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9,302 results for “DETECT”
Fraction of the GBM-190816 localization favored by INTEGRAL/ACS non-detection
<p>Fraction of the GBM-190816 localization favored by INTEGRAL/ACS non-detection</p>
Correction to "Detecting distortions of peripherally presented letter stimuli under crowded conditions"
<p>We discovered a bug in the code producing radial frequency distortions for our paper</p> <p>Wallis, T. S. A., Tobias, S., Bethge, M., & Wichmann, F. A. (2017). Detecting distortions of peripherally presented letter stimuli under crowded conditions. Attention, Perception, & Psychophysics. https://doi.org/10.3758/s13414-016-1245-x</p> <p>In this correction, we fixed the bug, remeasured data for Experiment 1 from the paper, and found that our substantive conclusions remain.</p>
Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor [Sample Dataset]
<p>[Sample Dataset File.]</p> <p>Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset. </p>
Raw Data for "Quantum non-demolition state detection and spectroscopy of single trapped molecules"
<p>Raw data and scripts (Matlab) for "Quantum non-demolition state detection and spectroscopy of single trapped molecules"</p>
Dataset for: Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning.
<p>The data presented in this article is related to the research article entitled ``Sky pixel detection in outdoor imagery using an adaptive algorithm and machine learning." \citep{Nice2019UC}.</p> <p>The dataset consists of a trained Inception V3 neural network model as well as the configuration files to train the neural network and run the inferences. The dataset also contains two sets of outdoor imagery (from Skyfinder and Google Street View) used to train the neural network and validate the sky pixel detection system in the linked article. The original images are included as well as rescaled imagery used to train the neural network, and sky masks used for validation.</p>
Supplementary material 1 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
This file contains the specimen data from the original trap samples, morphological identifications, molecular identifications, and resulting occurrence data.
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Hand Screw Clamp
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation
<p><strong>Abstract: </strong></p> <p>Swarms of drones offer an increased sensing aperture, and having them mimic behaviors of natural swarms enhances sampling by adapting the aperture to local conditions. We demonstrate that such an approach makes detecting and tracking heavily occluded targets practically feasible. While object classification applied to conventional aerial images generalizes poorly the randomness of occlusion and is therefore inefficient even under lightly occluded conditions, anomaly detection applied to synthetic aperture integral images is robust for dense vegetation, such as forests, and is independent of pre-trained classes. Our autonomous swarm searches the environment for occurrences of the unknown or unexpected, tracking them while continuously adapting its sampling pattern to optimize for local viewing conditions. We achieved an average positional accuracy of 0.39 m with an average precision of 93.2% and an average recall of 95.9%. Here, adapted particle swarm optimization considers detection confidences and predicted target appearance. We show that sensor noise can effectively be included in the synthetic aperture image integration process, removing the need for a computationally costly optimization of high-dimensional parameter spaces. Finally, we present a complete hard- and software framework that supports low-latency transmission and fast processing of extensive video and telemetry data.</p>
Figs. 6–7 in Figs. 6-7 in First Reported Detection of Ips calligraphus (Germar) and Ips grandicollis (Eichhoff) (Coleoptera: Curculionidae) in a Port of Entry and its Surrounding Forest in Spain
Figs. 6–7. Typhloelmis spangleri, new species collection sites in the Lower Canyons of the Rio Grande/Rio Bravo, Texas. 6) Type locality, an unnamed spring seep just below Lower Madison Falls, a) Rio Grande with adjacent seep in foreground, b) Seep with filamentous algae in which specimens were found; 7) Below Panther Spring concealed by giant cane, Arundo donax, above a backwater adjacent to the main river channel.
Figs. 2–3. Typhloelmis spangleri, new species. 2 in Figs. 6-7 in First Reported Detection of Ips calligraphus (Germar) and Ips grandicollis (Eichhoff) (Coleoptera: Curculionidae) in a Port of Entry and its Surrounding Forest in Spain
Figs. 2–3. Typhloelmis spangleri, new species. 2) Adult habitus (dry), holotype, length 3.55 mm, a) Dorsal view,
Figs. 4–5 in Figs. 6-7 in First Reported Detection of Ips calligraphus (Germar) and Ips grandicollis (Eichhoff) (Coleoptera: Curculionidae) in a Port of Entry and its Surrounding Forest in Spain
Figs. 4–5. Typhloelmis species, mature larvae, approximately 6 mm long, lateral views. 4) T. spangleri, new species; 5) T. finegan.
Fig. 5 in Biocontrol Agents Cross Borders: First Detection of the Knapweed Seed Head Weevil, Bangasternus fausti (Reitter) (Coleoptera: Curculionidae), in Canada
Fig. 5. Maximum-likelihood gene tree of Bangasternus, Rhinocyllus, and Larinus cytochrome c oxidase subunit I sequences. Black circles indicate 100% support with SH-aLRT ratios/ultrafast bootstrapping and gray circles indicate greater than 70% support. Dark red labels depict identified specimens from the William F. Barr Entomological Museum at the University of Idaho.
Figs. 2–4 in Biocontrol Agents Cross Borders: First Detection of the Knapweed Seed Head Weevil, Bangasternus fausti (Reitter) (Coleoptera: Curculionidae), in Canada
Figs. 2–4. Adult Bangasternus fausti (sensu Colonnelli and Whitehead 1990) from British Columbia, Canada. 2) Lateral habitus; 3) Dorsal habitus; 4) Male genitalia, dorsal view. Specimen in Figs. 2–3 from Peachland, British Columbia, Canada (49.739669, −119.769512), collected 27 June 2022 on Centaurea diffusa. Specimen in Fig. 4 from Kelowna, British Columbia, Canada (49.90889, −119.49167), collected 11 June 2022 on C. diffusa (specimen code Bf068). Photos by C. L. Cock.
Fig. 1. Search effort for Bangasternus between 2020–2022 in Biocontrol Agents Cross Borders: First Detection of the Knapweed Seed Head Weevil, Bangasternus fausti (Reitter) (Coleoptera: Curculionidae), in Canada
Fig. 1. Search effort for Bangasternus between 2020–2022. Symbol color represents presence or absence of B. fausti: white depicts presence, green depicts iNaturalist observations, and unfilled depicts absence. White triangles depict sites that had B. fausti and at least two species of Centaurea whereas unfilled triangles depict sites that had no B. fausti but had both C. diffusa and C. stoebe ssp. micranthos. Photo of B. fausti on C. diffusa by N. G. Earley. Figure prepared in QGIS version 3.16.6 with basemaps from https://github.com/stamen/terrain-classic and https://github.com/CartoDB/ basemap-styles.
Fig. 1 in New Turfgrass Host Associations and Regional Detections for the Flea Beetle Chaetocnema minuta Melsheimer (Coleoptera: Chrysomelidae) in the Southwestern United States
Fig. 1. Adult and larva of Chaetocnema minuta isolated from damaged creeping bentgrass in southern Utah (photo: A. Van DYke).
Figs. 1–4 in Detective Stories from Historical Collections: The Case of Eumolpus hobsoni Curtis, 1840 (Coleoptera: Chrysomelidae: Eumolpinae)
Figs. 1–4. Xanthonia decemnotata: 1) Neotype; 2) Labels of neotype. Eumolpus hobsoni: 3) Holotype; 4) Label of holotype.
Impurity gas detection dataset on 2/3-scaled canister mock-up
<p>This repository contains datasets used for training Variational Autoencoders (VAE) and Wasserstein Autoencoders (WAE) in the detection of impurity gases on a 2/3-scaled sealed canister mock-up. The research focuses on identifying abnormal gas concentrations using acoustic sensing techniques. These datasets were utilized in the study titled "Acoustic Sensing and Auto-encoder Approach for Abnormal Gas Detection on a Spent Nuclear Fuel Canister Mock-up."</p> <p>Files</p> <p>1. X_train_helium(argon).npy: This file contains the training data with helium in day-1.<br>2. Argon_Percentage.npy: This file contains the different impurity levels of argon used in the experiments.<br>3. impurity_data.npz: This file is a dictionary containing ultrasonic response data for various argon impurity levels.<br>4. X_train_helium(air).npy: This file contains the training data with helium in day-2.<br>5. Air_Percentage.npy: This file contains the different impurity levels of argon used in the experiments.<br>6. impurity_data.npz: This file is a dictionary containing ultrasonic response data for various air impurity levels.</p> <p><br>Data Structure</p> <p>X_train_helium(argon).npy<br>- Shape: (1,991, 301)<br>- Description: This array contains the training data, where:<br> - Each of the 1,991 rows represents a unique sample.<br> - The 301 columns represent the truncated ultrasonic response for each sample.</p> <p>X_train_helium(air).npy<br>- Shape: (988, 301)<br>- Description: This array contains the training data, where:<br> - Each of the 988 rows represents a unique sample.<br> - The 301 columns represent the truncated ultrasonic response for each sample.</p> <p>Argon_Percentage.npy or Air_Percentage.npy<br>- Shape: (41, 1)<br>- Description: This array contains the impurity levels of argon and air used in the experiments. There are 41 different impurity levels, each represented by a percentage in this file.</p> <p>impurity_data.npz<br>- Structure: This file is a dictionary containing 40 items, each representing data for a specific argon impurity concentration level.<br>- Description: Each item in the dictionary is structured as follows:<br> - Shape: (51, 301)<br> - Details:<br> - The 51 rows represent repeated ultrasonic response measurements at each impurity concentration level.<br> - The 301 columns represent the truncated ultrasonic response corresponding to each measurement.</p> <p>Contact<br>For any questions or issues related to this dataset, please contact the repository owner via bozhouzh@usc.edu.</p>
CSES (ZH-1) Satellite magnetic field detection data with whistlers
Open the record for dataset details and reuse information.
Dataset of "Detecting Weaknesses and Analyzing Cascading Failures in Critical Infrastructure Networks Using an Interconnected Simulation System"
<p>This article introduces an innovative simulator for critical infrastructure, developed to enable comprehensive<br>simulation of data and power grid and their interconnections. The simulator offers user-input functionality,<br>allowing for detailed modelling and analysis of various impact scenarios on infrastructures. A key feature<br>of this tool is the use of a state matrix to represent the current state of infrastructures, which is updated<br>after each simulation. This update facilitates accurate and dynamic modelling of changes in infrastructure<br>networks and their interconnections.<br>The simulator is designed to test the functionality of both individual components and the overall network<br>integrity, including the analysis of the cascading failures, where a failure in one part can impact other parts<br>of the infrastructure. This capability is essential for a deeper understanding of risks and for developing<br>effective strategies to protect and ensure the resilience of critical infrastructures. The simulator represents a<br>significant advancement in the field of critical infrastructure simulation, providing a tool for better prediction,<br>identification, and mitigation of potential threats, thereby enhancing the security and resilience of critical<br>systems.</p>
Participant Data and Code for "Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video"
<p>All code produced to analyze the participant response data and the participant response data itself are included in this repository. </p>
ScienceDex guides
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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.