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9,302 results for “DETECT”
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Black-Box Machine-Generated Text Detection
<p>Large language models (LLMs) are becoming mainstream and easily accessible, ushering in an explosion of machine-generated content over various channels, such as news, social media, question-answering forums, educational, and even academic contexts. Recent LLMs, such as ChatGPT and GPT-4, generate remarkably fluent responses to a wide variety of user queries. The articulate nature of such generated texts makes LLMs attractive for replacing human labor in many scenarios. However, this has also resulted in concerns regarding their potential misuse, such as spreading misinformation and causing disruptions in the education system. Since humans perform only slightly better than chance when classifying machine-generated vs. human-written text, there is a need to develop automatic systems to identify machine-generated text with the goal of mitigating its potential misuse.</p> <p>We offer three subtasks over two paradigms of text generation: (1) <strong>full text</strong> when a considered text is entirely written by a human or generated by a machine; and (2) <strong>mixed text</strong> when a machine-generated text is refined by a human or a human-written text paraphrased by a machine.</p>
Analysis of Circulating Biomarkers for Minimally Invasive Early Detection of Breast Cancer
<p>A nested case–control study was conducted on plasma samples of 65 cases and 66 controls (discovery set) and 32 cases and 127 controls (validation set). 10 circulating microRNAs have been analyzed by RT-qPCR and 7 microRNA ratios (as the difference between their threshold cycles) have been included in a model to predict the presence of breast cancer. Together with the women identifier (andromeda_id) and the seven microRNA ratios, menopause staus (menopause), body mass index (bmi_di), the interaction term between menopause and BMI (bmi_men), the life-style score according to the World Cancer Research Fund (score_wcrf), the breast density classification (tabar), the cancer diagnosis (status) and the type of control (class2 - only for the validation cohort where there were two types of controls: mammography negatives (N) and mammography false-positives(N2)) are reported in the first sheet (validation set) and in the third sheet (discovery set). The second sheet contains the time intervals between enrollment with blood sampling and breast cancer diagnosis. This information was used only for the validation set, where these intervals were much higher than in the discovery set.</p> <p>Raw threshold cycles (Ct) are available upon request.</p>
Leaf: an ultrafast filter for population-scale long-read SV detection: dataset 2
<p>Simulated PacBio Simuluated ONT reads for assessment</p>
Pedestrian Fall Detection in Various Image Degradation Scenarios Datastes
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Figure 6 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 6. Direct sequence results of two genotypes of the Trp-2027-Cys (G> C) mutation in Rottboellia cochinchinensis samples. (A) Wild type and (B) mutant type.
Figure 3 in Detection of the Trp-2027-Cys Mutation in Fluazifop-P-butyl-resistant Itchgrass (RottboelliO cochinchinensis) using High-Resolution Melting Analysis (HRMA)
Figure 3. High-resolution melting analysis (HRMA) for detection of mutation Trp-2027-Cys in the Rottboellia cochinchinensis carboxyl-transferase domain of the acetyl-coenzyme A carboxylase gene conferring resistance to fluazifop-P-butyl. Representative profiles of the melting curves (derivative melt curves) (A), normalized melt curves (B) and differential curves using susceptible (wild type) as reference genotype (C) for resistant (TGC, in red) and susceptible plants (wild type, TGG in blue at x-axis level).
Reproducibility archive for HCC detection and post-surgery monitoring using cfDNA methylomes
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Data and Sourcecode from: Neural Network-based Occupancy Detection on the Edge
<div> <h2>Environmental Data Collected for Data-Driven Occupancy Detection</h2> <h3>Version Information</h3> <p>v1.0: Holds repository alongside the dataset inside<br>v2.0: Holds dataset in a seperate zip folder</p> <h3>Dataset Information</h3> <div>The following data is collected from LoRa sensors of two rooms for a period of three months in an office building on the ground floor in Graz, Austria:</div> <div> <ul> <li>Open status of windows/doors</li> <li>Relative humidity</li> <li>CO2 concentration</li> <li>Ambient temperature</li> <li>PIR-based motion counter</li> <li>Light level</li> <li>IR-based occupancy (only room A)</li> <li>Average/peak sound level</li> <li>Radar-based people counter (left-to-right and right-to-left; only room A; <em><strong>no</strong> trustworthy ground truth!</em>)</li> </ul> </div> <div> </div> <div>Folder Organization in occupancy-detection-dataset.zip</div> <br> <div> ├── data</div> <div> │ ├── interim <- Intermediate data of room A and B that has been transformed.</div> <div> │ └── raw <- The original, immutable sensor data dump of room A and B.</div> <br><br> <div><strong>Raw Data</strong></div> <div>Raw sensor data of room A and B consisting of six and two work places respectively. Data is gathered in an interval of five minutes.</div> <br> <div>Note:</div> <ul> <li>Timezone ist UTC+00:00.</li> <li>Column "occupancy" in df_features.csv refers to IR based occupancy sensor from Elsys ERS Eye (Possible values 0-2).</li> <li>Column "motion" in df_features.csv refers to a PIR based motion counter.</li> <li>IR-based occupancy is not measured in room B.</li> </ul> <br> <div><strong>Intermediate Data</strong></div> <div>Event-based (door and window sensors) and interval based (humidity, CO2, temperature, ....) data is synchronized to retrieve a homogenous data set.</div> <div>Window columns are merged to represent the number of open windows. Nothing else was applied to the data.</div> <br> <div><strong>Ground Truth</strong></div> <div>Image-based occupancy ground truth data is separated in a file (df_occ.csv).</div> <div>It describes the number of occupants at a certain time stamp provided from images (manually labelled).</div> <h3>References</h3> <p>Coming soon.</p> </div>
Apple object detection code
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Apple Object Detection Dataset
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The 2023 Mw 6.0 Jishishan Earthquake: A Slow Unilateral Rupture on a Blind Thrust Fault Revealed by High-Precision Earthquake Detection, Location, and Dynamic modeling
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STROBE Checklist of capillary tube to detect blood clots
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FIGURE 10 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 10. Percentage of DCA-1 values obtained from simulations that fall within a 95% envelope of three DCA-1 values: (A) 0.0 (B) 1.0 and (C) 2.0. These values were selected as example observations from a hypothetical empirical data set and are marked by a green line in each subfigure. The bounds of the 95% confidence envelope were calculated by repeatedly simulating assemblages at the indicated empirical DCA-1 value and finding the two-tailed 95% quantile on the DCA-1 values obtained from those simulated assemblages. In the mixed assemblage simulations, background assemblages are simulated at a DCA-1 value of -0.5 and are mixed with an event assemblage with a DCA-1 value as given on the horizontal axis, with the proportion of mixing indicated on the vertical axis. In all three cases, the region the target DCA-1 value is observed in the simulations is a narrow ridge in probability space.
Domain-independent anomalies datasets (adaptions of the MVTec Anomaly Detection dataset)
<p>An adaption of the MVTec Anomaly Detection dataset, presented in the paper <em><a href="https://arxiv.org/abs/2407.02910">Domain-independent detection of known anomalies</a></em></p> <p>The datasets may be used to evaluate approaches on the hybrid task of detecting known anomalies across different, previously unseen objects.</p> <p>The source code for training and testing models on theses datasets can be found at: <a href="https://doi.org/10.5281/zenodo.11924708">https://doi.org/10.5281/zenodo.11924708</a></p>
Methanol (CH3OH), acetylene (C2H2), and propylene (C3H6) point-sources detected by the IASI infrared satellite instrument (2007-2023).
<p>This dataset includes the super-sampled IASI 0.01° × 0.01° CH3OH, C2H2, C3H6, and SO2 HRI dataset in GeoTIFF format and the catalogue of identified and categorized point-sources of CH3OH, C2H2, and C3H6 in kml format. The oversampling, wind rotation and supersampling prorcedure and codes are available in the paper of Clarisse <em>et al.</em> (2019) at <a href="https://doi.org/10.5194/amt-12-5457-2019">https://doi.org/10.5194/amt-12-5457-2019</a>.</p>
Figure 1 in Detection of enteroparasites in foliar vegetables commercialized in street- and supermarkets in Aparecida de Goiânia, Goiás, Brazil
Figure 1. Eggs and larvae of helminths detected in samples of lettuce and collard greens from street market and supermarkets in Aparecida de Goiânia, Goiás. (A) Ancylostomatidae / Strongyloididae; (B) Ascarididae; (C) Hymenolepis nana; (D) ovigerous capsule of Dipylidium caninum; (E) Taeniidae, (F) Toxocara sp.; (G) and (H) probably Trematodae; (I) probably Trypanoxyuris sp.; (J) unidentified nematode larvae.
MODERN MEDICAL METHODS OF EARLY DETECTION AND TREATMENT OF KIDNEY STONE DISEASE
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Yellow Sticky Trap Insect Images for Object Detection Training
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Computer Vision-Based Algorithm for Precise Defect Detection and Classification in Photovoltaic Modules
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Table S3 Total Protein detected by untargeted proteomics
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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.