ts-route-service, ts-train-service, ts-travel-service). For each combination of fault type and targeted service, we repeat the operation (i.e., fault injection and metrics data collection) five times, resulting in 100 failure cases for each benchmark microservice system.</p> <p><strong>Code</strong></p> <p>The code to reproduce the experimental results in the paper is available at <a href="https://github.com/phamquiluan/baro">https://github.com/phamquiluan/baro</a>.</p>
Detection of siRNA movement in Arabidopsis thaliana using P19 under root cell layer specific expression
GEO Series GSE113029. Arabidopsis thaliana. 8 samples. Type: Expression profiling by high throughput sequencing.
Traces, Metrics, and Logs for Anomaly Detection and Root Cause Localization in Microservices
<p>Here are the data used in our paper published at ICSE 2023: </p><p>"Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data".<br><br>Please make sure to cite our paper whenever you use the data in your research:<br><br>@inproceedings{DBLP:conf/icse/LeeYCSL23, author = {Cheryl Lee and Tianyi Yang and Zhuangbin Chen and Yuxin Su and Michael R. Lyu}, title = {Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data}, booktitle = {45th {IEEE/ACM} International Conference on Software Engineering, {ICSE} 2023, Melbourne, Australia, May 14-20, 2023}, pages = {1750--1762}, publisher = {{IEEE}}, year = {2023}, url = {https://doi.org/10.1109/ICSE48619.2023.00150}, doi = {10.1109/ICSE48619.2023.00150}, timestamp = {Wed, 19 Jul 2023 10:09:12 +0200}, biburl = {https://dblp.org/rec/conf/icse/LeeYCSL23.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }</p>
On Previously Root Canal-treated Patients Using an AI Program, Detect the Accuracy of it in the Detection of Root Canal Obturation Quality on CBCT Compared With Conventional PA Radiography, e.g., Void
ClinicalTrials.gov study NCT07056998. IPD Sharing: YES. Countries: 1. Publications: 0.
Artificial Intelligence to Detect Missed Root Canals in Previously Treated Lower Back Teeth Using 3D Dental Imaging (CBCT)
ClinicalTrials.gov study NCT07077694. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Accuracy of Artificial Intelligence Technology in Detecting Number of Root Canal of Human Mandibular Second Molar: Diagnostic Accuracy Experimental Study
ClinicalTrials.gov study NCT07319221. IPD Sharing: YES. Countries: 1. Publications: 0.
Accuracy of Artificial Intelligence Technology in Detecting Number of Root Canals in Human Mandibular First Molars Obturated and Indicated for Retreatment: Diagnostic Accuracy Experimental Study
ClinicalTrials.gov study NCT06325163. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Diagnostic Accuracy of Artificial Intelligence, CBCT, and Clinical Examination in Detecting Number of Root Canals in Conventional and Retreated Maxillary and Mandibular Molars
ClinicalTrials.gov study NCT06712160. IPD Sharing: NO. Countries: 1. Publications: 0.
Figure 1. Maximum parsimony rooted phylogenetic tree for a in A Coronavirus Detected in the Vampire Bat Desmodus rotundus
Figure 1. Maximum parsimony rooted phylogenetic tree for a segment of the ORF1b of the Coronavirus genus, showing each of the three groups in the genus and in bold and underlined the strain BatCoV DR/2007 detected in the enteric content of a Desmodus rotundus bat. Numbers at each node are 1,000 replicates bootstrap values.
Fingerprinting of the hNGF P61R100E gene expression in rat Dorsal Root Ganglion detected by microarray analysis
GEO Series GSE80257. Rattus norvegicus. 8 samples. Type: Expression profiling by array.
Supplementary Material - Dataset for "Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis"
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.